Je tiens tout d'abord à remercier les membres du jury, en particulier Petra Dittrich et Daniel Ricklin pour avoir accepté le rôle de rapporteur ainsi que pour la pertinence des commentaires et échanges à propos de mon travail. Je remercie également Marion Espéli et Andrew Griffiths pour la discussion très intéressante lors de la soutenance. Ensuite, je voudrais remercier mes deux encadrants, Jean et Klaus. Merci pour la confiance que vous m'avez accordée et votre implication sans faille dans l'accompagnement de mon projet de thèse. Je suis convaincu que c'est une grande chance d'avoir eu des encadrants aussi disponibles et investis, et je pense que vous m'avez réellement fait mûrir et progresser en tant que scientifique. Klaus, tu m'as appris une grande partie de mes connaissances en travail expérimental et m'y a donné goût par ton enthousiasme constant. Jean, je garderai un excellent souvenir de nos discussions et réflexions ainsi que de tes conseils avisés. Mes remerciements vont également à tous les membres du groupe Functional Immune Repertoire Analysis à l'ETH Zürich pour l'accueil lors de mes séjours en Suisse, et en particulier à Olivia, Inga, Kevin et Luca pour l'aide considérable que vous m'avez apportée. Le travail présenté dans cette thèse n'aurait pas pu être réalisé sans vous. Je voudrais maintenant remercier les membres du laboratoire LCMD. Ce fut un plaisir de travailler avec vous pendant ces quatre dernières années, dans une ambiance toujours conviviale et bienveillante. Je remercie en particulier Florence, pour ta gentillesse, ton aide précieuse et tout le travail que tu fournis pour l'organisation et le fonctionnement du labo, je ne sais pas comment on ferait sans toi ! Merci aussi à ceux qui se sont succédés dans le bureau E1.06 au fil du temps, pour les échanges, l'aide et la bonne humeur : Adbul, Magda, Paul, Matin, Antu. Merci aussi à Claire pour les discussions pleines d'humour à la pause déjeuner pendant cette thèse effectuée presque en simultané. Enfin, merci à Isa et Hélène pour l'aide indispensable au niveau de la gestion administrative, toujours faite parfaitement et avec le sourire ! Je tiens aussi à remercier mes amis de la promotion 134. Je ne peux pas tous vous citer ici, mais votre présence a été réellement indispensable pendant ces dernières années. Merci pour tous ces bons moments partagés et à venir ! Un immense merci à ma famille, et en particulier à mon frère et mes parents pour tout ce que vous m'avez apporté pendant ces 26 dernières années, l'inspiration et l'exemple que vous représentez pour moi. Enfin, merci à Pauline d'être toujours à mes côtés. i xii Résumé en français permise par notre bioessai. L'autre perspective serait de confirmer l'influence des agonistes des TLR observée ici en testant d'autres immunogènes et d'autres timings d'immunisations. Ensuite, la prochaine étape serait d'appliquer ces agonistes à la formulation de candidats vaccins, notamment la combinaison d'agonistes des TLR2, 4 et 9 qui n'a jamais été testée à notre connaissance.

Pour un bien plus grand niveau de détail ainsi que des figures et des références à la littérature, merci de vous référer à la version principale en anglais, présentée dans la suite de ce manuscrit.

Dans ce manuscrit, nous commençons par introduire le contexte dans un premier chapitre d'introduction. Ensuite, le chapitre II présente les développements techniques de bioessais effectués durant cette thèse. Ces développements ont été nécessaires pour deux études expérimentales, concernant respectivement l'activation du complément par des anticorps individuels, présentée dans le chapitre III, et le rôle des agonistes des récepteurs de type Toll en tant qu'adjuvants, présentée dans le chapitre IV. Nous concluons ensuite dans le chapitre V.

Chapitre I : Introduction

L'antibiorésistance désigne la capacité de certaines bactéries à résister aux traitements antibiotiques, ce qui les rend inefficaces pour le contrôle des infections. La montée de l'antibiorésistance est principalement attribuée à l'utilisation intensive des antibiotiques chez les humains et les animaux, ce qui exerce une forte pression de sélection sur les bactéries pour développer une résistance. Cette augmentation des pathogènes résistants aux antibiotiques représente une menace importante pour la santé publique, avec environ 1,27 million de décès dans le monde attribués à l'antibiorésistance en 2019. L'impact de l'antibiorésistance dépasse celui de maladies telles que le VIH, le cancer du sein ou le paludisme. Le problème est plus prononcé pour les pays en développement en raison d'un nombre d'options d'antibiotiques disponibles plus restreint. Une action mondiale urgente est nécessaire pour lutter contre la résistance aux antibiotiques. Les mesures préventives comprennent la réduction de l'utilisation inutile d'antibiotiques dans l'agriculture et la médecine, ainsi que le contrôle du rejet des effluents antibiotiques dans l'environnement. De plus, il est essentiel de trouver des alternatives aux antibiotiques existants. Les anticorps monoclonaux antibactériens, un type d'agent thérapeutique, montrent des résultats prometteurs car ils ciblent des antigènes bactériens spécifiques sans nuire au microbiote bénéfique. Cependant, le développement des anticorps monoclonaux antibactériens en est encore à ses débuts par rapport à ceux ciblant d'autres maladies, principalement en raison des coûts élevés comparé aux antibiotiques. Malgré cela, quelques anticorps monoclonaux ont été approuvés pour le traitement des infections bactériennes, et plusieurs autres font l'objet d'essais cliniques.

Les anticorps monoclonaux antibactériens disponibles ou en développement ciblent principalement le toxines bactériennes. Les anticorps qui neutralisent les toxines se sont révélés efficaces pour réduire la virulence de certaines bactéries infectieuses et améliorer les taux de survie. Cependant, ces anticorps n'éliminent pas directement les cellules bactériennes et atténuent plutôt les effets de l'infection pendant que le système immunitaire de l'hôte élimine les bactéries. Le ciblage des antigènes de surface des bactéries, tels que des protéines ou des polysaccharides, pourrait constituer un autre mode d'action pour les anticorps monoclonaux.

En se liant aux protéines responsables de l'adhésion des bactéries aux cellules hôtes, les anti-iv Résumé en français corps peuvent inhiber la colonisation et l'entrée des bactéries dans les cellules. Les anticorps possèdent également un fragment cristallisable (Fc) qui peut interagir avec les récepteurs des cellules immunitaires et assurer diverses fonctions effectrices. La phagocytose cellulaire dépendante des anticorps (ADCP) permet aux phagocytes d'internaliser et de dégrader les bactéries opsonisées par les anticorps. La cytotoxicité cellulaire dépendante des anticorps (ADCC) implique les lymphocytes NK qui reconnaissent les cellules hôtes recouvertes Le système du complément sert de pont entre l'immunité innée et l'immunité adaptative et peut être exploité par des anticorps monoclonaux thérapeutiques. La voie classique, qui implique les anticorps, est particulièrement efficace. Les anticorps IgM adoptent une conformation hexamèrique qui facilite la liaison de C1q, la première protéine du complément intervenant dans la voie classique, tandis que les anticorps IgG nécessitent un assemblage de six molécules pour une liaison efficace de C1q. L'activation de la cascade du complément amplifie le dépôt des protéines du complément sur les surfaces bactériennes, ce qui conduit à la formation de complexes plus importants et à la destruction éventuelle de la bactérie par le complexe d'attaque membranaire. Il a été démontré que des anticorps monoclonaux IgG pouvait activer efficacement le système du complément. Mais il n'est pas trivial de trouver de nouveaux anticorps monoclonaux activant le complément car de nombreux paramètres des anticorps rentrent en jeu (affinité, mais également sous-classe pour les IgG, densité de l'épitope cible, géométrie de l'épitope, distance à la membrane, . . . ). Cela nécessite de cribler des répertoires de lymphocytes B avec un système pouvant mesurer la sécrétion d'anticorps au niveau de la cellule unique (car chaque cellule produit un seul anticorps), sa spécificité pour une bactérie cible, ainsi que l'activation du complément, et ainsi sélectionner les meilleurs anticorps. Les mesures doivent être quantitatives pour différencier les anticorps les plus efficaces et le débit très haut pour trouver des évènements rares.

Différents systèmes pour des mesures de sécrétion d'anticorps (ou d'autres protéines) existent. Les méthodes standard de cytométrie de flux ne sont pas adaptées à cet objectif car elles ne peuvent pas établir de lien entre les cellules sécrétrices et les anticorps sécrétés.

Les techniques alternatives pour l'analyse de la sécrétion de protéines par une seule cellule comprennent l'ELISpot et ses dérivés, qui capturent les protéines sécrétées sur une surface v en vue d'une détection ultérieure. Cependant, ces techniques ne permettent pas de mesures quantitatives et leur débit est limité. Des méthodes basées sur la cytométrie existent; elles établissent un lien physique entre les cellules et les protéines sécrétées par le biais d'inhibiteurs de sécrétion ou de capture de surface avant l'analyse par un cytomètre en flux classique. Bien que ces méthodes offrent un débit élevé et des capacités de multiplexage, elles ne permettent pas d'effectuer des mesures quantitatives sur une seule cellule. Les plateformes microfluidiques sont apparues comme une option plus avancée, permettant la compartimentation de cellules uniques dans de petits volumes. Il existe trois principaux types de plates-formes microfluidiques : les essais basés sur les puits, les essais basés sur les chambres et les essais basés sur les gouttelettes. Chacune a ses avantages et permet d'améliorer les mesures de la sécrétion de protéines au niveau de la cellule unique. Les plateformes à base de gouttelettes offrent un débit élevé et une mesure précise de la dynamique de la sécrétion au fil du temps.

Ici nous avons utilisé la technologie DropMap existante, une plateforme microfluidique à gouttelettes capable de quantifier la sécrétion d'anticorps au niveau de cellules uniques à haut débit, avec des mesures de l'affinité et du taux de sécrétion. Elle fonctionne en compartimentant des lymphocytes B uniques obtenus typiquement de la rate de souris dans des gouttelettes de 50 pL, ainsi qu'un bioassay immunologique basé sur la relocalisation de la fluorescence sur des nanoparticules magnétiques fonctionnalisées présentes dans chaque goutte, alignées en une ligne verticale appelée beadline par l'application d'un champ magnétique.

Le système permet le phénotypage dynamique de dizaines de milliers de lymphocytes B en parallèle et le criblage des cellules d'intérêt.

Objectif : Activation du complément au niveau de l'anticorps unique (voir chapitre III) Ici, nous avons cherché à mettre au point une méthode pour découvrir des anticorps monoclonaux ciblant des bactéries pathogènes et activant le complément. À l'aide de la plateforme DropMap, nous avons créé un test permettant de quantifier la liaison de l'anticorps aux bactéries et l'activation du système du complément qui s'ensuit. La technologie DropMap a permis le criblage à haut débit de cellules individuelles, ce qui était essentiel en raison de la rareté des anticorps activant efficacement le complément. Nous avons démontré le criblage des répertoires de cellules B afin d'identifier les anticorps spécifiques aux bactéries activant le complément. Nous avons également fait de premiers essais pour l'intégration de l'essai biologique dans un pipeline de tri qui permettrait d'obtenir les séquences d'anticorps activateurs du complément sélectionnés. Cette étude représente une première étape vers une méthode de tri à haut débit pour les anticorps activant le complément.

Au-delà de l'activation du complément, nous sommes aussi intéressés par la caractérisation précise des anticorps détectés. Les anticorps ont en effet différentes caractéristiques qui déterminent leur rôle, notamment l'affinité, la densité de l'épitope cible, la géométrie de l'épitope, l'isotype et la sous-classe. L'affinité fait référence à la force de la liaison entre le domaine de liaison à l'antigène d'un anticorps et son antigène cible. L'affinité des anticorps vi Résumé en français augmente au cours de la réponse immunitaire en raison de l'hypermutation somatique, ce qui entraîne la sélection de lymphocytes B à affinité plus élevée. La mesure précise de l'affinité permise par DropMap est essentielle pour évaluer les réponses anticorps et développer des anticorps monoclonaux pour la thérapie. La densité de l'épitope cible joue un rôle dans la Pour détecter l'activation du complément, nous nous intéressons à la première étape de l'activation de la voie classique : le recrutement de la protéine C1q, qui se lie au domaine Fc des anticorps IgG ou IgM. Nous avons ajoutée une protéine C1q purifiée marquée avec un fluorophore dans les gouttes. Si une cellule sécrétant un anticorps spécifique capable d'activer le complément est présente dans une goutte, nous observerons donc un signal pour la relocalisation de l'anticorps et également de la protéine C1q. Nous sommes ainsi capable de quantifier la densité de l'anticorps et celle de la protéine C1q sur la surface des bactéries et donc de connaitre l'efficacité relative de l'anticorps pour l'activation du complément. Nous distinguons également ici les IgM, les IgG ainsi que leur sous-classe (IgG1 ou IgG2a/2b/3).

Nous avons démontré que cette détection marchait au niveau cellule unique pour des lymphocytes B obtenus de la rate de souris immunisées avec des bactéries fixées, comme nous le décrivons plus en détail dans l'étude présentée dans le chapitre III. C'est à notre connaissance la première description d'un système permettant de détecter l'activation du complément par des anticorps individuels tout en criblant des répertoires avec un haut débit. ix

Chapitre III : Activation du complément au niveau de l'anticorps unique

Dans la première étude expérimentale de cette thèse, nous avons immunisé des souris avec des vaccins antibactériens ou des bactéries pathogènes fixées au paraformaldéhyde afin de susciter des réponses anticorps spécifiques à ces bactéries cibles. Nous avons caractérisé précisément ces répertoires d'anticorps à l'aide de la plateforme DropMap et mesuré une fréquence satisfaisante de cellules sécrétrices d'anticorps spécifiques aux bactéries cibles respectives pour des immunisations avec le vaccin Infanrix (contre B. pertussis) ou des N. meningitidis B ou C fixées. Nous avons ensuite pu détecter des lymphocytes B uniques sécrétant des anticorps IgM ou IgG spécifiques qui recrutaient efficacement la protéine C1q du complément à la surface de la bactérie, ce qui constitue la première étape de l'activation de la voie classique du complément. Nous avons pu quantifier avec précision les densités de liaison des anticorps et de la protéine C1q à la surface des bactéries, et donc déterminer avec une grande précision l'efficacité des anticorps individuels pour l'activation du complément.

À partir de ces mesures, il est possible de déduire certains paramètres nécessaires pour l'activation de la voie classique du complément en fonction de l'isotype de l'anticorps. Tout d'abord, pour les anticorps IgM, une forte densité d'anticorps se liant à la surface bactérienne semble importante pour une liaison efficace de C1q. Toutefois, cela n'est pas suffisant, car seule une petite fraction des IgM spécifiques des bactéries se liant à haute densité recrute C1q dans nos expériences. Cela pourrait être dû à des exigences structurelles strictes pour la formation du complexe IgM-C1q qui ne permettent qu'aux anticorps IgM ciblant certains épitopes de lier C1q. Dans le cas des anticorps IgG, un premier paramètre nécessaire à l'activation du complément est la sous-classe d'IgG, seules les sous-classes IgG2a, 2b ou 3 liant C1q dans les expériences décrites ici, comme prévu dans la littérature. Pour les IgG, une forte densité d'anticorps liés est également nécessaire. Cela peut être attribué à la proximité des anticorps IgG nécessaire pour former des hexamères IgG sur les surfaces bactériennes -la conformation idéale pour la liaison de C1q. Lorsqu'une densité de liaison élevée est atteinte, la plupart des anticorps IgG des sous-classes IgG2a/2b/3 déclenchent la liaison de C1q, contrairement aux anticorps IgM. Ceci est probablement dû à une plus grande flexibilité de l'assemblage de l'hexamère IgG par rapport à une molécule IgM, ce qui permettrait la liaison de C1q pour la plupart des configurations d'épitopes. Cependant, nous avons encore observé de grandes différences dans l'efficacité de la liaison de C1q pour une densité équivalente d'IgG liés et même quelques IgG2a/2b/3 à haute densité qui ne recrutent pas C1q à un niveau détectable. Cela pourrait être dû à différentes sous-classes d'IgG (que nous ne pouvons pas distinguer davantage ici) ou à des contraintes dépendant de l'épitope, par exemple l'orientation de l'anticorps due à la géométrie de l'épitope, la distance de l'épitope à la membrane cellulaire ou la constante de dissociation k off . Il a été démontré dans d'autres études que tous ces paramètres influencent la liaison de C1q induite par des IgG monoclonaux, mais il n'a pas été possible ici d'attribuer les différences observées à des paramètres précis.

Ces variations dans l'efficacité de l'activation de la voie classique par des anticorps se x Résumé en français liant à des densités similaires soulignent l'intérêt de la méthode développée dans cette thèse qui permet de quantifier précisément le recrutement de C1q induits par des anticorps monoclonaux. En effet, pour trouver des anticorps activateurs de complément efficaces, il ne suffit pas de les sélectionner sur la base d'une forte affinité et d'une densité élevée d'épitopes cibles, mais il est également essentiel de mesurer l'activation du complément, comme nous l'avons fait ici en quantifiant la liaison de C1q. En outre, les anticorps liant C1q étaient généralement très rares, ce qui confirme la nécessité d'une méthode à haut débit telle que la plateforme DropMap pour cribler les anticorps activant le complément. Pour ces raisons, le système présenté ici parait prometteur pour la découverte de nouveaux anticorps monoclonaux activant le complément. Cela nécessiterait le développement d'un protocole de tri pour récupérer les gouttelettes contenant les cellules d'intérêt, pour lequel des essais préliminaires sont présentés dans cette thèse.

Chapitre IV : Influence d'agonistes des TLR sur le répertoire d'anticorps

Dans la deuxième étude expérimentale conduite durant cette thèse, nous nous sommes intéressés à la possibilité d'exploiter le système immunitaire inné pour moduler les réponses anticorps. L'idée était d'essayer d'influencer les paramètres importants des répertoires d'anticorps générés par les vaccinations, tels que l'affinité des anticorps ou, plus important encore, les distributions d'isotypes et de sous-classes d'IgG qui déterminent les fonctions effectrices induites par les anticorps. Cela aurait des implications pour le développement de vaccins plus efficaces. Une stratégie potentielle que nous avons explorée ici consisterait à ajouter des agonistes des TLR comme adjuvants aux vaccinations et à activer ainsi diverses cellules immunitaires par l'intermédiaire de leurs TLR, ce qui pourrait conduire à la modulation de la réponse humorale. Dans un système modèle simple (deux immunisations successives avec un antigène protéique unique adjuvanté avec de l'alun), nous avons caractérisé précisément l'effet de l'ajout de différents agonistes des TLR (de TLR2, 3, 4 ou 9) ou d'une combinaison d'agonistes des TLR2, 4 et 9 sur le répertoire d'anticorps généré et ce avec une résolution au niveau de l'anticorps unique. L'ajout d'agonistes des TLR n'a pas eu d'impact significatif sur la qualité de la réponse anticorps, d'abord en termes de distribution d'affinité pour l'antigène cible. Les distributions des isotypes et des sous-classes d'IgG étaient également similaires ; en particulier, les agonistes TLR n'ont pas induit de changement vers des sous-classes d'IgG fonctionnellement actives et n'ont pas pu reproduire l'effet des immunisations bactériennes complètes à cet égard.

Une explication probable serait que les agonistes TLR n'ont pas directement activé les cellules B de la rate dans les expériences présentées ici, ce qui nécessite l'engagement simultané du TLR et du BCR. En revanche, nous avons observé une forte augmentation de l'ampleur de la réponse anticorps, particulièrement importante avec l'ajout de l'agoniste du TLR2 ou de la combinaison, et dans une moindre mesure de l'agoniste du TLR9. L'augmentation de l'ampleur de la réponse humorale est attribuée ici à une augmentation de la fréquence des cellules sécrétrices d'anticorps (à la fois spécifiques et non spécifiques) ainsi que du nombre xi total de cellules B dans la rate. Cet effet est très probablement dû à la stimulation des cellules présentatrices d'antigènes via leur TLR localement au site d'injection, induisant une meilleure absorption de l'antigène, un transport et une présentation plus efficace dans la rate. Cela conduirait à une amélioration des réponses des centres germinaux et donc à la génération d'un plus grand nombre de cellules sécrétrices d'anticorps. En outre, nous avons observé des taux de sécrétion d'IgG plus élevés lorsque des agonistes des TLR2, 4 ou 9 (mais pas leur combinaison) étaient ajoutés aux immunisations, ce qui contribue à renforcer la réponse anticorps et constitue une nouvelle découverte à notre connaissance. En conclusion, ces résultats expliquent l'augmentation précédemment décrite des titres d'anticorps lorsque les agonistes TLR sont utilisés comme adjuvants, avec des détails sans précédent dans la caractérisation de la population de cellules sécrétrices d'anticorps. Cela confirme le potentiel des agonistes TLR en tant que candidats adjuvants de vaccins, en particulier l'agoniste du TLR2 Pam 3 CSK 4 , l'agoniste du TLR9 CpG ODN 1826 et leur combinaison avec l'agoniste du TLR4 MPL.

Chapitre V : Conclusion et perspectives

Dans le contexte de la recherche de nouveaux anticorps thérapeutiques antibactériens activant efficacement le complément, nous avons développé ici un système permettant de quantifier l'activation du complément par des anticorps individuels tout en criblant à haut débit, en nous basant sur la technologie microfluidique DropMap. C'est à notre connaissance la première description d'un tel système. La prochaine étape serait de coupler notre système à un protocole de tri des gouttes contenant des cellules d'intérêt, pour ensuite obtenir les séquences des anticorps monoclonaux correspondants. Ceux-ci pourraient ensuite être réexprimés et testés in vitro pour leur activité bactéricide. Un tel système pourrait avoir un grand potentiel pour la découverte de nouveaux anticorps thérapeutiques particulièrement efficaces pour l'élimination de bactéries pathogènes par l'activation du complément, qui pourraient être un atout important dans le traitement d'infections par des bactéries antibiorésistantes. D'autres perspectives de développement pour notre système pourraient être la détection de l'activation d'autres étapes de la cascade du complément dans les gouttes, ou bien l'application à d'autres fonctions effectrices induites par les anticorps, comme l'ADCC ou l'ADCP.

Nous avons également étudié l'influence des agonistes des TLR sur les réponses humorales et montrés qu'ils pouvaient grandement augmenter l'ampleur de ces réponses, mais sans en modifier la qualité dans nos expériences. Cela confirme donc leur intérêt comme adjuvants vaccinaux pour augmenter la réponse à des vaccins protéiques peu immunogènes. Une première perspective pour cette étude serait de tester un système ou l'antigène et l'agoniste sont liés de manière covalente, ce qui permettrait de stimuler directement les lymphocytes B par l'activation conjointe du TLR et du BCR. Selon la littérature, un tel système pourrait induire une modification de la qualité de la réponse anticorps, ce qui n'a encore jamais été mesuré avec un système permettant une analyse précise du répertoire d'anticorps, telle celle

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Introduction

In this introduction chapter, we will first start by explaining why antibacterial monoclonal antibodies are needed as a potential alternative to antibiotics in the context of rising antibiotic resistance in pathogenic bacteria. These monoclonal antibodies can be used as therapeutic agents to eliminate bacteria through the activation of immune effector functions. We will then go over a particularly efficient antibody-dependent effector function against bacteria: the activation of the classical pathway of the complement system. There is already evidence that monoclonal antibodies can activate the complement system on bacteria, but this function has not yet been exploited in therapeutic antibacterial antibodies, mainly because the discovery of new monoclonal antibodies that strongly activate complement is challenging. This requires high-throughput systems that can measure antibody secretion by single B cells with additionally a quantification of complement activation. We will then give an overview of technologies that enable the detection of antibody secretion at the singlecell level, which are mostly microfluidic platforms. One of these platforms is the DropMap technology, which additionally enables to perform custom functional assays, further characterizing the detected antibodies. During this thesis we developed a novel DropMap bioassay that detects bacteria-specific antibodies and their potential to activate the classical pathway of complement, that we used in a first experimental study presented in chapter III. Afterward in this introduction chapter, we will describe the different antibody parameters necessary for an efficient functional response, among which the antibody isotype and subclass play a particularly important role. We then wondered how the isotype and subclass distribution of an antibody response could be influenced, for example to improve vaccination efficiency.

We will explain why adding Toll-like receptor agonists as adjuvants to immunizations is a promising strategy to achieve that aim, that we explored in a second experimental study presented in chapter IV.

The issue of bacterial antimicrobial resistance

Bacterial antimicrobial resistance (AMR) is the ability of certain bacteria to resist to antibiotic compounds that would normally kill or inhibit them. that are eliminated by the antibiotic treatment. Bacterial AMR is a natural process that arises through evolution. Indeed, bacteria that develop resistance to antibiotics by mutations will be selected, and resistance genes to natural antibiotics were already present before the advent of therapeutic antibiotics [START_REF] Davies | Origins and Evolution of Antibiotic Resistance[END_REF]. However, there is a rise in bacterial AMR in recent years that is mostly due to the extensive use of antibiotics in human and animals. With this constant exposure to antibiotics, bacteria are facing a high selection pressure to develop resistance, leading to the apparition of more resistant strains.

This increase in the prevalence of antibiotic-resistant pathogenic bacteria is becoming a major public health issue. A recent study has estimated that 1.27 million deaths could be directly attributed to bacterial AMR in the world in 2019 [3]. This makes the resistance to antibiotics a leading cause of deaths among infectious diseases. As a comparison, the yearly death toll of bacterial AMR is higher than HIV (864 000), breast cancer (700 000) or malaria (643 000) [3]. In recent years, it has only been surpassed by COVID-19 that has led to approximately 6.9 million deaths over three years at the time of writing of this thesis [START_REF] Who | WHO Coronavirus (COVID-19) Dashboard[END_REF].

The leading pathogenic bacteria responsible for AMR-attributable deaths are Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus, Acinetobacter baumannii, Streptococcus pneumoniae and Mycobacterium tuberculosis. Combined, these six pathogens caused 929 000 AMR-attributable deaths in 2019 [3]. In addition, bacterial AMR also has a large economic impact, with associated additional healthcare costs and loss of productivity [START_REF] Smith | The true cost of antimicrobial resistance[END_REF]. AMR is a worldwide issue, but it is especially problematic in developing countries where the range of available antibiotics is narrower, leaving no alternatives when standard antibiotics are no longer effective. In general, the public health issue caused by bacterial AMR is rising and a report has even estimated that it could lead to 10 million annual deaths by 2050 [START_REF] O'neill | Tackling drug-resistant infections globally: final report and recommendations -The Review on Antimicrobial Resistance chaired by Jim O'Neill[END_REF].

Recently, the burden of AMR has worsened with the COVID-19 pandemic. The massive use of antibiotics as part of COVID-19 treatments fueled the development of AMR [START_REF] Rawson | Antimicrobial use, drug-resistant infections and COVID-19[END_REF], while the saturation of healthcare systems by the virus made it harder to pursue the existing efforts to combat antimicrobial resistance [START_REF]COVID-19 & Antibiotic Resistance[END_REF].

If no effective actions to fight bacterial AMR are taken, many bacterial pathogens could become much more lethal in the future, even some that are considered benign today as they can be quickly eliminated by antibiotics. AMR also increases the risk for many medical procedures such as cancer chemotherapy, organ transplantation or intubation where the patient is left vulnerable to bacterial infections and thus effective protection with antibiotics is crucial. Therefore, AMR is an urgent public health issue and various organizations agree that there is a need for a global coordinated plan to address it [START_REF]Antibiotic Resistance Threats Report[END_REF][START_REF] Who | Antimicrobial resistance[END_REF].

First and foremost, several prevention steps should be taken to reduce the use of antibiotics in order to maintain the efficiency of currently available compounds [START_REF] O'neill | Tackling drug-resistant infections globally: final report and recommendations -The Review on Antimicrobial Resistance chaired by Jim O'Neill[END_REF]. Indeed, antibiotics are usually very affordable, which allows their current excessive usage. For example, there is a need to drastically reduce the resort to antibiotics in agriculture, where 3 they are widely employed as prophylactic treatments and sometimes growth promoters for livestock. The rationalization of the usage of antibiotics for human treatment with rapid testing methods to determine the appropriate antibiotic is also necessary to decrease unnecessary use. Additionally, a better control of effluents of hospital, farms and mostly antibiotic manufacturing facilities could prevent the leakage of antibiotics into the environment where they promote the development of AMR.

On the other hand, there is a need to find alternatives to the most common antibiotics.

When antibiotic resistance is encountered, it is possible to use several antibiotics in combination or to shift to newer molecules to replace older antibiotics that are used more widely and thus resisted by more bacterial strains. However, there is a limited pool of antibiotic compounds available and it has been difficult to find new molecules. Indeed, there are currently very few new antibiotics in clinical trial, and most of them are derivatives of already existing classes [START_REF] Beyer | The Antibacterial Research and Development Pipeline Needs Urgent Solutions[END_REF]. These derivative molecules are unlikely to solve the issue of bacterial AMR since they act through the same or similar mechanisms. It is very challenging to find truly new classes of antibacterial molecules [START_REF] Payne | Drugs for bad bugs: Confronting the challenges of antibacterial discovery[END_REF], and the last discovery of a new class of antibiotics goes back to the 1980s [START_REF] Pew | A Scientific Roadmap for Antibiotic Discovery[END_REF]. Although there are currently new techniques and strategies being developed for the discovery of new antibiotics [START_REF] Mahler | Highly parallelized droplet cultivation and prioritization on antibiotic producers from natural microbial communities[END_REF][START_REF] Miethke | Towards the sustainable discovery and development of new antibiotics[END_REF], the pipeline of development of antibiotics is mostly dry with very limited perspectives of new antibiotics. For this reason, changing the way we used antibiotics by controlling their overuse and misuse as explained before is the main priority, as antibiotics are very precious treatments that cannot be easily replaced by equivalent new molecules. In parallel, it is necessary to turn to other alternatives. Among those alternatives, the most promising are antibacterial vaccines, probiotics and monoclonal antibodies [START_REF] Czaplewski | Alternatives to antibiotics-a pipeline portfolio review[END_REF]. We will focus here on antibacterial monoclonal antibodies as a potential solution to bacterial AMR, which we will describe in detail in the next section.

Antibacterial monoclonal antibodies

Monoclonal antibodies are widely used nowadays as therapeutic agents. These antibodies are usually fully human or humanized antibodies, identical or very similar to the antibodies that are naturally secreted by B cells as part of the adaptive immune response. They have had large successes and even sometime revolutionized the treatment of certain diseases such as autoimmune diseases [START_REF] Bang | Adalimumab: A review of its use in rheumatoid arthritis[END_REF], cancer [START_REF] Pierpont | Past, present, and future of Rituximab-The world's first oncology monoclonal antibody therapy[END_REF] or viral infections [START_REF] Kupferschmidt | Successful Ebola treatments promise to tame outbreak[END_REF] in the last thirty years.

Perks of monoclonal antibodies compared to small molecules are that they are generally well tolerated and have little toxicity even at high doses since they are naturally occurring human proteins. They also allow a high specificity for their target with low off-target effects or interactions with other drugs [START_REF] Castelli | The pharmacology and therapeutic applications of monoclonal antibodies[END_REF], and have typically longer half-lives in the body. A first drawback is the fact that monoclonal antibodies typically cannot penetrate cell membranes to reach intracellular targets. Monoclonal antibodies are also generally much more expensive than therapeutic small molecules, which strongly restricts their application in developing countries.

The general process to obtain monoclonal antibodies involves the immunization of laboratory animals (usually mice) with a target antigen and adjuvants and the harvest of B cells from the spleen. Afterward, these B cells are fused with myeloma cells in order to immortalize them. The produced hybridomas are then selected based on the binding of their antibody to the target antigen. The best clones are placed in separate cultures, and the secreted monoclonal antibodies can then be purified from the culture medium. Even if new methods have been introduced for the generation of monoclonal antibodies (phage display, single B cell selection and cloning, ...), the development process remains long and work-intensive. The mass production of monoclonal antibodies is afterward also complex, which explains the high cost of monoclonal antibodies compared to standard drugs.

With the rise of antibiotic resistance, there has been great interest in also applying monoclonal antibodies to the treatment of bacterial infections. An important advantage over traditional antibiotics in this context is that antibodies can target specific antigens of pathogenic bacteria and thus do not disturb the beneficial commensal microbiota, whereas it would be equally eliminated by antibiotic treatment. An important step during the development of antibacterial antibodies would be to verify that mutations that allow bacterial evasion do not arise quickly, but monoclonal still seem to be promising alternatives to antibiotic compounds against which resistance is already present. However, the development of monoclonal antibodies designed to treat bacterial infections is just at its beginning compared to other types of diseases [START_REF] Zurawski | Monoclonal antibodies as an antibacterial approach against bacterial pathogens[END_REF], in part owing to their high cost relative to antibiotics that were sufficient before the advent of AMR. While the first fully human antibody (adalimumab, targeting TNF-α) was approved by the FDA in 2002 and over 100 approved monoclonal antibodies are available at the time of writing of this manuscript, only three of them are designed for the treatment of bacterial infections. Two monoclonal antibodies, raxibacumab (approved in 2012) [START_REF] Tsai | Approval of Raxibacumab for the Treatment of Inhalation Anthrax Under the US Food and Drug Administration "Animal Rule[END_REF] and obiltoxaximab (approved in 2016) [START_REF] Greig | Obiltoxaximab: First Global Approval[END_REF] are used for the treatment of Bacillus anthracis (anthrax) infections, by binding to the protective antigen of anthrax toxin.

The third approved antibacterial monoclonal antibody is Bezlotoxumab [START_REF] Markham | Bezlotoxumab: First Global Approval[END_REF]. It targets the Clostridium difficile toxin B and is a treatment option for recurring Clostridium difficile infections. Additionally, there are currently around 20 monoclonal antibodies against bacteria undergoing clinical trials.

Antibody effector functions

Most of the current antibacterial monoclonal antibodies, approved or in clinical trial, target bacterial toxins. This is an effective approach that allows to strongly reduce the virulence of the infectious bacteria. For example, a monoclonal antibody against the alphatoxin of S. aureus was shown to improve the survival in mice infected with S. aureus, alone or in combination with antibiotics [START_REF] Hua | Assessment of an anti-alpha-toxin monoclonal antibody for prevention and treatment of Staphylococcus aureus-induced pneumonia[END_REF]. In that case, the protection relies on a first function of antibodies, which is the neutralization of its target (see figure I.2). By directly binding to toxins, monoclonal antibodies can inhibit their virulence. In this way, they are interesting 5 treatment options as they allow to decrease the deleterious effects of bacterial infections in cases where antibiotics have none or little effect. However, these types of antibodies targeting secreted toxins do not have a direct impact on the bacterial cells and thus do not help for their elimination. Instead, they dampen the effects of the infection while the host's immune system removes the pathogenic bacteria.

It seems that targeting instead bacterial surface antigens, either proteins or polysaccharides, would be an efficient alternative mode of action for monoclonal antibodies. This is desirable for persistent bacterial infections where the host immune system is not able to eliminate the pathogenic bacteria by itself. First, it is possible to target proteins responsible for the adhesion of bacteria to host cell receptors. This is an important mechanism for bacterial infection that allows the colonization of host tissues and also the entry of bacteria into the cells in the case of intracellular bacterial pathogens [START_REF] Pizarro-Cerdá | Bacterial adhesion and entry into host cells[END_REF]. It can be inhibited by monoclonal antibodies binding and neutralizing the appropriate targets. For instance, it has been shown that monoclonal antibodies binding to Bordetella pertussis surface proteins could efficiently prevent the adhesion of the bacteria to human cells [START_REF] Yougbare | Anti-FIM and Anti-FHA Antibodies Inhibit Bordetella pertussis Growth and Reduce Epithelial Cell Inflammation Through Bacterial Aggregation[END_REF]. Additionally, the binding to an antigen with the fragment antigen-binding (Fab) domains is not the only feature of monoclonal antibodies [START_REF] Lu | Beyond binding: Antibody effector functions in infectious diseases[END_REF]. Indeed, antibodies also possess a fragment crystallizable (Fc domain) [START_REF] Lu | Beyond binding: Antibody effector functions in infectious diseases[END_REF] Five different antibody isotypes exist in human and mouse: IgG, IgM, IgA, IgD and IgE. IgG, IgD and IgE are secreted as monomers, while IgM is secreted as a pentamer (as shown here) or hexamer, and IgA as a dimer (as shown here) or monomer. The antibody heavy chains are represented in blue while the light chains are represented in red. All the antibody isotypes possess fragment antigen-binding (Fab) domains, responsible for the binding to target antigens, and a fragment crystallizable (Fc) domain, that mediates antibody-dependent effector functions through interactions with cell surface receptors (Fc receptors) and complement proteins. of antibody effector functions. For monoclonal antibodies binding to bacterial surface antigens, the Fc-mediated effector functions are a crucial aspect that determines their efficiency to directly eliminate bacteria.

The Fc domain of therapeutic monoclonal antibodies can interact with Fc receptors (FcRs) present on other immune cells, in the same way as normal human antibodies do. If

Lysis of microbes

Antibodies

Neutralization of microbes and toxins

Fcγ receptor

Opsonization and phagocytosis of microbes

Phagocytosis of microbes opsonized

with complement fragments (e.g., C3b) important role in the immune response [START_REF] Moore | Igietseme. Fc receptor regulation of protective immunity against Chlamydia trachomatis[END_REF]. Therefore ADCC could be an efficient effector mechanism to target intracellular bacteria with monoclonal therapeutic antibodies. To our knowledge, there are no antibacterial monoclonal antibodies against intracellular bacteria under clinical trial yet, although monoclonal antibodies show potential to treat infections by intracellular bacteria, such as L. pneumophila [START_REF] Weber | Dissecting the Contribution of IgG Subclasses in Restricting Airway Infection with Legionella pneumophila[END_REF].

C3b receptor

Complement activation

NK cell

Phagocyte

Antibodydependent cellular cytotoxicity

Inflammation

Finally, IgM or IgG antibodies bound to a bacterial surface can trigger the activation of the classical pathway of complement. We will describe this effector function in more detail in the next section.

The complement system: a main antibody effector function against bacteria

As mentioned in the previous section, an immune effector function that can be triggered by bacteria-specific antibodies is the activation of the complement system by the classical pathway. The complement system is part of the innate immunity and is constituted of over 40 different serum and cell surface proteins [START_REF] Ricklin | Complement: A key system for immune surveillance and homeostasis[END_REF]. This innate system can be activated by different triggers, and follows a cascade of activation on cell surfaces (termed "complement cascade"), one protein activating the next one. The complement proteins interact with each other and with other components of the immune system in three connected pathways, displayed on figure I.3 and that we will describe in the next paragraphs. The complement system is tightly regulated by several mechanisms, so that it activates only on the surface of microbes and disfunctioning host cells but not on healthy host cells or in solution, which we will not go over in detail here.

The classical pathway

A first pathway of the complement system is the classical pathway. It will be our main focus here since it is the only complement pathway that involves antibodies, constituting one of the bridges between innate and adaptive immunity. For this reason, the classical pathway of complement is an effector function that can be harnessed by therapeutic monoclonal antibodies.

Chapter I. Introduction

The classical pathway is indeed initiated by the binding of antibodies of the IgM or IgG isotypes on a cell surface, for example the surface of a pathogenic bacteria. The main step for the activation of the classical pathway is then the binding of the complement protein C1q to one surface-bound IgM or to several surface-bound IgGs [START_REF] Murphy | Janeway's Immunobiology[END_REF], which will be our main focus during this thesis. C1q is a large multimeric protein (molecular weight of 410 kDa), composed of six chains that form a structure resembling an umbrella (see schematic on figure I.3). Each of the chains possesses a globular head connected by a collagen arm to a central stalk that links the six chains together. On each head of the C1q molecule, there is a domain that can bind specifically to a region of the Fc domain of IgM or IgG antibodies [30]. In this way, the C1q protein performs a recognition function of surface-bound IgM or IgG. Only certain subclasses of IgG can activate the classical pathway efficiently: IgG1, 2 and 3 in human, and IgG2a, 2b and 3 in mouse.

The IgM antibody is a multimeric molecule, either secreted as a pentamer (as shown on figure I.1) [START_REF] Perkins | Solution structure of human and mouse immunoglobulin M by synchrotron X-ray scattering and molecular graphics modelling. A possible mechanism for complement activation[END_REF] or less frequently an hexamer [START_REF] Hughey | Production of IgM Hexamers by Normal and Autoimmune B Cells: Implications for the Physiologic Role of Hexameric IgM[END_REF][START_REF] Müller | High-resolution structures of the IgM Fc domains reveal principles of its hexamer formation[END_REF]. Both pentameric and hexameric IgM antibodies adopt a C6-symmetric hexameric staple-like structure once bound to a surface [START_REF] Sharp | Insights into IgM-mediated complement activation based on in situ structures of IgM-C1-C4b[END_REF].

This change of conformation exposes the C1q binding site present on each of the Fc domains of the IgM, in an ideal conformation for the docking of the hexameric C1q protein [START_REF] Sharp | Insights into IgM-mediated complement activation based on in situ structures of IgM-C1-C4b[END_REF][START_REF] Czajkowsky | The human IgM pentamer is a mushroom-shaped molecule with a flexural bias[END_REF].

Regarding the activation of complement by IgG antibodies, which are secreted as monomers as shown on figure I.1, it has long been unclear how many IgG molecules are required for proper C1q binding. Early studies proposed that two or even one surface-bound IgG antibodies were sufficient for C1q recruitment. However, more recently Diebolder et al. have demonstrated that six IgG molecules binding to antigens on a cell surface can assemble in an hexameric shape that exposes the C1q binding site on each of the six IgG Fc domains [START_REF] Diebolder | Complement is activated by IgG hexamers assembled at the cell surface[END_REF].

The C1q protein can then dock to this IgG assembly with a high avidity, each of the six C1q globular heads binding to an IgG Fc domain. This is now thought to constitute the most efficient conformation of surface-bound IgG antibodies for C1q binding [START_REF] Diebolder | Complement is activated by IgG hexamers assembled at the cell surface[END_REF][START_REF] Ugurlar | Structures of C1-IgG1 provide insights into how danger pattern recognition activates complement[END_REF][START_REF] Wang | Molecular Basis of Assembly and Activation of Complement Component C1 in Complex with Im[END_REF]. In general, for both IgM or IgG antibodies, the binding of C1q requires a specific conformation of the antibodies that happens only when they are bound to a surface. This ensures that free circulating IgM or IgG cannot bind C1q and trigger the complement system in solution.

C1q is present in vivo as a complex with two C1r and two C1s proteases (C1qr 2 s 2 ) named C1 [START_REF] Arlaud | Structural biology of the C1 complex of complement unveils the mechanisms of its activation and proteolytic activity[END_REF][START_REF] Poon | Conformation and restricted segmental flexibility of C1, the first component of human complement[END_REF]. Upon binding of C1q to surface-bound antibodies, the C1 complex changes conformation, which activates the C1r proteases that then cleave and activate C1s [START_REF] Gaboriaud | Structure and activation of the C1 complex of complement: Unraveling the puzzle[END_REF]. C1s subsequently cleaves the C4 circulating complement protein into C4a and C4b, resulting in the covalent deposition of C4b on the bacterial surface in the immediate proximity of the activated C1-antibody complex. C4b then binds C2, which is in turn cleaved by C1s into C2a and C2b, generating the C4b2b classical pathway C3 convertase [START_REF] Murphy | Janeway's Immunobiology[END_REF].

The lectin and alternative pathways

The generation of a C3 convertase is the converging point of the different complement pathways. In the lectin pathway, functionally similar to the classical pathway, ficolin or Pathogens can be recognized by the complement system through three separate mechanisms which correspond to different activation pathways: the lectin, classical and alternative pathway (top row).

The three pathways converge in a central step that acts as an amplification loop, which results in the coating of the pathogen surface with large amount of the complement protein C3b (middle row).

The complement cascade then leads to several effector functions that contribute directly or indirectly to the elimination of the pathogen: inflammation, opsonization for phagocytosis and formation of a membrane-attack complex that causes cell lysis (bottom row).

Chapter I. Introduction

Finally, in the alternative pathway, the central complement protein C3, present in the serum and normally inert, is constantly cleaved at a low rate into C3a and C3b fragments [START_REF] Pangburn | Formation of the initial C3 convertase of the alternative complement pathway: Acquisition of C3b-like activities by spontaneous hydrolysis of the putative thioester in native C3[END_REF]. This cleavage exposes a reactive thioester on C3b than may then bind covalently to cell surfaces or is otherwise quickly inactivated by hydrolyzation, preventing the further activation complement in solution. The binding of C3b to surfaces is regulated so that it is amplified on foreign surfaces but restricted on host cells. Once C3b is bound to a cell surface, it can recruit the plasma protein Factor B, which can in turn be cleaved by the protease factor D [START_REF] Harboe | The alternative complement pathway revisited[END_REF]. This forms the alternative pathway C3 convertase, the C3bBb complex, stabilized by the protein properdin [START_REF] Murphy | Janeway's Immunobiology[END_REF].

Amplification and completion of the complement cascade

The C3 convertases generated by either of the three pathways of the complement system can in turn trigger again the alternative pathway by cleaving C3 and inducing surfacebinding of C3b. This forms a very efficient amplification loop which quickly increases the density of C3b and C3bBb deposited on the surface [START_REF] Murphy | Janeway's Immunobiology[END_REF]. The increase in density leads to the more likely formation of larger complexes by adding C3b to the C3 convertase, forming either C4b2b3b or C3bBb3b [START_REF] Ricklin | Complement: A key system for immune surveillance and homeostasis[END_REF]. These complexes then act as a protease to cleave another protein of the complement pathway, C5, which initiates the activation of the late steps of complement activation.

The cleavage of C5 releases the C5a and C5b fragments. C5b can then assemble with the complement proteins C6 and C7, forming a C5b-C6-C7 complex than inserts into the cell membrane. Through interaction with C8, this complex induces the recruitment of several C9 protein units that assemble to form a circular lytic pore in the membrane (see figure

I.
3) [START_REF] Tegla | Membrane attack by complement: The assembly and biology of terminal complement complexes[END_REF]. This pore, called the membrane attack complex (MAC), allows the free passage of water and ions and thus leads to the rapid osmotic swelling and bursting of the pathogenic cell [START_REF] Murphy | Janeway's Immunobiology[END_REF].

The formation of MAC is the terminal step of the complement pathway and is an effector function that allows the destruction of the targeted cell. However, this complement function is only efficient against Gram-negative bacteria, as the thick peptidoglycan layer of the membrane of Gram-positive bacteria cannot be penetrated by MACs [START_REF] Joiner | Studies on the mechanism of bacterial resistance to complement-mediated killing. III. C5b-9 deposits stably on rough and type 7 S. pneumoniae without causing bacterial killing[END_REF]. Additionally, certain bacterial specie have developed defense mechanisms that prevent the full formation of the MAC, for example B. burgdorferi [START_REF] Lambris | Complement evasion by human pathogens[END_REF]. In fact, the action of the complement system on pathogenic bacteria often relies on other effector functions than the formation of MAC [START_REF] Ricklin | Complement: A key system for immune surveillance and homeostasis[END_REF][START_REF] Dunkelberger | Complement and its role in innate and adaptive immune responses[END_REF].

Other effector functions of complement

First, C3a, C4a and C5a, the smaller fragments released by the cleavage of respectively C3, C4 and C5, induce an acute inflammatory response [START_REF] Dunkelberger | Complement and its role in innate and adaptive immune responses[END_REF][START_REF] Haas | Anaphylatoxins: Their role in bacterial infection and inflammation[END_REF][START_REF] Klos | The role of the anaphylatoxins in health and disease[END_REF]. All three fragments bind to mast cells and induce degranulation, releasing vasoactive mediators such as histamine that facilitate the infiltration of white blood cells into the infected tissue. C3a and C5a are also potent chemoattractants for effector cells, namely monocytes, neutrophils and macrophages.

11

Through this mechanism, these phagocytic cells are recruited to the site of complement activation where they can participate in the antibacterial immune response.

In addition, several of the complement proteins that coat the surface of the bacteria targeted by the complement system, in particular C3b and C4b, act as opsonins that facilitate phagocytosis [START_REF] Van Lookeren Campagne | Macrophage complement receptors and pathogen clearance[END_REF]. For example, the complement receptor CR1 present on neutrophils and macrophages binds to C3b and C4b [START_REF] Krych-Goldberg | Structure-function relationships of complement receptor type 1[END_REF] and can in this way induce phagocytosis of the opsonized cells. This mechanism is enhanced by additional interactions of bacteria-bound

IgG antibodies with Fcγ receptors on the phagocytic cells and by activation of macrophages by the proinflammatory cytokine IFN-γ [START_REF] Murphy | Janeway's Immunobiology[END_REF].

These two additional complement effector functions, proinflammatory signaling and facilitation of phagocytosis, are essential in the complement-mediated defense against most bacterial pathogens.

Central role of complement in the defense against bacterial pathogens

Although various bacteria have acquired a range of different complement evasion mechanisms throughout evolution [START_REF] Lambris | Complement evasion by human pathogens[END_REF][START_REF] Garcia | Novel Evasion Mechanisms of the Classical Complement Pathway[END_REF], the complement system and in particular the classical pathway is nonetheless the main defense mechanism against many bacterial pathogens. For example, there are numerous evidences that complement activation by the classical pathway is crucial in the host defense against Neisseria meningitidis [START_REF] Hellerud | Critical roles of complement and antibodies in host defense mechanisms against Neisseria meningitidis as revealed by human complement genetic deficiencies[END_REF][START_REF] Lewis | Meningococcal disease and the complement system[END_REF] and the presence of serum bactericidal antibodies (SBA, in vitro assay determining bacterial killing by serum samples containing antibodies in presence of complement) is the main correlate for protection against meningococcal disease [START_REF] Frasch | Bactericidal antibody is the immunologic surrogate of protection against meningococcal disease[END_REF]. For Vibrio cholerae, there is evidence of efficient bacterial lysis by antibody and complement and SBA is also good surrogate for protection in this case [START_REF] Attridge | Susceptibility of Vibrio cholerae O139 to antibody-dependent, complement-mediated bacteriolysis[END_REF]. Regarding Bordetella pertussis, which cause the infectious whooping cough disease, it is known that complement-mediated killing is important in vaccine-induced protection [START_REF] Geurtsen | Importance of (antibodydependent) complement-mediated serum killing in protection against Bordetella pertussis[END_REF]. The activation of the classical pathway of complement is also instrumental in the protection against Staphylococcus pneumoniae, an example of a Gram-positive bacteria efficiently targeted by the complement system [START_REF] Gil | Streptococcus pneumoniae interactions with the complement system[END_REF]. It has also been shown that classical pathway activation is essential over other antibody-mediated functions in the defense against several strains of Salmonella [START_REF] Gondwe | Importance of antibody and complement for oxidative burst and killing of invasive nontyphoidal Salmonella by blood cells in Africans[END_REF][START_REF] Rossi | The essential role of complement in antibody-mediated resistance to Salmonella[END_REF]. As a final example, it was also recently demonstrated that the activation of complement was the main antibody effector function in the protection against Neisseria gonorrhoeae with monoclonal antibodies [START_REF] Gulati | Complement alone drives efficacy of a chimeric antigonococcal monoclonal antibody[END_REF].

As we discussed in the previous section, Fc-mediated effector functions are central in the efficacy of antibodies to eliminate pathogenic bacteria, since the simple binding of antibodies to bacterial antigens is often not sufficient. Among these effector functions, the activation of the classical pathway of complement is the main one in the defense against many bacterial pathogens. Thus, in the aim of developing efficient antibacterial therapeutic antibodies as a potential solution to antibiotic resistance, it seems crucial to assess antibody-dependent complement activation. Therapeutic antibody candidates could be selected based on their efficiency to activate the classical pathway.

Activation of the complement system by monoclonal antibodies

It is well known for over a century that the serum of immunized human or animals is able to kill pathogenic bacteria in vitro [START_REF] Nesargikar | The complement system: History, pathways, cascade and inhibitors[END_REF]. This ability is due to a polyclonal mixture of bacteria-specific antibodies present in the serum that activate the classical pathway of complement, leading to complement-mediated cell lysis. This phenomenon is now widely used to assess the quality of complement-activating immune responses. Indeed, the bactericidal activity of serum samples in the presence of complement is commonly used as a correlate of protection after vaccination against bacterial pathogens, such as V. cholerae [START_REF] Attridge | Susceptibility of Vibrio cholerae O139 to antibody-dependent, complement-mediated bacteriolysis[END_REF] or N.

meningitidis [START_REF] Giuliani | Bactericidal antibodies against hypervirulent Neisseria meningitidis C field strains following MenC-CRM or MenACWY-CRM priming and MenACWY-CRM booster in children[END_REF][START_REF] Neri | Evaluation of Meningococcal Serogroup C Bactericidal Antibodies after Primary Vaccination: A Multicentre Study, Italy[END_REF][START_REF] Sadarangani | Persistence of immunity after vaccination with a capsular group B meningococcal vaccine in 3 different toddler schedules[END_REF][START_REF] Vesikari | Meningococcal serogroup B bivalent rLP2086 vaccine elicits broad and robust serum bactericidal responses in healthy adolescents[END_REF]. Serum bactericidal antibody measurements can also be used to evaluate the efficiency of the natural immunity in populations exposed to bacterial epidemics [START_REF] Trotter | Seroprevalence of Bactericidal, Specific IgG Antibodies and Incidence of Meningitis Due to Group A Neisseria meningitidis by Age in Burkina Faso 2008[END_REF].

Therefore, it is obvious that polyclonal bacteria-specific antibodies are able to activate the complement system.

However, in the perspective of using antibodies as clinical treatments for bacterial infections, there is a need for well-defined antibody preparations. Polyclonal antibody mixtures, for example purified from the serum of immunized individuals, do not fit this purpose. It is indeed impossible to produce such antibody preparations in large quantities with low batchto-batch variability. Instead, monoclonal antibodies preparations are needed in this case.

More recently, studies have started to assess the potential of monoclonal antibodies to trigger the complement system. There is now abundant evidence that certain monoclonal antibodies can indeed do so efficiently. For example, Szijártó et al. described three different humanized monoclonal antibodies against the LPS of a multidrug-resistant pathogenic E. coli strain that could each potently induce the activation of the classical pathway of complement, both in vitro and in vivo [START_REF] Szijártó | Bactericidal monoclonal antibodies specific to the lipopolysaccharide O antigen from multidrug-resistant Escherichia coli clone ST131-O25b:H4 elicit protection in mice[END_REF] To further enhance the capacity of monoclonal IgG antibodies to trigger the classical pathway, it is possible to modify their Fc domain by antibody engineering. A general strategy is to enhance the Fc-Fc interactions between several antibodies, in order to facilitate the formation of the IgG hexamer that can then bind C1q with very high avidity as mentioned before [START_REF] Diebolder | Complement is activated by IgG hexamers assembled at the cell surface[END_REF]. This can be done by introducing specific point mutations to the amino acid sequence of the IgG Fc domain [START_REF] Diebolder | Complement is activated by IgG hexamers assembled at the cell surface[END_REF][START_REF] Vor | Monoclonal antibodies effectively potentiate complement activation and phagocytosis of Staphylococcus epidermidis in neonatal human plasma[END_REF][START_REF] Cook | Antibodies That Efficiently Form Hexamers upon Antigen Binding Can Induce Complement-Dependent Cytotoxicity under Complement-Limiting Conditions[END_REF][START_REF] Jong | A Novel Platform for the Potentiation of Therapeutic Antibodies Based on Antigen-Dependent Formation of IgG Hexamers at the Cell Surface[END_REF]. It is also possible to modify the glycan present on the Fc domain of IgG antibody, which can greatly enhance the C1q binding activity, most likely also through improved IgG hexamer formation [START_REF] Dekkers | Decoding the human immunoglobulin G-glycan repertoire reveals a spectrum of Fc-receptor-and complement-mediated-effector activities[END_REF][START_REF] Peschke | Fc-Galactosylation of Human Immunoglobulin Gamma Isotypes Improves C1q Binding and Enhances Complement-Dependent Cytotoxicity[END_REF].

The question that remains is how to efficiently discover new complement-activating antibacterial monoclonal antibodies. It is in general difficult to anticipate the complementactivation potency of monoclonal antibodies based on simple criteria, such as affinity towards antigen, isotype and subclass, or glycosylation pattern [START_REF] Goldberg | Antibody-mediated complement activation in pathology and protection[END_REF]. Other parameters are at play, such as the exact antigen and epitope targeted as well as its density on the surface [START_REF] Diebolder | Complement is activated by IgG hexamers assembled at the cell surface[END_REF][START_REF] Cragg | Complement-mediated lysis by anti-CD20 mAb correlates with segregation into lipid rafts[END_REF],

the orientation of the bound antibodies due to epitope geometry [START_REF] Cragg | Complement-mediated lysis by anti-CD20 mAb correlates with segregation into lipid rafts[END_REF][START_REF] Bindon | Importance of antigen specificity for complement-mediated lysis by monoclonal antibodies[END_REF][START_REF] Teeling | The Biological Activity of Human CD20 Monoclonal Antibodies Is Linked to Unique Epitopes on CD20[END_REF] or the distance of the epitope to the cell membrane [START_REF] Cleary | Antibody Distance from the Cell Membrane Regulates Antibody Effector Mechanisms[END_REF][START_REF] Pawluczkowycz | Binding of Submaximal C1q Promotes Complement-Dependent Cytotoxicity (CDC) of B Cells Opsonized with Anti-CD20 mAbs Ofatumumab (OFA) or Rituximab (RTX): Considerably Higher Levels of CDC Are Induced by OFA than by RTX[END_REF][START_REF] Xia | Efficient complement-mediated lysis of cells containing the CAMPATH-1 (CDw52) antigen[END_REF]. These parameters are more difficult to apprehend and cannot be modified by antibody engineering. Therefore, we think that an effective approach to discover efficient complement-activating monoclonal antibodies is to take advantage of the natural diversity of antibodies that are generated during an immune response against bacteria.

This can be achieved by screening antibody repertoires of individuals that are immune to a given bacteria after an infection or a vaccination in order to select the antibodies that are most efficient at activating the classical pathway of complement. The screening method should enable single-cell resolution, since the aim is to select monoclonal antibodies as explained before and each B cell secretes a single antibody clone. Additionally a high throughput is needed given the large diversity of antibodies generated during an immune response. For each individual antibody, the method should allow a quantitative measurement of the binding of the antibody to the target bacteria and of the antibody-induced activation of complement.

Technologies for single-cell antibody secretion measurement

As we discussed in the previous section, the discovery of new complement-activating monoclonal antibodies from natural antibody repertoires requires high-throughput technologies with single-cell resolution. The goal would be to mine populations of antibody-secreting B cells to look for bacteria-specific antibodies most effective for complement activation. Since the antibody is a secreted protein, a link needs to be established between the secreting cell and the secreted antibodies to enable the detection of positive cells. For this reason, standard flow cytometry methods do not apply. Instead, this requires specific techniques that more generally enable single-cell protein secretion measurements [START_REF] Bucheli | Measuring single-cell protein secretion in immunology: Technologies, advances, and applications[END_REF]. These techniques are not limited to the analysis of antibody secretion but can be applied to a large range of secreted protein analytes, for example cytokines, chemokines or enzymes. An alternative method to screen antibody repertoires would be to sort memory B cells which do not secrete antibodies but express them on their surface, but we will not describe this alternative here.

In this section, we will first give an overview of the different technologies available for protein secretion analysis at the single-cell level, summarized on figure I.4, and then move on to a description of the DropMap technology [START_REF] Bounab | Dynamic single-cell phenotyping of immune cells using the microfluidic platform DropMap[END_REF][START_REF] Eyer | Single-cell deep phenotyping of IgG-secreting cells for high-resolution immune monitoring[END_REF] and why we chose to use it for this thesis.

The most basic single-cell protein secretion measurements are ELISpot and its derivatives (e.g. FluoroSpot) [START_REF] Saletti | Enzyme-linked immunospot assays for direct ex vivo measurement of vaccine-induced human humoral immune responses in blood[END_REF]. In these techniques adapted from sandwich immunoassays, cells are placed at the bottom of a well or on a detection membrane. The secreted proteins are captured on the surface in the proximity of the secreting cells and later detected with a secondary probe after an incubation period. The secreting cells can then be identified as forming distinct spots on the surface. Owing to their simplicity and low cost, these techniques are now widespread in laboratories and clinical settings, but they are also very limited. Indeed, their output is mostly the frequency of secreting cells, without quantitative measurements of secretion rates or dynamics for example, and they also have limited throughput and multiplexing possibilities. Additionally, the spot-based techniques do not allow the recovery of positives cells.

Other well-established protein secretion measurement techniques are adaptations from standard flow cytometry methods. As mentioned above, a physical link between the cells and their secreted proteins needs to be established to be able to analyze the secretome of single-cells with cytometry. This can be achieved through two main strategies. First, secretion inhibitors can be added to prevent the secretion of the protein analyte; the analyte then accumulates inside the cells where it can be stained with fluorescent detection reagents [START_REF] Jung | Detection of intracellular cytokines by flow cytometry[END_REF].

Alternatively, the protein analyte can be captured on the surface of the secreting cell, functionalized by different methods, such as biotin-avidin systems [START_REF] Manz | Analysis and sorting of live cells according to secreted molecules, relocated to a cell-surface affinity matrix[END_REF], lipid-anchored capture antibodies [START_REF] Kida | Cell surface-fluorescence immunosorbent assay for real-time detection of hybridomas with efficient antibody secretion at the single-cell level[END_REF] or bi-specific antibody-antibody complexes [START_REF] Pinder | Isolation and Characterization of Antigen-Specific Plasmablasts Using a Novel REFERENCES Flow Cytometry-Based Ig Capture Assay[END_REF]. By contrast with the spotbased methods, cytometry-based methods can have very high throughput (>10 4 cells/s), measure many secreted analytes and cell markers in parallel (panels with up to 30 parameters [START_REF] Liechti | OMIP-060: 30-Parameter Flow Cytometry Panel to Assess T Cell Effector Functions and Regulatory T Cells[END_REF]) and allow the sorting of positive cells. However, results obtained with these techniques are usually analyzed as population level-data (frequencies within gates) and do not enable quantitative single-cell measurements or fine selection of the sorted population.

In the last fifteen years, novel methods relying on microfluidic platforms have emerged, that enable much better resolved measurements of protein secretion by single-cells. These microfabricated systems allow the compartmentalization of single-cells in very small volumes,

6 Technologies for single-cell antibody secretion measurement 15 typically tens of picoliters to nanoliters. In these compartments, even analytes secreted at low rates by a unique cell rapidly reach concentrations at which they can be reliably detected.

Three main types of microfluidic platforms exist for the analysis of protein secretion at the single-cell level, based on wells, chambers or droplets.

First, in well-based methods the principle is to isolate single cells in microscopic wells, usually molded in polydimethylsiloxane (PDMS). Microfabricated chips can contain tens of thousand of wells, and each well constitutes a separate compartment to perform the protein The single-cell protein secretion methods include spot-and cytometry-based assays, as well as different microfluidic methods (well-, chamber-and droplet-based assays). Common features of all described technologies are the spatial separation of the individual cells, and their reliance on labeled reporters, especially antibodies and variants thereof. However, all the described technologies differ in several respects, for example in the read-out obtained, ease of application, multiplexing potential, throughput or temporal resolution, and therefore have different applications and limitations. The spot-based assays ELISpot and FluoroSpot, based on the seeding of a heterogeneous population of cells on a protein-binding membrane, allow for the rapid and simple determination of the frequency of secreting cells based on an antibody-based immunoassay. The frequency of cells stained positively with a detection reagent can be assessed using flow cytometry-based methods. With this technology, the heterogeneous population of cells is measured temporally and spatially separated from each other, and positive events are gated and counted as a frequency of total input cells. The three microfluidic methods shown on the right are all based on the same concept of individually trapping and analyzing cells in small volumes. Here, the secreted molecule rapidly reaches a detectable concentration due to the small volume, allowing more accurate and rapid quantification of protein secretion. The microfluidic methods can be further divided based on the enclosing structure used. Well-based assays encapsulate cells in small circular or square wells, whereas chamber-based use elongated chambers that fit a higher volume and allow spatially separating the detection of various secreted proteins. Lastly, droplet-based assays use two immiscible fluids to generate an emulsion to encapsulate individual cells in small volumes, and enable a surface-free, volume-based analysis of protein secretion in high-throughput and with high precision. (legend adapted from the original paper)

Chapter I. Introduction secretion measurement assay (commonly a fluorescence sandwich immunoassay). Two main approaches exist for the analysis of proteins secreted in the wells. It is possible to close the wells with a functionalized glass plate that then serves as the capture surface (referred to as micro-engraving) [START_REF] Ogunniyi | Screening individual hybridomas by microengraving to discover monoclonal antibodies[END_REF][START_REF] Ogunniyi | Profiling human antibody responses by integrated single-cell analysis[END_REF]. Alternatively, in open-well formats the secreted proteins are directly captured on the functionalized surfaces of the microwells [START_REF] Jin | Rapid isolation of antigen-specific antibody-secreting cells using a chip-based immunospot array[END_REF]. The main advantages of microwells systems are the small assay volumes, leading to a high sensitivity (detection limit as low as 10 3 molecules) with reduced incubation times (2-5 hours), and the high throughput (10 3 -10 5 individual cells in parallel). These systems also allow multiplexing to detect several secreted proteins in parallel, although this is limited by the number of available fluorescence detection channels (usually 4 to 6). Finally, since the position of the single cells of interest is known, it is possible to recover them after the secretion measurement.

A second category of microfluidic platforms comprises microchambers assays. In these systems that share similarities with well-based platforms, the analyzed cells are trapped in separated microchambers, also mostly fabricated in PDMS, that can be sealed with valves [START_REF] Dietsche | Multiplexed analysis of signalling proteins at the single-immune cell level[END_REF][START_REF] Ma | A clinical microchip for evaluation of single immune cells reveals high functional heterogeneity in phenotypically similar T cells[END_REF]. The added value is the possibility to precisely add or remove reagents throughout the experiments, at the cost of a higher complexity of fabrication and operation.

The detection of the target analytes is also typically done with sandwich immunoassays, with the capture taking place either on functionalized beads [START_REF] Junkin | High-Content Quantification of Single-Cell Immune Dynamics[END_REF][START_REF] Armbrecht | Quantification of Protein Secretion from Circulating Tumor Cells in Microfluidic Chambers[END_REF] or glass slides [START_REF] Ma | A clinical microchip for evaluation of single immune cells reveals high functional heterogeneity in phenotypically similar T cells[END_REF][START_REF] Su | Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line[END_REF].

The perks of such systems are the same as that of well-based systems, but with higher control and multiplexing possibilities. On the other hand the throughput is generally lower with around a thousand of chambers per microfluidic chip. Several companies commercialize chamber-based microfluidic systems, for example IsoPlexis or Berkeley Lights (recently merged as PhenomeX).

The last type of microfluidic platform is droplet-based assay. In these techniques, the single cells are encapsulated with an emulsion of two immiscible fluids, typically in an aqueous inner phase surrounded by an inert oil phase [START_REF] Köster | Drop-based microfluidic devices for encapsulation of single cells[END_REF]. With microfluidic droplet-makers, it is possible to produce monodisperse emulsions of picoliter-sized droplets with a very high throughput (several kHz). The cell concentration is adjusted so that most droplets contain either one cell or none, to then enable single-cell in-droplet assays. There are two main approaches to analyze the cell-containing droplets: flow-based or stationary. In flow-based droplets microfluidics, the droplets are passed at high rates through a detector that measures fluorescence, in a similar manner to flow cytometry. The secreted protein can be detected by FRET [START_REF] Dhar | Functional profiling of circulating tumor cells with an integrated vortex capture and single-cell protease activity assay[END_REF], or fluorescence relocation onto a target cell [START_REF] Gérard | High-throughput single-cell activity-based screening and sequencing of antibodies using droplet microfluidics[END_REF][START_REF] Shembekar | Single-Cell Droplet Microfluidic Screening for Antibodies Specifically Binding to Target Cells[END_REF] or a functionalized bead [START_REF] Gérard | High-throughput single-cell activity-based screening and sequencing of antibodies using droplet microfluidics[END_REF][START_REF] Mazutis | Single-cell analysis and sorting using droplet-based microfluidics[END_REF]. Flow-based droplet microfluidics platforms only allow an endpoint measurement for each droplet, so that the droplets containing secreting cells have to be incubated beforehand. Because of this single time point measurement, flow-based platforms cannot be applied to the precise analysis of secretion dynamics, but they are well-suited for screening and sorting of rare populations of secreting cells [START_REF] Gérard | High-throughput single-cell activity-based screening and sequencing of antibodies using droplet microfluidics[END_REF][START_REF] Shembekar | Single-Cell Droplet Microfluidic Screening for Antibodies Specifically Binding to Target Cells[END_REF][START_REF] Mazutis | Single-cell analysis and sorting using droplet-based microfluidics[END_REF]. By contrast, in stationary droplet-based microfluidic systems, the droplets are immobilized in an observation compartment after their formation [START_REF] Bounab | Dynamic single-cell phenotyping of immune cells using the microfluidic platform DropMap[END_REF][START_REF] Konry | Droplet-based microfluidic platforms for single T cell secretion analysis of IL-10 cytokine[END_REF][START_REF] Konry | Live single cell functional phenotyping in droplet nano-liter reactors[END_REF]. With similar methods for the detection of secretion as in flow-based systems, this allows multiple imaging time points to precisely measure secretion dynamics over time and quantify protein secretion rates. A constraint of droplet-based systems is that the content of the droplets cannot be modified after their formation, making it impossible to rinse or add reagents to conduct multi-step assays. Droplet-based microfluidics platforms for identification and sorting of cells of interest are developed and commercialized by several companies, for example Sphere Fluidics or Saber Bio.

The characteristics and estimations of complexity and costs of the different methods for secretion analysis at the single-cell level are recapitulated in the table below:

DropMap technology

The DropMap technology developed by Eyer et al. [START_REF] Eyer | Single-cell deep phenotyping of IgG-secreting cells for high-resolution immune monitoring[END_REF] that was used in this thesis belongs to the category of stationary droplet-based microfluidic platforms. Compared to earlier stationary droplet methods [START_REF] Konry | Droplet-based microfluidic platforms for single T cell secretion analysis of IL-10 cytokine[END_REF], the throughput and the accuracy of the secretion measurements are widely improved. The DropMap platform was first developed to quantify antibody secretion by B cells and later used in several studies of antibody responses after vaccination [START_REF] Eyer | The Quantitative Assessment of the Secreted IgG Repertoire after Recall to Evaluate the Quality of Immunizations[END_REF][START_REF] Rybczynska | The impact of frost-damage on the quality and quantity of the secreted antigen-specific IgG repertoire[END_REF] or in autoimmune disease [START_REF] Canales-Herrerias | High-affinity autoreactive plasma cells disseminate through multiple organs in patients with immune thrombocytopenic purpura[END_REF]. However, it can also be applied to different cell types and various secreted proteins, such as cytokines [START_REF] Bounab | Dynamic single-cell phenotyping of immune cells using the microfluidic platform DropMap[END_REF], as long as they are detectable by sandwich immunoassay. Alternatively, the DropMap platform can also be used to probe NK cell function [START_REF] Subedi | An automated real-time microfluidic platform to probe single NK cell heterogeneity and cytotoxicity on-chip[END_REF][START_REF] Subedi | Single-Cell Profiling Reveals Functional Heterogeneity and Serial Killing in Human Peripheral and Ex Vivo-Generated CD34+ Progenitor-Derived Natural Killer Cells[END_REF]

Mouse model

For studies involving measurements of antibody secretion by B cells, we generally work with a mouse model. The main reason is that it is much more difficult to obtain antibody-

Encapsulation

Using a microfluidic droplet-maker chip, the B cells are then compartmentalized into 50 pL droplets along with different assay reagents. The chip allows to rapidly produce monodisperse droplets surrounded by an inert oil phase, with an exactly constant concentration of assay reagents in the different droplets. The repartition of the B cells in the droplets follows a Poisson distribution and the cell concentration is adjusted accordingly, so that most droplets are either empty or contain a single cell, with very few droplets containing more than one cell. This ensures that the measurement will be conducted on single cells. Even if a large proportion of droplets will be empty, the large total number of droplets enables a high throughput (typically 15 000-20 000 single cells in parallel per measurement).

Imaging

Following the encapsulation procedure, the droplets are flown in a glass observation chamber. The height of the chamber is slightly lower than the diameter of the droplets, which enables the immobilization of the droplets into a 2D array (see figure I.5c). The ports of the observation chamber are sealed, and the droplets contained in the chamber can then be imaged over-time with an automated epifluorescence microscope.

In-droplet bioassay

The detection of secreted proteins relies on paramagnetic nanoparticles that are functionalized to capture the target protein on their surface, as shown on figure I.5b for antibodies.

Approximately 1 300 nanoparticles are present in each droplet, and by applying a magnetic field, the nanoparticles in each droplet align into an elongated object called beadline on which the secreted protein will relocate. A second fluorescent detection reagent, specific to the analyzed protein (IgG on figure I.5b), is also present in the droplets, and the reagent and thus fluorescence will relocate in a concentration-dependent manner onto the beadline if the analyte is secreted. The readout of the assay in each droplet is the ratio of the fluorescence on the beadline against the background fluorescence (fluorescence relocation signal, see figure

I.5c and also figure II.1). This fluorescence relocation signal can be assigned to a precise concentration of analyte in the droplet using a calibration curve.

Analysis

The time-lapse images of the droplets are analyzed using a custom MATLAB software.

The first part of the analysis, fully automated, consists in the detection of the position of the droplets on the brightfield image, followed by the computation of the fluorescence relocation on the beadline for each droplet and time point. In a second part of the procedure, the droplets that contain a secreting cell are selected for further analysis. This is done with a semi-automated method that includes a selection step based on several criteria on the fluorescence relocation signal and a visual verification step. The visual verification of the images of the droplet is critical when analyzing very rare events, to obtain certitude of their nature and ensure that they are not due to artifacts. The easy access to droplet images is therefore a perk of the DropMap technology.

For the example of IgG antibodies, precise secretion rates (from 5 to 500 IgG/s) and affinities for an antigen (e.g. tetanus toxoid, K D between 0.1 and 500 nM) can be measured for the individual IgG-secreting cells [START_REF] Eyer | Single-cell deep phenotyping of IgG-secreting cells for high-resolution immune monitoring[END_REF]. This is possible because of the time-lapse imaging of the droplets that enables to follow the kinetics of IgG secretion.

Aim of the study -Complement activation at the single antibody level (developed in chapter III)

Our aim here is to develop a method to discover new complement-activating monoclonal antibodies targeting pathogenic bacteria. Building on the DropMap platform, we developed during this thesis an assay to quantify the binding of the antibody secreted by single B cells to target bacteria as well as the potential subsequent activation of the complement system.

The goal is first to better understand the requirements for efficient activation of the classical pathway by monoclonal antibodies, as well as establish an assay for the quantification of complement activation that could later be applied to the sorting of complement-activating antibodies.

The DropMap platform is well adapted to the objectives of our study. A first reason is that we expect efficient complement-activating antibodies to be rare events in the cell population that we want to screen. Indeed, only a fraction of the screened B cells will secrete an antibody (around 1% of spleen B cells) and only part of these antibodies will be targeting a bacterial antigen. Additionally, it is possible that not all bacteria-specific antibody will be able to activate the classical pathway of complement, further reducing the frequency of positive events. Therefore, a high throughput is necessary to find these rare B cells secreting an antibody of interest and the DropMap technology allows to typically measure around 20 000 single cells in parallel. A key perk of the DropMap platform for this study is its versatility, with the possibility to develop custom bioassays: the nanoparticles used for the capture can be functionalized in different ways to adapt the target of the bioassay [START_REF] Heo | Deep phenotypic characterization of immunization-induced antibacterial REFERENCES IgG repertoires in mice using a single-antibody bioassay[END_REF] and various detection reagents can be seamlessly used in the droplets. In this study, we bound inactivated bacteria to the nanoparticles in order to detect antibodies that can bind to these bacteria, measured by the fluorescence relocation on the beadline. Subsequent complement activation could also be detected by the recruitment of the C1q complement component on the surfaces of the bacteria, also measured by fluorescence relocation. We did not need any rinsing step for our developed bioassays, so that the impossibility to rinse inherent to droplet-based systems was not problematic. Furthermore, the DropMap platform allows quantitative time-lapse measurements that will be crucial here to precisely measure the binding of antibodies to bacteria but also characterize the antibody-dependent complement activation and distinguish particularly efficient antibodies. Finally, the sorting of cells of 21 interest is an envisioned objective in this study and the droplet format of DropMap assays allows easy transferring to a sorting platform, either a microfluidic sorter chip or even a standard FACS instrument after double-emulsification of the droplets.

After the development of a novel bioassay for the DropMap platform (described in section II.4), we have shown that we could screen repertoires of B cells to identify individual bacteriaspecific antibodies that could efficiently activate the classical pathway of complement (see results presented in chapter III). To our knowledge, this is the first report of a system that allows the detection of complement activation at the single antibody level with highthroughput screening capabilities. At the end of chapter III, we also present preliminary trials for the integration of our bioassay to a sorting pipeline, which would enable to retrieve sequences of selected complement-activating monoclonal antibodies. The study conducted during this thesis thus represents a first step towards establishing a high-throughput sorting method of complement-activating antibodies. Such a system could have implications for the development of new therapeutic antibacterial monoclonal antibodies particularly efficient at the activation of the complement system.

The important parameters of monoclonal antibodies

Beyond antibody-dependent complement activation, we are also interested in precisely characterizing the single antibodies that we detect with the DropMap platform. Antibodies indeed have different characteristics that determine their role, from the binding to a target antigen to the mediated downstream effector functions. This is of course relevant for the discovery of complement-activating monoclonal antibodies as discussed in the previous sections but can also more generally be applied to the study of antibody responses. The different features of antibodies include for example the affinity, the density and geometry of the targeted epitope or the isotype and subclass, and they are crucial both for antibodies produced during the adaptive response and for monoclonal antibodies used in therapy. It is possible to precisely characterize these parameters for each antibody detected with the DropMap platform described in the previous section. Here, we will detail the different parameters of antibodies and explain the importance of their precise determination, both for studies of antibody responses and for screening of monoclonal antibodies.

Affinity

The affinity of the variable antigen-binding region of the Fab domain towards specific antigens is the central characteristic of antibodies. The affinity refers to the dissociation constant K D (dimension of a concentration, often expressed in nM) that characterizes the binding of an antibody's antigen-binding domain to its target antigen. A simple physical interpretation is that if the concentration of the free form of an antibody is equal to its K D , half of the total antigen molecules are bound by the antibody. A lower K D value corresponds to a higher affinity and a more efficient binding to the antigen.

Chapter I. Introduction

The affinity of antibodies towards a given antigen increase progressively over the course of an immune response, which constitutes one of the hallmarks of adaptive immunity [START_REF] Eisen | Affinity enhancement of antibodies: how low-affinity antibodies produced early in immune responses are followed by high-affinity antibodies later and in memory B-cell responses[END_REF].

This increase in affinity is due to somatic hypermutation (SHM) of the immunoglobulin genes encoding for the antigen-binding variable regions of antibodies [START_REF] Rajewsky | Clonal selection and learning in the antibody system[END_REF]. Through this process driven by activation-induced cytidine deaminase [START_REF] Muramatsu | Class switch recombination and hypermutation require activation-induced cytidine deaminase (AID), a potential RNA editing enzyme[END_REF], the sequences and thus binding affinities of the variable domains displayed on the B cell receptors (BCRs) are vastly diversified.

The somatic hypermutation of B cells takes place in structures of secondary lymphoid organs called germinal centers that appear upon encounter of foreign antigens during infections or after immunizations [START_REF] Victora | Germinal Centers[END_REF]. In germinal centers, the different mutated B cells proliferate and compete for survival signals that depend on the affinity of their BCR towards the antigen, resulting in the selection of higher affinity B cells throughout the germinal center reaction.

The B cells then follow different cell fates based on their affinity [START_REF] Smith | The extent of affinity maturation differs between the memory and antibody-forming cell compartments in the primary immune response[END_REF][START_REF] Viant | Antibody Affinity Shapes the Choice between Memory and Germinal Center B Cell Fates[END_REF]. Lower affinities cells tend to differentiate into memory B cells, which can be later reactivated upon encounter of the same or similar antigens. Higher affinity cells preferentially become plasma cells that secrete antibodies [START_REF] Kräutler | Differentiation of germinal center B cells into plasma cells is initiated by high-affinity antigen and completed by Tfh cells[END_REF][START_REF] Phan | High affinity germinal center B cells are actively selected into the plasma cell compartment[END_REF]. The secreted affinity-matured antibodies thus have a high affinity for their target antigen. Alternatively, antigen-affine antibody secreting cells can be generated outside of germinal center by extrafollicular B cell responses, that typically lead to lower affinity antibodies [START_REF] Murphy | Janeway's Immunobiology[END_REF]. However, the affinity towards the target antigen is critical for an efficient antibody-mediated protection. Indeed, a high affinity allows tight binding of the antibody to its target, for example to neutralize a toxin or opsonize a pathogen and trigger various antibody-dependent effector functions. In addition, a high affinity enables efficient binding at low antibody concentrations, which is often the case for a given antibody clone in the blood.

It is therefore of great interest to have experimental systems that can quantify the affinity of individual antibodies. This can first be applied to the precise evaluation of the quality of antibody responses post vaccination [START_REF] Rybczynska | The impact of frost-damage on the quality and quantity of the secreted antigen-specific IgG repertoire[END_REF]. In the context of the development of monoclonal antibodies for therapy, it is also central to measure affinity. Depending on the mode of action of therapeutic monoclonal antibodies, different affinities can be desirable. For antibodies meant to neutralize a toxin or opsonize target pathogens or tumor cells, higher affinity antibodies will yield better results through more efficient binding and low dissociation. However, for antibodies designed as agonists for immune receptors, an intermediate affinity is sometimes desirable. In this case, the binding of the antibody is only the first step of a sequential process, usually an intracellular signaling cascade, so that the antibody does not need to remain bound to the receptor after activation. Instead, relatively lower affinities allow the antibody to dissociate and engage other receptors to amplify the signal, which leads to bell-shaped dose-response curves regarding the affinity as shown recently by Yu et al. [START_REF] Yu | Reducing affinity as a strategy to boost immunomodulatory antibody agonism[END_REF]. This emphasizes the need for methods that can not only detect and select the antibodies with the highest affinities, but also precisely quantify affinity in order to characterize the full distribution of affinities and/or select antibodies with specific desired affinities.

The standard format of the DropMap system for antibody secretion measurement, as described by Eyer et al. [START_REF] Eyer | Single-cell deep phenotyping of IgG-secreting cells for high-resolution immune monitoring[END_REF], allows the precise quantification of the affinity of antibodies towards soluble antigens. The only requirement is that it should be possible to label the antigen with a fluorophore, which is the case for virtually every protein antigen.

Epitope density

Another parameter that plays a major role in the function of antibodies is the density of the epitope that it targets. The epitope density is particularly relevant in the case of the opsonization of pathogens (e.g. bacteria or parasites), infected cells or tumor cells. A higher epitope density allows more antibodies of a given clone to bind to the cell surface. In addition, for high densities of epitopes, IgG antibodies can bind the target epitope with both of their Fab domains simultaneously, leading to much stronger binding, referred to as avidity.

Similarly, efficient binding of the pentameric or hexameric IgM antibody requires a high target epitope density. In general higher densities of target epitopes and bound antibodies leads to enhanced antibody-dependent effector functions. It is the case for antibody-dependent cellular cytotoxicity (ADCC), antibody-dependent cellular phagocytosis (ADCP) and activation of the complement system. These different functions also require different antibody density thresholds for activation [START_REF] Oostindie | Avidity in antibody effector functions and biotherapeutic drug design[END_REF]. tion of an hexamer of surface-bound IgGs [START_REF] Diebolder | Complement is activated by IgG hexamers assembled at the cell surface[END_REF]. For this reason, the efficient IgG-dependent activation of complement requires a high density of bound IgG antibodies and thus a high epitope density, which had been observed before in different studies [START_REF] Garred | The IgG Subclass Pattern of Complement Activation Depends on Epitope Density and Antibody and Complement Concentration[END_REF][START_REF] Giuntini | Combined roles of human IgG subclass, alternative complement pathway activation, and epitope density in the bactericidal activity of antibodies to meningococcal factor H binding protein[END_REF]. The density of target epitopes is therefore a critical parameter for the activation of the different antibody-dependent effector functions.

It is challenging to measure the target epitope density of single antibodies, as for a given antibody concentration, the density of binding of an antibody to a surface will also depend on its affinity for the epitope, as schematized on figure I.

Figure I.6: Effect of epitope density and affinity on the binding density of antibodies

A high epitope density and a high affinity are important for an antibody to bind densely to a pathogen surface. The resulting proximity of Fc domains of antibodies is necessary for the optimal activation of antibody-dependent effector functions.

that we developed to detect complement activation by single antibodies binding to target bacteria (see section II.4 in chapter II), we are able to measure the relative density of IgG or IgM antibodies bound to the surface of target bacteria. This parameter depends on both the affinity of the antibody and the density of target epitope. We are nonetheless able to distinguish antibodies that can bind with a very high density to the bacterial surface, probably due to both high affinity and epitope density. Expressed as monoclonal antibodies, these clones will likely be very efficient at the activation of the different antibody-dependent effector functions, although conditioned by other parameters: the epitope geometry and the isotype and subclass of the antibody.

Epitope geometry

The density of target epitopes can also be modulated by the epitope geometry. Certain epitopes might be less accessible due to geometrical constraints and thus bound by fewer antibodies. Furthermore, the geometry of the target epitope can also influence the antibodydependent effector functions, depending on the availability of the Fc domain of the antibody after binding to the epitope. For example regarding complement activation, it has been shown that the epitope geometry can directly impact the complement-activating efficiency of an antibody by influencing the orientation of the bound antibody [START_REF] Cragg | Complement-mediated lysis by anti-CD20 mAb correlates with segregation into lipid rafts[END_REF][START_REF] Bindon | Importance of antigen specificity for complement-mediated lysis by monoclonal antibodies[END_REF][START_REF] Teeling | The Biological Activity of Human CD20 Monoclonal Antibodies Is Linked to Unique Epitopes on CD20[END_REF]. The distance of the target epitope to the cell membrane was also demonstrated to play a role, with closer proximity favoring antibody-dependent C1q-binding [START_REF] Pawluczkowycz | Binding of Submaximal C1q Promotes Complement-Dependent Cytotoxicity (CDC) of B Cells Opsonized with Anti-CD20 mAbs Ofatumumab (OFA) or Rituximab (RTX): Considerably Higher Levels of CDC Are Induced by OFA than by RTX[END_REF] and subsequent complementdependent cell lysis [START_REF] Cleary | Antibody Distance from the Cell Membrane Regulates Antibody Effector Mechanisms[END_REF][START_REF] Xia | Efficient complement-mediated lysis of cells containing the CAMPATH-1 (CDw52) antigen[END_REF].

The geometry of target epitopes is not a parameter that we can probe with the DropMap technology, but we were able to identify differences in complement activation potency of IgG antibodies that were not linked to the binding density (see chapter III). These differences might be attributed to epitope-specific geometrical constraints.

Isotype and subclass

Finally, the isotype and subclass is a main characteristic of antibodies. Here, we will discuss the different antibody isotypes and subclasses in the mouse, because we usually work with mouse B cells with the DropMap platform, as we did in the studies presented in this thesis. Similarly as humans, mice express five different antibody isotypes: IgM, IgD, IgA, IgE and IgG (see figure I.1). These different isotypes can be expressed on the surface of B cells [START_REF] Murphy | Janeway's Immunobiology[END_REF], as part of the B cell receptor (BCR), or secreted by plasmablasts or plasma cells [START_REF] Nutt | The generation of antibody-secreting plasma cells[END_REF]. The different isotypes differ in the structure of their heavy chains and thus Fc basophils and eosinophils, and in immune responses against parasites [START_REF] Murphy | Janeway's Immunobiology[END_REF]. We will focus here on the main antibody isotypes present in mouse serum, IgM and IgG [START_REF] Klein-Schneegans | Serum concentrations of IgM, IgG1, IgG2b, IgG3 and IgA in C57BL 6 mice and their congenics at the lpr (lymphoproliferation) locus[END_REF].

IgM are secreted as pentameric [START_REF] Perkins | Solution structure of human and mouse immunoglobulin M by synchrotron X-ray scattering and molecular graphics modelling. A possible mechanism for complement activation[END_REF] or less frequently hexameric molecules [START_REF] Hughey | Production of IgM Hexamers by Normal and Autoimmune B Cells: Implications for the Physiologic Role of Hexameric IgM[END_REF][START_REF] Müller | High-resolution structures of the IgM Fc domains reveal principles of its hexamer formation[END_REF]. IgM is therefore particularly suited to the binding of repeated epitopes, such as polysaccharides on bacterial surfaces. Their main function is to activate the classical pathway of complement, as discussed in section I.4.

IgG antibodies are secreted as monomers, and exist in four different subclasses in the mouse, IgG1, IgG2a, IgG2b and IgG3, that are usually secreted simultaneously during humoral responses in varying proportions depending on the antigen [START_REF] Collins | IgG subclass co-expression brings harmony to the quartet model of murine IgG function[END_REF]. These IgG subclasses have drastically different associated effector functions. The difference is first mediated by varying affinity of the different IgG subclasses for the IgG-specific FcRs (FcγRs) [START_REF] Bruhns | Mouse and human FcR effector functions[END_REF].

IgG1 mostly interacts with the inhibitory FcγRIIB and therefore can induce very little FcRmediated effector functions. On the contrary, IgG2a and IgG2b can interact with several activating FcγRs and are therefore efficient at activating FcR-mediated effector functions, such as ADCC or ADCP [START_REF] Collins | IgG subclass co-expression brings harmony to the quartet model of murine IgG function[END_REF]. IgG3 seems to only interact with FcγRI and by this way induce ADCP of opsonized pathogens [START_REF] Gavin | Identification of the mouse IgG3 receptor: implications for antibody effector function at the interface between innate and adaptive immunity[END_REF][START_REF] Saylor | Murine IgG1 and IgG3 Isotype Switch Variants Promote Phagocytosis of Cryptococcus neoformans through Different Receptors[END_REF]. Additionally, certain IgG antibodies can very efficiently activate the complement system as discussed in section I.4 and I.5. This is true for IgG2a, IgG2b and IgG3 subclasses, while murine IgG1 antibodies have little to no potential for complement activation [START_REF] Lilienthal | Potential of murine IgG1 and Human IgG4 to inhibit the classical complement and Fcγ receptor activation pathways[END_REF][START_REF] Michaelsen | The four mouse IgG isotypes differ extensively in bactericidal and opsonophagocytic activity when reacting with the P1.16 epitope on the outer membrane PorA protein of Neisseria meningitidis[END_REF][START_REF] Neuberger | Activation of mouse complement by monoclonal mouse antibodies[END_REF][START_REF] Schreiber | Variable region-identical monoclonal antibodies of different igg subclass directed to pseudomonas aeruginosa lipopolysaccharide o-specific side chain function differently[END_REF][START_REF] Silveira | Complement activation selectively potentiates the pathogenicity of the IgG2b and IgG3 isotypes of a high affinity anti-erythrocyte autoantibody[END_REF]. To summarize, it is known that murine IgG2a and IgG2b subclasses can very efficiently activate secondary effector functions, namely ADCC, ADCP and complement, and that IgG3 can efficiently activate complement and also has some potential at inducing ADCP, while IgG1 is mostly limited to neutralization. This low functional activity of IgG1 has been proposed to serve as a regulation mechanism, avoiding the over-activation of effector functions by the other IgG subclasses [START_REF] Collins | IgG subclass co-expression brings harmony to the quartet model of murine IgG function[END_REF][START_REF] Lilienthal | Potential of murine IgG1 and Human IgG4 to inhibit the classical complement and Fcγ receptor activation pathways[END_REF][START_REF] Strait | IgG1 protects against renal disease in a mouse model of cryoglobulinaemia[END_REF]. To note: in this thesis, we worked exclusively with mice of the BALB/c inbred strain which express the IgG2a subclass, but for certain inbred strains, for example C57BL/6, the IgG2a subclass is replaced by IgG2c. In that case, IgG2c antibodies have similar associated effector functions as IgG2a.

Since the different antibody isotypes and IgG subclasses have vastly different associated effector functions and therefore protective effects, it is critical to be able to measure the different antibody isotypes and IgG subclasses to evaluate antibody responses. To that aim, we developed during this thesis a DropMap bioassay that allows to detect the secretion of IgM and IgG antibodies separately and also distinguish between IgG antibodies of the IgG1

Influencing the antibody isotype and subclass distribution with TLR agonists?

As we have discussed above, the isotype and subclass within the IgG isotype constitute a major characteristic of antibodies that determine their potential for triggering effector functions. When considering therapeutic monoclonal antibodies, it is possible to choose the isotype and subclass of the expressed antibody. Most of therapeutic antibodies are of the IgG isotype, and it is possible to select the IgG subclass based on the desired antibodyeffector functions (or lack thereof). Furthermore, the Fc domain of therapeutic antibodies can be modified by antibody engineering to enhance or prevent certain effector functions, for example complement activation [START_REF] Vor | Monoclonal antibodies effectively potentiate complement activation and phagocytosis of Staphylococcus epidermidis in neonatal human plasma[END_REF][START_REF] Cook | Antibodies That Efficiently Form Hexamers upon Antigen Binding Can Induce Complement-Dependent Cytotoxicity under Complement-Limiting Conditions[END_REF][START_REF] Jong | A Novel Platform for the Potentiation of Therapeutic Antibodies Based on Antigen-Dependent Formation of IgG Hexamers at the Cell Surface[END_REF][START_REF] Dekkers | Decoding the human immunoglobulin G-glycan repertoire reveals a spectrum of Fc-receptor-and complement-mediated-effector activities[END_REF][START_REF] Peschke | Fc-Galactosylation of Human Immunoglobulin Gamma Isotypes Improves C1q Binding and Enhances Complement-Dependent Cytotoxicity[END_REF]. However, when considering natural antibody responses that follow an infection or a vaccination, an isotype distribution that can activate appropriate effector functions to mediate protection is critical, for example ADCC, ADCP and complement against bacteria. In the context of vaccination, we wondered about the possibility of modulating the isotype and subclass distribution of the generated antibody repertoire as well as other important antibody parameters presented in the previous section by adding additional components to the immunization, also known as adjuvants. This could have large implications for the development of more efficient vaccines, since antibodydependent effector functions are often a key parameter of vaccine-induced protection.

Adjuvants are added to vaccines to induce larger and longer lasting immune responses.

They are critical for vaccines composed of purified protein antigens, which are usually not sufficiently immunogenic without adjuvants [START_REF] Marrack | Towards an understanding of the adjuvant action of aluminium[END_REF][START_REF] Shi | Vaccine adjuvants: Understanding the structure and mechanism of adjuvanticity[END_REF]. The most widely used adjuvants are aluminium salts (alum AlK(SO 4 ) 2 , alhydrogel/aluminium hydroxide Al(OH) 3 , adju-phos Al(PO 4 ) 3 , ...), included in the large majority of human vaccines currently licensed by the FDA [START_REF] Shi | Vaccine adjuvants: Understanding the structure and mechanism of adjuvanticity[END_REF], owing to their efficacy and good safety profile [START_REF] Principi | Aluminum in vaccines: Does it create a safety problem?[END_REF]. Aluminium salts boost the humoral response by maintaining the antigens adsorbed to their solid surface (depot effect) and inducing a local inflammation that recruits immune cells at the site of injection [START_REF] Marrack | Towards an understanding of the adjuvant action of aluminium[END_REF]. In general, aluminium salts or other adjuvants present in licensed vaccines are primarily added in the aim of increasing the magnitude of the antibody response, and not of modulating isotype and subclass distributions to influence antibody-induced effector functions.

A possibility to achieve this objective would be to use Toll-like receptor agonists as adjuvants. These are molecules that interact with Toll-like receptors (TLRs), which are innate immune receptors involved in the recognition of pathogen-associated molecular patterns [START_REF] Murphy | Janeway's Immunobiology[END_REF].

TLRs are present on all types of immune cells, either on the cell surface or in endosomes (see table I pathway [START_REF] Kaisho | Toll-like receptor function and signaling[END_REF]. The activation of these signaling pathways has different effects depending on the cell type, but mainly results in cell activation and secretion of pro-inflammatory cytokines [START_REF] Takeda | Toll-like receptors in innate immunity[END_REF]. In this way, TLRs act as a first line pathogen-recognition mechanism; they can sense microbial infections, activate immune cells and trigger innate immune responses immediately, before the availability of antigen-specific adaptive immune responses.

In addition, since very early after their discovery, TLRs are known to have an influence on adaptive immune responses [START_REF] Medzhitov | A human homologue of the Drosophila toll protein signals activation of adaptive immunity[END_REF][START_REF] Schnare | Tolllike receptors control activation of adaptive immune responses[END_REF], and therefore constitute a bridge between innate and adaptive immunity. Indeed, stimulating TLRs with agonist molecules can promote antibody Chapter I. Introduction responses: both T cell-independent extrafollicular responses (low affinity antibodies) and T-cell dependent germinal center responses (high affinity antibodies) [START_REF] Defranco | Contribution of Toll-like receptor signaling to germinal center antibody responses[END_REF]. TLR agonists can mediate this effect by indirect mechanisms, through the activation of TLRs on innate immune cells such as antigen-presenting cells, that afterward participate to the enhancement of adaptive responses. In particular, dendritic cells can be activated by TLR agonists, which stimulates cytokine secretion [START_REF] Querec | Yellow fever vaccine YF-17D activates multiple dendritic cell subsets via TLR2, 7, 8, and 9 to stimulate polyvalent immunity[END_REF] but also enhances antigen uptake [START_REF] Watts | TLR signalling regulated antigen presentation in dendritic cells[END_REF][START_REF] West | Enhanced dendritic cell antigen capture via Toll-like receptor-induced actin remodeling[END_REF], processing and presentation [START_REF] West | Enhanced dendritic cell antigen capture via Toll-like receptor-induced actin remodeling[END_REF][START_REF] Cella | Inflammatory stimuli induce accumulation of MHC class II complexes on dendritic cells[END_REF]. The engagement of TLRs on dendritic cells also induces maturation and migration of the cells to the lymph nodes and the spleen [START_REF] Zanoni | Regulation of antigen uptake, migration, and lifespan of dendritic cell by Toll-like receptors[END_REF], where they can present the antigen to B and T cells and participate in this way to adaptive responses. It has been shown in several studies that TLR agonists could enhance germinal center responses by this mechanism [START_REF] Hou | Selective utilization of toll-like receptor and Myd88 signaling in B cells for enhancement of the antiviral germinal center response[END_REF][START_REF] Kasturi | Programming the magnitude and persistence of antibody responses with innate immunity[END_REF][START_REF] Rookhuizen | Toll-like receptor 9 signaling acts on multiple elements of the germinal center to enhance antibody responses[END_REF], leading to the generation of high-affinity antigen-specific antibodies.

On the other hand, TLR agonists can also have a direct effect on B cells; different TLRs are expressed by B cells, which secrete antibodies in response to their engagement [START_REF] Browne | Regulation of B-cell responses by Toll-like receptors[END_REF][START_REF] Gururajan | Toll-Like Receptor Expression and Responsiveness of Distinct Murine Splenic and Mucosal B-Cell Subsets[END_REF][START_REF] Rawlings | Integration of B cell responses through Toll-like receptors and antigen receptors[END_REF].

Also, the activation of TLRs on B cells is sometimes required for T-cell dependent antibody responses [START_REF] Pasare | Control of B-cell responses by Toll-like receptors[END_REF].

Due to their effects on the development of adaptive immunity, TLR agonists have raised interest as potential adjuvants for vaccines [START_REF] Coffman | Vaccine adjuvants: putting innate immunity to work[END_REF][START_REF] Toussi | Immune adjuvant effect of molecularly-defined Toll-like receptor ligands[END_REF]. The adjuvant effect of agonists to various TLRs has been investigated in numerous studies [START_REF] Basto | Targeting TLR2 for vaccine development[END_REF][START_REF] Li | Built-in adjuvants for use in vaccines[END_REF][START_REF] Lu | Formulation of aluminum hydroxide adjuvant with TLR agonists poly(I:C) and CpG enhances the magnitude and avidity of the humoral immune response[END_REF] and TLR agonists have been included in clinical trials of vaccines [START_REF] Hogenesch | Optimizing the utilization of aluminum adjuvants in vaccines: you might just get what you want[END_REF], for example targeting tuberculosis [START_REF] Coler | The TLR-4 agonist adjuvant, GLA-SE, improves magnitude and quality of immune responses elicited by the ID93 tuberculosis vaccine: first-in-human trial[END_REF],

malaria [START_REF] Ellis | Phase 1 Study in Malaria Naïve Adults of BSAM2/Alhydrogel R +CPG 7909, a Blood Stage Vaccine against P. falciparum Malaria[END_REF] or hepatitis B virus [START_REF] Cooper | CPG 7909 adjuvant improves hepatitis B virus vaccine seroprotection in antiretroviral-treated HIV-infected adults[END_REF]. Furthermore, several FDA-approved human vaccines now include TLR agonists [START_REF] Shi | Vaccine adjuvants: Understanding the structure and mechanism of adjuvanticity[END_REF]. Cervarix, a recombinant human papillomavirus vaccine is adjuvanted with AS04, composed of alum and MPL, a TLR4 agonist [START_REF] Didierlaurent | AS04, an Aluminum Salt-and TLR4 Agonist-Based Adjuvant System, Induces a Transient Localized Innate Immune Response Leading to Enhanced Adaptive Immunity[END_REF]. The recombinant zoster virus vaccine Shingrix is also adjuvanted with a TLR4 agonist [START_REF] Izurieta | Recombinant Zoster Vaccine (Shingrix): Real-World Effectiveness in the First 2 Years Post-Licensure[END_REF]. Also, the TLR9 agonist CpG-ODN 1018 is used as adjuvant in the recombinant hepatitis B vaccine Heplisav-B [START_REF] Campbell | Development of the CpG adjuvant 1018: A case study[END_REF]. In general, the desired effect of TLR agonists in vaccine formulations is an increase in the magnitude and duration of the antibody response, that can be assessed with antibody titer measurements [START_REF] Buffa | Evaluation of TLR Agonists as Potential Mucosal Adjuvants for HIV gp140 and Tetanus Toxoid in Mice[END_REF][START_REF] Giannini | Enhanced humoral and memory B cellular immunity using HPV16/18 L1 VLP vaccine formulated with the MPL/aluminium salt combination (AS04) compared to aluminium salt only[END_REF][START_REF] Weeratna | TLR agonists as vaccine adjuvants: Comparison of CpG ODN and Resiquimod (R-848)[END_REF].

However, another effect of TLR agonist is a potential to induce isotype switching from IgM to different IgG subclasses by class switch recombination [START_REF] Chen | Signaling control of antibody isotype switching[END_REF]. Indeed, TLR agonists activation have been associated with a shift in the type of adaptive response, favoring type [START_REF] Schnare | Tolllike receptors control activation of adaptive immune responses[END_REF]. Studies also demonstrated the stimulation of the secretion of T helper type 1-polarizing cytokines by TLR agonists [START_REF] Barton | Control of adaptive immune responses by Toll-like receptors[END_REF][START_REF] Lipford | CpG-DNA-Mediated Transient Lymphadenopathy Is Associated with a State of Th1 Predisposition to Antigen-Driven Responses[END_REF] or combination thereof [START_REF] Madan-Lala | Combinatorial Delivery of Dual and Triple TLR Agonists via Polymeric Pathogen-like Particles Synergistically Enhances Innate and Adaptive Immune Responses[END_REF][START_REF] Mitchell | Dual Stimulation of MyD88-Dependent Toll-Like Receptors Induces Synergistically Enhanced Production of Inflammatory Cytokines in Murine Bone Marrow-Derived Dendritic Cells[END_REF][START_REF] Napolitani | Selected Tolllike receptor agonist combinations synergistically trigger a T helper type 1 -polarizing program in dendritic cells[END_REF][START_REF] Surendran | TLR agonist combinations that stimulate Th type I polarizing responses from human neonates[END_REF]. There is also evidence of TLR agonists-induced switching towards type 1-associated mouse IgG subclasses, IgG2a [START_REF] Rookhuizen | Toll-like receptor 9 signaling acts on multiple elements of the germinal center to enhance antibody responses[END_REF][START_REF] Jegerlehner | TLR9 Signaling in B Cells Determines Class Switch Recombination to IgG2a[END_REF][START_REF] Tian | B Cell-Intrinsic MyD88 Signaling Promotes Initial Cell Proliferation and Differentiation To Enhance the Germinal Center Response to a Virus-like Particle[END_REF] or IgG2b/3 [START_REF] Pone | BCR-signalling synergizes with TLR-signalling for induction of AID and immunoglobulin class-switching through the non-canonical NF-κB pathway[END_REF],

mostly directly through the simultaneous activation of TLR and BCR on B cells.

Aim of the study -Influence of TLR agonists on the antibody repertoire (developed in chapter IV)

The exact mechanism from the activation of TLRs to the influence on the generated antibody repertoire is extremely complex to decipher. Nonetheless, there has been evidence of an influence of the activation of TLR by agonist molecules on the antibody repertoire, leading to larger and longer-lasting responses as well as a shift towards the isotypes associated with type 1 responses (in mice IgG2a, 2b and 3). These effects were however mostly evaluated with titer measurements, a crude method that determines the total amount of antigen-specific antibodies circulating in the blood but cannot provide a detailed picture of the generated antibody repertoires.

Here, we propose to use the DropMap platform to precisely characterize the effect of TLR agonists on antigen-specific antibody responses. We will use a simple system: two immunizations with a protein antigen and alum, sufficient to generate affinity-matured antibodies, to which we will add different TLR agonists (alone or in combination). The DropMap platform enables the characterization of the antibody repertoire at the single-antibody level, with a quantification of affinity and determination of isotype and subclass for every antibody, informations that are typically not accessible with other systems. This will allow us to describe the effect of TLR agonists on the antibody repertoire with unprecedented precision in order to evaluate their efficacy as adjuvants. Hopefully we will also gain insights on the mechanisms at play and most importantly the potential of TLR agonists to induce class-switching and thus modulate antibody-induced effector functions.

The results of this second study conducted during my Ph.D. thesis are presented in chapter IV.

Chapter II

Assay development

Contributions [START_REF] Heo | Deep phenotypic characterization of immunization-induced antibacterial REFERENCES IgG repertoires in mice using a single-antibody bioassay[END_REF].

In this chapter we will describe the different technical developments that were conducted during this thesis, building on the already existing DropMap platform. We first improved a DropMap assay that quantifies IgG secretion by single B cells. We also multiplexed the This principle of relocating the analyte of interest on the beadline and detecting it with a fluorescent probe is the central part of the DropMap technology and can be applied to various analytes depending on the capture reagent bound on the nanoparticles and the detection probe(s). The antibody capture reagent used in the assay described here targets the antibody kappa light chain and therefore binds to the different murine antibody isotypes (IgG, but also IgM, IgA, IgD and IgE) that include a kappa light chain (present in ∼95% of antibodies in mouse, regardless of isotype [START_REF] Larijani | The recombination difference between mouse kappa and lambda segments is mediated by a pair-wise regulation mechanism[END_REF]). We start by describing the detection of antibodies of the IgG isotype in this section, but we could also detect in parallel the secretion of IgM antibodies, similarly captured on the beadline, by adding an IgM-specific detection probe in the assay (see section II.2). Alternatively, it is possible to functionalize [START_REF] Bounab | Dynamic single-cell phenotyping of immune cells using the microfluidic platform DropMap[END_REF][START_REF] Eyer | Single-cell deep phenotyping of IgG-secreting cells for high-resolution immune monitoring[END_REF] and already applied to different studies investigating IgG secretion by mouse or human B cells [START_REF] Eyer | Single-cell deep phenotyping of IgG-secreting cells for high-resolution immune monitoring[END_REF][START_REF] Eyer | The Quantitative Assessment of the Secreted IgG Repertoire after Recall to Evaluate the Quality of Immunizations[END_REF][START_REF] Canales-Herrerias | High-affinity autoreactive plasma cells disseminate through multiple organs in patients with immune thrombocytopenic purpura[END_REF]. However, there were still certain aspects of the mouse IgG secretion assay that could benefit from an optimization, in particular the measurement range and the precision of the secretion rate quantification. We have worked on these technical developments and modified the DropMap protocol for IgG secretion measurement accordingly. The updated protocol will be published as a chapter of the Methods in Molecular Biology book series (first author, manuscript submitted). In this section we will summarize the different improvements that we have established for the DropMap IgG secretion assay.

Improvement of the assay measuring IgG secretion

As explained above, we can compute an IgG secretion rate for every individual IgGsecreting B cell with this DropMap assay. In our experiments, we typically observe that most mouse B cell have IgG secretion rates ranging from 10 to 500 IgG/s, with also a small fraction of IgG-secreting B cells with higher secretion rates, sometimes much higher than 500 IgG/s. The standard duration of measurement that we use to characterize IgG-secreting cell populations is around 30 minutes. After this duration, the concentration of IgG in a 50 pL droplet containing a cell secreting 10 IgG/s (very low secretor) is roughly 0.5 nM. This defines the lower limit of the measurement range that the DropMap IgG secretion assay should have. On the other hand, we need to be able to quantify the IgG concentration up to approximately 15 nM to measure the secretion rate of the high secreting cells that secrete around 500 IgG/s (quantification only during the first part of the 30 minutes duration is sufficient in that case).

Chapter II. Assay development

An issue that we encountered in early studies involving the detection of IgG secretion was the poor performance of the assay in the lower end of our desired measurement range (from 0.

Calibration of the IgG assay

The next step was to calibrate the assay using this new IgG detection probe. For this we used known concentrations of different monoclonal IgG antibodies, and adjusted the indroplet concentration of the detection probe to obtain the desired measurement range. We established that the optimal concentration in the droplets for the new IgG detection probe was 75 nM. The resulting optimized calibration curve is displayed on figure II.2, here plotted from 30 000 individual droplets for each IgG concentration. We see here that the fluorescence IgG concentration (nM) The calibration curve can be fitted using the following exponential model:

y = y plateau -(y plateau -y blank ) * e -K * x ⇔ x = -ln y plateau -y y plateau -y blank /K
where x is the concentration of the analyte, y is the fluorescence relocation, y blank is the median relocation of a blank sample, and y plateau is the fitted plateau value. The computed exponential fit is displayed as a dotted line on figure II.2, with the following fitted parameters: y plateau = 3.441, y blank = 1.072 and K = 0.09577. This exponential fit enables the computation of the IgG concentration in a given droplet based on the measured fluorescence relocation of the IgG detection probe.

However, the IgG concentration can only be computed with precision for a certain fluorescence relocation range that corresponds to the quantitative range of the assay (see figure II.2). First, the lower limit of the quantitative range is determined by the limit of detection (LoD) of the assay [START_REF] Armbruster | Limit of Blank, Limit of Detection and Limit of Quantitation[END_REF]. This limit of detection is the average signal obtained for the smallest concentration of IgG that can be reliably detected and distinguished from a blank sample. Its computation is detailed later in this section. The concentration of IgG corresponding to the LoD constitutes the lower limit of the quantitative range of the assay, here approximately 0.7 nM. The quantitative range also has a higher limit, above which no precise concentration of IgG can be computed. Indeed, because of the variability of the signal obtained (standard deviation values in figure II.2), when the fluorescence relocation signal approaches the plateau of the calibration curve, it is not possible to attribute a precise concentration to a given relocation value: the range of concentrations that could correspond to this relocation is too large. To define the upper limit of the quantitative range of the assay, we set a cutoff on the fluorescence relocation equal to the plateau value from the exponential fit minus the half-width of the prediction interval (90% confidence level). The upper limit of the quantitative range is the concentration of IgG that leads to a relocation equal to the cutoff value. Here, the fitted plateau value is 3.

Method to quantify IgG secretion

After this improvement and detailed calibration of the IgG detection assay, we then optimized the method to precisely quantify IgG secretion rates of single B cells with this DropMap assay.

Measurement and image analysis

Following the protocol published by Bounab et al. [START_REF] Bounab | Dynamic single-cell phenotyping of immune cells using the microfluidic platform DropMap[END_REF], we encapsulate single B cell in 50 pL droplets, immobilize the droplets in a 2D observation chamber and image them with an epifluorescence microscope (Nikon Eclipse Ti2-E). Typically we acquire a large image composed of 10x10 fields of view (square size of 1.3x1.3 cm with our acquisition setup), which enables to image approximately 40 000 droplets. The chamber is usually imaged every 8 minutes for 32 minutes in the brightfield and red fluorescence channel (corresponding to the IgG detection probe labeled with Alexa Fluor 647). We use the previously published DropMap MATLAB application for the image analysis of the obtained .nd2 file [START_REF] Bounab | Dynamic single-cell phenotyping of immune cells using the microfluidic platform DropMap[END_REF], which detects the droplets in the brightfield channel, saves their identity and position, and measures the fluorescence relocation on the beadline for each droplet and time point.

Computation of the limit of detection for IgG

For each measurement, we then compute the limit of detection (LoD) of IgG, using a population of empty droplets (without cell and thus without IgG secretion), as:

LoD = median empty droplets + 1.645 * 2 * σ σ = k * M AD empty droplets
where median empty droplets is the median relocation signal in empty droplets, M AD empty droplets is the median absolute deviation in empty droplets, k = 1.4826 and k * M AD is a consistent estimator of the standard deviation (SD) σ, considering the distribution of relocation signals for empty droplets as normal.

This is our adaptation of a usual definition of the LoD [START_REF] Armbruster | Limit of Blank, Limit of Detection and Limit of Quantitation[END_REF]:

LoD = mean blank + 1.645 * (SD blank + SD low concentration sample )
Here we assume that a blank and low concentration sample have the same standard deviation σ (as their SD values are very close in practice) and estimate mean blank and σ using the population of empty droplets at the first time point of a measurement. The relocation signals from the empty droplet population are normally distributed (p-value usually around 0.9 with a Kolmogorov-Smirnov test of the normality of the distribution). However, outliers (due to fluorescent clumps for example) whose signals are not part of this normal distribution might be present. We use median empty droplets and k * M AD empty droplets to estimate mean blank and σ, respectively, reliably despite the presence of those potential outlier values [START_REF] Leys | Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median[END_REF][START_REF] Rousseeuw | Alternatives to the Median Absolute Deviation[END_REF].

Similarly, we can compute the LoD for the calibration curve using a calibration sample without IgG in the droplets as the empty droplet population. Computing a LoD for each individual measurement ensures that it is adapted to the exact conditions of the measurement since the assay might vary slightly from measurement to measurement due to deviations in pipetting or mixing, to IgG secretion before encapsulation, or to slightly different focal planes of the images.

Selection of droplets containing IgG-secreting cells

Afterward, we use Microsoft Excel to perform the following step of the analysis, which consists in detecting the droplets that contain an IgG-secreting cell. The droplets are sorted according to the following three criteria:

• The droplet contains a detectable concentration of IgG: the fluorescence relocation of the IgG detection probe is above the LoD for at least one timepoint.

• The concentration of IgG increases over time: the fluorescence relocation of the IgG detection probe has a positive slope over the time series.

• The signal varies more over the time series than the analytical noise in the absence of Chapter II. Assay development not correspond to IgG secretion but instead probably to BCR expression on the surface of the cell or to a dead cell on which the IgG detection probe sticks.

Computation of the IgG secretion rate

If a droplet fulfills the three criteria and is then positively sorted during the manual verification it is classified as containing an IgG-secreting cell. For these droplets we can then compute the individual IgG secretion rates. We do this by first computing the IgG concentration at each time point from the fluorescence relocation signal using the calibration curve. From these concentration values, we then derive a average secretion rate SR in IgG/s over the full measurement with the following formula:

SR = N A * V n -1 n-1 i=1 [IgG] i+1 -[IgG] i ∆t
where N A is the Avogadro constant, V the volume of a droplet (50 pL), n is the number of time points, ∆t is the time interval (in seconds), and [IgG] i is the analyte concentration (in mol/L) at the time point i.

This approach of analyzing the IgG secretion rates as averages assumes that the B cells secrete IgG at an approximately constant rate throughout the measurement. This is the case for our measurements with a standard duration of 32 minutes: as visible on figure II.4a, the IgG-secreting cells with lower secretion rates display a mostly constant increase of the relocation signal. This is however not true for the cells with high secretion rates where the signal

Detection of antibody isotype and IgG subclasses

In the previous section, we described the detection and quantification of IgG secretion using a DropMap assay optimized during this thesis project. The analyzed B cells can alternatively secrete IgM antibodies which are involved in particular in complement activation.

Additionally, within IgG antibodies, the IgG subclass (IgG1, IgG2a, IgG2b and IgG3 in BALB/c mice) determines the effector functions (see section I.8) and is therefore an important information. This is why we were also interested in the measurement of IgM and IgG subclasses to conduct the experimental studies presented in chapter III and IV. To build on this assay, we then also added the parallel measurement of IgM secretion and the distinction between IgG1 (non-functional) and IgG2a/2b/3 (functionally active) subclasses for secreted IgG antibodies. We did this by adding IgM-and IgG1-specific detection probes in different fluorescence channels. The IgG detection probe that we mentioned in the previous section is labeled with a red fluorophore (Alexa Fluor 647; excitation max. 650 nm, emission max.

668 nm, Cy5 filter set), which enabled to use in parallel the blue (DAPI) and green (FITC) fluorescence channels for the detection of IgM and IgG1. Since the IgG capture reagent (CaptureSelect Biotin Anti-LC-kappa (Murine) Conjugate,

Detection and quantification of IgM secretion

Chapter II. Assay development

Thermo Scientific) that we use on the magnetic nanoparticles binds the kappa light chain of antibodies, present in all mouse antibody isotypes, it similarly captures IgM antibodies.

In the same manner as represented for IgG on figure II.1, both IgM antibodies secreted in the droplets and the fluorescent IgM detection probe relocate to the beadlines, resulting in a fluorescence relocation signal. We could therefore directly transpose the method for detection of IgG secretion described in the previous section to the measurement of IgM secretion.

Similarly to the process for the IgG detection probe, we first adjusted the concentration of the IgM detection probe to obtain the desired measurement range. Since we observed that the maximal IgM secretion rates observed are usually lower than those of IgG, we aimed for a measurement range with a lower higher limit for precise quantification. We determined that the optimal in-droplet concentration of the IgM detection probe for our application that led to an appropriate range of measurement was 75 nM. We then acquired a complete calibration curve and fitted it with an exponential model (see figure II.5). The LoD and cutoff values for this IgM calibration curve are computed with the same method as described in the previous section, and determine the quantitative range of this assay, from 0.3 to 5.5 nM IgM. Nonetheless, we were still able to measure IgM concentration in a range suitable to quantify the IgM-secretion rates that we typically observed in our experiments, for example those described in chapter IV.

Detection of IgG subclass

In parallel, we also wanted to be able to distinguish between the different subclasses of IgG (IgG1, IgG2a, IgG2b and IgG3) for cells secreting IgG antibodies. The detection of every single IgG subclass would require at least three additional fluorescence channels. This was not technically feasible since we also wanted to multiplex with the detection of IgM antibodies as described above, as well as the binding to a target antigen (see section II.3).

We settled with only distinguishing the IgG1 subclass from the other three IgG subclasses.

This distinction is interesting because IgG1 is the only IgG subclass that has very little effector functions and is thought to be merely limited to neutralization [START_REF] Collins | IgG subclass co-expression brings harmony to the quartet model of murine IgG function[END_REF]. Therefore, quantifying the fraction of an IgG repertoire that is of the IgG1 subclass gives an insight on the potential of this repertoire to trigger antibody-dependent effector functions.

In order to detect IgG1 antibodies we added an IgG1-specific secondary antibody (Fab fragment) to the bioassay, labeled in the green fluorescence channel (Zenon Alexa Fluor 488

Mouse IgG1 Labeling Kit, Z25002, from Invitrogen; excitation max. 490 nm, emission max.

525 nm, FITC filter set). We determined that the optimal in-droplet concentration for this reagent was 50 nM.

Here we did not need to measure the concentration of IgG1 based on the relocation of the IgG1 detection probe, since IgG1 antibodies were also detected by the general IgG detection probe and thus their concentration was already measured with the assay described in section II.1. Therefore, we did not need to obtain a full calibration curve for the IgG1 detection probe. Instead, we simply determined its LoD, as defined on page 36 in section II.1 but this time for the IgG1 detection probe. For cells detected as secreting an IgG antibody with the method described in section II.1, we then considered the fluorescence relocation signal of the IgG1 probe as a binary output:

• If the relocation is higher than the LoD of the IgG1 probe, the cell is classified as secreting an IgG1 antibody.

• If the relocation is lower than the LoD of the IgG1 probe, the cell is classified as secreting an IgG2a/2b/3 antibody.

Detection of antigen binding

We have shown in the two previous section that we were able to precisely quantify the secretion of IgM and IgG antibodies, and determine IgG subclasses. This allowed us for example to analyze the isotype and subclass distributions of repertoires of antibody secreting cells in the experimental studies presented in chapter III and IV. However, the most crucial parameter of a given antibody is certainly the affinity of its binding domain for a target antigen. We wanted to also be able to quantify this parameter with our DropMap bioassay to further characterize antibody repertoires.

For example, in the study presented in chapter IV, we used R-phycoerythrin (PE, 52412, Sigma-Aldrich) as a model target antigen. We immunized mice with PE and various TLRs With this method we are able to detect PE-specific antibody-secreting cells by the fluorescence relocation signal of the PE protein. We first set a limit of detection of PE binding, defined for each measurement as:

LoD = median sample + 1.645 * 2 * SD blank
where median sample is the median PE relocation of the droplets in the measurement and SD blank is the standard deviation of the PE relocation in a blank calibration sample (with PE but without antibody).

This is an adaptation of a usual definition of the LoD [START_REF] Armbruster | Limit of Blank, Limit of Detection and Limit of Quantitation[END_REF]:

LoD = mean blank + 1.645 * (SD blank + SD low concentration sample )
We assume that a low concentration sample has the same standard deviation for the PE relocation as a blank and compute it from a calibration sample without PE specific antibody.

It would otherwise depend on the affinity of an arbitrarily chosen anti-PE control monoclonal antibody. Then we replace mean blank by median sample , to take into account variations in the baseline signal that could for example be due to secretion of PE-specific antibody in bulk before encapsulation, or to slightly different focal planes of the images. We use median instead of mean for this to avoid taking into account outliers (e.g. due to fluorescent clumps).

Afterward, we analyze the PE relocation signal of every antibody-secreting cell detected.

If the relocation signal exceeds the limit of detection of PE binding for at least one time point of the DropMap measurement, the cell is classified as PE-specific.

We detected only few IgM-secreting cells with affinity towards PE so we only consider IgG-secreting cells in the rest of this section. We show results of this classification of single For a given IgG relocation value on the x axis, a higher PE relocation corresponds to a higher affinity of the antibody. The diagonal direction corresponds to IgGs with similar affinities at different concentrations. The dotted line represents a higher limit of PE binding that seems to be exceeded by only very few IgG antibodies and probably corresponds to a one-to-one binding of IgG to PE.

signals of the IgG detection probe (x axis). However, for distinct IgG antibodies with similar concentration, we observed very different relocations of the PE antigen. For a given indroplet concentration of IgG, a higher relocation of PE corresponds to more PE bound per IgG antibody, and therefore a higher affinity of the antibody towards the PE antigen. On figure II.7, we can first observe IgG-secreting cell that have no affinity for PE (dots below the horizontal yellow line, PE relocation value similar to the baseline even for high concentrations of IgG). We also observe PE-specific IgG-secreting cells with a wide range of affinities (dots above the yellow horizontal line). It seems that there is a higher limit of affinity (represented by the dotted line) that is very unlikely to be exceeded with only two cells with a much higher affinity. The limit probably corresponds to a one-to-one binding of the IgG antibodies to PE. The two cells that have a PE-binding value widely exceeding this limit are rare outliers that we cannot explain for now. We did not have the possibility of calibrating the assay to compute back a K D value in nM from the affinity slope during this thesis. This calibration would require at least two control monoclonal IgG antibodies with different affinity for PE measured by SPR and we only had access to one here. However we plan to perform this calibration in the near future.

To conclude, we have developed a bioassay that allows the precise characterization of antibody-secreting cell repertoires, with in parallel the detection of IgM

Design of the assay

Our objective was to develop a DropMap assay that allows to detect antibodies binding to target bacteria and quantify the potential subsequent activation of the classical pathway of complement.

In a previous study of our group [START_REF] Heo | Deep phenotypic characterization of immunization-induced antibacterial REFERENCES IgG repertoires in mice using a single-antibody bioassay[END_REF], it was demonstrated that it is possible to detect antibacterial antibodies with the DropMap platform by binding target bacterial cells on the magnetic nanoparticles that we normally use to capture antibodies (see figure II.9). This was done here by incubating the bacteria with a biotin-PEG-cholesterol linker; the cholesterol part of the linker integrates into the lipidic membrane of the bacteria, while the biotin part binds to the streptavidin-coated magnetic nanoparticles. In the antibacterial antibody detection assay described here, we also applied a magnetic field to the observation chamber which aligned the nanoparticles contained in each droplet into an elongated beadline coated with target bacteria that we term here "bactoline". The antibodies that bind to the target bacteria relocate to the bactoline. In a similar manner to the IgG detection assay that We also added the IgM-and IgG1-specific detection probes described in section II.2 at respectively 45 and 30 nM in-droplet concentration. This allowed the parallel detection of bacteria-specific IgM, that should be able to activate the classical complement pathway, as well as distinguishing IgG1 (no complement activation) from bacteria-specific IgG antibodies of the other subclasses.

Additionally, we wanted to add the detection of the classical pathway of complement to our assay. We chose to do this by looking at the first step of this pathway, which is the binding of the C1q protein to the Fc domain of antibodies bound to a bacterial surface (see section I.4). To simplify our in-droplet system as much as possible, we added only the C1q protein of complement to the assay (labeled in-house with Alexa Fluor 555). Therefore, no subsequent step of the complement cascade could take place in our assay, but we assumed that an antibody that properly recruited C1q to bacterial surfaces would be able to trigger the full cascade of the classical complement pathway in a system involving all complement proteins. The antibodies that bound C1q were considered to be complement-activating.

If a bacteria-specific antibody was able to activate the classical pathway, it recruited the labeled C1q protein once it binds to the membrane with a sufficient density, as represented on figure II.9. We could then quantify the amount of C1q bound to the bacteria by measuring the fluorescence relocation of the Alexa Fluor 555 fluorophore to the bactoline. As seen on the second row of figure II.10, the monoclonal anti-P. aeruginosa IgG2a is able to recruit C1q since we measured an increase in the fluorescence relocation of C1q to the bactolines when we added the antibody. In the following paragraphs, we will detail the different development and optimization steps that were conducted to first improve the DropMap assay to detect antibacterial antibodies and then add the precise quantification of antibody-dependent C1q binding.

Development and optimization of the assay

Culture of bacteria

Here we describe the culture conditions for the different bacterial species that were used as target bacteria in the assay. These culture conditions correspond to standard culture protocols for the respective bacterial species. We grew the cells for a defined duration before harvesting and inactivating them. The duration of growth was adjusted to obtain bacterial cells that were still in the growth phase or early stationary phase and thus with phenotypes 50

Chapter II. Assay development close to that of live cells infecting a host. Additionally, if the cells were kept in culture for a too long duration, the agar medium did tend to dry up which damaged the bacterial cells.

Culture conditions for Pseudomonas aeruginosa

To grow Pseudomonas aeruginosa (strain ATCC 10145), we used LB agar medium, prepared with LB broth and agar (both Sigma Aldrich), autoclaved and dispensed into petri dishes. The cultures were sealed with parafilm and incubated at 37 • C in an aerobic incubator for 24 hours before harvesting.

Culture conditions for Bordetella pertussis

To grow Bordetella pertussis (strain DMSZ 4923), we used Bordet Gengou agar plates (with sheep blood, BD). The cultures were sealed with parafilm and incubated at 36 • C in an aerobic incubator for five to seven days before harvesting.

Culture conditions for Neisseria meningitidis

To grow Neisseria meningitidis(strain ATCC 13090 or DSMZ 15464), we used chocolate agar with Vitox plates (Thermo Scientific). The cultures were sealed with parafilm and incubated at 37 • C in a CO 2 incubator (5% CO 2 ) for 30 hours before harvesting.

Inactivation by fixation with paraformaldehyde

As the different bacterial strains listed here belong to the biosafety level 2 category, we needed to inactivate the bacteria before using them in our DropMap assay (or for mouse immunizations). We chose to use fixation with paraformaldehyde, as discussed later in this section. This method allowed to completely inactivate the bacterial cells while preserving their surface antigens. We optimized the concentration of paraformaldehyde and duration of incubation to reliably obtain complete killing of the bacterial cells while minimizing the extent of the fixation. Here, we describe the protocol to fix the bacteria, the volume of reagents listed corresponding to one plate of bacterial culture. All steps where we handle live bacterial cultures were performed inside a biosafety cabinet in a BSL-2 lab. After the fixation, the cells were also handled in sterile conditions to avoid contamination with other biological agents.

For each type of bacteria, after the growth duration specified before, we harvested the bacterial cells present on the agar plates. To do this, we gently scraped the surface of the agar with a sterile inoculation loop. We tried to retrieve as much as possible of the bacterial colonies without damaging the agar and resuspended the collected bacteria in 2.5 mL sterile PBS contained in a 50 mL Falcon tube. We broke up the aggregates by pipetting up and down with a 1 mL pipette. We then added 17.5 mL of sterile PBS and vortexed the sample at high rate for 20 seconds to further individualize the bacterial cells. We washed the cells by centrifuging (4000 g, 5 minutes, room temperature; same parameters for all subsequent centrifugation steps), discarding the liquid and resuspending with 10 mL PBS.

We then added 10 mL 4% paraformaldehyde (Fluka) in PBS, mixed and incubated for 30 51 minutes at room temperature to fix the bacterial cells. Afterward, we collected the cells by centrifugation, discarded the liquid and resuspended with 20 mL sterile PBS. We repeated the centrifugation step, discarded the liquid and this time resuspended the fixed bacteria with 5 mL sterile PBS. We measured the concentration of cells by OD 600 and inoculated a plate of the adapted agar medium with a volume corresponding to approximately 10 8 cells.

The plate was then placed in an incubator (conditions corresponding to the bacterial specie, described before). We afterward aliquoted the rest of the fixed bacteria solution (typically in 300 µL volume aliquots) and stored the aliquots at -80 • C. After 7 days, we checked that there was no growth observed on the plate to ensure the sterility of the fixed bacteria sample.

With this method, we could obtain concentrated sterile solutions of the target fixed bacteria (usually OD 600 between 10 and 20). These solutions could be used to coat the nanoparticles that we used in the DropMap C1q binding detection assay (see next section).

Optimization of the preparation of bacteria-coated beads

As mentioned before, our study built on previous work done in our group by Heo et al. [START_REF] Heo | Deep phenotypic characterization of immunization-induced antibacterial REFERENCES IgG repertoires in mice using a single-antibody bioassay[END_REF]. In that study, the authors coated the magnetic nanoparticles of the DropMap system with commercial heat-killed bacteria of different species, namely S. typhimurium, P. aeruginosa and E. coli (HKST, HKPA, HKEB, all from Invivogen). This allowed them to successfully characterize with single-antibody resolution the antibacterial IgG repertoires induced by immunization of mice with the same heat-killed bacteria.

However, a main limitation of this first version of the assay to detect antibacterial antibodies was due to the quality of the purchased heat-killed bacteria. Indeed, the heat-killed bacteria were present as large clusters of sometime several hundreds of individual bacterial cells. These clusters were very hard to break up, and even after 30-time passage through a very thin 34G needle and centrifugation the clusters at least partially remained. After thesis), that differ substantially from the thin vertical shape of the beadlines that we typically use for the detection of secretion in the DropMap system (see for example figure II.1).

These irregular bactolines made the detection of secreted specific antibodies more difficult and unprecise.

To solve this issue, we instead used bacteria cultured and fixed in the lab, as described -Take 100 µL of fixed P. aeruginosa solution. Alternatively take 50 µL for B. pertussis or 15 µL for N. meningitidis.

-Centrifuge (4000 g, 5 minutes, 4 • C; all subsequent centrifugation steps are with the same parameters).

-Discard supernatant and resuspend in 20 µL of PBS with 1 mM biotin-PEG-cholesterol.

-Incubate overnight at 4 • C.

-Centrifuge, discard supernatant, wash with 1 mL PBS 4 times, discarding the supernatant each time to eliminate free biotin-PEG-CLS as much as possible.

-Take 50 µL of stock bead solution in a separate tube, collect the beads using a magnet, discard the liquid and resuspend the beads in the same volume of PBS.

-Resuspend the bacterial pellet with the bead solution, mix by pipetting up and down until the suspension is completely homogeneous.

-Incubate on a horizontal shaker for 2 hours at 4 • C.

-Collect the beads and discard the liquid containing unbound bacteria.

-Wash with 100 µL PBS by pipetting up/down while keeping the beads collected at the bottom of the tube with a magnet.

-Resuspend the beads with 100 µL assay buffer.

-Incubate for 30 minutes on ice.

-Collect the beads and discard the liquid.

-Wash once with 100 µL of assay buffer by resuspending and collecting the beads immediately, discard the liquid.

-Resuspend in 100 µL assay buffer.

-Add detection probes at desired concentrations.

Compared to the previously published protocol [START_REF] Heo | Deep phenotypic characterization of immunization-induced antibacterial REFERENCES IgG repertoires in mice using a single-antibody bioassay[END_REF], we increased the concentration of biotin-PEG-cholesterol up to the limit of saturation of the bacterial cell membranes, in order to maximize the binding efficiency of the bacteria to the beads. Additionally, we adjusted the volume of fixed bacteria used for each individual specie. The goal was to maximize the number of bacteria present on the bactoline in each droplet, while still retaining a thin and regular shape of the bactoline. We estimated that with this optimized protocol, there were approximately 200 target bacterial cells bound to the bactoline in each droplet.

Complement source, storage and handling

As mentioned previously, during the thesis we added the detection of antibody-dependent C1q binding to the assay, in order to detect complement-activating antibodies. For this we needed a source of C1q protein suitable for the in-droplet DropMap assay.

We first tested to add the complete complement proteins purified from mouse serum (mouse complement lyophilized, C201-0005, Rockland). However, we found that this preparation also contained a substantial concentration of mouse IgG antibodies. This strongly interfered with the detection of secreted bacteria-specific mouse IgG, as part of the IgG detection probe molecules bound to the unspecific IgG antibodies included in the purified complement proteins.

To circumvent this issue, we instead chose to only add purified C1q protein (C1740, Sigma-Aldrich) to the assay. According to the supplier specifications, this C1q protein preparation is almost entirely pure (≥95% by SDS-PAGE), and we could verify that the presence of IgG antibodies was indeed undetectable (data not shown). The drawback is that this C1q is human (purified from human serum). It would have been preferable to use a mouse C1q protein in the assay, but we did not have access to an equivalent source of mouse C1q at the time of the experiments. However, C1q is a protein that is highly similar in different mammalian species, and typically the C1q protein from a given specie can interact with the Fc domains of complement-activating antibodies from different species. Indeed, there are various studies investigating the antibody-dependent complement activation of mouse antibodies using complement proteins purified from the serum of different mammalian species. This is probably due in most cases to the difficulty of obtaining complement proteins from mouse serum (low serum volume available per animal). For example, Duncan et al. used guinea pig complement and Michaelsen et al. human complement with mouse IgG antibodies [START_REF] Michaelsen | The four mouse IgG isotypes differ extensively in bactericidal and opsonophagocytic activity when reacting with the P1.16 epitope on the outer membrane PorA protein of Neisseria meningitidis[END_REF][START_REF] Duncan | The binding site for C1q on IgG[END_REF]. Therefore we expect our results on antibody-dependent C1q binding to still be relevant even if we used a human source for the C1q protein instead of a murine source.

C1q is a very sensitive protein that can be activated for example by temperature changes.

In our assay, this could lead to antibody-independent C1q binding on the bacteria. When this happened, the C1q protein bound to the bacteria even in the absence of antibody and all droplets displayed a positive C1q binding signal, making it impossible to distinguish complement-activating antibodies. To avoid this issue, we stored the C1q protein at -80 • C in single-use aliquots. For each DropMap measurement with the C1q binding assay, we thawed quickly a single C1q aliquot and placed it on ice before use in the assay. The waiting time with the C1q protein stored on ice should be as short as possible, typically lasting only a few minutes.

Options for the detection of C1q binding

After adding C1q to our DropMap assay, we needed to be able to detect its relocation to the bactoline. We compared two different options: either using a fluorescently-labeled anti-C1q antibody in the assay, or directly labelling the C1q protein with a fluorophore.

Among the various anti-C1q commercial monoclonal antibodies that we tested during this thesis, only one gave satisfying results for the detection of C1q relocation in the droplets (MA1-40311, Invitrogen). This fluorescently-labeled monoclonal antibody is however a mouse IgG2a, which is problematic because it will also be bound by the IgG detection probe, creating an interdependency between IgG and C1q detection in the assay. Additionally, IgG2a is a mouse IgG subclass that can activate the complement pathway, and thus this C1q antibody might itself participate to the activation of the classical complement pathway on the bacterial surfaces. Also, adding another detection antibody to the assay might create steric hindrance and in this way disturb the binding of C1q to the Fc domains of bacteria-specific antibodies.

For these reasons, we instead decided to directly label the C1q protein with a fluorophore.

This was done with an Alexa Fluor 555 NHS Ester reactive dye (A37571, Invitrogen), following the protocol of the supplier and adjusting the amount of dye to obtain approximately seven fluorophore molecules per C1q protein. With this method we could successfully detect the antibody-dependent relocation of C1q to the bacterial surfaces (see figure II.10) and even obtained higher relocation values in equivalent calibration experiments compared to using the anti-C1q labeled antibody (data not shown). In this assay set-up, we also verified that the binding of the IgG detection probe did not significantly impact the IgG-dependent recruitment of C1q (data not shown).

Optimization of the concentration of detection reagents

After the optimization of the preparation of the bacteria-coated beads, the choice of the C1q protein source and detection method, we still had to optimize the concentration of the different reagents present in the in-droplet bioassay described on figure II.9. For this, we used calibration experiments with P. aeruginosa bactolines and a control anti-P. aeruginosa monoclonal IgG2a that induces C1q binding, as shown on figure II.10. We set a 40 nM constant concentration of the control IgG2a antibody, which corresponds to the higher range of IgG concentrations typically attained when measuring secreting B cells with our parameters (32 minutes measurements in 50 pL droplets). We then modified the concentration of single assay reagents to find the optimal concentration for each, in the aim of maximizing the relocation signals for IgG and C1q detection.

55

First, we varied the concentration of the labeled C1q protein from 5 to 40 nM in-droplet concentration. As shown on figure II.12, modifying the concentration of C1q had little effect on the measured relocation signal for C1q binding. Therefore, we chose the smallest concentration that we tried for C1q, 5 nM, for the assay. This concentration allowed to maximize the C1q binding signal while using only a small amount of C1q protein. Additionally, this small concentration limited the issue of antibody-independent C1q binding that we mentioned before in this section. did not increase compared to the negative control without antibody for low concentrations of bacteria-specific IgG2a (2.5 and 5 nM). At these concentrations, the density of IgG2a antibodies bound to the bacterial surfaces probably was not sufficient to recruit C1q. For higher concentrations of IgG2a, the relocation of C1q steadily increased with the concentration of specific antibody, and thus this antibody successfully recruited C1q at these densities on the bacteria. The increase in C1q binding stopped for concentrations of IgG2a higher than 40 nM, also reflecting here the saturation of the available target epitopes as mentioned above.

Analysis and results of measurements with the complement assay

We could then apply this assay to the analysis of B cell populations obtained from immunized mice. It allowed us to detect single B cells secreting a bacteria-specific antibody and quantify the recruitment of the C1q protein by the antibody. We typically ran DropMap measurements with five time points spaced by 8 minutes and then analyzed the data as described in the next paragraphs. Here, we detail the method to detect cells secreting bacteria-specific IgG antibodies, but in parallel we also detected cells secreting bacteria-specific IgM and quantified the IgM-dependent binding of C1q in the same way.

For each measurement we start by computing limits of detection (LoD) for the relocation of the IgG detection probe and for the relocation of the labeled C1q protein as: These definitions are directly equivalent to that of the LoD that we defined for the general IgG secretion assay, adapted from a usual definition of the LoD [START_REF] Armbruster | Limit of Blank, Limit of Detection and Limit of Quantitation[END_REF] (see section II.1).

LoD IgG =
For each droplet in the measurement, we then detect whether it contains a cell secreting a bacteria-specific IgG. It is classified as such if it fulfills all three of the following criteria:

• The droplet contains a detectable concentration of bacteria-specific IgG binding to the bactoline: the fluorescence relocation of the IgG detection probe is above the LoD IgG for at least one timepoint.

• The concentration of bacteria-specific IgG increases over time: the fluorescence relocation of the IgG detection probe has a positive slope over the time series.

• The signal varies more over the time series than the analytical noise in the absence of We can then detect whether the secreted IgG antibody is able to recruit C1q (and thus activate the classical pathway of complement). We classify the IgG-secreting cell as C1qbinding (C1q + ) if the relocation of C1q is above LOD C1q for at least one time point, corresponding to a detectable amount of C1q protein binding to the bacterial surfaces. In that case, we consider that the binding of C1q is induced by the bacteria-specific IgG antibody, as we do not observe droplets that do not contain a specific IgG-secreting cell fulfilling this criteria. We also quantify the density of bound C1q protein with a relative C1q binding value, defined in the same way as the previously mentioned IgG binding value. C1q binding is equal to the maximum relocation signal of C1q on the bactoline over the duration of the measurement, normalized by LOD C1q . A C1q binding value above 1 corresponds to an antibody that is able to recruit C1q, and higher C1q binding values reflect higher densities of C1q protein bound to the bacterial surfaces. In chapter III we will discuss the characterization of complement-activating antibody repertoires with this newly developed DropMap assay. There, we chose to represent the single bacteria-specific IgG or IgM antibodies detected as scatter plots of their maximum C1q binding value against their maximum IgG binding value (see e.g. figure III.6a). This allows to have an insight on the relative efficiency of the antibodies for C1q recruitment. Indeed, for two antibodies of a given isotype bound at a similar density on the bacteria (similar IgG binding value), a higher C1q binding value corresponds to more C1q recruited to the surface of the bacteria and therefore a higher efficiency.

To summarize, we have developed a novel DropMap bioassay that allows the detection of individual bacteria-specific antibodies of the IgG or IgM isotype, as well as the quantification of their potential to activate the classical pathway of complement, analyzed as the recruitment of the C1q complement protein. To our knowledge, this is the first report of an assay that enables the measurement of antibody-dependent complement activation at the single antibody level with high-throughput screening capabilities. This assay is used in the experimental study presented in chapter III.

Data representation and statistical analysis

In the rest of this thesis we show results of conditions that include several biological replicates. These biological replicates are separate mice, with three to six animals per condition. We usually represent comparisons between conditions with bar charts of the mean and standard deviation of each condition. Additionally, we always represent the values for the individual mice with overlayed circles (see for example figure IV.3).

To evaluate the significance of differences between conditions, we chose to use Welch's In the previous chapter, we described the development of a high-throughput droplet microfluidics assay to measure the activation of the classical complement pathway by individual antibodies specific against bacteria, characterized by the antibody-dependent binding of the C1q protein. We showed that our assay worked with a monoclonal antibody targeting P.

unequal
aeruginosa, and we now wanted to apply the assay to the screening of post-immunization antibody repertoires. Our first goal here was to have a proof of concept study confirming that we can detect and quantify antibody-dependent C1q binding at the single cell level with our assay. Additionally, we aimed at gaining insights into the important parameters for an efficient complement-activating antibody response against these bacteria. Measuring the activation of the complement system at the single antibody level is challenging, and to our knowledge has never been done with a high throughput system that allows the screening of antibody repertoires. This study represents a first step towards the development of a method to sort antibodies highly efficient for the activation of the classical pathway of complement based on quantitative measurement of C1q binding. Such a method could be applied to the discovery of new complement-activating monoclonal antibodies against pathogenic bacteria which are a potential alternative to antibiotics in the context of rising antibiotic resistance, as reviewed in the introduction chapter.

62

Chapter III. Complement activation at the single antibody level

Design of the study

We chose B. pertussis and N. meningitidis as model target bacteria for our study. Both species are clinically relevant, as targets of vaccination, and the complement system is involved in the protection. Indeed, B. pertussis displays strong interplay with the complement system during infection: it has developed several complement-escape mechanisms, and certain of its antigens trigger bactericidal antibodies, in particular the surface protein pertactin [START_REF] Geurtsen | Importance of (antibodydependent) complement-mediated serum killing in protection against Bordetella pertussis[END_REF][START_REF] Gotto | Biochemical and immunological properties of two forms of pertactin, the 69,000-molecular-weight outer membrane protein of Bordetella pertussis[END_REF]. Regarding N. meningitidis, it has been demonstrated that the antibodydependent activation of the complement system is a main defense mechanism involved in the protection [START_REF] Lewis | Meningococcal disease and the complement system[END_REF][START_REF] Frasch | Bactericidal antibody is the immunologic surrogate of protection against meningococcal disease[END_REF]. Therefore, it is relevant to study complement-activating antibody responses targeting these two pathogenic bacteria in detail.

Here we wanted to study the activation of complement by antibodies targeting these bacteria. A first step was to elicit bacteria-specific antibody responses in mice. We tried two different strategies to achieve that aim, either immunization with human antibacterial vaccines or fixed bacteria. The vaccines Infanrix (targeting B. pertussis among other pathogens), Bexsero (targeting N. meningitis serogroup B) and NeisVac-C (targeting N. meningitis serogroup C) were used to immunize different groups of mice, using a 100 µL dose (one fifth of the human dose) chosen based on a previous study of our group that used the same dose of another human vaccine in mice that proved to be both immunogenic and well tolerated by the animals [START_REF] Rybczynska | The impact of frost-damage on the quality and quantity of the secreted antigen-specific IgG repertoire[END_REF]. Alternatively, we also immunized mice with fixed N. meningitidis B or C. We chose a dose of 10 8 bacterial cells per immunization, adjuvanted with alum, based on a previous study of our group that used the same dose of commercial heatkilled bacteria also adjuvanted with alum for mouse immunizations [START_REF] Heo | Deep phenotypic characterization of immunization-induced antibacterial REFERENCES IgG repertoires in mice using a single-antibody bioassay[END_REF]. We immunized the mice twice with a four-week interval, replicating a standard vaccination scheme with primary and booster immunizations to obtain a complete immune response. The mice were sacrificed seven days after the booster immunization, at the peak of the antibody-secreting cell response in the spleen [START_REF] Eyer | Single-cell deep phenotyping of IgG-secreting cells for high-resolution immune monitoring[END_REF].

We then precisely characterized the antibody-secreting cell repertoires present in the spleen after the different immunization protocols. Indeed, we wanted to first verify that bacteria-specific antibody responses were successfully generated, and that they included complement-activating isotypes (IgM, IgG2a/2b/3). In a second step we quantified the antibody-dependent C1q recruitment for each individual bacteria-specific antibody with the assay described in section II.4. We quantified both antibody and C1q binding densities on the surface of the bacteria, to compare the C1q-binding efficiencies of different antibodies.

Materials and methods

We used three different commercial human vaccines for the immunizations, Infanrix DTPa-IPV (GlaxoSmithKline), Bexsero (GlaxoSmithKline) and NeisVac-C (Pfizer). We also used N. meningitidis bacteria ( 10 The mice were immunized twice with the same vaccine or fixed bacteria preparation with an interval of four weeks. Seven days after the second immunization, the mice were sacrificed and the spleens harvested.

From the spleens, single-cell suspensions were obtained and then enriched in B cells using the Pan B Cell Isolation kit II (mouse) and a MultiMACS device (both from Miltenyi Biotec).

The spleen B cells were then analyzed using the DropMap platform and the assays described in sections II.1 and II.2, to measure antibody secretion and isotype. In parallel, the spleen B cells were also analyzed with the DropMap bactoline assay described in section II.4, to detect bacteria-specific antibodies and their potential to activate the classical complement pathway. Performing both types of measurement in parallel allows the quantification of the fraction of the total antibodies that are bacteria-specific. For each of the bactoline measurements, the magnetic beads were coated with paraformaldehyde-fixed bacteria corresponding to the bacterial specie that the analyzed mouse was immunized against. We used fixed B. cells analyzed). In this case, a possible explanation could be that the targeted antigens are the recombinant proteins included in the vaccine, that would be present at a too low density on the surface of the fixed bacteria used in the assay for the antibody-binding to be detectable.

Regarding the NeisVac-C vaccine, we detected on average only respectively 0.006 and 0.013% of cells secreting a bacteria-specific IgM or IgG within the spleen B cells (corresponding to a total of four specific antibody-secreting cells per mouse). The general antibody secretion was also very low for this vaccine with only 0.14 ±0.03% IgM-secreting cells and 0.06 ±0.06%

IgG-secreting cells (see figure III.2), and thus it seems to be only weakly immunogenic in mice. Because of these extremely low bacteria-specific antibody responses, neither of the two N. meningitidis vaccines were suitable for our objective of studying complement-activating antibody responses with the newly developed DropMap assay.

Attempting to find an immunization protocol that would elicit a sufficient N. meningitidis-specific antibody response, we decided to immunize mice directly with paraformaldehydefixed N. meningitidis bacteria, either from the B or C serogroup, exactly similar to those present on the nanoparticles in the detection assay. This allows to diversify the available antigens as compared to the two acellular vaccines that contain only certain surface proteins or polysaccharides, and would therefore probably broaden the obtained antibody repertoire.

3 Antibody responses induced by antibacterial immunizations 67 Additionally, with this method of immunization, we ensure that the distribution of antigens is the same in the immunogen as in the DropMap detection assay. By contrast, we could not measure the abundance of the antigens contained in acellular vaccines on the bacteria that we use for the detection of specific antibodies. This would be problematic if they are not present at a sufficient density to allow the detection of bound antibodies, as could have been the case with the Bexsero vaccine for instance.

Both of these immunizations induced a general antibody response with mostly IgG-rather than IgM-secreting cells (see figure III.2). Within spleen B cells, the fixed N. meningitidis B immunizations resulted in 5.1 ±4.3% IgG-and 0.17 ±0.13% IgM-secreting cells, while the fixed N. meningitidis C immunizations resulted in 2.1 ±1.1% IgG-and 0.20 ±0.08%

IgM-secreting cells. The magnitude of the IgG response was very heterogeneous, especially for the mice immunized with N. meningitidis serogroup B: one of the three mice had a fifty times lower IgG-secreting cell frequency than the others in the group. This heterogeneity between mice immunized identically is frequently observed for immunizations with either heat-killed or fixed bacteria in our group (and here also with the Infanrix vaccine), and we do not have an explanation for this phenomenon yet. The N. meningitidis-specific responses also reflected this heterogeneity (see figure III.3a): for example in the fixed N. meningitidis B condition, one mice had a large specific IgG response, with 0.85% of the spleen B cells secreting a specific IgG, while we detected very few (0.014% IgG-secreting cells) or none for the other two mice of the group. Surprisingly, for both conditions the fraction of the generated IgG that were detected as binding to the target bacteria was quite low, on average below 5% of the IgG-secreting cells as shown on figure III.3b. Still, we could detect bacteria-specific IgM and IgG-secreting cells for all animals except one, and even observed a particularly large response for one of the mice immunized with N. meningitidis B, with 394 specific IgG-secreting cells detected out of the 46 000 B cells that we measured. In that regard, the immunizations with fixed N. meningitidis did fit our objective of finding immunization conditions with substantial bacteria-specific antibody responses, in order to study the activation of the classical pathway of complement in a second step.

As explained in section I.8, while all IgM antibodies can in theory trigger complement activation, the subclass of IgG antibodies has a strong influence on their capacity to activate the complement system: IgG1 are only very weakly activating the classical complement pathway, while IgG2a, IgG2b and IgG3 are the efficient subclasses for complement activation [START_REF] Collins | IgG subclass co-expression brings harmony to the quartet model of murine IgG function[END_REF][START_REF] Michaelsen | The four mouse IgG isotypes differ extensively in bactericidal and opsonophagocytic activity when reacting with the P1.16 epitope on the outer membrane PorA protein of Neisseria meningitidis[END_REF][START_REF] Neuberger | Activation of mouse complement by monoclonal mouse antibodies[END_REF]. Therefore, for the different groups of mice that we selected to study the antibody-dependent complement activation (mice immunized with Infanrix, or fixed N. meningitidis B or C), we also measured the subclass of the IgG-secreting cells, distinguishing between IgG1 and IgG2a/2b/3 with the assay described in section II. 4 Analysis of C1q-binding antibodies 69

We have shown here that we were able to measure substantial antibody responses targeted towards bacteria after immunizations, either with an anti-B. pertussis acellular vaccine or with fixed N. meningitidis (B or C serogroup). We could precisely characterize those antibody responses at the single-cell level, with determination of the antibody isotype, IgM or IgG, and of the IgG subclass, either IgG1 or IgG2a/2b/3. We detected bacteria-specific IgM and IgG2a/2b/3, isotypes that can activate the classical pathway of the complement system.

In the next section, we will analyze in detail their ability to recruit the C1q protein, which is the first step of the classical pathway.

Analysis of C1q-binding antibodies

As described in the previous section, we selected immunization protocols targeting our Additionally, there were only few cells secreting a B. pertussis-specific IgG binding with a high density on the surfaces of the fixed bacteria (24 out of the 180 specific IgGs), which To summarize, we observed in these different experiments that IgG-dependent C1q binding was only induced by IgGs binding at a high density on the target bacteria, and only by antibodies of the functionally active IgG2a/2b/3 subclasses. Additionally, a high density of bound IgG2a/2b/3 antibody was sufficient to trigger C1q binding in most cases.

Low density High density

We now looked in detail at the bacteria-specific IgM-secreting cells detected against the different model bacteria, B. pertussis and N. meningitidis B and C. We can analyze these

1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6
IgG binding 0.7 0.8 0.9 For the mice immunized with the Infanrix vaccine, the obtained B. pertussis-specific cells are represented on the figure III.8. For this condition we detected 125 specific IgM-secreting cells, secreting antibodies bound at various density on the bacteria, with 46% of IgMs binding at a low density and 54% at a high density. Additionally, four of the specific IgM antibodies could trigger C1q deposition on the B. pertussis bacteria. We observed here that only IgM antibodies binding with a high density were able to relocate C1q. However, a high density of IgM was not a sufficient condition for C1q binding, as only four out of the 68 IgM antibodies binding with a high density (6%) showed detectable C1q binding in these experiments.

The immunizations with fixed N. meningitidis from the B or C serogroups led to fewer specific IgM-secreting cells as compared to the Infanrix vaccine. We could detect only 17 N. meningitidis B-specific IgM antibodies. 71% of these specific IgMs bound the bacteria with a high-density, but we still detected no C1q + IgM antibody for this condition, as shown on figure III.9a. This was not surprising considering the low number of specific IgMs detected here, and the low occurrence rate of C1q-binding IgM that we observed against B. pertussis,

1 1.05 1.1 1.15 1.2
IgM binding 0.7 0.8 0.9

C1q -

Low density High density

Figure III.8: IgM-secreting cells specific against B. pertussis B. pertussis-specific IgM-secreting cells (125 in total), pooled from measurements of three separate mice immunized with Infanrix DTPa-IPV. IgM binding corresponds to the maximum relocation signal of the anti-IgM probe on the bactoline over the duration of the measurement, for individual specific IgM-secreting cells, normalized by the limit of detection for IgM. C1q binding corresponds to the maximum relocation signal of C1q on the bactoline over the duration of the measurement for the same IgM-secreting cells, normalized by the threshold used to detect C1q binding. Cells with a C1q binding value above 1 are classified as C1q-binding (C1q + ), displayed in green. An arbitrary limit between low and high density IgM binding was placed. Only high-density IgMs relocated C1q to the bacterial surface, but a high density of IgM was not sufficient to relocate C1q.

Chapter III. Complement activation at the single antibody level

High density IgM binding corresponds to the maximum relocation signal of the anti-IgM probe on the bactoline over the duration of the measurement, for individual specific IgM-secreting cells, normalized by the limit of detection for IgM. C1q binding corresponds to the maximum relocation signal of C1q on the bactoline over the duration of the measurement for the same IgM-secreting cells, normalized by the threshold used to detect C1q binding. Cells with a C1q binding value above 1 are classified as C1q-binding (C1q + ), displayed in green. No C1q + IgM-secreting cell was detected against the serogroup B, and only one against the serogroup C.

4 Analysis of C1q-binding antibodies 75 even for IgM bound at a high density. On the contrary, for the mice immunized with fixed N. meningitidis of the C serogroup, the number of specific IgM-secreting cells was slightly higher [START_REF] Pizarro-Cerdá | Bacterial adhesion and entry into host cells[END_REF], and we could detect one IgM bound at intermediate density that very efficiently relocated C1q to the surfaces of the bacteria (see figure III.9b).

These experiments with N. meningitidis as a target bacteria confirm our observation with the B. pertussis-specific IgMs: a high density of bound IgM antibody is not sufficient to trigger C1q binding in this experimental setting. In that regard, IgM displays an opposite behaviour compared to the functionally active subclasses of IgG, where a high density of bound antibody was mostly sufficient to relocate C1q as we described before.

In the same manner as for IgG, we can also represent the simultaneous evolution of IgM binding and C1q binding over the duration of the measurements, as shown for example on figure III.10 for N. meningitidis C-specific IgM-secreting cells. The only C1q + IgM detected here displays an almost perfectly linear trend and seems very efficient here with already detectable C1q recruitment at the first time point, when the density of bound IgM is still low. Here the C1q -IgMs have on the contrary a completely flat slope, which can be attributed to a complete absence of C1q binding.

We showed in this section that we were able to detect and quantify the ability to bind C1q of single bacteria-specific IgM and IgG antibodies with our assay, while screening immunization-induced antibody-repertoires with a high throughput. In addition to this proof-of-concept, we could also gain some insights on the mechanism of antibody-dependent C1q binding for monoclonal antibodies of the IgM and IgG isotypes. We compared the antibody density to the C1q binding efficiency, and showed that a high density of IgM seems to be important but not sufficient to efficiently recruit the C1q protein. By contrast, for IgGs of the complement-activating subclasses (IgG2a/2b/3) a high-density of IgG binding seems to be the only requirement in most cases for C1q recruitment.

Discussion

In a first part of this study, we set out to establish efficient immunization procedures to induce antibody response targeted towards our chosen bacterial species, Bordetella pertussis and Neisseria meningitidis. The children tetravalent vaccine Infanrix gave satisfactory anti-B. pertussis response in the mice, while we observed that two different meningitidis commercial vaccines, Bexsero and NeisVac-C only induced very low or undetectable N.

meningitidis-specific antibody responses. This weak effect of the meningitidis vaccines in mice was surprising, because those vaccines mediate satisfactory protection in human. Possible explanations could be that the immunogenicity of the vaccines is different depending on the specie, or that the protection in human relies on a small number of very efficient antibody-secreting cells that we could not detect here. As an alternative to the meningitidis vaccine for our study, we found that using fixed bacteria as immunogens was a more efficient strategy to induce anti-N. meningitidis IgM and IgG antibodies.

However, we observed that the fraction of the generated IgG that were detected as binding to the target bacteria was quite low, on average below 5% of the IgG-secreting cells for all three immunization protocols (see figure III.3b). For the mice immunized with the Infanrix, it is logical that only part of the IgG repertoire is directed against B. pertussis, as the tetravalent vaccine also contains various unrelated antigens that are included to also protect the recipient against tetanus, diphteria and polio, as well as the pertussis toxoid that is not present on bacterial surfaces. Surprisingly, this was also the case for the fixed N. meningitidis immunizations where the IgG repertoire should be more targeted towards the bacterial antigens (percent specificity of IgG-secreting cells of respectively 3.6 ±6.0% and 3.8 ±2.8% for N. meningitidis B and C). As a comparison, protein antigen immunization typically induce much more targeted antibody responses, with over half of the generated antibodies detected as antigen-specific (see for example the results of immunizations with phycoerythrin on fig-

ure IV.8b later in this thesis). Here, the low fraction of bacteria-specific antibodies within the IgG repertoire could have different explanations. First, the secretion rates of the IgGsecreting cells in general, not taking into account specificity, were particularly low for the mice immunized with N. meningitidis. This resulted in low IgG concentrations attained over the duration of the measurements for most single IgG-secreting cells, with 60% of the droplets containing IgG-secreting cells staying below 1 nM in-droplet IgG concentration. Since the 77 limit of detection for bacteria-specific antibodies depends on several parameters (affinity of the antibody for the target epitope, abundance of the epitope and antibody concentration), we cannot directly link the antibody concentration to the limit of detection of specific antibodies, but it is probable that part of the antibodies secreted at a very low rate fall below the limit of detection of bacteria-specific antibodies, even if they bind an antigen present on the surface of the fixed bacteria. Additionally, part of the antibody-response might be directed against intracellular proteins of the bacteria, that would be available for affinity maturation in the spleen once the fixed bacteria are processed. However, those antigens are not available for antibody binding on the surface of the fixed bacteria present in the assay, so the corresponding antibody-secreting cells would not be detected as specific. Finally, the fixed bacteria immunizations might result in a general activation of B cells leading to the secretion of non-specific IgG antibodies.

Once we selected efficient antibacterial immunization conditions, the next results shown here demonstrated that our assay was able to detect single bacteria-specific C1q-binding antibodies, either IgM or IgG2a/2b/3, while screening post-immunization spleen B cell populations with a high-throughput. Additionally, we could link several parameters to the C1q-binding efficiency of the bacteria-specific antibodies.

First, we observed that IgG1 antibodies cannot bind C1q, even when coating with a high density the surfaces of the bacteria, as seen for example on figure III.6. This is coherent with previous reports that the IgG1 subclass in mouse induces very weak to undetectable activation of the classical pathway of complement [START_REF] Collins | IgG subclass co-expression brings harmony to the quartet model of murine IgG function[END_REF][START_REF] Lilienthal | Potential of murine IgG1 and Human IgG4 to inhibit the classical complement and Fcγ receptor activation pathways[END_REF][START_REF] Michaelsen | The four mouse IgG isotypes differ extensively in bactericidal and opsonophagocytic activity when reacting with the P1.16 epitope on the outer membrane PorA protein of Neisseria meningitidis[END_REF][START_REF] Neuberger | Activation of mouse complement by monoclonal mouse antibodies[END_REF][START_REF] Silveira | Complement activation selectively potentiates the pathogenicity of the IgG2b and IgG3 isotypes of a high affinity anti-erythrocyte autoantibody[END_REF], contrasting with the other mouse IgG subclasses. In our experiments no IgG1 was above the limit of detection that we set for positive C1q binding, but some IgG1 antibodies, in particular bound at a high density, had a C1q binding signal close to that limit (e.g. see figure III.5), which might reflect the very weak but non-zero C1q-binding ability of some IgG1 antibodies described before [START_REF] Michaelsen | The four mouse IgG isotypes differ extensively in bactericidal and opsonophagocytic activity when reacting with the P1.16 epitope on the outer membrane PorA protein of Neisseria meningitidis[END_REF][START_REF] Neuberger | Activation of mouse complement by monoclonal mouse antibodies[END_REF].

On the other hand, we detected as expected C1q binding induced by single antibodies of the different isotypes known to activate the classical pathway of complement, IgM and the IgG subclasses IgG2a, IgG2b and IgG3. However, a high density of surface-bound antibodies seemed to be necessary for antibody-dependent C1q binding, both for IgG (figure III.6) and IgM (figure III.8). Accordingly, we did not observe any C1q-binding IgG antibodies against B. pertussis, as all high-density IgGs were of the IgG1 subclass in that case (see figure III.5).

The importance of a high density of antibodies bound to the pathogen surface for efficient classical pathway activation has been described before for IgG antibodies of various species [START_REF] Giuntini | Combined roles of human IgG subclass, alternative complement pathway activation, and epitope density in the bactericidal activity of antibodies to meningococcal factor H binding protein[END_REF][START_REF] Wright | Dimeric, trimeric and tetrameric complexes of immunoglobulin G fix complement[END_REF]. The reason for this density requirement is that the C1q protein only has a very low affinity for a single IgG Fc domain, with a K D on the order of 100 µM [START_REF] Hughes-Jones | Reaction between the isolated globular subunits of the complement component Clq and IgG-complexes[END_REF]. Therefore, it needs to bind to several IgG Fc domains at once, up to six, with its six globular heads Chapter III. Complement activation at the single antibody level that each contain a binding domain, drastically increasing the avidity to a K D of roughly 10 nM [START_REF] Hughes-Jones | The reaction between the complement subcomponent C1q, IgG complexes and polyionic molecules[END_REF][START_REF] Lin | Interaction of human Clq with insoluble immunoglobulin aggregates[END_REF]. Earlier models proposed that the C1q protein should bind to the Fc domain of at least two IgG antibodies, each binding surface antigens with both of their Fab domains. However, more recent studies have proposed that efficient C1q binding would instead require six IgG antibodies in close proximity [START_REF] Diebolder | Complement is activated by IgG hexamers assembled at the cell surface[END_REF][START_REF] Ugurlar | Structures of C1-IgG1 provide insights into how danger pattern recognition activates complement[END_REF][START_REF] Wang | Molecular Basis of Assembly and Activation of Complement Component C1 in Complex with Im[END_REF]. Diebolder et al. first found that six IgG molecules binding to antigens on a cell surface can assemble in an hexameric shape through non-covalent interactions between their Fc domains, forming a circular Fc platform on top with six C1q-binding domains available, on which the C1 complex can dock with high avidity to initiate the classical complement cascade [START_REF] Diebolder | Complement is activated by IgG hexamers assembled at the cell surface[END_REF] (see figure III.11). In the IgG hexamer, each of the IgG molecules is bound to the surface with only one of its two Fab arms. This hexameric assembly of IgG antibodies closely resembles the structure of an IgM antibody binding to a surface, and seems to be the main efficient mechanism for C1q binding by IgG antibodies [START_REF] Diebolder | Complement is activated by IgG hexamers assembled at the cell surface[END_REF]. It does not exclude that C1q binding to less than six IgG Fc domains is possible, as was reported by early studies [START_REF] Borsos | Complement fixation on cell surfaces by 19S and 7S antibodies[END_REF][START_REF] Rosse | Structural Characteristics of Hemolytic Antibodies As Determined by the Effects of Ionizing Radiation[END_REF][START_REF] Rosse | Quantitative immunology of immune hemolytic anemia. I. The fixation of C1 by autoimmune antibody and heterologous anti-IgG antibody[END_REF], but this lower avidity C1q binding would result in lower complement activity [START_REF] Pawluczkowycz | Binding of Submaximal C1q Promotes Complement-Dependent Cytotoxicity (CDC) of B Cells Opsonized with Anti-CD20 mAbs Ofatumumab (OFA) or Rituximab (RTX): Considerably Higher Levels of CDC Are Induced by OFA than by RTX[END_REF].

We will compute an order of magnitude of the density of surface-bound IgG antibodies on the bacterial surfaces present in our in droplet assay, to determine whether IgG antibodies An obvious first parameter that could be at play here would be the exact subclass of the IgG antibody. Indeed, in our experiments we could not distinguish between the mouse IgG2a, IgG2b and IgG3 subclasses that can all activate the classical complement pathway.

These three subclasses were however reported to have different activities for complement activation, measured as in vitro serum bactericidal activity [START_REF] Michaelsen | The four mouse IgG isotypes differ extensively in bactericidal and opsonophagocytic activity when reacting with the P1.16 epitope on the outer membrane PorA protein of Neisseria meningitidis[END_REF] or C1q deposition [START_REF] Lilienthal | Potential of murine IgG1 and Human IgG4 to inhibit the classical complement and Fcγ receptor activation pathways[END_REF],

or in vivo hemolytic activity [START_REF] Silveira | Complement activation selectively potentiates the pathogenicity of the IgG2b and IgG3 isotypes of a high affinity anti-erythrocyte autoantibody[END_REF]. These studies do not classify the different mouse IgG subclasses in the exact same order, probably owing to an influence of the experimental setting, in particular the target epitope and the source of complement (human or mouse).

Nonetheless they point to an influence of the exact IgG subclass on complement activation, and thus on C1q binding efficiency, since it is the only step of the complement cascade where the antibody plays a major role.

Another parameter influencing the C1q-binding efficiency of complement-activating IgGs in our experiments could be the target antigen and epitope. Since we used full fixed bacteria for the immunizations, the complete diversity of bacterial antigens was available for the affinity maturation of the antibody-secreting cells, and thus the specific IgG-secreting cells that we detected could be targeting very different surface antigens and also different epitopes within the antigens. Various studies have reported before that potent complement activation, often measured as complement-dependent cytotoxicity (CDC), depended on the antigen and epitope. Cragg et al. have even reported that certain target antigens completely abrogate the ability to activate complement even for monoclonal IgGs binding at a high density [START_REF] Cragg | Complement-mediated lysis by anti-CD20 mAb correlates with segregation into lipid rafts[END_REF], which we also observed in this study with one very densely bound N. meningitidis B-specific IgG2a/2b/3 antibody failing to bind C1q at a detectable level (see figure III.6a). The varying complement activation efficiency could be for instance attributed to the orientation of bound IgG due to epitope geometry [START_REF] Cragg | Complement-mediated lysis by anti-CD20 mAb correlates with segregation into lipid rafts[END_REF][START_REF] Bindon | Importance of antigen specificity for complement-mediated lysis by monoclonal antibodies[END_REF][START_REF] Teeling | The Biological Activity of Human CD20 Monoclonal Antibodies Is Linked to Unique Epitopes on CD20[END_REF] or to the fluidity of the antigen on the cell membrane [START_REF] Cragg | Complement-mediated lysis by anti-CD20 mAb correlates with segregation into lipid rafts[END_REF][START_REF] Teeling | Characterization of new human CD20 monoclonal antibodies with potent cytolytic activity against non-Hodgkin lymphomas[END_REF] (probably very limited in our experiments involving paraformaldehyde-fixed bacteria). The distance of the target epitope to the cell membrane also seemed to be an important factor, with closer distances favoring C1q-binding avidity [START_REF] Pawluczkowycz | Binding of Submaximal C1q Promotes Complement-Dependent Cytotoxicity (CDC) of B Cells Opsonized with Anti-CD20 mAbs Ofatumumab (OFA) or Rituximab (RTX): Considerably Higher Levels of CDC Are Induced by OFA than by RTX[END_REF] and subsequent complement-dependent cell lysis [START_REF] Cleary | Antibody Distance from the Cell Membrane Regulates Antibody Effector Mechanisms[END_REF][START_REF] Xia | Efficient complement-mediated lysis of cells containing the CAMPATH-1 (CDw52) antigen[END_REF]. Finally the off rate k off of bound IgGs also played a role [START_REF] Teeling | Characterization of new human CD20 monoclonal antibodies with potent cytolytic activity against non-Hodgkin lymphomas[END_REF]. A simple interpretation of those findings is that all of the different factors mentioned impact the formation of the hexameric IgG assembly, leading to more or less efficient C1q docking and explaining the differences in C1q binding efficiency observed in our experiments, although we cannot attribute those differences to specific parameters here.

Regarding IgM antibodies, we also observed in the results described here that a high density of bound IgM seemed to usually be important for efficient C1q binding. However, by contrast with IgG, the high density of antibody was not sufficient in this case, as only few of the densely bound IgMs could recruit C1q at a detectable level (see figure III.8, only 10% of the high density IgMs are C1q + ). This was true for all three of the bacterial models studied here, B. pertussis and N. meningitidis B or C serogroup, with only few or none of the specific IgM binding C1q, despite the detection of IgM antibodies binding at a high density for all three bacterial strains. In addition, we also observed C1q recruitment starting at low IgM density for one particularly efficient IgM antibody (see figure III.10), contrasting with the antibody density threshold for C1q binding that we observed for IgG. We propose to discuss in detail the structure of IgM binding to a surface and recruiting C1q as described in the literature to try to understand this phenomenon.

The IgM antibody can be secreted as a pentameric [START_REF] Perkins | Solution structure of human and mouse immunoglobulin M by synchrotron X-ray scattering and molecular graphics modelling. A possible mechanism for complement activation[END_REF] or less frequently hexameric [START_REF] Hughey | Production of IgM Hexamers by Normal and Autoimmune B Cells: Implications for the Physiologic Role of Hexameric IgM[END_REF][START_REF] Müller | High-resolution structures of the IgM Fc domains reveal principles of its hexamer formation[END_REF] molecule and undergoes a conformational change from a mostly planar shape in solution to a staple-like shape when bound to surface antigens [START_REF] Czajkowsky | The human IgM pentamer is a mushroom-shaped molecule with a flexural bias[END_REF][START_REF] Feinstein | Conformation of the free and antigen-bound IgM antibody molecules[END_REF]. This conformation change is thought to fully expose the binding sites for the C1q globular heads, according to mechanistic models and cryo-atomic force microscopy observations [START_REF] Czajkowsky | The human IgM pentamer is a mushroom-shaped molecule with a flexural bias[END_REF], whereas these sites are mostly concealed when the IgM is in solution, limiting the activation of complement to bound IgM antibodies. Recently, Sharp et al. have described the IgM structure in more detail through cryoelectron tomography of IgM binding to antigen adsorbed on liposomes, also studying the interaction of IgM with the C1 protein [START_REF] Sharp | Insights into IgM-mediated complement activation based on in situ structures of IgM-C1-C4b[END_REF]. They showed that both pentameric and hexameric IgM form an equivalent C6-symmetric hexameric structure once bound to surface antigens and C1, where the missing domain is replaced by a J chain in the pentameric form of IgM. Thus, the structure of the IgM-C1 assembly is very similar to that of the IgG hexamer that enables efficient C1q binding (first described by Diebolder et al. and mentioned previously in this discussion [START_REF] Diebolder | Complement is activated by IgG hexamers assembled at the cell surface[END_REF]), both for the pentameric and hexameric form of IgM, which was not clear until then. This structure displays a ring of five or six Fc units on top of the dome formed by the IgM molecule, with C1q binding sites exposed, to which either five or six of the globular heads of C1q are bound. Additionally, the authors show that shown that the F(ab') 2 domains of IgM can only pivot as a rigid unit and lack the flexibility of the equivalent domain in IgG, where both Fab arms can move independently [START_REF] Chen | Cryomicroscopy reveals the structural basis for a flexible hinge motion in the immunoglobulin M pentamer[END_REF].

Our hypothesis to explain that even a high density of IgM binding to a bacterial surface does not necessarily induce C1q binding in our experiments would then be that the ideal conformation of the IgM-C1q assembly has rather strict geometrical constraints, as described above, with in particular a complete binding of the different Fab domains of the IgM on surface antigens. It is possible that only certain antigens and epitopes allow to fulfill these conditions, for instance requiring not only a high antigen density, but also a specific location and orientation of the binding site on the target epitope. The fact that the bacterial surfaces are fixed by crosslinking with paraformaldehyde in our experiments and thus probably allow limited antigen rearrangement may also add to these geometrical constraints. If these specific structural requirements are not met, the bound IgM would not be stabilized in an suitable conformation for C1q docking, for example with the C1q binding domains not properly exposed, and the binding avidity of C1q would be drastically reduced. This would explain our observation that only few of the detected bacteria-specific IgM antibodies could bind C1q in our experiments. However, it seems that when targeting an adequate epitope, IgM antibodies can recruit C1q with an efficiency equivalent to that of highly efficient IgG antibodies: for example, the only C1q-binding antibody detected against N. meningitidis C (figure III.9b) has a very high C1q binding value, similar to some of the most efficient C1q +

IgG antibodies that we detected in our measurements (figure III.6). In addition, for this antibody, the evolution of C1q binding versus IgM binding is almost perfectly linear and C1q activation starts for low IgM binding density (see figure III.10). An interpretation is that IgM-dependent C1q recruitment directly correlates here with the number of IgM antibodies present on the surface of the bacteria and can be achieved very efficiently for this epitope (each bound IgM recruiting a C1q molecule?), allowing its detection with our system even at very low IgM binding density. Since a single IgM molecule is sufficient for C1q binding, it is logical that there is no threshold in IgM binding density required to initiate C1q binding, contrasting with IgG in that regard.

We have shown here that our assay was able to detect IgM and IgG antibodies binding the C1q protein, and distinguish subtle details in their efficiency to do so. However, there are certain limitations inherent to the design of the assay. First, we used pure C1q protein in our experiments, while the complement-initiating C1 complex also contains two monomers of the C1r and C1s proteases that are assembled with C1q. Studies have however shown that C1q binding is not the only determinant of the efficiency of antibody-dependent C1 activation and that the presence of C1r 2 C1s 2 in the C1 complex also plays a role, albeit probably not central.

For instance, Parce et al. have described different C1 activation potential for conditions with similar levels of surface-bound C1q [START_REF] Parce | Measurement of antibody-dependent binding, proteolysis, and turnover of C1s on liposomal antigens localizes the fluiditydependent step in C1 activation[END_REF], and more recently Zwarthoff et al. showed that C1r 2 C1s 2 enhanced the binding of C1q to target-bound human IgG [START_REF] Zwarthoff | C1q binding to surface-bound IgG is stabilized by C1r2s2 proteases[END_REF]. This difference between C1q and the functional C1 complex might also extend to IgM, because it has been observed that the IgM-C1 assembly adopts a slightly skewed hexagonal conformation due to the presence of C1r 2 C1s 2 , different to the perfect hexagonal shape in the projected C1q-only arrangement [START_REF] Sharp | Insights into IgM-mediated complement activation based on in situ structures of IgM-C1-C4b[END_REF], which might result in moderately different binding dynamics.

Another limitation is that we used fluorescent probes to detect the relocation of IgM and IgG on the bacterial surfaces, as well as distinguishing the IgG subclass (see sections II.1 and II.2). These probes are either fluorescently-labeled full IgG antibodies (IgG Fc probe, goat), F(ab') 2 fragments (IgM probe, donkey) or Fab fragments (IgG1-specific probe, goat), and we cannot exclude that they interfere to a certain extent with the binding of the C1q protein by steric hindrance. Regarding IgG, it is also possible that the IgG Fc probe, possessing two binding domains as a full IgG, helps to crosslink the bacteria-specific IgG antibodies and maintain them bound on the bacterial surface, maybe facilitating IgG hexamer formation and C1q binding. Nonetheless, we expect the antibodies that were distinguished as very efficiently recruiting C1q in our measurements to also be the most effective for complement activation in an in vivo setting without the presence of the detection probes. Therefore, we still think that our system is relevant to detect C1q-binding bacteria-specific antibodies.

Overall, we observed that several parameters are required for efficient antibody-dependent recruitment of C1q, which probably contributed to make C1q-binding antibodies rare events in our experiments. As an example, on average only one out of 3000 spleen B cells secreted a C1q-binding IgG after the N. meningitidis B immunizations, and this was true for only one mouse out of the group of three, while the other mice did not have any C1q-binding antibody response. For this reason, a high-throughput method is necessary to screen for complement-activating antibodies against target bacteria. We highlighted here that a high density of bound antibodies is necessary for optimal complement activation and therefore a first requirement when developing complement-activating monoclonal antibodies is to make sure that they target surface antigens present at a high density. However, we also showed here that it would not be sufficient to select antibodies based on very high affinity and target epitope density to find efficient activators of the complement system. Instead, it is crucial to also measure subsequent C1q binding. Indeed, only few IgM antibodies were able to bind C1q and although most functionally active IgGs relocated C1q, they did so with widely varying efficiency. This validates our approach that allows a fine quantification of an antibody's ability to induce C1q binding.

In a next step, we aim to couple our detection assay with high-throughput sorting and single-cell sequencing of the antibodies most efficient for C1q recruitment. We would sort the antibody-secreting cells based on maximal C1q binding density and also on maximal antibody 6 Perspective: sorting of complement-activating antibodies 83 binding density on the bacterial surfaces. This would then allow to express monoclonal antibodies with the sequences of interest, in order to study their interactions with the target bacteria and complement in vitro and identify the target antigens. A first perspective would be to characterize in more detail the molecular mechanism of C1q binding by the antibodies that were particularly efficient in that regard. Additionally, this approach could be used to screen for potential therapeutic monoclonal antibodies against pathogenic bacteria that would induce strong activation of the complement system. Preliminary trials to establish this coupling with high-throughput sorting are presented in the next section.

Perspective: sorting of complement-activating antibodies

As mentioned in the previous section, the next objective of this project is to develop a workflow to sort the droplets containing a cell secreting a bacteria-specific antibody that is able to efficiently activate the classical complement pathway. In a second step, we would then use single-cell transcriptomics to obtain the sequences of these antibodies of interest.

Here, we present preliminary work on the high-throughput sorting step that was conducted with the help of Daniela Rodrigues, Kevin Portmann and Luca Schlotheuber.

The droplets that we use in the DropMap system are aqueous droplets in an oil outerphase. Therefore they cannot be flowed directly through a conventional cell sorter which requires the sorted objects to be in an aqueous solution. A simple solution is to form double emulsions (water/oil/water) from the droplets containing our complement detection assay and B cells with a vortexing protocol [START_REF] Sukovich | Bulk double emulsification for flow cytometric analysis of microfluidic droplets[END_REF]. To do this, we add the oil solution containing droplets to an aqueous outer-phase buffer in a 1:10 ratio. The outer-phase buffer is composed of PBS with 10% w/v PEG35K, 4% v/v Tween 20 and 1% v/v Pluronic F-68 (all from Sigma-Aldrich). We then vortex the sample (5 cycles of 20 seconds at maximum speed)

Chapter III. Complement activation at the single antibody level

We then applied our method to the sorting of a double emulsion sample containing spleen B cells from a mouse immunized with N. meningitidis B (condition described before in this chapter) and fixed N. meningitidis B-coated nanoparticles. The double emulsions were incubated for 30 minutes at 37 • C to induce antibody secretion. Here, we could not distinguish any clear positive double emulsion (containing a N. meningitidis-specific IgG), as seen on figure III.13c. This was not surprising as the frequency of double emulsions containing a specific IgG-secreting cell was know to be quite low as inferred from the DropMap measurements of the same spleen B cell sample (roughly one positive double emulsion out of 2000).

We were additionally limited by the sorting time to encounter intact double emulsions: it was difficult to observe more than 100 of these events per minute. This is at least partly due to the heterogeneous nature of the double emulsions that we could form with the vortexing protocol. As visible on the microscopy image on figure III.12, the sample also contains smaller oil-in-water droplets that are by-products of this crude double emulsification process, that cannot be easily separated and also have to be flowed through the cell sorter, greatly increasing the sorting time.

Taking into account the slow sorting speed of double emulsions in these first experiments and the low frequency of bacteria-specific antibody-secreting cells, we estimate that it would have taken approximately 10 days to sort the minimal number of B cells of interest required for the sequencing process (roughly 1000 viable cells). This sorting time is obviously not realistic and thus the process requires extensive optimization to increase the speed of sorting.

First, a solution to the heterodispersity of the double emulsion would be to use a microfluidic double emulsion droplet generator, such as the Fluigent Raydrop Double Emulsion system, that allows to produce perfectly monodisperse double emulsions. We estimate that this modification of the quality of the double emulsions compared to our crude samples generated with the vortexing protocol could increase the sorting rate of intact double emulsions by a factor 10. The Raydrop device was however not available in the lab at the time of the experiments due to delivery delays outside of our control. Additionally, an optimization of the double emulsion concentration and of the sorter flow rate could probably further increase the sorting rate of positive events by another factor 10. With the implementation of these optimization steps, we could probably sort a sufficient number of target cells a few hours maximum, allowing to proceed with the single-cell sequencing.

To conclude, these first trials for the sorting of droplets containing a specific IgG-secreting cells show that the process seems feasible, but would need further optimization to considerably increase the sorting speed in order to obtain enough positive events for the sequencing process in a reasonable sorting time. We did not have the possibility to pursue any optimization of the double emulsion sorting, but we listed several options that should make the sorting process technically successful, allowing to move on to the single-cell sequencing. Alternatively, a commercial solution for the sorting of droplets containing beadline relocation assays, the Comet device from Saber Bio, will soon be available.

Chapter IV

Influence of TLR agonists on the antibody repertoire

Contributions: The work with mice (handling, immunizations, harvest of spleen) was conducted by Olivia Bucheli, Ingibjörg Sigvaldadóttir, Kevin Portmann and Luca Schlotheuber.

Afterward, the rest of the experiments and analyses described in this chapter were conducted by myself.

In the previous chapter, using full fixed bacteria as immunogens, we observed sometimes agonists per immunization; these doses were chosen by referring to previous studies that used similar doses in mouse, not necessarily through the same immunization route [START_REF] Bremer | Injection route and TLR9 agonist addition significantly impact heroin vaccine efficacy[END_REF][START_REF] Caproni | MF59 and Pam3CSK4 Boost Adaptive Responses to Influenza Subunit Vaccine through an IFN Type I-Independent Mechanism of Action[END_REF][START_REF] Fransen | Agonists of toll-like receptors 3, 4, 7, and 9 are candidates for use as adjuvants in an outer membrane vaccine against Neisseria meningitidis serogroup B[END_REF][START_REF] Jayakumar | TLR1/2 Activation during Heterologous Prime-Boost Vaccination (DNA-MVA) Enhances CD8+ T Cell Responses Providing Protection against Leishmania (Viannia)[END_REF]. As a scale reference, the dose of 10 8 fixed bacterial cells used in the previous chapter corresponds to roughly 100 µg dry weight.

2 Materials and methods

89

We added alum to all immunizations here, including those containing TLR agonists, because it is necessary as a basis adjuvant in our experimental setting to boost the antibody response, since pure protein immunizations are not sufficiently immunogenic to measure antibody-secreting cell responses at the single cell level. They typically lead to extremely low numbers of observed antibody-secreting cells with the throughput accessible with the DropMap system (screening of 20 000 -30 000 total B cells per measurement, only a few secreting cells typically detected), comparable to naive mice, and are thus not suitable to compare differences between conditions.

For each condition, we immunized the mice twice with a four-week interval, replicating a standard vaccination scheme with primary and booster immunizations to obtain a complete immune response. We measured antibody secretion by the spleen B cells 6 days after the second immunization, which correspond to the peak of the antibody response in the spleen after the boost [START_REF] Eyer | Single-cell deep phenotyping of IgG-secreting cells for high-resolution immune monitoring[END_REF]. We quantified antibody secretion at the single-cell level, separating IgM and IgG, the main antibody isotypes secreted in the spleen, and also distinguishing the IgG1 subclass from the IgG2a, IgG2b and IgG3 subclasses. The reasoning behind this distinction is that mouse IgG1 is not functionally active: it does not activate complement by the classical pathway and only has very little potential to activate FcR-mediated effector functions, as it binds mostly to the inhibitory receptor FcγRIIb [START_REF] Bruhns | Mouse and human FcR effector functions[END_REF], and thus is mostly limited to the neutralization of toxins and viruses [START_REF] Collins | IgG subclass co-expression brings harmony to the quartet model of murine IgG function[END_REF]. On the contrary, IgG2a, IgG2b and IgG3 subclasses efficiently fix complement and activate effector functions by binding to activating Fc receptors. Therefore, measuring side-by-side the secretion of those three IgG subclasses and IgG1 gives an insight on the potential of the IgG response to trigger effector mechanisms. Additionally, we measured the binding of the single antibodies to the PE antigen, resulting in a classification of the antibodies as PE-specific or non-specific, with a scale of affinity for the specific antibodies, as explained in section II.3.

Materials and methods

We used R-phycoerythrin (PE, from Sigma-Aldrich) for the immunizations (70 µg per immunization, optimization of the dose described in appendix A.2). We also added to the We then immunized i.p. 8-10 weeks old female BALB/c mice (Janvier) with 100 µL of the prepared solutions. Each mouse was immunized a second time four weeks later with the same antigen solution, and six days after the animals were sacrificed and full spleen and blood samples were collected. From the spleens, single-cell suspensions were obtained and The spleen B cells were then analyzed using the DropMap platform and the assays described in sections II.1 and II.2, with the measurement of the affinity of the secreted antibodies against PE (20nM in-droplet concentration, see section II.3).

After analyzing the results of the antibody secretion measurements, it became apparent that some of the mice in this study did not respond to the immunizations, or at least much less than the rest of the mice from the same condition. For these mice we detected either no PE-specific IgG-secreting cells or a frequency at least ten times lower than the average of the rest of the group. The origin of these differences is still unclear, but we chose to exclude those "non-responder" mice from the results of the study as they stood out as outliers. In total, we used 37 mice (six mice per condition, seven for the PE only condition) and excluded 5 non-responders (two in the PE only group, and one in the PE+TLR2 agonist, PE+TLR3 agonist and PE+TLR9 agonist groups), leading to either five or six animals per condition in the final results presented throughout this chapter.

The Cantonal ethics committee of Zürich validated and approved all the experiments described in this study under the license number ZH215/19.

In the different bar charts of this chapter, each dot represents one mouse and the mean and standard deviation are plotted for each condition. Throughout the chapter, the pvalues were computed to compare each condition with TLR agonists added to the PE alone condition but not for any other pair of conditions, except when otherwise specified. See section II.5 for additional details on statistical analysis.

Influence on the antibody repertoire and its isotype distribution

As explained in the introduction, the isotype of the antibody plays an important role in determining its effector functions. Here we focused on IgM and the different IgG subclasses.

With the assay described in section II.2, we were able to detect IgM-secreting cells in the measured spleen B cell populations. Their frequency are displayed on figure IV.3a, and range 91 from 0.2 to 1.1% of the spleen B cells for the different conditions. We observed a significant increase of the IgM-secreting cells frequency with the addition of the combination of TLR agonists (0.94 ±0.29% compared to 0.38 ±0.31% for PE alone), as well as a trend increase with the TLR2 and TLR3 agonists (1.08 ±1.06% and 0.83 ±0.63% respectively). This increase in IgM-secreting cells does not seem to correspond to a reduction in the efficiency of class switching towards IgG, because the fraction of IgG-secreting cell within B cells also increased for the same conditions (see figure IV.3b). Indeed, we could also detect the IgGsecreting cells (see section II.1), that accounted for 0.19 ±0.15% of the spleen B cells with PE alone, and observed a large increase of their frequency for all the TLR agonists tested

92

Chapter IV. Influence of TLR agonists on the antibody repertoire except the TLR4 agonist, significant for the TLR2 and TLR9 agonist (0.58 ±0.15% and 0.54 ±0.28% respectively). There was also a significant increase for the combination of the TLR2, 4 and 9 agonists, by a factor four, to 0. We hypothesized that the TLR agonists could influence the IgG subclass distribution, but we did not observe any significant change for this parameter when adding the different TLR agonists or the combination of TLR agonists (see figure IV.5). For all the conditions, the subclass of the IgG-secreting cells is always around 90% of IgG1 and 10% of IgG2a/2b/3

(not distinguishable with our assay described in section II.2, 14 ±5% for PE alone). Again, Here, we showed that the different TLR agonists and the combination induced increases in the IgM-and IgG-secreting cell frequencies, without significantly changing the ratio of those two isotypes, expect for the TLR9 agonist that resulted in a shift towards IgG. Additionally, we did not observe any change in the distribution of IgG1 versus IgG2a/2b/3 subclasses when adding TLR agonists.

P E P E + T L R 2 a

Increase in the IgG secretion rates

Looking in more details at the secreted antibody repertoire, we also quantified the secretion rate of single IgM-or IgG-secreting cells with the method described in section II.1 to determine whether adding the different TLR agonists could have an influence on this parameter. The median IgM and IgG secretion rates for mice immunized with PE alone were 86 ±18 and 94 ±18 Ig/s respectively. We observed no effect of the addition of the different TLR agonists or combination thereof on the median IgM-secretion rate. By contrast, we found a significant increase in the median IgG secretion rate for the mice immunized with PE and the TLR2 agonist, up to 146 ±28 IgG/s, and moderate increases for both the TLR4 and TLR9 agonists (see figure IV.6), but not for the TLR3 agonist.

However, the increase in the IgG secretion rate is lost when the TLR2, 4 and 9 agonists are To summarize, we observed increases in the secretion rates of the population of IgGsecreting cells for the TLR2, 4 and 9 agonists but not for their combination. For the TLR2 and the TLR9 agonists, this adds up to the increase of the frequency of IgG-secreting cells described in section IV.3, further amplifying the IgG response. Medians and standard deviations are overlayed. The total number of IgG-secreting cells for each condition is specified at the bottom. The high secretors (>500 IgG/s, no precise quantification of the secretion rate) are displayed above the dotted line, as well as the fraction of the IgG-secreting cells of the group that they represent.

P E P E + T L R 2 a g o n is t P E + T L R 4 a g o n is t P E + T L R 9 a g o n is t P E + c o m b in a ti o n a v g T L R 2 ,

Increase of the PE-specific cells frequency

We then looked at the PE-specific antibody response by measuring the PE-specific IgM and IgG-secreting cells in the spleen. Within IgG-secreting cells, PE-specific cells were detected with the method described in section II.3. Their frequency for the respective conditions are shown on figure IV.8a, averaging between 0.05 and 0.5% of the total spleen B cells, a range similar to previous DropMap measurements for different protein antigens, for example tetanus toxoid [START_REF] Eyer | Single-cell deep phenotyping of IgG-secreting cells for high-resolution immune monitoring[END_REF]. By contrast, only very few PE-specific IgM-secreting cells were detected. As an order of magnitude less than one out of 200 IgMs showed binding of PE, while more than half of the IgG-secreting cells were PE-specific overall (see figure IV.8b).

This does not seem to be a technical issue, as we did observe several cells secreting an IgM antibody that very clearly relocated PE to the beadline. A possible explanation would be that most PE-affine cells underwent a class-switching process, for example switching their isotype from IgM to IgG.

We observed a significant increase in PE-specific IgG-secreting cells when the TLR2 agonist or the combination were added to the immunizations, from 0.13 ±0.12% for PE alone to respectively 0.45 ±0.11% and 0.38 ±0.18% of the B cells secreting a PE-specific antibody. We also observed a more modest increase for the TLR9 and TLR3 agonists (see figure IV.8a). This is coherent with the higher antigen-specific serum titers reported before when adding the different TLR agonists [START_REF] Buffa | Evaluation of TLR Agonists as Potential Mucosal Adjuvants for HIV gp140 and Tetanus Toxoid in Mice[END_REF][START_REF] Giannini | Enhanced humoral and memory B cellular immunity using HPV16/18 L1 VLP vaccine formulated with the MPL/aluminium salt combination (AS04) compared to aluminium salt only[END_REF][START_REF] Weeratna | TLR agonists as vaccine adjuvants: Comparison of CpG ODN and Resiquimod (R-848)[END_REF][START_REF] Fransen | Agonists of toll-like receptors 3, 4, 7, and 9 are candidates for use as adjuvants in an outer membrane vaccine against Neisseria meningitidis serogroup B[END_REF]. We also measured the serum IgG titers at the time point of spleen harvest (results in appendix A.1).

We showed here that this increase in the frequency of PE-specific IgG-secreting cells was mostly due to the general increase in the frequency of IgG-secreting cells in the spleen B cells that we described in section IV.3. Indeed, the fraction of specific cells within IgG-secreting cells was already high in the PE alone condition (64 ±12%) and did not increase significantly with the addition of the different TLR agonists, as seen on figure IV.8b. Overall, slightly more than half of the IgG-secreting cells were PE-specific in the different conditions, with only an increase for the TLR2 agonist, up to 79 ±7% (not significant, p = 0.052), and even a significant decrease for the TLR3 agonist.

Whether an IgG-secreting cell was PE-specific or not did not seem to have any link with its IgG subclass, because the respective fractions of IgG1 and IgG2a/2b/3 were similar for the subset of PE-specific IgG-secreting cells as compared to the non-specific cells (data not shown). Similarly, for each condition, the IgG secretion rates (median and single-cell level distribution) were essentially similar when considering all the IgG-secreting cells or only the PE-specific subset (data not shown).

No change in the average affinity

With the PE-binding assay described in section II.3, we can distinguish cells that secrete an antibody that binds the PE antigen, but we can also precisely determine differences in the affinity of the antibody for the antigen. We do this for the IgG-secreting cells by computing the slope of the evolution of PE relocation against IgG relocation over the duration of the measurement as explained in section II.3. The obtained affinity slope is a direct correlate of the affinity of individual IgGs for the PE antigen, with higher affinity slopes corresponding to higher affinities, or lower dissociation constants K D .

As visible on figure IV.9, here the addition of the different TLR agonists or combination thereof did not result in any significant modification of the average affinity slope of the generated PE-specific IgG antibodies. The average affinity slope is 1.54 ±0.29 for PE alone and range from 1.20 to 1.55 for the different TLR agonists and combination. Affinity slopes of single PE-specific IgG-secreting cells, pooled for each condition from all the mice of the group. Medians and standard deviations are overlayed. The total number of cells for each condition is specified at the bottom. Cells with an affinity slope higher than 5 are displayed above the dotted line (slope of 5.9, 11.5, 6.5 and 5.9 from left to right).

P E P E + T L R 2 a g o n is t P E + T L R 4 a g o n is t P E + T L R 9 a g o n is t P E + c o m b in a ti o n P E + T L R 3 a g o n is t 0 0.5 1

Discussion

We showed here that the different TLR agonists and the combination had significant effects on several parameters that determine the magnitude of the antibody response (frequency of IgG-SCs, both specific and non-specific IgG secretion rates, ...), but that they did not impact the quality of the antibody response in our experiments: no change in the affinity of the obtained IgGs and no switch towards functionally active IgG isotype.

Quality of the antibody response

As we showed in the previous section, the different TLR agonists or the combination did not have any significant effect on the affinities of the generated PE-specific antibody repertoire, either considered as the average affinity or as the distribution of affinity of individual antibodies.

In the literature, TLR agonists added to immunizations have been associated with increased germinal center responses that are usually linked with the generation of higher affinity antibodies [START_REF] Defranco | Contribution of Toll-like receptor signaling to germinal center antibody responses[END_REF][START_REF] Rookhuizen | Toll-like receptor 9 signaling acts on multiple elements of the germinal center to enhance antibody responses[END_REF]. In line with this, an enhancement of affinity maturation by the addition of TLR agonists has been described, e.g. for the TLR4 agonist MPL [START_REF] Budroni | Antibody avidity, persistence, and response to antigen recall: comparison of vaccine adjuvants[END_REF][START_REF] Delgado | Lack of antibody affinity maturation due to poor Tolllike receptor stimulation leads to enhanced respiratory syncytial virus disease[END_REF]. However, the effect of TLR agonists on the affinity of the antibody repertoire is not always clear. For example in the case of the TLR9 agonist CpG ODN 1826, different studies have reported contradictory results regarding its effect on affinity maturation. Rookhuizen et al. have described higher affinity antibodies induced by the addition of CpG [START_REF] Rookhuizen | Toll-like receptor 9 signaling acts on multiple elements of the germinal center to enhance antibody responses[END_REF], while Akkaya et al.

have instead reported that CpG antagonized affinity maturation in their experiments [START_REF] Akkaya | Toll-like receptor 9 antagonizes antibody affinity maturation article[END_REF].

In general, reports of quantitative affinity measurements of antibody repertoires resolved at the single antibody level are rare, mainly because of the lack of technologies that enable such measurements. We have not been able to find studies that presented this type of measurement of affinity to assess the effect of TLR agonists on antibody responses. Instead, the affinity of antibodies repertoires is often measured on the polyclonal mix of antibodies present in the serum, for example with ELISA [START_REF] Rookhuizen | Toll-like receptor 9 signaling acts on multiple elements of the germinal center to enhance antibody responses[END_REF][START_REF] Delgado | Lack of antibody affinity maturation due to poor Tolllike receptor stimulation leads to enhanced respiratory syncytial virus disease[END_REF] or surface plasmon resonance [START_REF] Akkaya | Toll-like receptor 9 antagonizes antibody affinity maturation article[END_REF].

These methods can only access the average affinity of antibodies present in the blood at a given time point and not the affinity of individual antibody clones and therefore cannot precisely characterize the affinity distribution. For example, high affinity antibodies present at a low numbers in the blood will not be accounted for. With the DropMap technology, we can on the contrary quantify the affinity of IgG antibodies secreted by single B cells, and our results are thus difficult to compare with measurements of polyclonal serum antibodies.

Nonetheless, in our experiments we did not find any significant effect of the addition of the different TLR agonists or combination on antibody affinities for the PE antigen.

The TLR agonists were also not efficient at modifying the obtained antibody effector functions, determined by the isotype, in our experimental setting. In the antibody-secreting cells, we observed around half of unswitched IgM-secreting cells, in contrast to bacterial immunizations, and only the TLR9 agonist seemed to induce a shift towards IgG-secreting cells (see figure IV.4). Within the IgG-secreting cells, there were mostly cells of the IgG1 subclass and fewer IgG2a/2b/3 secreting cells for all conditions, regardless of the addition of TLR agonists (see figure IV.5).

This IgG subclass distribution is not surprising for the first condition (PE alone), as all immunization solutions were prepared with alum added, which preferentially induces Th2 responses [START_REF] Marrack | Towards an understanding of the adjuvant action of aluminium[END_REF][START_REF] Victoratos | Regulation of cytokine gene expression by adjuvants in vivo[END_REF] with IgG1 as associated IgG subclass in mice. Additionally, we used mice of the BALB/c inbred strain for these experiments, which is generally considered a Th2-dominant strain [START_REF] Heinzel | Reciprocal expression of interferon γ or interleukin 4 during the resolution or progression of murine leishmaniasis. Evidence for expansion of distinct helper T cell subsets[END_REF][START_REF] Hsieh | T cell genetic background determines default T helper phenotype development in vitro[END_REF]. Therefore, it was expected that the subclass of IgG-secreting cells in mice immunized with PE and alum would be mostly IgG1.

However, we hypothesized that adding the TLRs agonists corresponding to bacterial pathogens (TLR2, 4 and 9 agonists) or their combination could reproduce the distribution observed with fixed bacteria immunizations (see chapter III), with mostly IgG2a/2b/3 (Th1 response). This seemed reasonable, because these different TLR agonists have been reported to induce Th1-polarizing cytokine responses, characterized by the secretion of proinflammatory cytokines by dendritic cells in vivo, in particular IFN-γ [START_REF] Barton | Control of adaptive immune responses by Toll-like receptors[END_REF][START_REF] Lipford | CpG-DNA-Mediated Transient Lymphadenopathy Is Associated with a State of Th1 Predisposition to Antigen-Driven Responses[END_REF]. TLR3, which uses a different signalling pathway (TRIF) compared to the other TLR agonists (MyD88 for all, TLR4 uses both MyD88 and TRIF), also showed the same type of response in vivo [START_REF] Victoratos | Regulation of cytokine gene expression by adjuvants in vivo[END_REF]. Different groups have observed that combining TLR agonists to stimulate dendritic cells in vitro even had a synergistic effect on Th1-associated cytokine secretion in general [START_REF] Mitchell | Dual Stimulation of MyD88-Dependent Toll-Like Receptors Induces Synergistically Enhanced Production of Inflammatory Cytokines in Murine Bone Marrow-Derived Dendritic Cells[END_REF][START_REF] Napolitani | Selected Tolllike receptor agonist combinations synergistically trigger a T helper type 1 -polarizing program in dendritic cells[END_REF][START_REF] Surendran | TLR agonist combinations that stimulate Th type I polarizing responses from human neonates[END_REF]. Madan-Lala et al. included the same TLR2, 4 and 9 agonists combination bound on pathogen-like particles in their study and also demonstrated activation and increased cytokine secretion in bone-marrow-derived dendritic cells, namely of IL12p70, a mediator of the Th1-type response (although this specific combination was not the most efficient among the various ones they tested) [START_REF] Madan-Lala | Combinatorial Delivery of Dual and Triple TLR Agonists via Polymeric Pathogen-like Particles Synergistically Enhances Innate and Adaptive Immune Responses[END_REF].

We showed here that this was not the case: we did not find any difference in the IgG subclass distribution when the different TLR agonists or the combination were added compared to PE alone (see section IV.3), for both the MyD88 (TLR2, 4 and 9) and TRIF (TLR3 efficiently, but that TLR activation alone does not trigger CSR [START_REF] Pone | BCR-signalling synergizes with TLR-signalling for induction of AID and immunoglobulin class-switching through the non-canonical NF-κB pathway[END_REF]. Studies that have shown efficient switching to IgG2 subclasses in vivo usually used immunogens that include antigens and TLR agonists in physical proximity, such as virus-like particles [START_REF] Jegerlehner | TLR9 Signaling in B Cells Determines Class Switch Recombination to IgG2a[END_REF][START_REF] Tian | B Cell-Intrinsic MyD88 Signaling Promotes Initial Cell Proliferation and Differentiation To Enhance the Germinal Center Response to a Virus-like Particle[END_REF]. In our experimental setting, there is no physical link between the injected protein antigen and the various TLR agonists; they are initially both adsorbed on alum, but this effect probably does not persist when the antigens are transported from the injection site into the spleen.

Thus, it is unlikely that there is simultaneous activation of the BCR and TLRs on spleen B cells, respectively by the PE antigen and the TLR agonists. In contrast, with the full fixed bacteria that we used as immunogen in chapter III, the bacterial antigens and TLR agonists are both present on the bacteria and can co-activate the BCR and TLR on B cells. This would explain the very efficient CSR that we observed, with almost exclusively IgG-and not IgM-secreting cells, and IgG2a/2b/3 subclasses within IgG-secreting cells. Additionally, it has been shown that the multimericity of the interactions with B cells has an important role for CSR in vitro [START_REF] Pone | Multimericity Amplifies the Synergy of BCR and TLR4 for B Cell Activation and Antibody Class Switching[END_REF], and repeated antigens as well as TLR agonists (such as LPS) are available at the surface of the fixed bacteria for multiple interactions with the B cell BCR/TLRs, further improving the efficiency of class switching. What might be missing in our experimental design to induce a Th1-type antibody response associated with functionally active IgG2a/2b/3 subclasses against PE would then be a physical link between the TLR agonists and the antigen for co-presentation to the B cells. This might also lead to the enhancement of the affinity of the generated antibodies. The physical link could be achived for example through covalent linkage [START_REF] Eckl-Dorna | BCR-mediated uptake of antigen linked to TLR9 ligand stimulates B-cell proliferation and antigen-specific plasma cell formation[END_REF][START_REF] Xu | Bioconjugation Approaches to Producing Subunit Vaccines Composed of Protein or Peptide Antigens and Covalently Attached Toll-Like Receptor Ligands[END_REF] or coating on nanoparticles [START_REF] Kasturi | Programming the magnitude and persistence of antibody responses with innate immunity[END_REF], which have already been partially explored as vaccination strategies. Finally, another simpler explanation for the absence of effect of the TLR agonists on the isotype distribution in our experiments could be that they are not efficiently transported into the spleen but rather have a local effect at the injection site, and in that case have no impact on B cell CSR in the spleen.

Magnitude of the antibody response

If they had little effect on the quality of the antibody response as discussed above, the different TLR agonists and the combination did strongly increase the magnitude of the Furthermore, we can also consider the total number of B cells present in the spleen at the time point of harvest, that we estimate with the B cell count after the enrichment process, done from the total amount of splenocytes obtained from the full mouse spleen. We generally observed an increase in the B cell count compared to PE alone, in particular for the TLR2 agonist and the combination with a significant and large increase, by a factor three and five respectively (see figure IV.11a). Here, the TLR2, 4 and 9 seemed to have an additive impact on the B cell count when used in combination. We can then multiply the average antibody secretion rates per B cell computed before by the spleen B cell count for each mouse, and we obtain a correlate of the total amount of antibody secreted in the spleen, distinguishing IgM, total IgG and PE-specific IgG, as shown on figure IV.11b. The TLR2 and TLR9 agonists as well as the combination induced a significant increase for total IgG and PE-specific IgG, especially large for the TLR2 agonist and the combination (at least a factor 10 for each). Also, the combination of TLR agonists significantly increased the production of IgM in the spleen, by an order of magnitude. These results point to a very large increase of the magnitude of the antibody response in the spleen. However, there are some limitations to this measurement. First, there is an uncertainty about the accuracy of measuring the B cell count after the enrichment process to quantify the number of B cells in the spleen, as the enrichment relies on eliminating cells with specific non-B cell markers rather than precisely selecting the B cell population. Additionally, this measurement only quantifies the antibody 103 secretion in the spleen and not in other compartments where antibody-secreting cells are present, such as the bone marrow, and is limited to a single time point so that it cannot take into account differences in the kinetic of the response.

P E P E + T L R 2 a g o n is t P E + T L R 4 a g o n is t P E + T L R
Overall, despite those limitations, we showed that there is a large increase in the magnitude of the antibody response when the TLR2 agonist, the combination of TLR agonists or to a lesser extent the TLR9 agonist are added to the immunization, with both an increase in Ig-secretion per B cell and total B cell count after enrichment from the spleen. The addition of the TLR3 agonist also resulted in a large increase of the magnitude of the antibody response but only in two of the five mice of the group, while there was no effect for the others.

We suggest that this stimulation of the antibody response is first due to a local effect of the TLR agonists at the injection site. Indeed, TLR agonists have the capacity to rapidly stimulate the innate immune system by activation of various innate cell types through two different signalling pathways, TRIF for TLR3 and MyD88 for the rest of the TLRs. Both of these pathways promote the secretion of pro-inflammatory cytokines, further increasing the local innate response post-immunization [START_REF] Kaisho | Toll-like receptor function and signaling[END_REF][START_REF] Kasturi | Programming the magnitude and persistence of antibody responses with innate immunity[END_REF]. Furthermore, studies have reported that TLR signalling in dendritic cells (the most efficient kind of antigen-presenting cells) activates the cells, leading to the secretion of inflammatory cytokines [START_REF] Querec | Yellow fever vaccine YF-17D activates multiple dendritic cell subsets via TLR2, 7, 8, and 9 to stimulate polyvalent immunity[END_REF], but also to maturation and transient enhancement of the uptake of soluble antigens and immune complexes [START_REF] Watts | TLR signalling regulated antigen presentation in dendritic cells[END_REF]. West et al. showed in vitro that for bone-marrow and splenic dendritic cells, exposure to TLR2, 3, 4 or 9 agonist induced a rapid and acute stimulation of antigen uptake, as well as an arrest of migration during this transient phase [START_REF] West | Enhanced dendritic cell antigen capture via Toll-like receptor-induced actin remodeling[END_REF]. This is also accompanied by enhanced antigen processing and presentation on MHC class II molecules [START_REF] West | Enhanced dendritic cell antigen capture via Toll-like receptor-induced actin remodeling[END_REF][START_REF] Cella | Inflammatory stimuli induce accumulation of MHC class II complexes on dendritic cells[END_REF]. Afterward, the role of TLR agonists extends to the further maturation of the dendritic cells that enables their migration to the lymph nodes and the spleen [START_REF] Zanoni | Regulation of antigen uptake, migration, and lifespan of dendritic cell by Toll-like receptors[END_REF]. In our experiments, TLR agonists retained at the site of injection due to their adsorption on alum could then have a direct effect on antigen-presenting cells, promoting antigen uptake, presentation and migration of those cells to the spleen where they can participate to the adaptive response.

Once they have migrated to the spleen, TLR-activated dendritic cells presenting the antigen can activate T FH cells, which then provide help to the B cells initiating the antigenspecific antibody response. It has been shown that TLR agonists lead to enhanced germinal center reaction through their signalling in dendritic cells by this mechanism [START_REF] Hou | Selective utilization of toll-like receptor and Myd88 signaling in B cells for enhancement of the antiviral germinal center response[END_REF][START_REF] Kasturi | Programming the magnitude and persistence of antibody responses with innate immunity[END_REF][START_REF] Rookhuizen | Toll-like receptor 9 signaling acts on multiple elements of the germinal center to enhance antibody responses[END_REF][START_REF] Defranco | The germinal center antibody response in health and disease[END_REF].

For example, Rookhuizen et al. demonstrated using mice deficient in MyD88 in specific cell subsets that TLR9 signalling in dendritic cells impacted the magnitude of the antibody response by increasing the number of T FH cells and antigen-specific GC B cells [START_REF] Rookhuizen | Toll-like receptor 9 signaling acts on multiple elements of the germinal center to enhance antibody responses[END_REF]. They also showed that a second effect of the TLR9 agonist was its signalling on B cells, that led to enhanced selection for high-affinity antibodies and class-switch to IgG2a. Since in our experiments we observed an increased magnitude of the antibody response with an increase in spleen B cells and specific antibody-secreting cell frequency but not better affinity maturation or IgG2a class switching, it suggests that in our case the TLR agonists activate dendritic cells but cannot efficiently interact directly with B cells. Again, this is probably due to the fact that we used separate soluble antigens and TLR agonists for immunizations, whereas

Rookhuizen et al. used a conjugate of their model antigen and a CpG TLR9 agonist. Thus, in our experiments, the drastic increase in the magnitude of the antibody response caused by the different TLR agonists most likely reflects their impact on the dendritic cells, with increased antigen uptake at the site of infection, migration to the spleen and antigen presentation to T FH cells. This effect seems similar for both types of TLR signalling pathways (MyD88 for TLR2, 4 and 9, TRIF for TLR3), since we also observed increases in antibody-secreting cell and PE-specific IgG-secreting cell frequencies with the TLR3 agonist, although smaller than with the TLR2 agonist, TLR9 agonist or the combination. The TLR3 agonist also resulted in higher spleen B cell count (see figure IV.11a), but only for part of the mice, which we cannot explain yet.

TLR agonists have also been reported to induce activation, sometimes class switching and antibody secretion in transitional B cells [START_REF] Capolunghi | CpG Drives Human Transitional B Cells to Terminal Differentiation and Production of Natural Antibodies[END_REF][START_REF] Ueda | T-Independent Activation-Induced Cytidine Deaminase Expression, Class-Switch Recombination, and Antibody Production by Immature/Transitional 1 B Cells[END_REF], B1 cells [START_REF] Genestier | TLR Agonists Selectively Promote Terminal Plasma Cell Differentiation of B Cell Subsets Specialized in Thymus-Independent Responses[END_REF] and marginal zone B cells [START_REF] Genestier | TLR Agonists Selectively Promote Terminal Plasma Cell Differentiation of B Cell Subsets Specialized in Thymus-Independent Responses[END_REF][START_REF] Oliver | Marginal zone B cells exhibit unique activation, proliferative and immunoglobulin secretory responses[END_REF], which contributes to increase the number of antibody-secreting cells and the magnitude of the antibody response. However, those subsets of B cells that lack affinity maturation typically secrete polyspecific antibodies that can only have low affinities for foreign protein antigens. By contrast, TLR agonists alone were not efficient at triggering antibody secretion from follicular B cells [START_REF] Genestier | TLR Agonists Selectively Promote Terminal Plasma Cell Differentiation of B Cell Subsets Specialized in Thymus-Independent Responses[END_REF] or reactivate memory B cells for antibody secretion [START_REF] Richard | The Agonists of TLR4 and 9 Are Sufficient to Activate Memory B Cells to Differentiate into Plasma Cells In Vitro but Not In Vivo[END_REF]. Thus, direct stimulation of B cells by TLR agonists for antibody secretion is limited to immature B cell subsets, leading to low affinity antibody secreting cells, but not high affinity antigen-specific antibody-secreting cells. In our experiments, we observed an increase in IgM-secreting cell frequency when the TLR agonists were added (see figure IV.3a), which could be due to this phenomenon. Regarding the IgG-secreting cells, while we observed an increase of their frequency with the addition of TLR agonists, the fraction of PE-affine cells stayed similar, or even increased for the TLR2 agonist IV.8b, so the secretion of non-specific antibodies by the different immature B cell subsets could only account for a minor part of the increase in the magnitude of the IgG response.

We were first surprised that the large increase in spleen B cells and antigen-specific antibody-secreting cells that we observed in particular for the TLR2 agonist and the combination (see figure IV.11) did not lead to at least partially reduced antibody affinities.

Indeed, there should be less antigen available per B cell during affinity maturation, and we have shown that we obtained lower affinity antibodies when we reduced the dose of PE antigen injected (see appendix A.2). To counterbalance this effect, a more efficient transport of the antigen to the spleen would be necessary in the conditions where the magnitude of the B cell response in the spleen is greatly increased, in order to maintain an equivalent amount of antigen available per B cell in the germinal center reaction. This also speaks for an effect of the TLR agonists on the dendritic cells that increase their antigen uptake, transport and presentation in the spleen, as discussed before.

Since their discovery, there has been great interest in TLR agonists for their potential as vaccine adjuvants because of their ability to harness the innate immune system to enhance adaptive responses [START_REF] Coffman | Vaccine adjuvants: putting innate immunity to work[END_REF]. To evaluate vaccination strategies, the measured output parameters are usually specific antibody titers and protection upon pathogen encounter.

The antibody titer indicates the amount of antibody circulating in the serum that bind to the target epitope. However it cannot take into account the affinity of single antibodies, and thus higher titer can be caused by an increase in the quantity of specific antibody or an increase in the average affinity of the antibodies, without the possibility of distinguishing those two parameters. The single-cell data presented here allow instead a precise characterization of the antibody-secreting cell repertoire. Our results indicate that the increased antibody titers that has been measured for various TLR agonists or combinations [START_REF] Buffa | Evaluation of TLR Agonists as Potential Mucosal Adjuvants for HIV gp140 and Tetanus Toxoid in Mice[END_REF][START_REF] Giannini | Enhanced humoral and memory B cellular immunity using HPV16/18 L1 VLP vaccine formulated with the MPL/aluminium salt combination (AS04) compared to aluminium salt only[END_REF][START_REF] Weeratna | TLR agonists as vaccine adjuvants: Comparison of CpG ODN and Resiquimod (R-848)[END_REF] reflects an increased magnitude of the antibody response rather than a better quality with higher affinity antibodies, at least in settings where there is no physical link between the TLR agonists and the antigen. Additionally, we have observed the ability of the TLR2, TLR4 and TLR9 agonists to boost the antibody secretion rate (see section IV.4), probably by direct interaction with B cells. This enhanced IgG secretion rate participates to the magnitude of the humoral response and has not been reported before to our knowledge.

To conclude, this study confirms with precise single-cell level measurements of the antibodysecreting cell repertoire that TLR agonists are interesting adjuvants candidates. In particular, we observed the largest effects with the TLR2 agonist Pam 3 CSK 4 , the TLR9 agonist CpG ODN 1826 and the combination of TLR2, TLR4 (MPLA) and TLR9 agonists. Various TLR2 agonists have already been pursued as potential vaccine adjuvants [START_REF] Basto | Targeting TLR2 for vaccine development[END_REF][START_REF] Li | Built-in adjuvants for use in vaccines[END_REF] and TLR9 agonists CpG ODN are included in a commercial vaccine [START_REF] Campbell | Development of the CpG adjuvant 1018: A case study[END_REF] and have been used

in different clinical trials [START_REF] Ellis | Phase 1 Study in Malaria Naïve Adults of BSAM2/Alhydrogel R +CPG 7909, a Blood Stage Vaccine against P. falciparum Malaria[END_REF][START_REF] Cooper | CPG 7909 adjuvant improves hepatitis B virus vaccine seroprotection in antiretroviral-treated HIV-infected adults[END_REF]. However, to our knowledge the combination of TLR2, 4 and 9 agonists that we studied here has not yet been explored in a vaccination strategy.

Chapter V

General conclusion and outlook

In this thesis, we focused on the understanding of functional antibody responses, by characterizing antibody responses at the single-cell level using the droplet microfluidic platform DropMap [START_REF] Bounab | Dynamic single-cell phenotyping of immune cells using the microfluidic platform DropMap[END_REF][START_REF] Eyer | Single-cell deep phenotyping of IgG-secreting cells for high-resolution immune monitoring[END_REF]. This technology allowed to probe different parameters of antibody repertoires with single-antibody resolution, such as affinity, isotype and subclass as well as an antibody effector function, the activation of the classical pathway of the complement system. In particular, two experimental studies were described, that respectively covered the activation of complement by monoclonal antibodies and the influence of TLR agonists on antibody repertoires. Initially, several technical developments described in chapter II were necessary to conduct these experimental studies.

First, we improved the existing DropMap assay that measures IgG secretion, resulting in an optimization of the measurement range and a more precise quantification of IgG secretion.

I also established a new method to precisely and reliably compute the secretion rate of single IgG-secreting cells, with a quantification range spanning from 5 to 500 IgG/s. This method will soon be published as a chapter of Methods in Molecular Biology (first author, manuscript submitted).

In the bacteria. This allows the dynamic measurement of C1q binding efficiency at the single antibody level with a high throughput, which has not been reported before to our knowledge.

After the development of these different DropMap assays, we moved on to the two experimental studies in chapter III and IV.

In the context of rising bacterial antimicrobial resistance [3,[START_REF] O'neill | Tackling drug-resistant infections globally: final report and recommendations -The Review on Antimicrobial Resistance chaired by Jim O'Neill[END_REF], monoclonal antibodies targeting bacteria are a potential alternative to antibiotic treatments. Therapeutic monoclonal antibodies are already widely used in the treatment of different diseases, such as autoimmune diseases [START_REF] Bang | Adalimumab: A review of its use in rheumatoid arthritis[END_REF], cancer [START_REF] Pierpont | Past, present, and future of Rituximab-The world's first oncology monoclonal antibody therapy[END_REF] or viral infections [START_REF] Kupferschmidt | Successful Ebola treatments promise to tame outbreak[END_REF]. By contrast, monoclonal antibodies have been far less explored for the treatment of bacterial infections, with only few available options that mostly target bacterial toxins [START_REF] Troisi | A new dawn for monoclonal antibodies against antimicrobial resistant bacteria[END_REF]. To effectively eliminate pathogenic bacteria, monoclonal antibodies could directly activate secondary effector functions against the bacteria. In particular, the complement system is a main antibody-dependent function against From these measurements, it is possible to infer certain requirements for the activation of the classical complement pathway depending on the isotype of the antibody. First, for IgM antibodies, a high density of antibodies binding to the bacterial surface seemed important for efficient C1q binding. However it was not sufficient since only a small fraction of bacteria-specific IgM binding at high density did relocate C1q. This could be due to strict structural requirements for the formation of the IgM-C1q complex [START_REF] Sharp | Insights into IgM-mediated complement activation based on in situ structures of IgM-C1-C4b[END_REF] that only allow IgM antibodies targeting certain epitopes to bind C1q. In the case of IgG antibodies, a first necessary parameter for complement activation was the IgG subclass, with only IgG2a, 2b or 3 subclasses binding C1q in the experiments described here, as expected from the literature [START_REF] Collins | IgG subclass co-expression brings harmony to the quartet model of murine IgG function[END_REF]. For IgG, a high density of bound antibodies was also necessary. This can be attributed to the close proximity of IgG antibodies required to form IgG hexamers on the bacterial surfaces -the ideal conformation for C1q docking [START_REF] Diebolder | Complement is activated by IgG hexamers assembled at the cell surface[END_REF]. When a high binding density was achieved, most IgG antibodies of the IgG2a/2b/3 subclasses did successfully trigger C1q binding contrarily to IgM antibodies. This is probably due to a higher flexibility of the IgG hexamer assembly as compared to an IgM molecule, which would allow C1q binding for most epitope configurations. However, we still observed large differences of C1q binding efficiencies for equivalent density of bound IgG and even a few high density IgG2a/2b/3 that did not recruit C1q at a detectable level. These differences might be caused by different IgG subclasses (which we cannot further distinguish here) or epitope-dependent constraints, for example the orientation of the antibody due to epitope geometry [START_REF] Cragg | Complement-mediated lysis by anti-CD20 mAb correlates with segregation into lipid rafts[END_REF][START_REF] Bindon | Importance of antigen specificity for complement-mediated lysis by monoclonal antibodies[END_REF][START_REF] Teeling | The Biological Activity of Human CD20 Monoclonal Antibodies Is Linked to Unique Epitopes on CD20[END_REF], the distance of the epitope to the cell membrane [START_REF] Pawluczkowycz | Binding of Submaximal C1q Promotes Complement-Dependent Cytotoxicity (CDC) of B Cells Opsonized with Anti-CD20 mAbs Ofatumumab (OFA) or Rituximab (RTX): Considerably Higher Levels of CDC Are Induced by OFA than by RTX[END_REF] or the off rate k off [START_REF] Teeling | Characterization of new human CD20 monoclonal antibodies with potent cytolytic activity against non-Hodgkin lymphomas[END_REF]. All of these parameters have been shown to influence IgG-induced C1q binding, but it was not possible to attribute the differences to specific parameters in these experiments.

These variations in the efficacy of classical pathway activation by antibodies binding at similar densities highlight the interest of the method developed in this thesis that enables the precise quantification of antibody-dependent C1q recruitment. Indeed, to find efficient complement-activating antibodies it is not sufficient to select them based on high affinity and epitope density, but it is also critical to measure the activation of complement, as we did here by quantifying C1q binding. Furthermore, the C1q-binding antibodies were rare events in general, which validates the need for a high throughput method such as the DropMap platform to screen for complement-activating antibodies. For these reasons, I think that the system presented here is promising for the discovery of new complement-activating monoclonal antibodies. This would require the development of a sorting protocol to retrieve the droplets containing cells of interest, for which I presented preliminary trials in this thesis.

There are several options to pursue the project about complement-activating antibodies. A first technical optimization would be to replace the human purified C1q protein by C1q purified from a murine source, which can for example be purchased from Complement Technology Inc. Matching the specie of the C1q protein and the analyzed antibodies would eliminate a bias present in this study. Alternatively, it would be interesting to use the full mouse complement system in the droplet bioassay and detect its activation also further down in the cascade, e.g. as C3b deposition or formation of the membrane attack complex. Antibodies specific for C3b or the C5b-C9 complex are available commercially and could be easily labeled with a fluorophore for use as detection probes in the droplets. The challenge here relies on finding a source of intact mouse complement and keeping all the different proteins functional while performing the in-droplet assay. Indeed, complement proteins are very sensitive, for example to temperature changes, and working with only C1q in the droplets already required delicate handling of the protein. However, detecting antibody-dependent complement activation at different steps of the cascade should be feasible and would be interesting to assess whether the epitope dependency that we observed here for C1q binding also applies -maybe differently -to the rest of the cascade. Another obvious perspective for transport [START_REF] Zanoni | Regulation of antigen uptake, migration, and lifespan of dendritic cell by Toll-like receptors[END_REF] and presentation [START_REF] West | Enhanced dendritic cell antigen capture via Toll-like receptor-induced actin remodeling[END_REF][START_REF] Cella | Inflammatory stimuli induce accumulation of MHC class II complexes on dendritic cells[END_REF] in the spleen. This would lead to enhanced germinal center responses as reported before [START_REF] Kasturi | Programming the magnitude and persistence of antibody responses with innate immunity[END_REF][START_REF] Rookhuizen | Toll-like receptor 9 signaling acts on multiple elements of the germinal center to enhance antibody responses[END_REF] and therefore to the generation of more antibody-secreting cells. In addition we observed higher IgG secretion rates when TLR2, 4 or 9 agonists (but not their combination) were added to the immunizations, which further participates to the enhancement of the antibody response and constitutes a new finding to our knowledge. To conclude, these results explain the previously described increase of antibody titers when TLR agonists are used as adjuvants [START_REF] Buffa | Evaluation of TLR Agonists as Potential Mucosal Adjuvants for HIV gp140 and Tetanus Toxoid in Mice[END_REF][START_REF] Giannini | Enhanced humoral and memory B cellular immunity using HPV16/18 L1 VLP vaccine formulated with the MPL/aluminium salt combination (AS04) compared to aluminium salt only[END_REF][START_REF] Weeratna | TLR agonists as vaccine adjuvants: Comparison of CpG ODN and Resiquimod (R-848)[END_REF], with unprecedented details in the characterization of the antibody-secreting cell population. This confirms the potential of TLR agonists as vaccine adjuvants candidates, in particular the TLR2 agonist Pam 3 CSK 4 , the TLR9 agonist CpG ODN 1826 and their combination with the TLR4 agonist MPL.

A first perspective to continue this study would be to investigate in more detail the influence of the TLR agonists that were most promising in these experiments (TLR2, 9

and the combination of TLR2, 4 and 9). This could be done by testing their addition to different target antigens, with or without alum, and varying the immunization schedule and measurement time point. In this way we could consolidate our first observations of their effect as adjuvants. Afterward, their addition to vaccine candidates progressing into clinical trials could be envisioned, in particular for the previously unexplored combination of the TLR2, 4 and 9 agonists. As a second perspective, it would be interesting to try to link physically the antigen to the different TLR agonists, for example through covalent linkage [START_REF] Eckl-Dorna | BCR-mediated uptake of antigen linked to TLR9 ligand stimulates B-cell proliferation and antigen-specific plasma cell formation[END_REF][START_REF] Xu | Bioconjugation Approaches to Producing Subunit Vaccines Composed of Protein or Peptide Antigens and Covalently Attached Toll-Like Receptor Ligands[END_REF] or coating on nanoparticles [START_REF] Kasturi | Programming the magnitude and persistence of antibody responses with innate immunity[END_REF]. This would allow the simultaneous activation of TLR and BCR on specific B cells, which has been reported as necessary for TLR-dependent class switching and shifting towards Th1 humoral responses [START_REF] Pone | BCR-signalling synergizes with TLR-signalling for induction of AID and immunoglobulin class-switching through the non-canonical NF-κB pathway[END_REF], as discussed in section IV.7. Such an approach seems promising to modulate the IgG subclass distribution of the generated antibody repertoires towards functionally active subclasses, which independent antigen and TLR agonists did not achieve here. This would most likely be transferable to antibody responses in humans and thus have implications for the design of vaccines more efficient at the activation of antibody-dependent effector functions.

Going back to the novel DropMap assay for the detection of complement activation, I think an interesting perspective would be to use this system to identify target antigens that are particularly suitable for antibody-dependent complement activation, and thus could be of great interest as part of antibacterial vaccines. A first step would be to sort and sequence the most efficient activators of complement as previously discussed, and then use the expressed antibodies to detect their target bacterial antigen in vitro. These antigens could be purified from bacteria or produced as recombinant proteins depending on their nature and included in vaccine formulations. In these vaccines, combinations of TLR agonists such as the one identified here could be used as adjuvants to enhance the humoral response to the identified target antigens, as purified antigens are often not sufficient to induce large antibody responses by themselves [START_REF] Marrack | Towards an understanding of the adjuvant action of aluminium[END_REF][START_REF] Shi | Vaccine adjuvants: Understanding the structure and mechanism of adjuvanticity[END_REF]. In this way we would link the two experimental studies of this thesis, fitting in the broad context of improving the protection against bacterial pathogens, this time by the means of antibacterial vaccines designed for complement activation.

114

Appendix A. Supplementary information

We then measured the PE-specific IgG and IgG subclasses titers, as shown on figure A.2.

For the total PE-specific IgG titers (figure A.2a), we observed a significant increase when the TLR3 agonist was added to the PE immunizations, and small increases with the TLR2 and TLR9 agonists, but not with the combination. When looking at the IgG subclasses PEspecific titers (figure A.2b), we observed the same trend increase for the TLR9 and TLR3

agonists, but no significant differences. The IgG1 and IgG2a/2b specific titers reflected the distribution measured with DropMap, with more IgG1 than IgG2a and IgG2b for all the conditions. However, the relative fractions seem to vary between the different conditions in the titer measurements (e.g. for the TLR9 and TLR3 agonists), which was not the case in the DropMap single-cell measurements (see figure IV.5). In general, the results of the PE-specific IgG titer measurements go in the same direction as previous studies which showed an increase in the antigen-specific titer using the different TLR agonists [START_REF] Buffa | Evaluation of TLR Agonists as Potential Mucosal Adjuvants for HIV gp140 and Tetanus Toxoid in Mice[END_REF][START_REF] Giannini | Enhanced humoral and memory B cellular immunity using HPV16/18 L1 VLP vaccine formulated with the MPL/aluminium salt combination (AS04) compared to aluminium salt only[END_REF][START_REF] Weeratna | TLR agonists as vaccine adjuvants: Comparison of CpG ODN and Resiquimod (R-848)[END_REF][START_REF] Fransen | Agonists of toll-like receptors 3, 4, 7, and 9 are candidates for use as adjuvants in an outer membrane vaccine against Neisseria meningitidis serogroup B[END_REF]. However, the time point of measurement is different: we measured the titer 6 days after secondary immunization, whereas the standard procedure is to measure 14 days afterward. This could explain that we observe only small effects, because the titer reflects the IgG secreted over the previous days, the half-life of IgG in mice being about a week [START_REF] Vieira | The half-lives of serum immunoglobulins in adult mice[END_REF]. In this regard, our time point of titer measurement does not effectively take into account the IgG-secreting cells newly generated after the secondary immunization.

Those would appear a few days after the immunization, and would only have started to secrete shortly before the titer measurement. Their secreted IgGs would only represent a small fraction of the PE-specific titer compared to a titer measurement performed 14 days after the second immunization.

2 Influence of the dose of PE on the antibody response 115 Additionally, the titers measured here do not directly correlate with the single-cell antibody secretion described in chapter IV. Indeed the antibody secretion measurements describe the population of secreting cells at a given moment, and not integrated over time like the titer, as explained above, but also they only take into account the antibody-secreting cells present in the spleen. By contrast, the titer reflects the antibody secretion in the full organism, and IgG-secreting cells are present in other compartments, such as the lymph nodes and the bone marrow. Those two reasons could explain the moderate increases observed here for the TLR2 and TLR9 agonists and the absence thereof for the combination, when large effects were observed in the DropMap results (figures IV.8a, IV.6 and IV.11b), as well as the discrepancies mentioned before for the IgG subclasses distributions.

Influence of the dose of PE on the antibody response

Before planning the experiments described in chapter IV where TLR agonists are added to PE immunizations, we tested the influence of the dose of PE used in the immunizations.

Indeed, the dose of antigen can have important effects on the humoral response, in particular on the affinity maturation [START_REF] Baer | The effect of the dose of bacterial polysaccharide antigen on antibody production in mice[END_REF][START_REF] Foote | Kinetic and affinity limits on antibodies produced during immune responses[END_REF][START_REF] Molari | Quantitative modeling of the effect of antigen dosage on b-cell affinity distributions in maturating germinal centers[END_REF][START_REF] Rhodes | Dose finding for new vaccines: The role for immunostimulation/immunodynamic modelling[END_REF]. We wanted to quantify its influence on the different parameters of the antibody response before adding the TLR agonists and also choose the and A.4a). The higher dose (150 µg) did result in a decrease for both, however not significant due to the large variability observed for the two lower doses. Additionally, with the higher dose the percentage of PE-specific cells within the IgG-secreting cells was significantly lower, as seen on figure A.4b.

Interestingly, the intermediate dose did increase the percentage of IgM-secreting cells in antibody-secreting cells (see figure A.5a). This effect is due to a higher IgM response and does not mean that the IgG response diminishes, for example due to a less efficient class switching process, because for 70 µg of PE the IgG-secreting cell frequency is similar to the lower dose and higher than the maximum dose (see figure A.3). Finally, the dose of PE had no effect on the IgG subclass distribution, as seen on figure A.5b; we were also not able to influence this parameter with the addition of the different TLR agonists (see section IV.3).

The effect of the dose variation was most striking on the affinities of the PE-specific IgG-secreting cells subset. As showed on figure A.6, the average affinity slope is significantly higher for the intermediate dose of PE, corresponding to a better average affinity (lower K D , see section II.3). This is also visible at the level of the single-cell affinity slope distributions (see figure A.7), with a clear shift of the distribution towards higher slopes for 70 µg PE. This corresponds to previous observations and modelling that the response to immunizations follows a bell-shaped response depending on the dose, with the highest response obtained at intermediate doses, in particular for the affinities of the generated specific IgGs [START_REF] Molari | Quantitative modeling of the effect of antigen dosage on b-cell affinity distributions in maturating germinal centers[END_REF][START_REF] Rhodes | Dose finding for new vaccines: The role for immunostimulation/immunodynamic modelling[END_REF].

An explanation would be that lower doses are not sufficient to trigger an efficient immune response, while higher doses decrease the selection pressure during affinity maturation, leading to lower affinity antibodies [START_REF] Baer | The effect of the dose of bacterial polysaccharide antigen on antibody production in mice[END_REF][START_REF] Kang | Affinity Inequality among Serum Antibodies That Originate in Lymphoid Germinal Centers[END_REF][START_REF] Tam | Sustained antigen availability during germinal center initiation enhances antibody responses to vaccination[END_REF].

To note: The results for the affinity slope showed on figure A.6 include only two replicates for the intermediate PE dose because a mistake was made when adjusting the concentration of PE inside the droplets for the other three biological replicates of this condition (included in the other figures). Because of this difference in the in-droplet PE concentration (68 nM versus 20 nM), we cannot compute the affinity slope for those replicates in a comparable way to the normal PE-binding assay described in section II.3, so they are excluded from this figure.

To conclude, compared to 10 µg and 150 µg, 70 µg of PE induced the highest response, both in term of frequency of secreting cells, frequency of specific IgG-SCs and mostly affinity.

Therefore, we chose this dose of PE for the experiments testing the effect of the addition of TLR agonists described in chapter IV. For each PE-specific IgG-secreting cell, the linear slope of the evolution of the PE relocation against the IgG Fc probe relocation over time is computed. It is a correlate of the affinity of the individual antibody: the higher the slope, the higher the affinity. The average affinity slope of all the PE-specific IgG-secreting cells is computed for each mouse and the mean and SD for each condition are plotted here. The p-values were computed for all permutations of conditions.

  d'anticorps et libèrent des facteurs cytotoxiques pour tuer les cellules infectées. Ces fonctions effectrices peuvent être efficaces contre les bactéries extracellulaires et intracellulaires, respectivement. Une autre fonction effectrice importante des anticorps est l'activation du système du complément, et il a été démontré que cette fonction est la principale pour l'éliminations de diverses bactéries pathogènes. Dans la voie classique d'activation du complément, les anticorps des isotypes IgM ou IgG peuvent se lier aux surfaces bactériennes et recruter la protéine C1q du complément. Cela déclenche une cascade d'activations des protéines du complément, conduisant à la formation de complexes d'attaque membranaire qui détruisent les cellules bactériennes ciblées, ainsi qu'à une inflammation et l'opsonisation des bactéries cibles pour la phagocytose.

  fonction d'un anticorps, en particulier pour les fonctions effectrices induites. Une densité d'épitope élevée permet à un plus grand nombre d'anticorps de se lier et renforce les fonctions effectrices telles que l'ADCC, l'ADCP et l'activation du complément. La géométrie de l'épitope influence également la liaison des anticorps et les fonctions effectrices, en modifiant l'orientation et la proximité des anticorps liés à la membrane cellulaire. Enfin, l'isotype et la sous-classe des anticorps déterminent leurs fonctions effectrices. Les principaux isotypes d'anticorps étudiés ici sont l'IgM et l'IgG, l'IgG étant subdivisé en sous-classes. Chaque sousclasse est associée à des fonctions effectrices différentes. Chez la souris, les sous-classes d'IgG IgG2a, IgG2b et IgG3 peuvent activer diverses fonctions effectrices, y compris le complément, tandis que l'IgG1 se limite à la neutralisation. La mesure des isotypes et des sous-classes d'anticorps est donc essentielle pour évaluer les réponses humorales fonctionelles, et la plateforme DropMap permet de détecter et de distinguer les différents isotypes et sous-classes avec un nouvel essai développé durant cette thèse. Dans l'ensemble, la compréhension de ces caractéristiques des anticorps est cruciale pour l'étude des réponses des anticorps, en particulier l'isotype et la sous-classe. En effet, dans le contexte de la vaccination, ils permettent aux anticorps de déclencher les fonctions effectrices appropriées. Nous nous sommes donc demandé comment il serait possible d'influencer la distribution d'isotypes et de sous-classes d'IgG obtenue. Une possibilité serait d'utiliser des agonistes des récepteurs de type Toll (TLR) comme adjuvants. Ces récepteurs sont présents sur les cellules immunitaires et peuvent détecter des motifs moléculaires spécifiques à différents agents pathogènes. Une fois activés par un ligand, les TLR déclenchent des voies de signalisation qui conduisent à des réponses immunitaires innées, avec l'activation de cellules immunitaires innées et la sécrétion de cytokines pro-inflammatoires. Les TLR influencent également les réponses immunitaires adaptatives, agissant comme un pont entre l'immunité innée et l'immunité adaptative. La stimulation des TLR peut favoriser les réponses anticorps par l'activation de cellules présentatrices d'antigènes et l'amélioration de la présentation d'antigènes aux lymphocytes B et T. Les agonistes des TLR ont été étudiés en tant qu'adjuvants dans les vaccins, avec l'objectif principal d'augmenter l'ampleur des réponses anticorps. Cependant, les agonistes des TLR peuvent également induire une modulation de la réponse immunitaire adaptative vers une réponse de type Th1 et la production des isotypes et sous-classes d'anticorps associés (IgG2a/2b/3 chez la souris). Objectif : Influence des agonistes des récepteurs de type Toll sur les réponses anticorps (voir chapitre IV) Il a été démontré que l'activation des TLR influence le répertoire d'anticorps, entraînant des réponses renforcées et plus durables, ainsi qu'une évolution vers des sous-classes d'anticorps vii associées à une réponse de type Th1. Cependant, les méthodes actuelles d'évaluation de ces effets sont limitées. Pour y remédier, la plateforme DropMap sera utilisée pour caractériser précisément l'impact des agonistes TLR sur les réponses anticorps spécifiques à un antigène modèle. En examinant les anticorps individuels, y compris l'affinité, l'isotype et la sous-classe, cette plateforme offre un aperçu sans précédent du répertoire des anticorps. Cette étude vise à évaluer l'efficacité des agonistes TLR en tant qu'adjuvants et à étudier leur capacité à induire un changement de classe des anticorps et donc une modulation des fonctions effectrices induites. Chapitre II : Développement de bioessais Ce chapitre aborde les développements techniques effectués durant la thèse, notamment les améliorations apportées à la plateforme DropMap. Un bioessai DropMap amélioré mesure précisément le taux de sécrétion d'IgG par des cellules B uniques, et une nouvelle version multiplexée quantifie la sécrétion d'IgM et classe les anticorps IgG en sous-classes. Nous avons également développé un nouveau bioessai qui détecte les cellules sécrétrices d'anticorps spécifiques aux bactéries et mesure le recrutement de la protéine C1q, qui correspond à la première étape de l'activation de la voie classique du complément. La technologie DropMap utilise des puces microfluidiques, des nanoparticules magnétiques de capture et une détection fluorescente pour relocaliser et quantifier la concentration d'analytes cibles (ici des anticorps) dans des gouttelettes de 50 pL. La sécrétion d'anticorps est mesurée de manière dynamique grâce à la microscopie time-lapse, ce qui permet de quantifier précisément le taux de sécrétion, l'affinité ou des fonctions effectrices (ici l'activation du complément). Quantification précise du taux de sécrétion d'IgG de lymphocytes B individuels Nous avons amélioré le bioessai existant pour la mesure de la sécrétion d'IgG en remplaçant l'anticorps de détection fluorescent par un nouveau donnant un signal plus intense et surtout similaire pour les différentes sous-classes d'IgG. Une nouvelle calibration a été effectuée, qui permet à l'aide d'une méthode nouvellement définie de précisément quantifier la concentration d'IgG dans les gouttes entre 0.5 et 15 nM. Cela correspond à des taux de sécrétions compris entre 10 et 500 IgG/s pour des mesures durant environ 30 minutes. Les lymphocytes B sécrétant plus de 500 IgG/s sont également détectés, mais classifiés en tant que haut sécréteurs sans quantification précise du taux de sécrétion. Multiplex pour la détection d'IgM et des sous-classes d'IgG Nous avons développé un nouveau bioessai qui permet de détecter et quantifier la sécrétion d'anticorps de l'isotype IgM, d'une manière similaire au bioessai pour les IgG décrit précédemment. Cette détection des IgM se fait dans un autre canal fluorescent, et peut donc être faite en parallèle. Une calibration a également été conduite, qui permet de mesurer précisément la sécrétion d'IgM dans la gamme 0.3 à 5.5 nM, correspondant à des taux de sécrétions de 5 à 185 IgM/s. viii Résumé en français Il est également intéressant de pouvoir distinguer les différentes sous-classes d'IgG : chez les souris, les IgG1 ont peu de fonctions effectrices associés et sont limités à la neutralisation, tandis que les sous-classes IgG2a, IgG2b et IgG3 peuvent activer efficacement les fonctions effectrices, notamment le système du complément. Dans cet objectif, nous avons ajouté la détection des IgG1 en parallèle des bioessais précédemment décrits et nous pouvons ainsi classifier les IgG en IgG1 ou IgG2a/2b/3 de manière fiable. Détection de la liaison d'anticorps à un antigène Le système DropMap permet de détecter la liaison d'un anticorps à un antigène cible en incluant cet antigène avec un label fluorescent dans les gouttes. Ici, nous avons utilisé l'antigène protéique modèle phycoérythrine (PE), qui est naturellement fluorescent dans un quatrième canal, et peut donc être détecté directement en parallèle dans notre bioessai. Nous avons établi une méthode qui permet d'avoir une mesure relative de l'affinité d'anticorps individuel pour cet antigène. Détection d'anticorps spécifiques aux bactéries et de l'activation induite du complément L'objectif ici était de développer un bioessai qui permettrait de détecter les cellules individuelles sécrétant des anticorps spécifiques contre des bactéries cibles dans un premier temps, et également ensuite de quantifier l'activation du complément induite par les anticorps monoclonaux sur la surface des bactéries. Pour ce faire nous avons modifié le bioessai standard de la plateforme DropMap en remplaçant l'agent de capture d'anticorps présent sur les nanoparticules par des bactéries inactivées. Différentes espèces de bactéries (ici P. aeruginosa, B. pertussis et N. meningitidis) ont été testées, toutes inactivées par fixation avec du formaldéhyde avant l'utilisation dans notre système. Cela permet ensuite de détecter la présence d'anticorps spécifiques se liant à ces bactéries par la relocalisation d'un agent de détection des anticorps fluorescent sur la beadline.

(Figure I. 1 :

 1 Figure I.1: Structures of the different antibody isotypes adapted from Lu et al., Nature Reviews Immunology, 2018[START_REF] Lu | Beyond binding: Antibody effector functions in infectious diseases[END_REF] Five different antibody isotypes exist in human and mouse: IgG, IgM, IgA, IgD and IgE. IgG, IgD and IgE are secreted as monomers, while IgM is secreted as a pentamer (as shown here) or hexamer, and IgA as a dimer (as shown here) or monomer. The antibody heavy chains are represented in blue while the light chains are represented in red. All the antibody isotypes possess fragment antigen-binding (Fab) domains, responsible for the binding to target antigens, and a fragment crystallizable (Fc) domain, that mediates antibody-dependent effector functions through interactions with cell surface receptors (Fc receptors) and complement proteins.

  the antibody is bound to a bacterial surface antigen, this can trigger different effector functions against the bacteria (see figure I.2). A first function is antibody-dependent cellular phagocytosis (ADCP). In this case, the pathogenic bacterium is opsonized by antibodies bound on its surface. The Fc domains of the antibodies are recognized by phagocytes (e.g. monocytes, macrophages or neutrophils) by their surface FcRs. Through multiple Fc-FcR interactions, the phagocyte can stretch itself around the bacterium and engulf it. Inside the phagocyte, the engulfed bacterium is then degraded in phagolysosomes. Monoclonal antibodies can efficiently eliminate extracellular pathogenic bacteria through ADCP, as for example Diago-Navarro et al. have demonstrated in the case of Klebsiella pneumoniae infection [29].

Figure I. 2 :

 2 Figure I.2: Antibody effector functions from Cellular and Molecular Immunology, 9 th edition, Abbas et al., Elsevier, 2016[30] Antibodies have different effector functions designed to counter pathogens. First, the antibody can simply bind to a target antigen and block its action, for example neutralizing a bacterial toxin. Additionally, antibodies can trigger several functions through their Fc domain. The interaction with Fc receptors present on phagocytes can induce antibody-dependent cellular phagocytosis (ADCP) of opsonized bacteria. Infected cell coated by antibodies can be recognized by natural killer (NK) cells bearing Fc receptors and lysed through antibody-dependent cellular cytotoxicity (ADCC). Finally, antibodies can trigger the activation of the complement system which leads to several effector functions: opsonization for phagocytosis, inflammation and lysis of the bacterial cells.

7 A

 7 second antibody-induced effector function is antibody-dependent cellular cytotoxicity (ADCC). This mechanism is relevant only against intracellular bacteria that infect host cells and replicate inside them, for example Listeria monocytogenes, Chlamydia trachomatis, Legionella pneumophila or Mycobacterium tuberculosis. The infected cell displays some bacterial antigens on its surface, which can then be bound by specific IgG antibodies. A type of innate lymphoid cell known as natural killer cell (NK cell) can recognize the antibody-coated host cell through a FcR specific for IgG antibodies (FcγRIII or CD16). If the density of IgG antibodies present on the surface of the cell is sufficient to crosslink the FcγRIII receptors, the NK cell releases cytotoxic factors that lyse the host cell in a targeted manner. It has been shown for example for C. trachomatis infections that activation of ADCC played an

Figure I. 4 :

 4 Figure I.4: Schematic representation of the different technologies that enable the measurement of protein secretion at the single-cell level from Bucheli et al., European Journal of Immunology, 2021 [90]The single-cell protein secretion methods include spot-and cytometry-based assays, as well as different microfluidic methods (well-, chamber-and droplet-based assays). Common features of all described technologies are the spatial separation of the individual cells, and their reliance on labeled reporters, especially antibodies and variants thereof. However, all the described technologies differ in several respects, for example in the read-out obtained, ease of application, multiplexing potential, throughput or temporal resolution, and therefore have different applications and limitations. The spot-based assays ELISpot and FluoroSpot, based on the seeding of a heterogeneous population of cells on a protein-binding membrane, allow for the rapid and simple determination of the frequency of secreting cells based on an antibody-based immunoassay. The frequency of cells stained positively with a detection reagent can be assessed using flow cytometry-based methods. With this technology, the heterogeneous population of cells is measured temporally and spatially separated from each other, and positive events are gated and counted as a frequency of total input cells. The three microfluidic methods shown on the right are all based on the same concept of individually trapping and analyzing cells in small volumes. Here, the secreted molecule rapidly reaches a detectable concentration due to the small volume, allowing more accurate and rapid quantification of protein secretion. The microfluidic methods can be further divided based on the enclosing structure used. Well-based assays encapsulate cells in small circular or square wells, whereas chamber-based use elongated chambers that fit a higher volume and allow spatially separating the detection of various secreted proteins. Lastly, droplet-based assays use two immiscible fluids to generate an emulsion to encapsulate individual cells in small volumes, and enable a surface-free, volume-based analysis of protein secretion in high-throughput and with high precision. (legend adapted from the original paper)

  . A general protocol has been published byBounab et al. in 2020 in a Nature Protocol article of which I am co-author[START_REF] Bounab | Dynamic single-cell phenotyping of immune cells using the microfluidic platform DropMap[END_REF]. As part of the work conducted during my Ph.D. thesis, we also recently submitted a manuscript for a chapter of Methods in Molecular Biology that describes in more detail the quantification of protein secretion, applied to IgG or IFN-γ. We will give an overview of a typical workflow with the DropMap technology in the following paragraphs, also represented on figure I.5.

Chapter I.Figure I. 5 :

 5 Figure I.5: DropMap technology adapted from Eyer et al., Nature Biotechnology, 2017 [92] (a) Overview of the workflow and timing. B cells are enriched from immunized mice samples and compartmentalized in 50 pL droplets using a microfluidic droplet-maker chip. Two aqueous phases (I and II), one containing cells or calibration antibodies (I) and the other containing paramagnetic nanoparticles and fluorescently labeled detection reagents (II) are mixed on-chip into droplets surrounded by an oil continuous phase using a flow-focusing junction. After generation, the droplets are loaded and immobilized into an observation chamber to form a 2D droplet array allowing the over-time measurement of IgG secretion and affinity. The insert shows 40 000 droplets, stained with different concentrations of sulforhodamine B for visualization. (b) The bioassay utilizes magnetic nanoparticles (∼1 300/droplet) functionalized with an antibody capture reagent. A secondary anti-IgG Fc antibody, fluorescently labeled with Alexa Fluor 647, binds to immobilized IgG antibodies. If the immobilized IgG possesses affinity for the antigen, here phycoerythrin (PE), the fluorescent antigen also relocates onto the bead surface. (c) Top: brightfield image of beadlines and splenocytes in an array of 50 pL droplets. To enable the measurement of fluorescence relocation onto the bead surfaces, the nanoparticles are aligned in an observable micrometer-sized structure termed beadline by applying a magnetic field. Bottom: the image series shows brightfield, red and orange fluorescence images for a non-binding (left) and PE-binding (right) IgG antibody secreted by a B cell.

  ADCC and ADCP are triggered by interactions of the Fc domain of IgG antibodies with Fcγ receptors on effector cells, respectively natural killer cells and phagocytes. Both functions require multiple Fc-FcγR binding for activation and thus a high density of bound antibodies. Regarding the activation of complement, as we have mentioned before in section I.4, Diebolder et al. demonstrated that the most efficient configuration for activation of the classical pathway by IgG antibodies is through the forma-

  domain, which allow them to interact with specific Fc receptors (FcRs) present on immune cells and mediate different effector functions[START_REF] Bruhns | Mouse and human FcR effector functions[END_REF]. Early in B cell development, IgM and IgD are co-expressed by naive B cells. During B cell maturation, the isotype of a given B cell can switch to either IgA, IgE or IgG. IgA antibodies are mainly secreted in the mucosal surfaces of the respiratory and intestinal tract as a dimeric molecule and IgA is the main isotype involved in mucosal immunity. IgE antibodies are secreted at very low concentrations in the serum, and are mainly involved in allergic responses, by interactions with mast cells,

(

  non-functional) or IgG2a/2b/3 (functionally active) subclasses. A first version of the assay to detect IgG1 and IgG2a/2b/3 that I developed at the beginning of my Ph.D. thesis has already been published in 2020 as part of a Nature Protocol paper by Bounab et al. (coauthor) [91]. The development of the current version of the assay is described in section II.2 of chapter II.

Figure II. 1 :

 1 Figure II.1: DropMap fluorescence relocation assay to measure IgG secretion (a) Droplet content: Functionalized magnetic nanoparticles coated with the IgG capture reagent, labeled IgG detection reagent and a B cell. A magnetic field is applied to align the nanoparticles into a beadline. (b) The B cell secretes an IgG antibody. The IgG molecules are bound by the fluorescent detection reagent and captured on the surface of the nanoparticles, relocating the fluorescence to the beadine. For the calibration of the assay, the secreting cell is replaced with a known concentration of a control monoclonal IgG. (c, d) DropMap images: droplets immobilized in a 2D observation chamber containing calibration samples. (c) Droplets containing a blank sample without IgG, no fluorescence relocation. (d) Droplets containing a constant concentration of a calibration IgG (10 nM IgG1), fluorescence relocation is visible on the beadlines.

1

  Precise quantification of the IgG secretion rate of single B cells 33 the nanoparticles in a different manner, as we did in a second assay presented in section II.4 by binding target fixed bacteria on the nanoparticles instead of the antibody capture reagent. This alternative method allows the capture of bacteria-specific antibodies on the beadline, which can then also be detected by the relocation of fluorescent probes. After the encapsulation process, the droplets containing secreting cells are imaged over time with a fluorescence microscope. The readout of the assay in each droplet is the ratio of the fluorescence on the beadline against the background fluorescence (termed fluorescence relocation signal, see figure II.1d), assigned to a precise concentration of analyte, IgG in this example, using a calibration curve. Since the droplets are imaged over time, it is possible to attribute a secretion rate to the individual cells, computed from the measurement of the concentration at each time point. 1 Precise quantification of the IgG secretion rate of single B cells In a first technical project of this PhD thesis, we set out to improve the quantification of IgG secretion with the DropMap technology. As mentioned in the section I.7, the measurement of IgG secretion with DropMap has been established for several years

Figure II. 2 :

 2 Figure II.2: Calibration curve for IgGMedian fluorescence relocation values displayed along with standard deviations and the computed exponential fit (dotted red line). Limit of detection (LoD) and plateau values are also displayed. The cutoff value corresponds to the relocation above which no precise concentration of IgG is computed: due to the variability of the obtained signal, when the relocation is close to the plateau of the calibration curve it is not possible to attribute a precise concentration to a given relocation value. Thus, the assay has a quantitative range, from 0.7 to 15 nM IgG, and a qualitative range, above 15 nM.

Figure II. 3 :

 3 Figure II.3: Example of droplet images and rules for manual verification Images of single droplets from an IgG secretion measurement of a murine B cell sample, after 32 minutes; top: brightfield channel, bottom: red fluorescence channel. (a) Droplet containing a cell that does not secrete IgG, automatically classified as negative. (b) Droplet containing a cell that secretes IgG (fluorescence relocation on the beadline), positively evaluated during manual verification. (c) Droplet containing a cell with relocation of the fluorescent detection reagent on the cell only, sorted out during manual verification. (d) Droplet containing a fluorescent clump, sorted out during manual verification.

Figure II. 4 :

 4 Figure II.4: Results of single-cell IgG secretion measurements Measurements of B cells enriched from spleens of mice immunized with PE and a TLR9 agonist (CpG ODN 1826), harvested 6 days after secondary immunization. (a) Time-lapse measurement of the fluorescence relocation for selected IgG-secreting cells, along with their mean secretion rates. Precise secretion rates are computed only for the time intervals where both relocation signals are below the cutoff value. (b) Distribution of the mean secretion rates of IgG-secreting cells (pooled values from separate measurements of 6 replicate mice, 562 IgG-secreting cells represented here out of 103 754 total B cells).

First, to detect

  the secretion of IgM antibodies by single B cells, we added an anti-IgM secondary antibody to the in-droplet bioassay (AffiniPure F(ab') 2 Fragment Donkey Anti-Mouse IgM, µ chain specific, 715-476-020, Jackson ImmunoResearch), labeled with DyLight 405 (blue fluorescence; excitation max. 400 nm, emission max. 420 nm, DAPI filter set).

Figure II. 5 :

 5 Figure II.5: Calibration curve for IgMMedian fluorescence relocation values displayed along with standard deviations and the computed exponential fit (dotted blue line). Limit of detection (LoD) and plateau values are also displayed. The cutoff value corresponds to the relocation above which no precise concentration of IgM is computed: due to the variability of the obtained signal, when the relocation is close to the plateau of the calibration curve it is not possible to attribute a precise concentration to a given relocation value. Thus, the assay has a quantitative range, from 0.3 to 5.5 nM IgM, and a qualitative range, above 5.5 nM.

  Chapter II. Assay developmentOn figure II.6 we show results of this assay to detect the subclass of single IgG-secreting cells. These IgG-secreting cells have different secretion rates, leading to widely varying indroplet IgG concentrations at the final time point of the measurements (after 32 minutes) and therefore also a wide range of relocation signals of the IgG detection probe. For each cell, we also measured the relocation of the IgG1-specific probe at the same time point, and we represented them as a scatter plot of the IgG1 probe relocation against the IgG Fc probe relocation. As explained above, we could classify the cells as IgG1 or IgG2a/2b/3 based on their IgG1 probe relocation signal. We observed a clear separation between IgG1 and the other IgG subclasses, although the exact relocation of the IgG1 probe for a given relocation of the IgG Fc probe seems variable for IgG1 antibodies, probably due to noise inherent to the assay. We also verified that these populations of IgG-secreting cells corresponded to the signals obtained with monoclonal antibodies of known IgG subclass (median and SD of calibration experiments overlayed in black). This showed that the IgG1 detection probe was indeed specific for IgG1, with high relocations observed only for IgG antibodies of the IgG1 subclass. In addition, we verified that the IgG1 detection probe did not bind the IgM or IgA mouse antibody isotypes (data not shown).

Figure II. 6 :

 6 Figure II.6: Detection of IgG subclasses Single IgG-secreting cells from mice immunized with PE and TLR agonists (2525 in total, pooled from the groups presented in chapter IV), represented as a scatter plot of IgG1 detection probe against IgG Fc detection probe relocation signals, at the final time point of the DropMap measurements (32 minutes). The cells are either classified as IgG1 (red points) or IgG2a/2b/3 (green points), based on the relocation signal of the IgG1 probe (respectively above or below the LoD of IgG1). For low concentrations of IgG (greyed area), the separation between IgG1 and IgG2a/2b/3 is performed manually. High concentrations of IgG3 can be separated and are represented in blue. Median and SD of calibration experiments with known concentrations of monoclonal control IgGs from the different subclasses are also represented in black.

  agonists and we needed to measure the PE-specific antibody repertoire after the immunizations. With this objective in mind, we added a known concentration of the PE protein(20 nM) in the composition of the DropMap assay. As we have described in the previous sections, secreted antibodies, either IgG or IgM, are captured to the beadlines and detected by the relocation of fluorescent detection reagents (see figure II.1). If these antibodies have an affinity for PE, the protein also relocates to the beadline, bound by the antibodies captured on the magnetic nanoparticles (see figure I.5b for a schematic of the antigen binding assay and figure I.5c for an image of a droplet with PE relocation on the beadline). PE has an intrinsic bright orange fluorescence (excitation max. 565 nm, emission max. 578 nm, TRITC filter set), that allows the detection of its relocation to the beadline without additional labeling with a fluorescent moiety. Alternatively, for a different protein antigen that would not already be fluorescent, it would be possible to label it with a reactive fluorescent dye (e.g. Alexa Fluor 555 NHS Ester, A37571, Invitrogen) to obtain a similar result. In this way, the detection of PE binding described in this section could be transposed to basically any other protein antigen.

Figure II. 7 :

 7 Figure II.7: Classification of IgG-secreting cells as PE-specific or non-specificSingle IgG-secreting cells from mice immunized with PE and TLR agonists (2525 in total, pooled from the groups presented in chapter IV), represented as a scatter plot of the relocation of the antigen (PE) against the relocation of the IgG Fc probe, at the endpoint of the DropMap measurements. The yellow horizontal line shows the threshold used to classify the cells as PE-specific (above) or nonspecific (below). For a given IgG relocation value on the x axis, a higher PE relocation corresponds to a higher affinity of the antibody. The diagonal direction corresponds to IgGs with similar affinities at different concentrations. The dotted line represents a higher limit of PE binding that seems to be exceeded by only very few IgG antibodies and probably corresponds to a one-to-one binding of IgG to PE.

  Since we measured the cells over time, we could analyze the affinity towards PE of the different IgG-secreting cells more precisely by taking all time points into account. We computed the over-time slope of PE relocation against IgG relocation, as shown on figureChapter II. Assay development II.8, for PE-specific IgG-secreting cells detected as described before. For the computation of the slope we only considered time points where the relocation of the IgG detection probe is below the cutoff defined in section II.1, because the quantification of IgG concentration is otherwise not precise enough. Within the quantitative range of the assay (IgG detection probe relocation below 2.83, see section II.1), the evolution of antigen relocation against IgG relocation is usually linear (see example on figure II.8, computed R 2 = 0.976 ±0.033 for a typical sample with 54 PE-affine cells), justifying the use of the slope of a linear fit. The obtained slope (termed "affinity slope" here) is a correlate of the affinity of the IgG antibody towards PE. A higher affinity slope value corresponds to a higher affinity (lower dissociation constant K D ).

Figure II. 8 :

 8 Figure II.8: Example of affinity slopes computed for single PE-specific IgG-secreting cells For each PE-specific IgG-secreting cell detected, we compute a linear fit of the evolution of the PE relocation signals versus the IgG relocation signals over time (dotted lines). The slopes, termed "affinity slopes", are displayed next to the individual fits. They are a correlate of the affinity of the single IgG antibodies for the PE protein antigen. The affinity slopes are only computed within the quantitative range of the assay (see section II.1): time points with IgG detection probe relocation above the IgG cutoff are not taken into account (last cyan-colored point for example).

4

  Detection of antibacterial antibodies and activation of the classical pathway of complementAs mentioned in chapter I, we were interested in studying antibacterial antibody responses at the single cell level with the DropMap platform. In particular, we wanted to measure the activation of the classical pathway of the complement system by single antibodies. In this section, we describe the development of the DropMap assay necessary to conduct this study, which allowed the detection of single bacteria-specific antibody-secreting cells and the quantification of the ability of the antibodies to trigger the classical complement pathway. The activation of the classical pathway was measured here by its first step, the antibody-induced recruitment of the C1q protein to bacterial surfaces. We first detail the design of the assay, and then move on to the technical steps for the development and optimization of the assay. Finally, we describe the analysis of B cell secretion measurements using this assay and show examples of the obtained results.

Figure II. 9 :

 9 Figure II.9: Schematic of the DropMap assay to detect antibacterial and C1q-binding antibodies Fixed target bacteria are bound on the same 300 nm magnetic nanoparticles that we also used in the previous sections, using a biotin-PEG-cholesterol linker. There are approximately 1200 magnetic beads and 200 fixed bacteria included in each droplet. The beads can be aligned using a magnetic field, forming vertical bacteria-coated beadlines, termed bactolines (see e.g. figures II.10 and II.11). Bacteria-specific antibodies (here IgG) will bind to the surface of the fixed bacteria, and can be detected with the Alexa Fluor 647-labeled IgG detection probe. We can distinguish the subclass of the IgG antibodies binding to the bacteria with an IgG1-specific probe labeled with Alexa Fluor 488. Alternatively, a DyLight 405-labeled IgM detection probe is also present in the assay to detect bacteria-specific IgM antibodies. If the antibody can recruit the C1q protein, we can detect the antibody-dependent relocation of C1q with a fluorescent Alexa Fluor 555 label, conjugated to the purified C1q protein that is included in the assay.

Figure II. 10 :

 10 Figure II.10: Calibration images of the assay to detect antibacterial antibodies and C1q binding Calibration experiments with P. aeruginosa bactolines and a constant concentration of a monoclonal anti-P. aeruginosa IgG2a (B11 clone, 0 or 40 nM) in the droplets. On the first row are shown the fluorescence of the IgG detection probe (computed relocation values overlayed in white). On the second row are shown the fluorescence of the C1q complement protein (labeled with Alexa Fluor 555). Adding the control IgG2a induces the relocation of the C1q protein to the bactolines.

Figure II. 11 :

 11 Figure II.11: Quality of bactolines depending on the type of inactivation of bacteria Bactolines obtained by binding (a) commercial heat-killed or (b) cultured and fixed P. aeruginosa bacteria on the nanoparticles. The white scale bar is 50 µm long.

  previously in this section. Although requiring additional work in an BSL-2 bacterial culture lab, this method allowed to obtain solutions of intact inactivated bacteria where the bacterial cells were perfectly individualized. This led to the formation of bactolines of a much better quality in the DropMap assay, as shown on figure II.11b. In the following paragraph, we give an updated protocol to prepare such bactolines from the fixed bacteria solutions: Volume of reagents are for 100 µL final volume of magnetic nanoparticle (also named beads) solution and considering a fixed bacteria stock solution of OD 600 = 20 (measured as OD 600 of 1:20 dilution = 1). Scale accordingly for a different final volume or concentration of fixed bacteria stock. Throughout the protocol, keep the different solutions containing fixed bacteria on ice or at 4 • C as much as possible to avoid the degradation of the cells.

Figure II. 12 :Figure II. 13 :Figure II. 14 :

 121314 Figure II.12: Optimization of labeled C1q concentration Signal obtained for the relocation of the IgG Fc probe (red) and labeled C1q (yellow) when varying the in-droplet concentration of labeled C1q protein. We used P. aeruginosa bactolines, 40 nM anti-P. aeruginosa B11 IgG2a and 45 nM IgG Fc probe for all points. Median fluorescence relocation values are displayed along with standard deviations. The chosen optimal concentration is 5 nM of C1q.

  median IgG empty droplets + 1.645 * 2 * k * M AD IgG empty dropletsLoD C1 q = median C1 q empty droplets + 1.645 * 2 * k * M AD C1 q empty droplets where median empty droplets is the median relocation signal in empty droplets, M AD empty droplets is the median absolute deviation in empty droplets, k = 1.4826 and k * M AD is a consistent estimator of the standard deviation σ, considering the distribution of relocation signals for empty droplets as normal.

  bacteria-specific IgG: max f luorescence relocation -min f luorescence relocation > 1.645 * 2 * k * M AD IgG empty droplets 58 Chapter II. Assay development For the cells that secrete an IgG antibody binding to the target bacteria, we can obtain a relative measurement of the density of IgG binding to the surfaces of the bacteria present on the bactoline. We compute this IgG binding value as the maximum relocation signal of the anti-IgG Fc probe on the bactoline over the duration of the measurement normalized by the LoD for IgG. A higher IgG binding value corresponds to a higher density of IgG binding to the bacterial surfaces. The normalization makes IgG binding values comparable between separate measurements.

Figure II. 15 :

 15 Figure II.15: Images of droplets containing single C1q + antibody-secreting cells Images from DropMap measurement of spleen B cells from mice immunized with fixed N. meningitidis C. The example droplets shown here contain either (a) an IgG-or (b) IgM-secreting cell. Both of these antibodies bind to the target fixed N. meningitidis C present on the bactolines since the relocation of the antibody detection probe increases over time (measured (a) in the Cy5 channel for the Alexa Fluor 647-labeled IgG probe or (b) in the DAPI channel for the DyLight 405-labeled IgM probe). The antibodies are also able to recruit the C1q protein since we observe an increase of the fluorescence relocation of the Alexa Fluor 555-labeled C1q over time. The images might not be well readable once printed, for this reason the fluorescence relocation values are also displayed on the images.

Figure II. 16 :

 16 Figure II.16: Secretion measurement of specific and complement-activating antibodies against N. meningitidis serogroup C Evolution of the C1q fluorescence relocation signal over time for the specific antibody-secreting cells (27 IgM and 64 IgG), pooled from measurements of three mice immunized with fixed N. meningitidis C, normalized by the threshold used to detect C1q binding. Cells with a C1q binding value above 1 for at least one time point are classified as C1q-binding (1 IgM, 10 IgG).

8 Figure III. 1 :

 81 Figure III.1: Immunization scheduleThe mice were immunized twice with the same vaccine or fixed bacteria preparation with an interval of four weeks. Seven days after the second immunization, the mice were sacrificed and the spleens harvested.

Figure III. 2 :Figure III. 3 :

 23 Figure III.2: Frequency of antibody-secreting B cells generated in the spleen in response to immunizations with antibacterial vaccines or fixed bacteria Frequency of IgM-and IgG-secreting cells within spleen B cells, in mice immunized with either Infanrix DTPa-IPV vaccine (acellular pertussis vaccine), Bexsero or NeisVac-C (acellular meningitis vaccines, either against serogroup B or C), or fixed N. meningitidis bacteria from the B or C serogroup.

2 .Figure III. 4 :

 24 Figure III.4: Respective fractions of IgG1 and IgG2a/2b/3 in the IgG-secreting cells Classification of IgG-secreting cells into either IgG1 (not functionally active) or IgG2a/2b/3 (functionally active, complement activation among other effector functions) for either (a) all IgG-secreting cells without taking specificity into account, or (b) IgG-secreting cells detected as bacteria-specific. The mice immunized with Bexsero or NeisVac-C were not considered here because of the very low number of IgG-secreting cells detected (bacteria-specific or not).

  chosen model bacterial species, either B. pertussis (Infanrix acellular vaccine), or N. meningitidis B or C (fixed bacteria immunizations). Following those immunizations, we observed IgM-and IgG-secreting cells with affinity for surface antigens of the respective target bacteria using the DropMap bactoline assay (see figure III.3a). To see whether those bacteria-specific antibodies could activate the classical complement pathway, we analyzed the results obtained with the complement assay described in section II.4, that allows the quantification of the antibody-dependent C1q binding on the surface of the bacteria, the first step of the classical pathway of complement. As explained before, we do not measure activation further down in the complement cascade and no other complement protein is present in the assay; we consider that if an antibody is able to relocate the C1q protein at the surface of the bacteria, it would be able to fully activate the classical pathway of the complement system, for example in an in vivo setting.We first looked more precisely at the bacteria-specific IgG-secreting cells obtained after the different immunization protocols. For the group of three mice immunized with the Infanrix vaccine, we detected in total 180 individual B. pertussis-specific IgG-secreting cells. For these cells we could measure the binding density of the secreted IgG antibody on the surfaces of the target bacteria present in the detection assay, as well as the potential subsequent binding density of the C1q protein, if the antibody is able to recruit C1q (see section II.4 for more details). We can then represent the cells as a scatter plot of their C1q binding density against their IgG binding density, as shown on figure III.5. Here, none of the B. pertussisspecific IgG antibodies could trigger C1q binding at a detectable level. However, there were only few IgG2a, IgG2b or IgG3 among the B. pertussis-specific IgG-secreting cells (36 out of 180), which are the complement-activating IgG subclasses, and instead mostly IgG1 that are unable to activate the classical complement pathway according to the literature[START_REF] Collins | IgG subclass co-expression brings harmony to the quartet model of murine IgG function[END_REF].

IgG1 IgG2a/2b/ 3 Figure III. 5 :

 35 Figure III.5: IgG-secreting cells specific against B. pertussis B. pertussis-specific IgG-secreting cells (180 in total), pooled from measurements of three mice immunized with Infanrix DTPa-IPV. IgG binding corresponds to the maximum relocation signal of the anti-IgG Fc probe on the bactoline over the duration of the measurement, for individual specific IgG-secreting cells, normalized by the limit of detection for IgG. C1q binding corresponds to the maximum relocation signal of C1q on the bactoline over the duration of the measurement, normalized by the threshold used to detect C1q binding. No C1q + B. pertussis-specific IgG were detected. No high-density IgG2a/2b/3 were detected.

Figure III. 6 :

 6 Figure III.6: IgG-secreting cells specific against N. meningitidis Specific IgG-secreting cells pooled from measurements of three mice immunized with fixed N. meningitidis, either from the B (panel a) or the C serogroup (panel b). The definition of IgG binding and C1q binding as well as the arbitrary limit between low and high density IgG binding are the same as in figure III.5. Most high-density IgG2a/2b/3 were C1q + .

Figure III. 7 :

 7 Figure III.7: Traces of C1q-binding induced by IgG-secreting cells specific against N. meningitidis serogroup B The high-density IgG from figure III.6a are isolated and the individual traces of C1q binding against IgG binding over the duration of the measurement are plotted here. Time stamps are shown next to one of the traces, and the dots represent the final time point of each trace. For C1q + IgG2a/2b/3, the evolution of C1q binding versus IgG binding is mostly linear, up to the point where the IgG binding saturates (constant or diminishing IgG binding at the end of part of the traces).

4

  Analysis of C1q-binding antibodies 73 cells in the same way as the specific IgG-secreting cells, comparing this time the IgM binding density on the target bacterial surfaces to the IgM-dependent C1q binding density.

Figure III. 9 :

 9 Figure III.9: IgM-secreting cells specific against N. meningitidis Specific IgM-secreting cells pooled from measurements of three mice immunized with fixed N. meningitidis, either from the B (panel a) or the C serogroup (panel b). IgM binding corresponds to the maximum relocation signal of the anti-IgM probe on the bactoline over the duration of the measurement, for individual specific IgM-secreting cells, normalized by the limit of detection for IgM. C1q binding corresponds to the maximum relocation signal of C1q on the bactoline over the duration of the measurement for the same IgM-secreting cells, normalized by the threshold used to detect C1q binding. Cells with a C1q binding value above 1 are classified as C1q-binding (C1q + ), displayed in green. No C1q + IgM-secreting cell was detected against the serogroup B, and only one against the serogroup C.

Figure III. 10 :

 10 Figure III.10: Traces of C1q-binding induced by IgM-secreting cells specific against N. meningitidis serogroup C The same N. meningitidis C-specific IgM-secreting cells shown in figure III.9b are represented here as individual traces of C1q binding against IgM binding over the duration of the measurement. Time stamps are shown next to one of the traces, and the dots represent the final time point of each trace. Here, only one IgM antibody could bind C1q. For this C1q + IgM, the evolution of C1q binding versus IgG binding follows almost perfectly a linear profile.

Figure III. 11 :

 11 Figure III.11: Structure of the IgG hexamer and C1-IgG 6 complex Panel (a) is from Diebolder et al., Science, 2014 [40]; Panels (b) and (c) are from Ugurlar et al., Science, 2018 [41]. (a) Crystal structure of an hexamer of human IgG1 (not functionally similar to mouse IgG1). The dashed enclosure indicates a single IgG molecule. The site for C1q binding is indicated in red. (b and c) 3D reconstructions from cryo-electron microscopy analyses of hexamers of human IgG1 bound by the C1 complex (C1qr 2 s 2 ). Only the Fc domains of the IgG1 antibodies are shown here; they form a circular platform on which C1 can dock. (b) Two sides views of the resulting complex. (c) Top and side views of the bottom platform, showing the globular heads of C1q (gC1q) binding to the IgG1 Fc domains.

5 Discussion 81 all

 581 Fab arms of IgM are binding to surface antigens in the IgM-C1 assembly: for each IgM monomer, both Fab domains are bound. The IgM-C1 complexes were more homogeneous and well-resolved in their observations as compared to similar experiments with IgG-C1 complexes[START_REF] Ugurlar | Structures of C1-IgG1 provide insights into how danger pattern recognition activates complement[END_REF], probably reflecting a lower flexibility of the complement-activating assembly in the case of IgM antibodies. In addition to these findings, Chen et al. have very recently

Figure III. 12 :Figure III. 13 :

 1213 Figure III.12: Proposed workflow for the sorting and sequencing of complement-activating bacteria-specific antibodies Double-emulsification: Double emulsions (water/oil/water) obtained by vortexing droplets containing the DropMap assay for detection of bacteria-specific and complement-activating antibodies (here with a specific control monoclonal IgG) added to outer-phase buffer. Brightfield and Cy5 images superimposed, with red fluorescence relocation on the aggregate of bacteria and magnetic nanoparticles due to the specificity of the monoclonal IgG for the bacteria (here P. aeruginosa). Sorting: High-throughput sorting using a BD FACSAria III cell sorter, based on IgG and C1q relocation signals. Sequencing: Single-cell mRNA sequencing to obtain heavy-and light-chain variable regions sequences of the individual antibodies of interest, using a 10x Genomics Chromium. Images of the instruments are from BioRender.com.

Figure IV. 1 :

 1 Figure IV.1: Difference between spleen antibody-secreting cells responses induced by a protein antigen (PE) or fixed bacteria immunizations (a) The fixed bacteria result in much larger, albeit heterogeneous, IgG-secreting cell responses in the spleen as compared to the protein antigen. (b) With the protein antigen most of the IgG-secreting cells in the spleen are of the IgG1 subclass, not functionally active, while with fixed bacteria immunizations most of the cells secrete IgG2a/2b/3, subclasses that efficiently activate effector functions. The pvalues displayed here compare the fixed bacteria conditions to the PE protein condition.

immunizations either Pam 3 CSK 4 (

 34 TLR2 agonist, 10 µg), poly(I:C) HMW (TLR3 agonist, 50 µg), monophosphoryl lipid A from S. minnesota R595 (MPLA, TLR4 agonist, 5 µg) and CpG ODN 1826 (TLR9 agonist, 15 µg; all from Invivogen), or a combination of Pam 3 CSK 4 , MPLA and CpG ODN 1826 (respectively 10, 5 and 15 µg). All the antigen solutions were prepared in PBS (from Gibco) and mixed 1:1 with 2% Alhydrogel adjuvant (alum, from Invivogen).

Figure IV. 2 :

 2 Figure IV.2: Immunization scheduleThe mice were immunized twice with the PE and TLR agonist preparation with an interval of four weeks. Six days after the second immunization, the mice were sacrificed and the spleens harvested.

Figure IV. 3 :

 3 Figure IV.3: Influence of the different TLR agonists on the antibody repertoire Increase in frequencies of IgM-(a) and IgG-secreting cells (b) within spleen B cells. (c) Ratio of the frequencies of IgG-over IgM-secreting cells, both within spleen B cells, increasing for the TLR9 agonist.

Figure IV. 6 :

 6 Figure IV.6: Increase in IgG secretion ratesThe secretion rate is computed for each antibody-secreting cell and the median IgM and IgG rate is derived for each mouse. The means and SDs for each condition are displayed here. The values for the TLR2, 4 and 9 groups are also averaged for comparison with the combination (bars with dotted outline).

Figure IV. 7 :

 7 Figure IV.7: Single-cell IgG secretion ratesSecretion rates of single IgG-secreting cells, pooled for each condition from all the mice of the group. Medians and standard deviations are overlayed. The total number of IgG-secreting cells for each condition is specified at the bottom. The high secretors (>500 IgG/s, no precise quantification of the secretion rate) are displayed above the dotted line, as well as the fraction of the IgG-secreting cells of the group that they represent.

Figure IV. 8 :

 8 Figure IV.8: Increase in PE-specific IgG-secreting cells frequency, mostly due to an increase in IgG-secreting cells (a) Frequency of IgG-secreting cells classified as PE-specific within spleen B cells. (b) Percentage of cells secreting a PE-specific IgG within IgG-secreting cells.

Figure IV. 9 :Figure IV. 10 :

 910 Figure IV.9: Average affinity slope of PE-specific IgG antibodiesFor each PE-specific IgG-secreting cell, the linear slope of the evolution of the PE relocation against the IgG Fc probe relocation over time is computed. It is a correlate of the affinity of the individual antibody: the higher the slope, the better the affinity (or lower K D ).

and 4 )

 4 TLR signalling pathways, meaning that a potential secretion of Th1-associated cytokines due to the TLR agonists (not measured here) was not sufficient to induce efficient class switching to IgG2a/2b/3 in our experiments. Another more efficient way for TLR agonists to induce class switching would be to directly interact with TLRs on B cells. B cells express all the different classes of TLRs, except TLR5 and 8, with slight differences in the expression pattern depending on the subset (naïve, memory, follicular, marginal zone, B-1 and Peyer's patch) and respond to TLR engagement[START_REF] Browne | Regulation of B-cell responses by Toll-like receptors[END_REF][START_REF] Gururajan | Toll-Like Receptor Expression and Responsiveness of Distinct Murine Splenic and Mucosal B-Cell Subsets[END_REF]. For example, marginal zone B cells are activated to proliferate and secrete antibody upon TLR engagement, including TLR1/2, TLR4, TLR6, TLR7 and TLR9[START_REF] Rawlings | Integration of B cell responses through Toll-like receptors and antigen receptors[END_REF]. The activation of TLRs directly on B cells can also induce isotype switching, but it requires specific conditions. Indeed, Pone et al. have demonstrated in vitro and in vivo (6 days after primary immunization) that the co-activation of the B-cell receptor (BCR) and TLR can trigger class-switch recombination (CSR) very 101

  response. Indeed, they impacted the frequency of IgM-(TLR2 agonist and the combination, see figure IV.3a) and IgG-secreting cells, PE-specific and non-specific (TLR2 and 9 agonists and the combination, see figures IV.8a and IV.3b), as well as the IgG secretion rates (TLR2, 4 and 9 agonists, see section IV.4). The increase of both of these parameters, IgG-secreting cell number and individual secretion rates, is clearly visible on figure IV.7. Interestingly, the effect seemed additive when comparing the combination of the TLR2, 4 and 9 agonist to the single agonists regarding the IgG-secreting cells frequency (see figure IV.3b), but not regarding the secretion rates, for which the increase observed for each of the single agonists was abolished with the combination (see figure IV.6 and IV.7).We can take into account both of these parameters simultaneously, frequency and secretion rate, to estimate the average antibody secretion per spleen B cell in the different 102 Chapter IV. Influence of TLR agonists on the antibody repertoire conditions by summing the individual secretion rates of the single antibody-secreting cells in each DropMap measurement, and dividing it by the total number of B cells measured.

Figure IV. 11 :

 11 Figure IV.11: Total spleen immunoglobulin secretion (a) After the enrichment process with the Miltenyi Pan B cell isolation kit II (mouse), the cells are pooled in 3 mL of buffer and the cell concentration is measured with a Countess 3 automated cell counter, leading to the total number of B cell enriched from the full spleen for each mouse. (b) Total IgM, IgG and PE-specific IgG secreted in the spleen (taking into account B cell count after enrichment), normalized by the IgM and IgG values of the PE group.

  addition, I introduced new multiplexing possibilities for the DropMap murine antibody secretion assay. It is now able to measure in parallel the secretion of IgM antibodies, IgG antibodies, and further classify IgG antibodies into the murine subclasses IgG1 (nonfunctionally active) or IgG2a/2b/3 (functionally active). At the same time it is possible to quantify the binding of individual antibodies to model protein antigens, here phycoerythrin, which directly correlates with the affinity towards the antigen. As a final and main technical development, I established a new DropMap assay that allows the detection of secreted antibody binding to target bacteria and the subsequent activation of the first step of the classical complement pathway, the recruitment of the C1q protein. Herein, paraformaldehyde-fixed bacteria are bound to magnetic nanoparticles, which enables the formation of bacteria-coated beadlines in the droplets. Antibodies specific to the target bacteria are then detected by fluorescence relocation, and if they are able to activate the classical pathway of complement, fluorescently-labeled C1q also relocates to the surface of 108 Chapter V. General conclusion and outlook

  many bacterial pathogens[START_REF] Frasch | Bactericidal antibody is the immunologic surrogate of protection against meningococcal disease[END_REF][START_REF] Geurtsen | Importance of (antibodydependent) complement-mediated serum killing in protection against Bordetella pertussis[END_REF][START_REF] Gil | Streptococcus pneumoniae interactions with the complement system[END_REF][START_REF] Rossi | The essential role of complement in antibody-mediated resistance to Salmonella[END_REF]. For this reason, complement-activating monoclonal antibodies are of great interest to treat infections by antibiotic-resistant bacteria. However, the discovery of such antibodies has remained challenging due to the lack of methods that allow to probe antibody-dependent complement activation by single antibodies clones with a high throughput. The experimental study presented in chapter III represents a step towards a better understanding of the activation of the classical pathway of complement by monoclonal antibodies and the establishment of a method to sort for single antibodies particularly efficient at the activation of complement.In this study, we immunized mice with antibacterial vaccines or paraformaldehyde-fixed pathogenic bacteria to elicit bacteria-specific antibody responses. I precisely characterized these antibody repertoires with the DropMap platform, and measured satisfactory bacteriaspecific antibody-secreting cell frequencies for the B. pertussis vaccine Infanrix and fixed N. meningitidis B and C immunizations. I could then detect single B cells secreting bacteriaspecific IgM or IgG antibodies that successfully recruited the complement protein C1q at the surface of the bacteria, which constitutes the first step of the activation of the classical pathway. I could precisely quantify the antibody and C1q binding densities on the bacterial surfaces, and therefore determine the efficiency of the individual antibodies for complement activation with a high precision.

Figure A. 2 :

 2 Figure A.2: PE-specific titersThe PE-specific titers were measured by coating the wells of 96-well plates with PE, adding the serum of the immunized mice, and then performing an ELISA with anti-IgG or IgG1, 2a or 2b labeled detection antibodies. The p-values were computed as comparisons with the PE alone condition.

  optimized dose in term of magnitude of the antibody response. Following the immunization schedule described in chapter IV, we immunized mice with either 10, 70 or 150 µg of PE and measured the antibody-secreting cell response in the spleen 6 days after the secondary immunization using the DropMap platform and the same assays as in chapter IV. The lower (10 µg) and intermediate (70 µg) dose of PE induced a similar response in term of frequency of IgG-secreting cells and PE-specific IgG-secreting cells (see figures A.3

Figure A. 3 :

 3 Figure A.3: Antibody secreting cell response -IgM and IgGFrequency of IgM-and IgG-secreting cells within spleen B cells. The p-values were computed for all permutations of conditions, but no significant difference was found.

Figure A. 4 :Figure A. 5 :

 45 Figure A.4: PE specific IgG repertoires (a) Frequency of IgG-secreting cells classified as PE-specific within spleen B cells. (b) Percentage of cells secreting a PE-specific IgG within IgG-secreting cells. The p-values were for all permutations of conditions.

Figure A. 6 :

 6 Figure A.6: Average affinity slopeFor each PE-specific IgG-secreting cell, the linear slope of the evolution of the PE relocation against the IgG Fc probe relocation over time is computed. It is a correlate of the affinity of the individual antibody: the higher the slope, the higher the affinity. The average affinity slope of all the PE-specific IgG-secreting cells is computed for each mouse and the mean and SD for each condition are plotted here. The p-values were computed for all permutations of conditions.

Figure A. 7 :

 7 Figure A.7: Distribution of the affinity slopes The distribution of the affinity slopes of individual PE-specific IgG-secreting cells are represented here for the different doses of PE used for the immunizations. Each distribution is fitted with a normal density function whose parameters µ and σ are displayed.

  Structures of the different antibody isotypes . . . . . . . . . . . . . . . . . . . I.2 Antibody effector functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I.3 The different pathways of the complement system . . . . . . . . . . . . . . . . I.4 Schematic representation of the different technologies that enable the measurement of protein secretion at the single-cell level . . . . . . . . . . . . . . . . . .11 Quality of bactolines depending on the type of inactivation of bacteria . . . . II.12 Optimization of labeled C1q concentration . . . . . . . . . . . . . . . . . . . . II.13 Optimization of IgG Fc probe concentration . . . . . . . . . . . . . . . . . . . II.14 Calibration curve of the complement assay with P. aeruginosa bactolines . . . II.15 Images of droplets containing single C1q + antibody-secreting cells . . . . . . . II.16 Secretion measurement of specific and complement-activating antibodies against Traces of C1q-binding induced by IgM-secreting cells specific against N. meningitidis serogroup C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III.11 Structure of the IgG hexamer and C1-IgG 6 complex . . . . . . . . . . . . . . . III.12 Proposed workflow for the sorting and sequencing of complement-activating bacteria-specific antibodies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III.13 Sorting of double emulsions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Immunization schedule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV.3 Influence of the different TLR agonists on the antibody repertoire . . . . . . . IV.4 Respective fractions of IgM and IgG in the antibody-secreting cells and comparison with fixed bacteria immunizations . . . . . . . . . . . . . . . . . . . . IV.5 Respective fractions of IgG1 and IgG2a/2b/3 in the IgG-secreting cells . . . .

	IV.1	Difference between spleen antibody-secreting cells responses induced by a pro-
		tein antigen (PE) or fixed bacteria immunizations . . . . . . . . . . . . . . . .
	I.1 IV.2
	III.3	Specific IgM and IgG responses towards bacteria in the spleen following im-
		munizations with antibacterial vaccines or fixed bacteria . . . . . . . . . . . .
	III.4	Respective fractions of IgG1 and IgG2a/2b/3 in the IgG-secreting cells . . . .
	III.5	IgG-secreting cells specific against B. pertussis . . . . . . . . . . . . . . . . . .
	III.6	IgG-secreting cells specific against N. meningitidis . . . . . . . . . . . . . . . .
	III.7

I.5 DropMap technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I.6 Effect of epitope density and affinity on the binding density of antibodies . . . II.1 DropMap fluorescence relocation assay to measure IgG secretion . . . . . . . . II.2 Calibration curve for IgG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II.3 Example of droplet images and rules for manual verification . . . . . . . . . . II.4 Results of single-cell IgG secretion measurements . . . . . . . . . . . . . . . . II.5 Calibration curve for IgM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II.6 Detection of IgG subclasses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II.7 Classification of IgG-secreting cells as PE-specific or non-specific . . . . . . . . II.8 Example of affinity slopes computed for single PE-specific IgG-secreting cells . II.9 Schematic of the DropMap assay to detect antibacterial and C1q-binding antibodies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II.10 Calibration images of the assay to detect antibacterial antibodies and C1q binding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IIN. meningitidis serogroup C . . . . . . . . . . . . . . . . . . . . . . . . . . . . III.1 Immunization schedule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III.2 Frequency of antibody-secreting B cells generated in the spleen in response to immunizations with antibacterial vaccines or fixed bacteria . . . . . . . . . . . Traces of C1q-binding induced by IgG-secreting cells specific against N. meningitidis serogroup B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III.8 IgM-secreting cells specific against B. pertussis . . . . . . . . . . . . . . . . . . III.9 IgM-secreting cells specific against N. meningitidis . . . . . . . . . . . . . . . . xvi III.10 IV.6 Increase in IgG secretion rates . . . . . . . . . . . . . . . . . . . . . . . . . . . IV.7 Single-cell IgG secretion rates . . . . . . . . . . . . . . . . . . . . . . . . . . . IV.8 Increase in PE-specific IgG-secreting cells frequency, mostly due to an increase in IgG-secreting cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV.9 Average affinity slope of PE-specific IgG antibodies . . . . . . . . . . . . . . . IV.10 Single-cell affinity slopes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV.11 Total spleen immunoglobulin secretion . . . . . . . . . . . . . . . . . . . . . . A.1 Serum IgG titers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.2 PE-specific titers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.3 Antibody secreting cell response -IgM and IgG . . . . . . . . . . . . . . . . . A.4 PE specific IgG repertoires . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.5 Isotype distribution depending on the quantity of PE used for the immunizations117 A.6 Average affinity slope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.7 Distribution of the affinity slopes . . . . . . . . . . . . . . . . . . . . . . . . .

The different pathways of the complement system adapted

								9
	mannose-binding lectin (MBL) play a parallel role to the C1q protein by recognizing the
	surface of bacterial pathogens, this time directly without the need for bound antibodies (see
	figure I.3) [46]. Both MBL and ficolin are present as complexes with MBL-associated serine
	proteases (MASPs) that are activated upon binding to the surface and cleave C4 and C2
	forming the same C3 convertase (C4b2b) as the classical pathway [47].
	LECTIN PATHWAY		CLASSICAL PATHWAY		ALTERNATIVE PATHWAY
	Mannose-binding lectin (MBL) and ficolins recognize and bind carbohydrates on pathogen surface	C1q interacts with pathogen surface or with antibodies bound to surface	C3 undergoes spontaneous hydrolysis to C3(H 2 O) to initiate eventual deposition of C3 convertase on microbial surfaces
				C1q		factor D	factor B
	ficolin	MASPs	MBL	C1r	C1s	C3(H 2 O)		properdin (factor P)
		pathogen						C3bBb
		surface
	MBL/ficolin, MASP-2 C4 C2		C1q, C1r, C1s C4 C2			factor D factor B properdin C3
				All pathways generate a C3 convertase, which
				cleaves C3, leaving C3b bound to the microbial
				surface and releasing C3a
				C3
				C3a
				C3b
	C3a and C5a recruit phagocytic cells to the site	Phagocytes with receptors for C3b engulf and	Completion of the complement cascade leads to
	of infection and promote inflammation	destroy the pathogen		formation of a membrane-attack complex (MAC),
						which disrupts cell membrane and causes cell lysis
						C5b		MAC
		C3a			Phagocyte	C6
				C3b
		infected tissue		complement receptors	C8	C7	C9
	Figure I.3: from Janeway's Immunobiology, 9 th edition, Kenneth Murphy & Casey Weaver, Garland
	Science, 2017 [34]

Table I .1: Comparison of the different technologies for the analysis of protein secretion at the single-cell level adapted

 I from Bucheli et al., European Journal of Immunology, 2021[START_REF] Bucheli | Measuring single-cell protein secretion in immunology: Technologies, advances, and applications[END_REF]

  6. With the new DropMap assay

	High epitope density Low affinity	Low epitope density High affinity	High epitope density High affinity
			efficient effector functions

Table I .

 I

	Toll-like receptor	Ligand		Cell type distribution
	TLR1:2/TLR2:6	Cell-wall components of bacteria and fungi	Monocytes, dendritic cells, mast cells, eosinophils, basophils
	TLR3	Double-stranded RNA (viruses), poly I:C	Macrophages, dendritic cells, intestinal epithelium
	TLR4	LPS (Gram-bacteria) Lipoteichoic acids (Gram+ bacteria)	Macrophages, dendritic cells, mast cells, eosinophils
	TLR5	Flagellin (bacteria)		Intestinal epithelium, macrophages, dendritic cells
	TLR7	Single-stranded RNA (viruses)	Plasmacytoid dendritic cells, macrophages, eosinophils, B cells
	TLR8	Single-stranded RNA (viruses)	Macrophages, neutrophils
	TLR9	DNA with unmethylated CpG (bacteria and herpesviruses)	Plasmacytoid dendritic cells, eosinophils, B cells, basophils
	TLR11 (mouse only)	Profilin and profilin-like proteins (T. gondii, uropathogenic bacteria)	Macrophages, dendritic cells, epithe-lial cells from liver, kidney, bladder
	TLR12 (mouse only) Profilin (T. gondii)		Macrophages, dendritic cells, epithe-lial cells from liver, kidney, bladder
	TLR13 (mouse only)	Single-stranded RNA (bacterial ribosomal RNA)	Macrophages, dendritic cells
		TLR	Adapter protein
		TLR1:2/TLR2:6 MyD88/MAL
		TLR3	TRIF
		TLR4	MyD88/MAL TRIF/TRAM
		TLR5	MyD88
		TLR7	MyD88/MAL
		TLR8	MyD88
		TLR9	MyD88
		TLR11	MyD88
		TLR12	MyD88
		TLR13	MyD88

.2). Different TLRs exist (twelve in total in mouse) that usually function as homodimers (although TLR2 forms heterodimers with TLR1 or TLR6) and are specific for conserved molecular patterns of different pathogens. For example TLR3, 7, 8 and 9 can sense various viral components, while TLR1:2, 2:6, 4, 5, 9, 11, 12 and 13 recognize different bacterial molecules (see table I.2), with each a different specificity. Once they are bound by Table I.2: Mouse Toll-like receptors adapted from Janeway's Immunobiology, 9 th edition, Kenneth Murphy & Casey Weaver, Garland Science, 2017

[START_REF] Murphy | Janeway's Immunobiology[END_REF]

List of Toll-like receptors (TLRs), as well as their ligands and cellular distribution. Cell surface TLRs are listed in blue, while endosomal TLRs are listed in red. Most of the mouse TLRs are also present in human with the same ligand specificity, although their cellular distribution can vary. The TLR-11, 12 and 13 are only present in mouse and not in human. 3: Adapter molecules involved in signaling by the different TLRs their ligand, TLRs can interact with different adapter molecules (see table

I

.3), that activate two main signaling pathways: the MyD88-dependent pathway and the TRIF-dependent

  5 to 5 nM IgG). More precisely, the fluorescence relocation signals obtained for antibodies of the IgG2a and IgG2b subclasses were too low with the detection probe that we used at the time (Alexa Fluor 647 AffiniPure F(ab') 2 Fragment Rabbit Anti-Mouse IgG, Fcγ fragment specific, 315-606-046, from Jackson ImmunoResearch). This made the detection of low concentrations of IgG2a/2b (below 5 nM) difficult, as the signal was barely higher than the noise inherent to the assay. For this reason, the IgG detection probe was replaced with another labeled antibody targeting the Fc domain of IgG (Goat Anti-Mouse IgG Fc -Alexa Fluor 647, 1033-31, from SouthernBiotech), selected because it led to high and similar relocation signals with control antibodies of all the different murine IgG subclasses (IgG1, IgG2a, IgG2b and IgG3). In addition, we verified that this new IgG detection probe did not bind to the IgA and IgM antibody isotypes that can also be secreted by spleen B cells.

  compute a correlate of the affinity of these antibodies for the antigen. The different analytes, reagents and detection channels are summarized in the following table, ordered by ascending peak emission wavelength λ em :

	Analyte	Reagent	Concentration	Fluorophore	λem	Channel
	IgM	AffiniPure F(ab') 2 Fragment Donkey	75 nM	DyLight 405	420 nm	DAPI
		Anti-Mouse IgM, µ chain specific
		(Jackson ImmunoResearch)
	IgG1	Zenon Mouse IgG1 Labeling Kit	50 nM	Alexa Fluor 488	525 nm	FITC
		(Invitrogen)
	PE	R-phycoerythrin	20 nM	PE	578 nm	TRITC
	(antigen)	(Sigma Aldrich)
	IgG	Goat Anti-Mouse IgG Fc	75 nM	Alexa Fluor 647	668 nm	Cy5
		(SouthernBiotech)

secretion, IgG secretion (classified into IgG1 or IgG2a/2b/3 subclasses), as well as the quantification of the binding of the single antibodies to the model target antigen R-phycoerythrin. As an output, 47 we can

  variances t-test. This is the statistical test used by default to compute p-values throughout this thesis, except when otherwise specified. Welch's t-test is more robust than Student's t-test for samples with unequal variances, which is usually the case for the different conditions considered here. Additionally, Welch's t-test is better suited to unequal sample sizes, also present in this thesis. Throughout this thesis, all p-values are computed but only significant differences are displayed. Exception: when the p-value is very close to the first significance level, the exact value is displayed, without a star.

	Chapter III
	Complement activation at the
	single antibody level
	Correspondence p-values -stars:
	p < 0.05: *
	p < 0.01: **
	p < 0.001: ***
	p < 0.0001: ****

Contributions: The work with mice (handling, immunizations, harvest of spleen) was conducted by Luca Schlotheuber, Olivia Bucheli and Kevin Portmann. Afterward, the rest of the experiments and analyses described in this chapter were conducted by myself, with the exception of the preliminary trials for the sorting of complement-activating antibodies (presented in section III.6) which were done with the help of Daniela Rodrigues, Kevin Portmann and Luca Schlotheuber.

  ). On the other

	4 Analysis of C1q-binding antibodies					71
	1.7
	1.6								IgG1
									IgG2a/2b/3
	1.5
	1.4
	1.1 1.2 1.3 C1q binding
	1								C1q + C1q -
	0.9
	0.8
				Low density High density
	0.7	1	1.2	1.4	1.6	1.8	2	2.2	2.4	2.6
					IgG binding
				(a) N. meningitidis serogroup B
	1.7
	1.6								IgG1 IgG2a/2b/3
	1.5
	1.4
	1.3
	1.2
	1.1
	1								C1q + C1q -
	0.9
	0.8
	0.7	1	1.2	1.4	1.6	1.8	2	2.2	2.4	2.6

  80 ±0.31% (see figure IV.3b). For those conditions, this corresponded to a general increase in the antibody-secreting cell responses, both IgM and IgG, while maintaining a similar balance of IgM over IgG as seen on figure IV.3c. The exception is the TLR9 agonist, for which the class switching process from IgM to IgG seems to be boosted since the ratio of IgG-over IgM-secreting cells significantly increased (1.28 ±0.28% compared to 0.56 ±0.17% for PE alone).Overall, for the PE or PE+TLR agonists groups, unswitched IgM-secreting cells still represent around half or the antibody-secreting cells measured (65 ±7% for PE alone, 45 ±6%for PE + TLR3 agonist). This is drastically different from the isotype distribution in mice immunized with fixed bacteria: on figure IV.4 we compare the respective fractions of IgM and IgG in antibody-secreting cells with two groups immunized with fixed N. meningitidis B or C serogroup (10 8 cells, measured 7 days after second immunization, described in chapter III). For those immunizations, the antibody response in the spleen is largely dominated by IgG-secreting cells (93 ±7% and 90 ±5% respectively).

Respective fractions of IgM and IgG in the antibody-secreting cells and comparison with fixed bacteria immunizations

  For the PE+TLR agonists groups, the p-values are computed as comparisons with the PE alone condition, the displayed p-value thus compares PE + TLR9 agonist to PE alone. For the N. meningitidis B and C groups, the fraction of IgM and IgG are significantly different from all the PE alone or with TLR agonists groups, with respectively always p < 0.004 or p < 0.0008 (not displayed).

		0.001
	100%	**
	80%
	60%
		IgG
		IgM
	40%
	20%
	0%
		g o n is t P E + T L R 4 a g o n is t P E + T L R 9 a g o n is t P E + c o m b in a ti o n P E + T L R 3 a g o n is t N . m e n in g it id is B N . m e n in g it id is C
	Figure IV.4:

Respective fractions of IgG1 and IgG2a/2b/3 in the IgG-secreting cells

  For the PE+TLR agonists groups, no significant differences compared to the PE alone group. For the N. meningitidis B and C groups, the fraction of IgG1 and IgG2a/2b/3 are significantly different from all the PE alone or with TLR agonists groups, with respectively always p ≤ 0.0005 or p < 0.015 (not displayed).

	4 Increase in the IgG secretion rates	93
	100%
	80%
	60%
	IgG2a/2b/3
	IgG1
	40%
	20%
	0%
	P E P E + T L R 2 a g o n is t P E + T L R 4 a g o n is t P E + T L R 9 a g o n is t P E + c o m b in a ti o n P E + T L R 3 a g o n is t N . m e n in g it id is B N . m e n in g it id is C
	Figure IV.5:

there is a clear difference when comparing PE with fixed bacteria immunizations, for which the IgG-secreting cells are mostly of the functionally active IgG2a/2b/3 subclasses, as seen on figure IV.5, with 82 ±6% and 72 ±14% of IgG2a/2b/3 for N. meningitidis B and C respectively.

Remerciements

Remerciements

Résumé en français

No No Yes (FACS) Yes Yes Yes No Complexity * * *(*) *(*) **(*) *** *(*) Investment

Chapter III. Complement activation at the single antibody level

We also used a limit between C1q binding and non-binding antibodies, that corresponds to the threshold for C1q binding detection, computed as described in section II.4.

Antibody responses induced by antibacterial immunizations

As mentioned above, we chose B. pertussis and N. meningitidis (B and C serogroups) as model bacteria to study complement-activating antibody responses. A first step was to find immunization conditions that induced sufficient specific antibody responses in mice against these different bacterial species. In particular, we were interested in IgM and IgG responses, since they are the isotypes that can activate the classical complement pathway.

To immunize the mice against B. pertussis, we used a reduced dose of the human vaccine Infanrix DTPa-IPV, a tetravalent acellular vaccine that includes two B. pertussis surface protein antigens, filamentous hemagglutinin and pertactin. The vaccine is adjuvanted with alum and also contains pertussis toxoid, not relevant in our case as the toxin is secreted and thus not an antigen avalaible on the surface of our fixed bacteria, as well as diphteria and tetanus toxoids and inactivated polioviruses of several strains. This vaccine mediates satisfactory B. pertussis-specific antibody titers and protection from disease in immunized children. Although it seems to induce a complement-activating response only in certain recipients [START_REF] Weiss | Acellular pertussis vaccines and complement killing of Bordetella pertussis[END_REF], at least one of the antigens contained in the vaccine, the surface protein pertactin, has been shown to induce bactericidal antibodies in mice [START_REF] Gotto | Biochemical and immunological properties of two forms of pertactin, the 69,000-molecular-weight outer membrane protein of Bordetella pertussis[END_REF]. Thus, we expected to measure B. pertussis-specific antibody responses after immunizations with the Infanrix vaccine, and it was probable that those responses could also trigger C1q binding.

Regarding the immunizations against N. meningitidis, we selected two acellular human vaccines, either protecting against the B serogroup (Bexsero) or the C serogroup (NeisVac-C).

The composition of the vaccines are different: Bexsero is composed of recombinant surface proteins and outer membrane vesicles, while NeisVac-C is composed of polysaccharides conjugated to tetanus toxoid (to enhance their immunogenicity and induce T-cell dependent antibody responses). Both vaccines mediate a satisfactory protection against their respective target strain in humans [START_REF] Deghmane | Product review on the IMD serogroup B vaccine Bexsero R[END_REF][START_REF] Pizza | Vaccines against meningococcal diseases[END_REF], and we assumed they did so through the activation of the complement system because it is widely accepted that complement is a main immune function against N. meningitidis [START_REF] Lewis | Meningococcal disease and the complement system[END_REF]. In particular the classical pathway seems to be crucial, since the presence of serum bactericidal antibodies is a very good correlate of protection [START_REF] Frasch | Bactericidal antibody is the immunologic surrogate of protection against meningococcal disease[END_REF].

We therefore also expected those two vaccines to induce specific and complement-activating antibody responses in mice.

All three of these human vaccines are adjuvanted with alum (Al(OH) 3 ) to increase their immunogenicity. A perk of using vaccines approved for clinical use in human is that they are usually well tolerated by the mice in our experiments, using one fifth of the human dose per mouse immunization.

Chapter V. General conclusion and outlook

this project is to continue working on the sorting pipeline. As we discussed in section III.6, the protocol for sorting double-emulsified droplets still needs extensive optimization from the preliminary trials that we could conduct here, but this seems technically feasible. Being able to sort droplets containing cells secreting antibodies particularly efficient at complement activation would allow to obtain the sequences of these antibodies, express them and test them in vitro for bactericidal activity. This would first be an important validation of our approach if the monoclonal antibodies indeed strongly activate complement in standard in vitro assays. Additionally, we would in this way obtain sequences of bactericidal monoclonal antibodies that could have great therapeutic potential. Since the system can straightforwardly be transferred to different species of pathogenic bacteria, the bioassay described here could be applied to the discovery of antibacterial monoclonal antibodies in a flexible manner, once coupled with a sorting protocol. Finally, I focused here on a particular antibody effector function, but it would be interesting to work on the detection of other effector functions with the DropMap system, such as ADCC or ADCP. This would fall within the broader objective of better understanding the functional humoral responses against different pathogens, not necessarily bacteria.

In a second experimental study, we were interested in the possibility of harnessing the innate immune system to modulate antibody responses. The idea was to try to influence important parameters of antibody repertoires generated by immunizations, such the affinity of antibodies or most importantly the isotype and IgG subclass distributions which determine antibody-dependent effector functions [START_REF] Bruhns | Mouse and human FcR effector functions[END_REF][START_REF] Collins | IgG subclass co-expression brings harmony to the quartet model of murine IgG function[END_REF]. This would have implications for the development of more efficient vaccines. A potential strategy that we explored here would be to add TLR agonists as adjuvants to immunizations and in this way activate various immune cells through their TLRs [START_REF] Coffman | Vaccine adjuvants: putting innate immunity to work[END_REF]. In a simple model system (two successive immunizations with a single protein antigen adjuvanted with alum), I precisely characterized the effect of the addition of different TLR agonists (of TLR2, 3, 4 or 9) or a combination of TLR2, 4 and 9 agonists on the generated antibody repertoire with single antibody resolution.

Adding TLR agonists did not significantly impact the quality of the antibody response, first in term of affinity distribution towards the target antigen. The isotype and IgG subclass distributions were also similar; in particular TLR agonists did not induce a shift towards functionally active IgG subclasses and could not reproduce the effect of full bacteria immunizations in that regard. A probable explanation would be that the TLR agonists did not directly activate spleen B cells in the experiments presented here, which requires simultaneous engagement of TLR and BCR [START_REF] Pone | BCR-signalling synergizes with TLR-signalling for induction of AID and immunoglobulin class-switching through the non-canonical NF-κB pathway[END_REF]. On the other hand, we observed a large increase in the magnitude of the antibody response, particularly important with the addition of the TLR2 agonist or the combination, and to a lesser extent of the TLR9 agonist. The increased magnitude of the humoral response could be attributed here to an increase of antibodysecreting cell frequency (both specific and non-specific) as well as of the total number of B cells in the spleen. This effect is most likely due to the local TLR-dependent stimulation of antigen-presenting cells at the injection site [START_REF] Querec | Yellow fever vaccine YF-17D activates multiple dendritic cell subsets via TLR2, 7, 8, and 9 to stimulate polyvalent immunity[END_REF], inducing enhanced antigen uptake [START_REF] Watts | TLR signalling regulated antigen presentation in dendritic cells[END_REF],

Appendix A

Supplementary information 1 TLR agonists increase the PE-specific titer

For the different conditions described in chapter IV, additionally to the single-cell antibody response in the spleen, we also measured the serum antibody titers. Since the mice were sacrificed to harvest the spleens 6 days after secondary immunization, we also collected the blood at that time point and not later, which might not be ideal as discussed below.

First, we verified that the total IgG and IgG1, IgG2a and IgG2b titers (without taking into account antigen specificity) did not change between two different conditions, as showed on figure A.1. Indeed, the total immunoglobulin G and subclasses concentrations in the blood are regulated and should not differ significantly between different mice, even immunized differently. This verification was only done for two conditions for time purposes, and we assumed that the same result would be observed for the other conditions.

MOTS CLÉS

ABSTRACT

Antibodies that activate the complement system are critical in the immune response against pathogenic bacteria. Such monoclonal antibodies used as therapeutic agents could be important assets in the fight against antibiotic-resistant bacterial infections, a rising issue. To better understand the activation of the classical pathway of complement by monoclonal antibodies, we developed a bioassay to quantify complement activation at the single antibody level and screen for rare complement-activating antibacterial antibodies. We built on the existing DropMap technology, a droplet-microfluidic platform able to quantify antibody secretion at the single cell level with high throughput, with measurements of affinity and secretion rate. It works by compartmentalizing single B cells in 50 pL droplets, along with a fluorescence relocationbased immunoassay. The system allows dynamic phenotyping of tens of thousands of B cells in parallel and screening for cells of interest. Here, we adapted and characterized assays to detect B cells secreting antibodies against specific pathogenic bacteria, including P. aeruginosa, B. pertussis, and N. meningitidis. The developed bioassay measures the specific antibodies' capacity to activate the classical complement pathway by quantifying the relocation of complement component C1q on the surface of fixed bacteria after antibody binding. In an experimental study, we immunized mice with fixed bacteria or antibacterial vaccines and screened purified B cells from the spleen. We demonstrated activation of the classical pathway by individual IgM-or IgG-secreting cells. Additionally, we could observe different requirements for efficient complement activation depending on antibody isotype, which are in agreement with previously described molecular mechanisms. This study is also a first step towards a sorting platform to discover new complement-activating monoclonal antibodies. In a second project, we studied the influence of conserved molecular patterns found in bacteria on the humoral response. These compounds are agonists of Toll-like receptors, which act as a bridge between innate and adaptive immune responses. We showed that adding Toll-like receptor agonists as adjuvants to protein immunizations could greatly enhance the magnitude of the antibody responses of mice while preserving their quality.

KEYWORDS

Immunology, complement system, microfluidics, single-cell, microbiology