The emergence of genomics technologies, especially those combining single-cell resolution and high throughput sequencing, is enabling us to characterize physiopathological mechanisms at a resolution never achieved before. Single-cell genomics sequencing allows researcher to highlight previously hidden intra-tissue cellular heterogeneity and its influences on diseases development. Our ability to generate high-throughput and single-cell data opens new perspectives but also brings new challenges to face. In my thesis, I focused on implementing new strategies to address some of these limitations providing with new evidence toward a better understanding of early disease mechanisms, focusing on two models: the early epigenetics programming of hematopoietic stem and progenitor cells (HSPCs) and the Alzheimer's Diseases (AD) susceptibility gene BIN1 function on study.

In my first model, I leveraged single-cell genomics to decipher early influence of being large for gestational age (LGA) on HSPCs plasticity. We characterized the transcriptional and functional consequences of DNA methylation alterations observed in LGA HSPCs compared to appropriately grown neonates (CTRL) combining single-cell epigenomics, single-cell transcriptomics, and in vitro analysis. We found that DNA hypermethylation is associated with hematopoietic stem cells (HSC) specific chromatin rearrangement in the regulatory network of EGR1, KLF2, and KLF4 transcription factors, affecting downstream genes known to sustain HSCs quiescence like SOCS3, JUNB, and DUSP2. Furthermore, we found that this network was enriched for genes with decrease expression in LGA compared to CTRL, supporting transcriptional consequences of these epigenetics alterations. Finally, leveraging both single-cell resolution of our transcriptomics data and in vitro differentiation analysis, we found a reduce ability for LGA HSC to stay quiescent/undifferentiated in response to stimulations.

Together, this single-cell genomics integrative approach supports that human fetal overgrowth affects HSC quiescence signaling via epigenetic remodeling.

With my second model, I leveraged single-cell transcriptomics to investigate the role of BIN1, the 2 nd most AD associated gene, on human brain models. We investigated the cellular effect of BIN1 deletion on both 2D neuronal culture and 3D cerebral organoid derived from human iPSC. We found that BIN1 loss-of-function leads to specific transcriptional alterations in glutamatergic neurons, resembling to the one found in AD brains, involving several genes associated with calcium homeostasis, ion transport and synapse function. Using functional assay, we found that calcium homeostasis and neural networks activity were dysregulated in BIN1 deleted brain models, and that BIN1 was able to interact with voltage-gated calcium channel Cav1. Pharmacological calcium channel blocker was able to partially rescue BIN1 mediated neuronal activity dysregulation supporting important BIN1 role in calcium channel regulation. These single-cell approaches have allowed to show neuronal specific Remerciements/ Acknowledgement 9 Remerciements/ Acknowledgement Ces trois années de thèse ont été une expérience exceptionnelle pour moi, autant d'un point de vue scientifique que d'un point de vue humain. J'ai appris énormément sur le plan technique, scientifique, mais aussi personnel, et cela grâce aux diverses et multiples interactions que j'ai pu avoir avec chacun d'entre vous tout au long de ces 3 années. Pour cela, je vous en serais eternellement reconnaissant. Tout d'abord, un grand (grand) merci au Dr Fabien Delahaye, qui été le directeur de thèse que beaucoup aimerait avoir, d'un soutien indéfectible, arrangeant, à l'écoute et authentique. Nos interactions, riches et variées, scientifiques ou non, ont largement contribué à mon épanouissement et au fait que je n'ai pas vu passer ces 3 années. A l'heure où j'écris ces lignes, je ne pense ne pas avoir totalement réalisé à quel point cette relation était unique, j'espère qu'elle continuera pour de nouveaux projets scientifiques. Merci pour tous. Un grand merci également au Pr. Philippe Froguel de m'avoir permis d'intégrer son laboratoire puis partagé son expérience, expertise et vision de la recherche. Vous m'avez toujours donné de bon conseils pour la thèse ou l'après thèse. L'apport de votre recul et encouragements à certain temps clé de la thèse ont été très important

Abstract

6 alteration of BIN1 deletion, and functionally validate role of BIN1 in calcium homeostasis related neuronal activity, while highlight its potential role in AD pathogenesis.

Résumé 7

Résumé

L'émergence des approches de génomiques, en particulier depuis l'apparition des technologies de séquençage à l'échelle de la cellule unique (SGC), nous a permis de caractériser des mécanismes physiopathologiques à une résolution jamais atteinte auparavant. Le SGC a mis en évidence l'hétérogénéité cellulaire intra-tissulaire ainsi que son influence sur le développement des maladies, et ouvert de nouvelles perspectives vers une meilleure compréhension de ces mécanismes. Dans ma thèse, j'ai mis en oeuvre de nouvelles stratégies d'analyse permettant l'intégration de ces données à l'échelle de la cellule unique dans deux modèles : la programmation épigénétique précoce des cellules souches et progéniteurs hématopoïétiques (CSPH), et l'étude du gène BIN1 dans la maladie d'Alzheimer.

Dans mon premier modèle, j'ai exploité la technologie de SGC pour étudier l'influence d'un excès de croissance gestationnelle (macrosomie) sur la plasticité des CSPH. Nous avons caractérisé les conséquences transcriptionnelles et fonctionnelles de l'altération de la méthylation de l'ADN observées chez les nouveau-nés macrosomes (NNM). Pour cela, nous avons intégré des données épigénétiques transcriptionnelles, et fonctionnelles. Nous avons découvert que l'hyperméthylation de l'ADN chez les NNM était associée à un réarrangement de la chromatine dans les cellules souches hématopoïétiques (CSH), touchant spécifiquement les facteurs de transcription EGR1, KLF2, et KLF4 connus pour soutenir la quiescence des CSH et réguler leur activation. Ce réseau de facteurs de transcriptions inclut notamment SOCS3, JUNB et DUSP2, et est enrichi en gènes dont l'expression est réduite chez les NNM, suggérant que les altérations épigénétiques ont des conséquences sur l'expression de ces gènes. Enfin, grâce à la résolution à la cellule unique de nos données, et à l'analyse de la différenciation in vitro des CSH, nous avons constaté une capacité réduite des CSH à rester quiescentes/indifférenciées en réponse aux stimulations chez les nouveau-nés macrosomes. Notre approche intégrative s'appuyant sur l'étude des différents facteurs régulant l'expression génique à l'échelle de la cellule unique nous a permis de confirmer que l'excès de croissance foetale affectait la signalisation régulant la quiescence des CSH par le biais d'un remodelage épigénétique.

Dans mon deuxième modèle, j'ai exploité le SGC pour étudier le rôle de BIN1, le deuxième gène le plus associé à la maladie d'Alzheimer, sur des modèles de cerveau humain. Nous avons étudié l'effet de la délétion de BIN1 dans une culture neuronale en 2D et sur un organoïde cérébral en 3D tout deux dérivés de cellules souches pluripotentes induites (iPSC) humains. Nous avons découvert que la perte de fonction de BIN1 entraînait des altérations transcriptionnelles spécifiquement dans les neurones glutaminergiques, ressemblant au changement d'expression trouvé chez les individus affectés par la maladie d'Alzheimer. Ces gènes sont fortement associés à la régulation du calcium dans les neurones, Résumé 8 ainsi qu'au transport ionique et à la fonction des synapses. En utilisant des tests fonctionnels in vitro, nous avons découvert que la régulation du calcium et l'activité des réseaux neuronaux étaient altérés dans les modèles cérébraux ou BIN1 était inactivé, et que BIN1 était capable d'interagir avec le canal calcique Cav1. Le blocage de ce canal par un inhibiteur pharmacologique permet d'empêcher partiellement l'altération de l'activité neuronale médiée par BIN1, soutenant son rôle important dans la régulation des canaux calciques. Cette approche de SGC a permis de mettre en avant une altération spécifique des neurones due à la délétion de BIN1, et valider fonctionnellement le rôle de BIN1 dans l'activité neuronale liée à la régulation du calcium intracellulaire, tout en soulignant son rôle potentiel dans la pathogenèse de la maladie d'Alzheimer.

Remerciements/ Acknowledgement 10 Pasteur. Sans vous, la plateforme de single-cell n'aurait pas eu la même convenue. Merci également au Dr Sylvianne Pied et le reste de son équipe pour son aide technique et matériel notamment pour le partage de la salle de culture.

Une thèse serait dure à mener sans le soutien de bons amis qui comprennent ce que tu vis.

Lucas mon compère des « presques » premières heures, tu as été un soutient énorme et devenu un ami, très rapidement. Cette connexion unique que l'on a et nos diverses discussions scientifiques, (géo)politiques ou philosophiques (et même économique !) y ont largement contribué et m'ont apporté recul et légèreté nécessaire pour apprécier cette aventure. Merci d'être entré dans ma vie.

Vincent, tu es arrivé il y a seulement un an, et pourtant j'ai l'impression qu'on se connait depuis 10 ans. Merci énormément pour tous ces moments partagés avec toi et Lucas, à l'open space étudiants ou ailleurs (surtout ailleurs), pour ta fraicheur, finesse d'esprit et d'humour, mais également pour ton soutient dans les moments plus stressant. Je suis fier de te compter parmi mes amis.

Erwan, Dolores, vous êtes les suivants, évidemment. Merci à vous aussi, pour votre gentillesse, sympathie et folie qui nous a si facilement réunis, pour ces moments où l'on a chanté à tue tête au karaoké, ou où l'on a escaladé des montagnes avec plus ou moins de succès.

Une attention également à toi Nawel, la battante au grand coeur, même si l'on ne se voyait pas tous les jours, moi qui était à Pasteur la plupart du temps, tu m'as toujours apporté ton soutient et écoute dès les premiers mois de thèse, et je t'en remercie.

Ma thèse n'aurait également pas eu la même saveur sans mes compères de Pasteur, alors merci Inès, Jérémy, pour votre soutien et pour les nombreux moments de détentes que l'on a pu partager à l'Alchimiste ou ailleurs.

Merci également à toi Théo, copain de thèse en bioinformatique devenu «el presidente » de Bioaddoct. On n'a pas eu beaucoup de temps pour se voir la dernière année, mais ton soutien lors des deux premières années m'a été très précieux.

La famille c'est très important pour surmonter cette épreuve, et heureusement, j'en ai une formidable. Merci du fond coeur Maman, Papa, Amandine, pour votre soutien indéfectible même dans les moments les plus durs, je suis fier d'avoir une famille comme vous et tellement reconnaissant. Je vous aime.

. Adult chronic diseases

Adult chronic diseases (ACDs) are chronic diseases whose onset increase with age, leading to progressive and permanent consequences. ACDs are multifactorial, relying on a detrimental interplay between (epi)genetics and environmental factors in the context of aging. Not restricted to genetic heritage and current environment, factors of ACDs also include epigenetics mechanisms and past exposure. ACDs encompass more than 90 diseases with the most important in term of incidence and mortality being cardiovascular diseases (CVD), cancer, Alzheimer's diseases (AD), and type 2 diabetes (T2D) 2,3 . ACDs are interconnected, i.e. one can be a risk factor of another (for example type 2 diabetes with cardiovascular diseases 4 ) and share common features still important gaps remain in our understanding of ACDs etiology. I will present in this thesis how genomics approaches help us identify factors involved disease etiology focusing on T2D and AD, two major ACDs that I have been studying.

I.1.a. Type 2 diabetes

Type 2 diabetes (T2D) is a form of diabetes characterized by a hyperglycemia caused by a relative lack of insulin secretion by pancreas on a context of age and obesity associated insulin resistance. It is diagnosed by blood test if during two occasions fasting plasma glucose is over 7mmol/L or if plasma glucose is over 11.1 mmol/L two hours after a glucose tolerance test. One major risk factor of T2D is obesity, still evidence support strong genetics and epigenetics influences. T2D is responsible for severe complications such as heart disease and stroke, with half of diabetic people dying from CVD but will also impact eyes, kidneys, and nerves leading blindness or amputation 5 . With the increasing aging population in both wealthy and low income/middle-income countries, according to WHO diabetes will be the seventh leading cause of death in 2030 5,6 .

I.1.b. Alzeihmer's disease

Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive neuronal degradation in the brain associated with memory and cognitive loss. Even if the definitive diagnosis can be performed only after brain autopsy, cognitive test and PET scan help to have a clinical diagnosis of the disease. It is the cause of 60-70% of cases of dementia and are one of the major causes of disability and dependency among older people globally. First symptoms are short term memory loss and inability to acquire new information as the results of the reduce neuronal plasticity 7 . Cognitive and motor function progressively decline after years as the consequence of neurotoxic aggregates INTRODUCTION 18 spreading across the brain. While the causal mechanisms remain poorly understood, several environmental and genetics factors increase the risk to develop AD. A major genetics risk is APOE4, found in ~60% of AD career, while in 15% of the global population 8,9 .

I.2. What is Genomics?

Genomics is a recent field in biology corresponding to the study of biological mechanisms at genome A wide level rather than at gene level. Genomics study the genetics information encoded in DNA of an organism, their interrelations and influence on the organism. Compared to previous targeted approaches, genomics allows the unsupervised discovery of genes or other molecular elements involved in a physiological or pathological condition. In the context of ACDs, which are polygenic diseases, i.e. their development is dependent on several genetics risks, genomic research comes into its own to decipher these different genetics factors and interaction. It initiates through the development of high-density DNA micro-arrays 25 years ago allowing the simultaneous interrogation of thousands of genes 10 . Then, high-throughput sequencing rapidly emerged enabling measurement of millions of genetics element in one assay 11 . Several 'omics' field are derived from genomics focusing on a downstream or parallel molecular layer, including transcriptomics, studying RNAs, epigenomics, studying the epigenome, or proteomics, studying proteins. However, advance in the specific field largely depend on the technology available and our ability to analyze them. In this thesis, I will mainly focus on genomics, transcriptomics and epigenomics approaches.

I.2.a. Transcriptomics

Transcriptomics study the set of RNAs produce by a cell or a population of cells in a given condition. The gene expression profile, i.e. the transcriptome, is tissue or cell type specific allowing specific proteome expression and therefore cellular activity. Because RNAs, compared to proteins, can be easily isolated and sequenced, transcriptomics analysis is a method of choice to study these cellular activities or assess role of genes in physiological or pathological conditions. This is one of the most used approaches in functional genomics, studying the role of genes in an organism, because of its mature technologies and wide application in fundamental or translational studies. They are essential to study the functional impact of a disease in a tissue/ cell type, as well as deciphering the cellular response to a treatment, useful notably for drug screening or understand drug resistance mechanism [12][13][14] . They are also commonly used in combination with microarray-based genotyping to study tissue specific impact of genetics variants on gene expression (known as eQTL for expression Quantitative Traits Locis). The latter are largely used to investigate the putative impact of risk loci on A The genome is all genetics information of an organism or a population.

INTRODUCTION

19 gene expression at tissue / cell type level and thus give biological insights on genetics risk and diseases mechanisms 15 . The main transcriptomic approach was DNA microarray, but was now largely outperformed by RNA sequencing (RNA-seq), allowing unsupervised assessment of gene expression profile in a tissue or in immune-phenotypically defined cell type.

I.2.b. Epigenomics

The epigenomics approaches allow the study of the epigenome. Epigenetics mechanisms are critical aspect in multicellular organism to allow cell specialization and identity. Epigenetics mean 'above' genetics, and was first conceptualized by Conrad Waddington in 1956, when he succeeded to demonstrate inheritance of characteristic in a population in response to an environmental stimulus 16 , showing existence of mechanism of inheritance 'above' standard genetics. What is consider as "epigenetics" has largely evolved since then. Even if is still debated, it can be defined as "all molecular or structural change that stably regulate expression of genes without altering DNA sequence". They are for the most part stable across cell division, allowing cell identity, differentiation and related specific gene expression profile. They allow also cells to adapt to a specific environment and can therefore testify about past environmental exposure with long term functional consequences on cell activity. Therefore, they have a central role in disease susceptibility by mediating the long-term consequences of current or past exposure.

In the DNA landscape, epigenetics mechanisms mainly include the CpG methylation and histone marks modification (e.g. specific lysine acetylation or methylation of histone tails) which, by their coordinated remodeling regulate chromatin accessibility to DNA-binding protein like transcription factors (TFs), and activity of the transcriptional machinery leading to the control of gene transcription. Epigenetics mechanisms can also, depending on the definition, include non-coding RNA activity like miRNAs, which targets specific coding transcripts, and regulate their expression by reducing their stability (figure 2).

I.2.b.i. DNA methylation

Cytosine, by their chemical structure, can be methylated in the 5 th position of the pyrimidine ring, forming 5-Methylcytosine, or 5mC. In mammals, this methylation mainly occurs in the CpG context, a cytosine followed by a guanine. Transfer of methyl group to Cytosine is catalyzed by DNA methyltransferase (DNMT) enzymes DNMT1, and DNMT3 family. DNMT1 is responsible of maintaining DNA methylation pattern across cell division. DNMT3 family, including DNMT3A and DNMT3B are involved in de novo DNA methylation. Around 75% of CpG are methylated across the human genome 17 . Due to their chemical resemblance with Thymine, they can mutate by error during replication, which have led during evolution to a global CpG depletion. However, certain CpGs rich region have been INTRODUCTION 20 conserved, called CpG island which have an important role in regulating transcription. They are mainly un-methylated and located in gene promoter regions. Methylation of CpG island has been shown to repress transcriptional expression through two putative independent mechanisms: i) the physical constraint to the transcriptional machinery 18 , and ii) the recruitment of methyl-CpG-binding domain proteins (MBDs) which can recruits others epigenetics modifiers like histone modifiers, leading to chromatin conformation change 19 . DNA methylation also plays a key role in repressing transposable elements and are found in gene body of highly transcribed genes where they could regulate splicing and repress activity of cryptic intragenic transcriptional units [20][21][22] . Outside of promoter and gene body region, DNA methylation could have a TF and context dependent role in promoting or repressing the TF binding in cis regulatory element like enhancer 23 . The main approach to study DNA methylation is genome wide DNA methylation microarray, assessing methylation in hundreds of thousands of CpG sites at genome scale, but approach based on sequencing are also widely used especially to study more targeted regions or focus on a specific population or disease model.

I.2.b.ii.

Histone modification DNA roll up on nucleosomes formed by complexes of proteins called histones, that allows for compaction of the DNA pellet. Several posttranslational histone modifications can alter their affinity to DNA modulating its accessibility to transcriptional machinery. Epigenetics modification of histones are cell type specific and are mainly involved the acetylation or methylation of lysine located in the histone tails. Histone marks are regulated by different epigenetic modifiers enzyme and structure the epigenetics landscape. Each histone modification has a specific role, allowing specific epigenetics readers recruitment and thus regulatory activity [24][25][26] . For example, H3K27ac (acetylation of the Lysine in the 27 th position of the Histone H3) are enriched in enhancer allowing their activation. H3K4Me3 are enriched in promoter and activate transcription, while H3K9Me3 repress them. H3K36Me3 are enriched in gene body of transcriptionally active gene. These histone marks are remodeling through differentiation allowing cell specific chromatin profile and associated transcriptional program.

Different consortia were created aiming to generate cell type specific epigenomics data to better understand genomics context, like the Roadmap Epigenomics Program which regroup more than 2800 cell type specific epigenomics data 27 . The main approach to study histone marks profile is ChIPsequencing (ChIP-seq) combining Chromatin ImmunoPrecipitation (ChIP) and high throughput sequencing.

I.2.b.iii. Non coding RNAs

Non-coding RNAs include all RNA, which are not translate into protein. They principally regulate or modify expression of protein coding RNAs but can also have a direct role in cells activity.

The main non-coding RNAs regulating protein-coding gene expression are miRNAs and long non-coding

INTRODUCTION

21 RNA (lncRNA). Micro-RNAs (miRNAs) are single stranded RNA of about 22 nucleotides that regulate gene expression through RNA silencing. MiRNA is complementary to a sequence of the targeted gene allowing its binding to the mRNAs and that silenced its translation. About 2000 miRNAs were identified in the human genome and have been shown to regulate 60% of the human coding genes [28][29][30] . lncRNAs are non-coding RNA with size greater than 200pb and have diverse activities, including regulation of gene transcription, epigenetic marks regulation, and post transcriptional regulation. The most known lncRNA is Xist, which inactivate the 2 nd X chromosome in female placental mammals through irreversible chromatin modifications 31 . Non-coding RNAs can be studied using (deep) RNA sequencing, but microarrays also exists, allowing notably to profile miRNAs in large cohorts, even if suffer from quantification issues 32 . First prominent genome wide assay was based on DNA microarray, allowing interrogating of thousands of genetics elements in one assay. DNA microarray has largely evolved since its birth in the 90's 33 , but principle remain similar: thousands of oligonucleotides are bound to a surface and used to measure relative concentration of labeled nucleic acids. These measures are allowed thanks to the complementary sequence hybridization and subsequent quantitative detection of hybridization B https://www.hematology.org/research/ash-agenda-for-hematology-research/epigenetic-mechanisms

INTRODUCTION 22
events. Recent microarrays are based on microbeads cover by multiple copies of an oligonucleotide probe, which span hundreds of thousands of microwells, link to optic fibers allowing fluorescence detection. Today, microarrays are still largely used for genotyping allowing genome wide association studies (GWAS) or to profile DNA methylome in large cohort allowing epigenome wide association studies (EWAS).

I.3.a.ii.

GWAS and genetics risk

Genome wide association studies (GWAS), study association between genetics variant and a phenotypic trait. GWAS used large cohort of case (possessing the certain phenotype/disease) and control individuals with genome wide assay to associate genetics variants to the specific trait. They allow identifying variants, and by extension genes, involved in a multifactorial /polygenic disease like ACDs.

For example for T2D, 245 independents variants and 18 putative causal genes have been identified in the most recent meta-analysis of 32 GWAS 34 . This study has highlighted how increases sample size and variants diversity affects discovery of causal T2D risk alleles, giving then new insight into the T2D genetics mechanisms as well as clinical benefits. For AD, the most recent meta-analysis have identified a total of 75 risk loci and around 100 putative causal genes, with strong enrichment for genes express in immune related tissues, and lipid related processes 35 .

While GWAS are important to identify genes involved in the disease and to estimate individual genetics susceptibilities, they do not, or only partially, take into account the environmental and epigenetics component in diseases development. Age, BMI, and sex are often the only environmental factors integrated in GWAS model limiting ability to find gene-environment interactions. Other limitations are that GWAS discovery is generally limited to frequent variants (>5% minor allele frequency; MAF) present in genome wide array (typically 1.8M variants with the 6.0 affymetrix array), but methods are used to impute genotype for variant with <1% frequency in population. Furthermore, the power of GWAS is highly dependent to cohort size. To tackle this limitation, meta-analysis of several GWAS is frequently performed, and more and more studies tend also to assess the variantvariant or gene-gene interaction on disease risk using notably polygenic risks score (PRS), which can considerably increase discovery power and highlight multigenics effect on diseases development [36][37][38] .

In addition to identify genetics risk and genes involved in diseases, GWAS allow also to assess causality between two traits using mendelian randomization. Mendelian randomization studies leverage the fact that genetics variants are randomly distributed across the population to assess effect of one trait (e.g. blood cholesterol level) on another (e.g. having T2D). They used for that genetics variants influencing the first trait (e.g. genetics variants reducing blood cholesterol) to assess if

INTRODUCTION

23 individuals carrying such genetics variants have less or more risk to have the second trait (e.g. having T2D). For example, for AD, GWAS have allow the study of the causal relationship between cognitive related traits and AD, as well as identified a protective effect of cognitive ability and educational attainment on AD risk 39 . However, such studies are limited by the availability of GWAS of the putative causal traits as well as presence of enough independent causal genetics variants in the population. Yet, to validate the causality of an environmental or (epi)genetics variable, interventional studies are needed such as randomized controlled clinical trial, in vitro studies and/or in vivo models.

I.3.a.iii. EWAS and epigenetics mechanism

Epigenome wide association studies (EWAS), accordingly to GWAS, study the associations between epigenetics factors and a phenotypic trait. Most of the EWAS are based on genome wide DNA methylation assay, interrogating association between CpG methylation site and disease. This is typically performed thanks to methylation microarrays, like the Infinium MethylationEPIC interrogating 850k methylation sites. These studies aim to identify disease related epigenetics biomarkers and associated biological pathways. For example, in T2D, EWAS on CD4 + T cells has allowed the identification of a CpG methylation within the ABCG1 gene associated with blood insulin and insulin resistance 40 . In peripheral white blood cells, 798 CpGs were associated with insulin resistance 41 . In a study of obesity, EWAS have found HIF3A methylation in adipose tissue and blood cells, and SOCS3 methylation in blood cells, as the most obesity associated genes methylation 42,43 . In AD brain, cross cortex meta-analysis of EWAS study have found 220 CpGs methylation associated with neuropathology targeting 121 genes 44,45 . Some of these association are brain region specific with notably CpG sites link to ABCA7 gene and HOXA5/HOXA3/HOX-AS3 cluster in the superior temporal gyrus region, while CpG sites link to MCF2L gene in the inferior frontal gyrus region. The first method to sequence DNA was developed by Frederick Sanger and colleagues in 1977 46 . The original Sanger method relied on an in vitro targeted DNA replication of a DNAs sample, using an oligonucleotide primer, a DNA polymerase, classical deoxynucleotides (dNTPs), and a small amount of a modified dideoxynucleotide (ddNTP). Before the DNA replication, DNA sample is divided into 4 separate reactions to incorporate one of the 4 ddNTPs (ddATP, ddGTP, ddCTP, or ddTTP) that when incorporated into the nascent DNA strand block the new strand synthesis and generate a panel of single strand DNA fragments of different size, which can then be separated by electrophoresis. The DNA bands are then visualized using classical autoradiography or UV methods and the DNA sequence of the interrogated genomic region is determined based on these bands (figure 3). Derivatives of this method are still used today because of its very low error rate 47 . This method was used through the Human Genome Project. This unique initiative launched in October 1990 and completed in April 2003, aimed to sequence the entire human genome costing around 3 billions of dollars 48 . Currently, a whole genome can be sequenced in one day for an approximate cost of ~1000$ [START_REF] Behjati | What is next generation sequencing?[END_REF] . This new generation sequencing (NGS) technology, also called highthroughput sequencing, are now largely used across scientific community with various application in genomics, epigenomics and transcriptomics research.

I.3.b.ii. Principle of high throughput sequencing

Several high-throughput sequencing methods exist but the widely used are synthesis-based method, as the one developed by Illumina [START_REF]Next-Generation Sequencing (NGS) | Explore the technology[END_REF] . They allow sequencing of hundreds of millions of DNA fragments of 100-300 bp in a massively parallel way. NGS is used for a variety of applications including the whole genome sequencing (WGS), the transcriptome sequencing (RNA-seq) and the epigenome sequencing (Bisulfite-Seq or ChIP-seq). For RNA-seq, a preliminary step of retro-transcription is necessary to convert RNA into DNAs.

The method consists of 4 steps: the DNA preparation, the clusters generation in a flow cell, the sequencing by synthesis, and the sequence mapping on a reference (figure 4). The DNA preparation include the DNA fragmentation into 100-300pb fragment and ligation with adaptors including the sequencing by synthesis relies on nucleotide-by-nucleotide synthesis of the complementary strand of the amplified DNA fragments. This synthesis is based on cycle of 3 steps. A mix of dATPs, dGTPs, dCTPs and dTTPs chemically modified to contain a specific fluorescent tag, which block integration of subsequent dNTPs. The polymerase adds one of this modified dNTPs to the nascent strand, which block the synthesis (i). Then, the nature of the nucleotide newly integrated is determined by reading the fluorescence (ii). Finally, the fluorescent tag is removed from the new nucleotide (iii), and the synthesis can continue (step i), until the defined number of cycles wanted (typically 100 or 250pb). The fluorescence is read simultaneously for all clusters at each cycle generating finally the composition of nucleotides of hundreds of millions of fragments simultaneously. These millions of sequences, called "reads" are then aligned on a reference genome or transcriptome depending on the usage, and allow variety of downstream analysis including mutation analysis, gene expression measurement, DNA methylation and histone marks profiling according to the assay/ starting material.

INTRODUCTION

26 The main advantage of NGS compared to microarray-based assay are their ability to measure a genetic information in an unsupervised way. This allows the identification of new biological features in genomics and related research.

I. 3.b.iii. Whole genome sequencing to discover new mutations Whole-genome sequencing (WGS) allow the detection of new or rare variants that are not present in microarrays. An alternative of it is whole-exome sequencing (WES), targeting coding region to increase discovery power of diseases-associated rare variants, which are likely to have greater impact on genes function.

For GWAS needed large cohort, this is quite rare to use WGS/WES because of their cost and amount of data produced. However, some studies have done this effort to understand contribution of rare variant in ACD inheritability. In a study focusing on its question for T2D, no evidence of significant contribution of rare variants was found explaining T2D inheritability [START_REF] Fuchsberger | The genetic architecture of type 2 diabetes[END_REF] . To contrast, a recent WGS based GWAS have found two novel AD associated genes region, DLG2 and DNTB. These AD associated genes region was found thanks to association with rare variants using a sliding window approach across the genome [START_REF] Prokopenko | Region-based analysis of rare genomic variants in wholegenome sequencing datasets reveal two novel Alzheimer's disease-associated genes: DTNB and DLG2[END_REF] .

Beyond inherited genetics variants, WGS can also be used to identify somatic mutations.

Somatic mutations are DNA mutation within non-germinal cells due to DNA replication error or incorrect DNA repair. It's estimated to occur at a rate of 2-10 mutations per diploid genome per cell division [START_REF] Martincorena | Somatic mutation in cancer and normal cells[END_REF] leading to mosaicism within each individual, i.e. cells of an organism will not have the exact same genomic information. Using a greater sequencing depth, somatic mutation can detected using WGS, which can be helpful for ACDs research. Indeed, somatic mutation is a well-recognized factor in cancer [START_REF] Martincorena | Somatic mutation in cancer and normal cells[END_REF] but play also an important role in others ACD [START_REF] Jaiswal | Age-Related Clonal Hematopoiesis Associated with Adverse Outcomes[END_REF][START_REF] Lodato | Aging and neurodegeneration are associated with increased mutations in single human neurons[END_REF] . For example, somatic-mutation-driven clonal hematopoiesis and clonally expanded chromosomal alterations in blood was associated with an increased incidence of CVD and T2D [START_REF] Jaiswal | Age-Related Clonal Hematopoiesis Associated with Adverse Outcomes[END_REF][START_REF] Jaiswal | Clonal Hematopoiesis and Risk of Atherosclerotic Cardiovascular Disease[END_REF][START_REF] Bonnefond | Association between large detectable clonal mosaicism and type 2 diabetes with vascular complications[END_REF] . The causal role of somatic-mutation-driven clonal hematopoiesis has been functionally validated for one of them, the TET2 loss-of-Function mutation, which drive clonal hematopoiesis while aggravate insulin resistance in aged mice and in obese mice [START_REF] Fuster | TET2-Loss-of-Function-Driven Clonal Hematopoiesis Exacerbates Experimental Insulin Resistance in Aging and Obesity[END_REF] .

In brain it was shown that neuronal somatic mutation increase with age and are associated with neurodegenerative disorders [START_REF] Lodato | Aging and neurodegeneration are associated with increased mutations in single human neurons[END_REF][START_REF] Park | Brain somatic mutations observed in Alzheimer's disease associated with aging and dysregulation of tau phosphorylation[END_REF] . Putative deleterious somatic mutations was found in 27% of AD brains and particularly enriched for genes contributing to AD pathogenesis Bulk RNA-sequencing (RNA-seq) can assess the entire genes expression profile (i.e. the transcriptome), of a tissue or a cell type, permitting to characterize tissue specific genes activity but also disease associated gene expression. To do so, RNAs are isolated from a mix of cells from a tissue or a cell line, and retro transcripts into cDNAs for NGS based sequencing. It was used to build atlas of tissue expression in differents species [START_REF] Söllner | An RNA-Seq atlas of gene expression in mouse and rat normal tissues[END_REF][START_REF] O'rourke | An RNA-Seq based gene expression atlas of the common bean[END_REF][START_REF] Leader | FlyAtlas 2: a new version of the Drosophila melanogaster expression atlas with RNA-Seq, miRNA-Seq and sex-specific data[END_REF] , notably in human with the Genotype-Tissue Expression (GTEx) project 63,[START_REF]The Human Protein Atlas[END_REF] or with the Human Protein Atlas program [START_REF] Karlsson | A single-cell type transcriptomics map of human tissues[END_REF] . Furthermore, RNA-seq is also widely used as first approach to assess impact of a disease in a tissue. It allow to identify differentially expressed genes (DEGs) in the diseases condition compared to an healthy control condition, highlighting putative biological process or signaling pathways altered or involved in the disease. It is also the primary analysis to identify function of gene in physiological or pathological conditions.

Indeed, RNA-seq is also used in first approach to assess the functional impact of modifying or deleting a studied gene in a specific tissue. In ACDs research, RNA-seq data are also useful to assess impact of a genetics variant on gene expression, either through eQTL studies [START_REF] Schwartzentruber | Genome-wide meta-analysis, finemapping, and integrative prioritization implicate new Alzheimer's disease risk genes[END_REF] (see part I.3.a) or direct in vitro/in vivo characterization using genome editing methods [START_REF] Tcw | Cholesterol and matrisome pathways dysregulated in astrocytes and microglia[END_REF] (see part I.4). RNA-seq can also be used as readout in drug screening study 14,[START_REF] Keenan | The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations[END_REF] . Drug screening employ a large bank of drugs or molecular compound to characterize impact of these compounds on cells, which can be used for drug discovery or drug repositioning. This approach was largely used for personalized cancer treatment [START_REF] Xia | A cross-study analysis of drug response prediction in cancer cell lines[END_REF] , but similar strategies was also deploy for AD [START_REF] Wang | Transformative Network Modeling of Multi-omics Data Reveals Detailed Circuits, Key Regulators, and Potential Therapeutics for Alzheimer's Disease[END_REF] . Then, RNA-seq have broad application and was therefore a major advance in functional genomics research. However, the major drawback of this technique if the lack of the cellular heterogeneity consideration that we will discuss in next parts.

To note, RNA-seq is also used for epigenomics studies, because having the ability to detect non coding RNA, notably miRNAs that are important in diseases mechanisms and are promising circulating biomarkers [START_REF] Cogswell | Identification of miRNA Changes in Alzheimer's Disease Brain and CSF Yields Putative Biomarkers and Insights into Disease Pathways[END_REF][START_REF] Calin | MicroRNA signatures in human cancers[END_REF][START_REF] Cheng | Circulating miRNAs: Roles in cancer diagnosis, prognosis and therapy[END_REF] .

I.3.b.v. Sequencing based epigenomics studies

High throughput sequencing is also largely used to characterize the epigenome, its dynamics across tissue and diseases and its ability to regulate gene expression. The main methods to investigate epigenome using NGS is Bisulfite seq, ChIP seq, and ATAC-seq.

Bisulfite-Seq allow whole genome DNA methylation profiling in a tissue. It is based on bisulfite treatment of DNA followed by sequencing. Bisulfite treatment convert non-methylated cytosine into thymine, allowing segregation of methylated Cytosine from unmethylated Cytosine by sequencing.

However, this technic requires a large amount of DNA material so is difficult to perform if the starting cells number are limited [START_REF] Ziller | Coverage recommendations for methylation analysis by whole-genome bisulfite sequencing[END_REF] .

INTRODUCTION 28

ChIP-seq allows genome wide Histone marks profiling but also profiling of DNA binding proteins on DNA. It is based on the immunoprecipation of chromatin using specific antibody followed by sequencing. Briefly, DNA and DNA binding proteins are first cross-linked using formaldehyde and DNA are randomly fragmented using restriction enzyme or DNA sonication. Then, DNA fragments containing histone marks or TF are pull down using an antibody recognizing this specific histone marks/TF (this is, chromatin immunoprecipation, or ChIP). These fragments are then sequenced and mapped onto a reference genome similarly to others high throughput sequencing based assay. Epigenomics assay using sequencing has allowed the discovery of several epigenetics mechanisms involved in ACDs. Bisulfite-seq has notably highlighted that aberant DNA methylation often occurs before cancer development, induced by different life events including acute infection, chronic alcohol consumption, or dysregulated inflammation, and can persist throughout all the lifetime of an individual, even if the carcinogenic factor is no longer present [START_REF] Takeshima | Accumulation of genetic and epigenetic alterations in normal cells and cancer risk[END_REF][START_REF] Hlady | Epigenetic signatures of alcohol abuse and hepatitis infection during human hepatocarcinogenesis[END_REF][START_REF] Lambert | Aberrant DNA methylation distinguishes hepatocellular carcinoma associated with HBV and HCV infection and alcohol intake[END_REF][START_REF] Takeshima | Degree of methylation burden is determined by the exposure period to carcinogenic factors[END_REF] . This approach has also showed the important role of DNA methylation as an alternative way to silence tumor suppressor genes, similarly to genetics. Such genome wide methylated DNA sequencing has also allowed to identify important methylation differences within T2D discordant monozygotic twins, with the stronger change being in MALT1 locus, a gene regulating insulin and glycemic pathway [START_REF] Yuan | An integrated epigenomic analysis for type 2 diabetes susceptibility loci in monozygotic twins[END_REF] . ChIP-seq has allowed the discovery of "super-enhancer" regions in cancer, regions enriched in H3K27ac histone mark allowing stable oncogene activation [START_REF] Lamoureux | Selective inhibition of BET bromodomain epigenetic signalling interferes with the bone-associated tumour vicious cycle[END_REF][START_REF] Lovén | Selective Inhibition of Tumor Oncogenes by Disruption of Super-Enhancers[END_REF] . In AD, such analysis has identified that H3K27ac and H3K9ac marks correlate with upregulation of chromatin and transcription related genes and contribute to amyloid-β42-driven neurodegeneration [START_REF] Nativio | An integrated multi-omics approach identifies epigenetic alterations associated with Alzheimer's disease[END_REF] . H3K9me3, mediating heterochromatin condensation, was also found enriched in AD brains, leading to downregulation of proximal genes mainly involved in synaptic transmission and plasticity [START_REF] Lee | Epigenome signatures landscaped by histone H3K9me3 are associated with the synaptic dysfunction in Alzheimer's disease[END_REF] . This assay has also identified that hyperglycemia led to important histone acetylation changes in specific genomics regions, which are associated to persistent expression of proinflammatory genes [START_REF] Pirola | Genome-wide analysis distinguishes hyperglycemia regulated epigenetic signatures of primary vascular cells[END_REF][START_REF] Brasacchio | Hyperglycemia Induces a Dynamic Cooperativity of Histone Methylase and Demethylase Enzymes Associated With Gene-Activating Epigenetic Marks That Coexist on the Lysine Tail[END_REF] Cis-eQTLs are used in complement of GWAS to functionally characterize disease risk loci, linking them to a gene with putative tissue specific impact. Some eQTLs studies have been designed to specifically characterized ACD related regulatory link and involvement of genetics modifications in T2D

and AD molecular mechanims [START_REF] Wang | Transformative Network Modeling of Multi-omics Data Reveals Detailed Circuits, Key Regulators, and Potential Therapeutics for Alzheimer's Disease[END_REF][START_REF] Varshney | Genetic regulatory signatures underlying islet gene expression and type 2 diabetes[END_REF] . In T2D, a study analyzed genomics and transcriptomics data from 112 islet samples associated with ATAC-seq based chromatin profile and showed that T2D risk alleles were enriched in islet specific enhancer and disturb the islet Regulatory Factor X (RFX) activity [START_REF] Varshney | Genetic regulatory signatures underlying islet gene expression and type 2 diabetes[END_REF] . In AD, a study regrouping 364 donors has characterized thousands of molecular changes and neuronal gene subnetwork associated to AD neuropathology or severity [START_REF] Wang | Transformative Network Modeling of Multi-omics Data Reveals Detailed Circuits, Key Regulators, and Potential Therapeutics for Alzheimer's Disease[END_REF] ). This number of tests reduces the statistical power of discovery, then a large population is needed to identify significant association after p-value correction for multi testing.

Other QTLs study can be performed, including methylation Quantitative Traits Loci (mQTLs), deciphering role of genetics variants in DNA methylation landscape [START_REF] Gibbs | Abundant Quantitative Trait Loci Exist for DNA Methylation and Gene Expression in Human Brain[END_REF] .

I.3.c.ii.
Others multi-omics integration (associate epigenomics with transcriptomics..)

Omics integration is not limited to QTL studies. Others association between different regulatory layers can be study to decipher molecular mechanisms behind a phenotype or a diseases development. Different methods exist to integrate multiple omics layer. These methods try to find correlation between these layers either with an unsupervised or supervised approach [START_REF] Huang | More Is Better: Recent Progress in Multi-Omics Data Integration Methods[END_REF] . Most frequent omics integration are DNA methylation with gene expression, or chromatin accessibility with gene expression, giving insight into epigenetics influences on gene expression but also gene regulatory network involved in physiological or pathological processes.

Epigenomic and transcriptomic data integration allows to correlate epigenetics change to gene expression to better understand functional consequences associated to epigenetic changes. This analysis can be named expression Quantitative Traits Methylation (eQTM) analysis in reference to eQTLs. Several DNA methylation and gene expression correlation have been found within T2D. For examples, DNA methylation of the insulin promoter and the PDX-1 gene were shown to be associated with reduced insulin expression and increased HbA1c levels in pancreatic islet of T2D patients [START_REF] Yang | Increased DNA Methylation and Decreased Expression of PDX-1 in Pancreatic Islets from Patients with Type 2 Diabetes[END_REF] . In AD, integration of DNA methylation and gene expression in multiple brain regions has allowed the discovery of genes epigenetically regulated in AD including ANKRD30B as well as several genes related to immunity and calcium homeostasis (figure ) [START_REF] Semick | Integrated DNA methylation and gene expression profiling across multiple brain regions implicate novel genes in Alzheimer's disease[END_REF] .

Others multi-omics integration can give insight into diseases development, including integration of gene expression with miRNA profile, histone marks, but also metabolomics or microbiota (i.e. metagenomics) [START_REF] Wang | Biomarker Identification through Multiomics Data Analysis of Prostate Cancer Prognostication Using a Deep Learning Model and Similarity Network Fusion[END_REF][START_REF] Narayana | Similarity network fusion for the integration of multi-omics and microbiomes in respiratory disease[END_REF] . This multi layers integration allows to characterize the whole molecular network involved in a specific phenotype (Figure 5). In AD brains, integration of transcriptomic, proteomic and epigenomics data have contributed to the identification of major epigenome reconfiguration including increase H3K27ac and H3K9ac associated to upregulation of concordant genes regulating transcription and histone marks as well as AD related pathways [START_REF] Nativio | An integrated multi-omics approach identifies epigenetic alterations associated with Alzheimer's disease[END_REF] . In addition to better understand disease mechanism, it was shown that multi-omics integration allows better patient stratification, diseases subtyping, and give insight into diseases subgroup specific molecular signatures [START_REF] Chierici | Integrative Network Fusion: A Multi-Omics Approach in Molecular Profiling[END_REF][START_REF] Ma | Integrate multi-omic data using affinity network fusion (ANF) for cancer patient clustering[END_REF] . For examples, integration of methylation, gene expression, and miRNAs has allowed to identify a set of multi-omics biomarkers associated with subtype of prostate adenocarcinoma with high risk of recurrence [START_REF] Wang | Biomarker Identification through Multiomics Data Analysis of Prostate Cancer Prognostication Using a Deep Learning Model and Similarity Network Fusion[END_REF] . In AD,

INTRODUCTION

31 proteomics, metabolomics, and lipidomics integration of cerebrospinal fluid in healthy and AD brain with different cognitive severity has identified new central nervous system pathway alteration in AD and contributed to a better AD prediction and associated cognitive decline based on four multi-omics molecular markers [START_REF] Clark | An integrative multi-omics approach reveals new central nervous system pathway alterations in Alzheimer's disease[END_REF] . siRNAs allow knock down (KD) of specific gene expression using RNA-induced silencing complex (RISC) cellular machinery. siRNAs, designed to be complementary to a specific mRNA region (mostly the 3' end untranslated region), is integrated in the cell by RISC and allow specific mRNA binding and cleaving. It is mostly used for transitory downregulation of expression but can also be stably transfected if using short hairpin RNA (shRNA) system. Pharmacological inhibitors are synthetized compound with a specific molecular structure, which, by resembling to natural substrate or ligand of an enzyme or receptor will interfere with the protein activity. Specific pharmacological inhibitors can be difficult to synthetized and can have off target effect limiting their application. Gene transfection using plasmid vector or lentivirus is used to induce expression of an exogenous gene in a cell. This approach is interesting to study the impact of gene overexpression or a specific mutation but can have limited physiological relevance.

To increase physiological relevance, genome editing methods are used [START_REF] Adli | The CRISPR tool kit for genome editing and beyond[END_REF] . Rather than adding an exogeneous gene, they allow to modify sequence of the endogenous gene or genetic element.

These methods mostly used targeted DNA double strand break (DSB) and endogenic homologous recombination-based DNA repair processes to edit genome(Figure 6). genomics sequences for gene knock-out (KO) or knock-in (KI) but can also be used for various other application when using a modified version of the system (Figure 7). For example, gene silencing (called CRISPR interference, or CRISPRi) can be performed with such assay by using Cas13 instead of Cas9, an enzyme that targets RNA instead of DNA. In the same way than genome editing, some epigenetics editing method have been developed

to study role of epigenetics elements. For DNA methylation, epigenetics drugs like S-Adenosyl methionine (SAM), the principal substrate of methyl group transfer can be used to modify DNA methylation but does not allow for targeted epigenetics modification . To get a targeted epigenetics editing, most promising approaches used the CRISPR system. They used inactivated Caspase like dCas9 coupled with epigenetics modifiers catalytic domain to edit specific chromatin marks. Notably, several

I.5. Main limit of classical genomics approaches

These genomics approaches cited above still suffered from a major gap: the consideration of cellular heterogeneity within a tissue. Indeed, these genomics assays are performed in bulk, i.e. with a RNAs/DNAs mix from several cells. This mix of cells is performed at tissue level or using presorted cells based on cell surface markers. Bulk approach was required to have enough genomics material to perform genomics library preparation for sequencing, but lead to the loss of crucial heterogeneity within this cell population. Notably, the biological insight of RNA-seq based studies is limited because it fails to explain from which cells (subpopulation) the effect observed come from. Similar objection can be raised for EWAS or epigenomics study. Epigenetics influence is cell type specific, then epigenetic change in a cell type cannot be generalize to others but rather highlight cell type specific epigenetics mechanism and role in disease development. Cellular heterogeneity is a key feature of multicellular organism. It allows asymmetric cell-tocell interactions, and emergence of complex functions and behaviors. The cell specialization relies on epigenetics remodeling, which give rise to a specific cell structure and activity. During development, this mechanism allows the formation of tissue, i.e. grouping of cells specialized in a specific task.

However, this cellular heterogeneity goes beyond the tissue level. Indeed, there is different cell type and cell states in each tissue allowing regulation of tissue functions. Cellular heterogeneity is even found within a same cell type according the cell cycle phase, the micro-environment, the cell to cell communication, but also according to somatic mutation and epigenetics mosaicism as seen in normal aging. Furthermore, the cellular plasticity, which allow organism to adapt to environmental change, is also an important factor of cellular heterogeneity. Cellular plasticity is the ability for cells, to change their activity, or to differentiate, in response to environmental cues. Adult stem cells, located in cellular niche across each tissue, can differentiate to regenerate tissue following damaged or for physiological turnover of cells. Differentiated cells itself can be reprogrammed into another cell type, a process called transdifferentation, allowing further organism plasticity to environmental exposure. For example, astrocyte can differentiate in neurons after brain injury [START_REF] Duan | Striatal astrocytes transdifferentiate into functional mature neurons following ischemic brain injury[END_REF][START_REF] Shen | Neurogenic effect of VEGF is related to increase of astrocytes transdifferentiation into new mature neurons in rat brains after stroke[END_REF] and white adipocytes can differentiate in brown adipocyte following cold exposure [START_REF] Barbatelli | The emergence of cold-induced brown adipocytes in mouse white fat depots is determined predominantly by white to brown adipocyte transdifferentiation[END_REF] . Then, accounting for intra-tissue heterogeneity in our genomics approaches appears crucial to better understand the biological system and how its dysregulation can lead to diseases development (Figure 8).

II.2. Cells heterogeneity alteration in diseases

Several evidence support the role of cellular heterogeneity or plasticity alteration in the early development of ACDs. Notably, the stem cells heterogeneity alteration is an important hallmark of aging and contribute to ACDs risk . This stem cells heterogeneity alteration is mainly driven by the clonal expansion of defective stem cells across time. Indeed, as exemplified in hematopoiesis, somatic mutations and epi-mutations I accumulate with age according a fitness advantage to certain clone compared to others. Such clonality directly affects the heterogeneity and plasticity of hematopoietic stem cells (HSC) niches driving hematopoietic dysfunction and increasing ACDs susceptibilities [START_REF] Jaiswal | Age-Related Clonal Hematopoiesis Associated with Adverse Outcomes[END_REF][START_REF] Jaiswal | Clonal Hematopoiesis and Risk of Atherosclerotic Cardiovascular Disease[END_REF] .

In T2D, imbalance between Beta cells and alpha cells in pancreas as well as Beta cells dedifferentiation lead to decrease insulin secretion and diabetes development [100][101][102] An important implication of cellular heterogeneity is the ability to develop asymmetrical cell to cell communication essential for the organism homeostasis and tissue synchronization. This cross talk can be altered and played an important role in ACDs. During atherosclerosis leading to CVD, the inflammatory cross talk between macrophage and endothelial cells failed to resolve and lead to accumulation of senescent lipid rich macrophages on the subendothelial space upon rupture 106 . In AD, defective cross talk between neurons, astrocytes and microglia appears primordial in AD pathogenesis especially in the Ab plaques maturation and propagation 107 . This cell-to-cell interaction further highlights the importance to consider cellular heterogeneity when studying ACDs. Considering cell to cell interaction adds complexity in our models but will improve identification of actionable targets. For example, all AD clinical trials focusing on treatment of astrocytes and microglial-mediated neuroinflammation have failed 108 suggesting that targeting only neuroinflammation is not sufficient and better understanding of the cells crosstalk involved in immune cells activation is needed. Recent studies suggest that microglial activation could be necessary to decrease neurons dysfunction and amyloid beta (Abeta) accumulation 105,109 . Complex cellular models integrating this cellular heterogeneity like organoid coupled with single-cell genomics assay appear then a first choice strategy to decipher these cross talks.

III. Single-cell genomics approaches, their interests and how to manage them

The intra-tissue heterogeneity plays a key role in cellular homeostasis, tissue function regulation, and disease development but was often disregarded due to the lack of available tools to characterize it. The recent emergence of single-cell genomics technologies allows us to tackle this limitation.

INTRODUCTION

III.1. Principle

Well-based assay used limiting dilution or micromanipulation to isolate one cell by well allowing specific barcoding of their RNAs. They allow for visualization and confirmation of cell isolation process, and are classically associate to Smart-seq protocol, a RNA-seq library preparation method, allowing full-length whole transcriptome sequencing 111 . However, such methods suffer from a limited number of cells that can be processed simultaneously.

Rapidly, FACS methods have been developed to reduce manipulation and time while allowing sorting of thousands of cells in plate. Such methods are classically associated with SMART-seq2

protocol, an improve protocol of Smart-seq allowing also full length transcriptome sequencing but with greater sensitivity 111,112 . A specificity that allows the interrogation of gene isoforms, or alternative splicing. This workflow is also useful when studying exome based somatic mutation, and can be used to perform lineage tracing experiments, based on spontaneous mitochondrial DNA mutation monitoring 113 . However, FACS based technics still have a limited cells throughput (of about 1000 cells) while requiring a large number of starting materials (>100k cells to good recovery). Furthermore, such assay is also expensive according to the large reagent volume it requires. Indeed, the critical steps of INTRODUCTION 39 such assay, the retro transcription, the tagmentation and the PCR, are performed in each individual well requiring large amount of associated enzymes and buffers.

Since 2015, a new isolation method has emerged allowing sequencing of thousands of cells in one assay 114 . This method is a microdroplet-based microfluidics approach. It allows cell isolation through an oil-based emulsion of water micro droplets containing all necessary materials to perform retro transcription and cell resolution RNA barcoding. This design reduces dramatically reagent volume and cost while increasing number of cells that can be simultaneously profiled. This technic, called Dropseq, was then commercialized by 10X Genomics and widely used across scientific community. This assay is not designed to interrogate full length RNA like Smart-seq2 methods, limiting splicing analysis / isoforms profiling as well as (somatic) mutation analysis.

Regardless of the cell isolation methods applied, the principle of single genomics methods remains the same (Figure 9). Cells are dissociated from a tissue, and isolated to allow the cell level barcoding of the genomics element of interest (RNA or DNA). If single-cell RNA-seq, is performed, the RNA is retrotranscripted into DNA, and the resulting barcoded DNA is sequenced according to classical NGS workflow. After mapping reads on the reference genome, we obtain a gene expression (or epigenetic) profile for every cell sequenced. These profiles are then compared between sequenced cells to identify the different cell subpopulations present in our original tissue.

III.2. Gain of resolution associated to single-cell approaches

As seen previously, genomics assays were usually performed in bulk, i.e. from a mix of cells leading to a loss of cell specific information. Therefore, only giving us access to an estimate of the predominant features from the whole cell population failing to highlight the cell type specific features and associated regulatory network (Figure 10). Bulk approaches also failed to assess if difference between two conditions reflects a change in cell population or a feature change within a same cell subpopulation which are two separate mechanisms essential to adequately understand disease etiology. Before the emergence of single cell technologies, the study of tissue heterogeneity was limited to cell surface markers-based phenotyping. Such studies are supervised relying on a limited number of cell surface markers chosen by researchers which is likely to affect the resolution of the study. With single cell genomics assay, gene or whole genome features are assessed in an unsupervised way allowing objective cell subpopulation definition and heterogeneity studies. Furthermore, molecular characterization of cell fate decision, an inherent unicellular process, was not possible with bulk genomics approach because only average cells states were captured. With single cell genomics, it is now possible to capture every cell state in the process of differentiation, allowing differentiation program characterization at transcriptomic and epigenomics level. Such gain of objectivity and resolution has already revolutionized our understanding of developmental processes and cell fate decisions 113,115 , but also of disease related cellular and molecular mechanisms 116,117 . For example, in a pioneer study in AD brains using single cell genomics, an AD specific microglial cell type was discovered (and molecularly characterized),a discovery that would not have been possible with previous supervised and/or bulk analysis 105 . This analysis has notably shown a disease specific cross talk between choroid plexus epithelium and brain astrocytes as well as oligodendrocytes and microglia in the brain of severe COVID19 patient 128 .

INTRODUCTION

Similarly, a disease related crosstalk between smooth muscle cells and fibroblast was observed in coronary artery disease 129 . Furthermore, a TNF-α mediated autocrine microglia activation as well as a

TGFβ2 mediated regulation of microglial activation by neurons was demonstrated at early stage of diabetic retinopathy 130 . In AD, researchers have also identified a TGF-β mediated overstimulation of perivascular fibroblast driven by other cerebrovascular cells 131 .

III.2.a.iv. Functional studies at single-cell resolution

Genetic editing coupled with scRNA-seq can decipher the role of genetic element in tissue function and disease development at cellular level, highlighting cell type specific transcriptional alteration and putative functional consequences. In a recent remarkable contribution, scRNA-seq was used both to identify T2D specific regulatory networks in pancreatic islet and a master regulator, BACH1, driving the metabolic inflexibility and endocrine progenitor/stem cell features of a T2D-specific subpopulation 132 . Authors then showed that a knockout of this master regulator reverse the T2D specific cellular features up to a non-diabetic phenotype. In another outstanding article 133 , APOE4, the main genetic risk of AD, has been induced in a mouse model of AD, and reveal that its selective removal in astrocytes was able to decrease AD signature in astrocytes but also in neurons, oligodendrocytes, and microglia.

Other advantage of scRNA-seq analysis is that we can leverage the single cell resolution to perturb several gene in one assay. Perturb-seq allows to study the function of several genes at the same time in a tissue and cell type specific manner 134 . Perturb-seq is based on pooled CRISPR screen

INTRODUCTION

43 with scRNA-seq read out. Dozens of sgRNA can be designed to target specific genes and transduced in limited dilution to have one or two sgRNA by cells, allowing single perturbation at single-cell resolution.

These transduced cells can then be processed through any high throughput scRNA-seq methods, but droplet based is more appropriate because it allows the sequencing of a greater number of cells increasing the resolution of the approach. This method was used to study the impact of 200 oncogenic variants on lung cancer cells 135 . The authors were able to classify variants into gain of function, loss of function or dominant negative variants and discovered that KRAS variants span a continuum of gain of function phenotype rather than a discrete functional alteration. Considering TFs perturbation, perturbseq enable the study of TFs downstream target genes in a cell type specific manner as demonstrated by a pilot study focused on TF regulating dendritic cells to lipopolysaccharides 134 . They also demonstrated the ability of this method to infer cell type specific TFs associated regulatory network.

scRNA-seq can also be used to assess the cell specific effect of a drug or a targeted therapy. In addition to assess the effectiveness on a specific cell type, scRNA-seq has the key advantage to also measure putative side effect or off target effect on other cell types. For example, drug use to mediate FOXO inhibition in pancreatic islet was shown to also induce dedifferentiation of both alpha and Beta cells 136 . In addition, effect of morphine in brain cell type was assessed showing an oligodendrocytes specific cell response never observed before 137 .

III.2.b. Single-cell epigenomics

In parallel of scRNA-seq, other single-cell genomics assays were developed focusing on other layer of the regulatory landscape, ranging from whole DNA sequencing to epigenomics assay. Even if some single-cell DNA methylation and Histone marks profiling were developed, because of their single molecules level, loss of material and contamination or measurement error can have a strong impact.

Then generating such data produce typically high noise and dropout rates (zero inflated data due to missing value) limiting their application 138 ScATAC-seq droplet-based method are similar to the one for scRNA-seq assay except that accessible DNA are captured instead of RNA (Figure 11). Before encapsulation, cells (or nuclei) are transposed using the mutant transposase tn5, which, as for classical bulk ATAC-seq method, cuts DNA in accessible region while adding a tag. After cell/nuclei encapsulation, this tagged accessible DNA fragments can be amplified with addition of a cell specific barcode. Mapping these CREs across cell type is then crucial to refine functional consequences of a variant or epigenetics alterations on gene expression. Indeed, a variant or epigenetic alteration like DNA methylation falling in pancreatic Beta cells specific CREs are likely to alter specifically Beta cells expression. This can be very useful for functional interpretation of GWAS variants that often fall in noncoding region 143,144 . For example, in T2D, scATAC seq has allowed to demonstrate that the causal T2D variant at the KCNQ1 locus targets specifically Beta cells specific enhancer

III.2.c. Single-cell multimodality

Recently new single-cell assays have been developed to simultaneously measure different modalities in the same cell allowing the investigation of interaction between different regulatory layers. Interrogating protein level as well as RNA level at cell level highlight correlation between gene transcription and protein expression. This is possible with CITE-seq technics, which, in addition to capture mRNA, label cell surface protein with oligo linked antibody. The oligo will be subsequently sequenced to estimate protein level. However, this technic still relies on a limited number of proteins (~200s for cell immune profiling), and only cell surface markers, limiting their scale.

Another important development is the assessment of chromatin accessibility and RNA expression within the same cell. This approach allows unbiased assessment of interaction between chromatin remodeling and gene expression at genome-wide resolution. In particular, we can better identify genomics region influencing gene expression, i.e. CREs. Indeed, if chromatin accessibility at one genomics region correlates with expression of neighbor gene at cell level, it is likely that this region influences its expression. Then, we can identify which TFs are likely to bind these CREs using TF motif analysis and TF footprint analysis. By this way, we can estimate the TFs regulatory activity on downstream target gene and infer a TF-gene regulatory network at subpopulation level. This approach can be very useful to demystify the gene regulatory network involved in differentiation process or in disease development. Few studies have been performed to date because of their recent accessibility, but they already impacted our understanding of cell type or diseases specific gene regulatory network identification 146,147 . Thanks to this approach, it has been shown that TF expression and TF motif accessibility correlate and their activation precede transcriptional expression of targeted genes, highlighting the key role of chromatin accessibility in programming differentiation of cells 140,148

III.3. Computational challenges

Analysis of single-cell genomics data requires important computational skills and remains quite challenging.

III.3.a. Single-cell data analysis pipeline

High-througphut sequencing generates thousands of reads per cells that need to be mapped to a reference genome to generate a gene-cell count matrix, representing the number of transcripts detected for each gene in each cell. Excepted the need for computational resources, generating this count matrix is trivial because it relies on ready to use bioinformatics pipelines.

INTRODUCTION 48

The next step after generating this count matrix is the cell cluster generation based on transcriptome similarities (Figure 12). Because data are at genome wide and cell level, the associated matrix is of high dimensionality (typically ~30k genes for human transcriptome) with numerous observations (n= the number of cells). High dimensional data are challenging to appropriately cluster.

Furthermore, because data generation relies on little amount of genomics material, single-cell genomics data represents a sparse matrix, with lot of zero, making it even more difficult to cluster properly. Therefore, there is a need to reduce the dimensionality by looking at common variability between genes. Several methods exist to reduce dimensionality including the classical principal component analysis (PCA) or more sophisticated one including latent semantic indexing (LSI).

PCA is a widely used reduction dimension method trying to maximize variability explanation in a limited number of dimensions, the principal components, using orthogonal vectors. It is quick to compute and give a linear projection of cells on principal components that explain the greatest covariance of genes across cells. However, PCA is a linear reduction method so does not catch nonlinear variability/pattern. PCA reduction is mostly used for scRNA-seq data as a first step reduction method. LSI is a dimensionality method allowing to give higher weight to rare feature mainly used to reduce scATAC-seq data 179,180 . Such reduction method typically reduces the data to ~50 dimensions, keeping the linear structure of the data while removing the zero biased, thus facilitating cluster identification. Clustering usually implements a graph-based method relying on shared nearest neighbor graph. It links cells according to their proximity in the dimension reduced space and further refines their link weight based on their mutual cell neighbors. Once this graph is produced, graphbased clustering algorithm, like the Louvain algorithm 181 aiming to optimize the modulatory (i.e.

module/cluster of highly connected cells), is performed to produce a clustering of cells reflecting their transcriptomic similarities relative to others cells. After this clustering step, cells cluster can be annotated thanks to identification of cell type specific markers. This step requires manual curation and knowledge about the interrogated tissue/sample. It is now also possible to annotate your cells based on scRNA-seq references, highlighting the interest of the human cell atlas and similar initiative 182 . To note, because cell type definition is dependent of the transcriptional profile in scRNA-seq, it is sometime hard to estimate if change between two conditions rely on cell subpopulation difference or rather reflect cell activity change. For this reason, it is important to define the cell type prior to perform differential expression analysis.

Once subpopulations are identified, it is then possible to compare different conditions at subpopulation level. Two main analyses are classically done: differential expression and differential cell-type abundance. Differential expression can be performed within each subpopulation to identify cell-type specific transcriptional alterations. This step is not trivial as gold standard has not yet emerge

INTRODUCTION 49
for the statistical part and the analysis will be highly dependent on your sample design. Indeed, to avoid technical bias or inflated p-value due to large number of cells, it is recommended to do this analysis at pseudo bulk level. To do that, single-cell count is summed for each sample replicates within each subpopulation producing a sample gene count matrix by subpopulation of interest. However, we need to have enough sample replicates to perform such analysis (at least N>3), which is often not the case due to the experiment cost and time. In the other case, differential expression analysis can be performed at cell level. In this case, the first step is to normalize for cellular sequencing depth and stabilized for variance using a regularized negative binomial model, to reduce technics dependent bias 183 . Then, a standard Wilcoxon rank-sum test can be used to highlight genes differentially expressed in your tested condition without making assumption on the sample distribution.

Difference of cell type abundance between conditions can also be analyzed. Accordingly, to previous part, depending on the number of biological replicates, the tests to use will be different. Chisquared test can be used if comparing two proportions with just one replicate by conditions, assuming that these proportions represent the whole population. Otherwise, if multiple replicates are available, a Wilcoxon test should be performed to compare cell type proportions between conditions. There are tests that have been developed specifically for single-cell data 184,185 , including Milo, a statistical framework that used cell-cell similarity k-nearest neighbor graphs, which have shown better performances than alternative methods to perform differential abundance testing. This method enable the identification of perturbation in cell composition that are hidden when discretizing cells into clusters, identifying notably the decline of fate-biased epithelial progenitors in aging mouse thymus.

Then, several others downstream analysis can be performed as explain in previous parts (Applications parts), including pseudotime analysis, RNA velocity, and TF activity measurement, each with specific set of statistical considerations and challenges that I won't develop here.

Integrative analysis

An important other challenge, maybe the major one, is data integration.

III.3.b.i. Batch integration

First level of integration is considering same modality datasets from different experiments / laboratory / sample / species. The challenge is then to remove batch dependent technical bias while conserving the biological cell type specific variation. Several methods have been developed to perform this task with different efficiency depending on the complexity of the datasets to integrate 186 . Most popular tools are based on mutual nearest neighbors (MNN) graph which will link cells from different dataset according to their common cell neighbors in common low dimensional space 187,188 . Another widely used method is Harmony, which corrects directly in low dimensional space the cell position to remove batch specific variability 189 . For more complex integration task (e.g. multiple species integration) deep learning based method like scVI or scGen are more efficient, because they can adapt for nonlinear variation 186 .

INTRODUCTION 51 III.3.b.ii.

Single-cell multi omics integration

A second level of integration is the integration across multiple modalities. First of all, between scRNA-seq and scATAC-seq data. Similar tool can be used if scATAC-seq data is also considered at gene level but this will result in a loss of scATAC-seq specific information, which reduce biological significance. Further methods have been developed to perform this specific task based on gene and chromatin accessibility co-variability 142 , or on reference multimodal data 190 but a lot of work remains to be done to effectively integrate both modalities.

The single-cell multimodal assay allows simultaneous profiling of epigenetic and transcriptional landscape in the same cells, which avoid the integration step and enable new applications and challenges. The main challenge is then to correctly associate an open chromatin region (a peak) to a neighborhood gene expression. In other words, the interest is to find which peak (putative regulatory region) regulates which gene, and in which cell type. If these links can be found, then we could define cell type specific CREs, and predict gene expression only based on open chromatin data, and inversely. This problem is still challenging as I am writing this thesis 191 . For the moment, only few methods have been proposed and are still trivial. SHARE-seq article proposes to simply link peak to gene based on the spearman correlation and compared against randomly selected features matching genomics region, to evaluate its statistical significance thanks to a ground truth 148 .

This approach can evaluate if the peak accessibility explains gene expression but miss the co-variation with others peaks and the sparsity of the chromatin accessibility data, which can hide some complex associations. ArchR a software design for single-cell analysis of regulatory chromatin used nearest neighbor method to group resembling cells and then merge count by group to reduce the sparsity problem 192 . However, more sophisticated or clever methods should emerge to better associate these two modalities. Finally, another promising avenue with this single-cell multimodal data is to infer cell type specific as well as disease relevant gene regulatory network [193][194][195][196] , but we are still in the very beginning.

III.3.b.iii. Integrate single-cell with bulk data

Another challenge in the genomics field is to integrate single-cell data with bulk data.

Deconvolution methods exist for bulk transcriptome in order to find single-cell composition 197 but they rely on cell specific datasets references (scRNA-seq or bulk RNAseq on isolated subpopulation). In addition, integration of different modalities from different resolutions appears more challenging.

Typically, genome wide DNA methylation are performed in bulk due to the limited recovery of actual single-cell methylation assay 155 . If cell type composition is known, deconvolution can be performed to fit with scRNA-seq data. Otherwise, linear comparison with every subpopulation expression could be done to evaluate cell type specific DNA methylation impact. In any case, a critical step is the need to

INTRODUCTION 52
integrate DNA methylation with transcriptomic data. DNA methylation is at CpG level and need to be compared to transcriptomic, which is at gene level. Several approaches can be used to do that but are still biased. In my first model I will develop a method trying to improve this link, integrating both TSS distance, tissue specific chromatin profile, and eQTL information.

III.3.c. New tools

To help overcome all these challenges, new computational tools are emerging. Most specifically, deep learning-based framework are very promising to perform complex task 186 . Deep learning is based on in silico neural networks resembling to the functioning of the biological one for lot of aspect. They allow complex task integration better than standard statistical tool because they can catch nonlinear pattern in a semi-automated way, and at high scale 198,199 198,201,202 .

In this thesis, I will present how I took advantage of single-cell genomics data using preexisting tool and developing new approaches in order to decipher cell type specific molecular mechanisms involved in adult chronic diseases development. To do that, I focus on two models: the epigenetics programming of hematopoietic stem and progenitor cells (HSPC) in the context of early programming of adult chronic diseases, and the Alzheimer's Diseases susceptibility genes BIN1 function.

INTRODUCTION

53

IV. Model 1: Epigenetics programming of hematopoietic stem and progenitor cell (HSPC)

IV.1. Early Programming of chronic metabolic diseases

Chronic metabolic diseases, including type 2 diabetes (T2D), obesity and cardiovascular diseases (CVD) constitute approximately 70% of deaths worldwide (WHO, 2017), thereby becoming the most significant burden to healthcare systems. Although inherited genetic risk and lifelong environmental exposure contribute to their development, they cannot explain alone the distressing rise in obesity and diabetes of these recent years 6,203 . Several epidemiological and experimental studies indicate that perinatal exposure (fetal and early development, up to 1000 days after birth) to a metabolic stress increase susceptibility to the development of chronic complex diseases several decades later. Perinatal development is a critical period of rapid growth and differentiation when organs shape and acquire their function. It is then a period of intense epigenetics remodeling and sensibility to environments which can have durable impact on organ structure and function 204 .

Our society have known radical changes this last century, notably in food industry, robotization and tertiary deployment, which have significantly modify our lifestyle and influence our exposure to nutrient as early as in utero. In 2010, more than half of pregnant women were considered obese in US 205 . In France, a 90% increase of obesity rate among adult woman have been observed between 1997 and 2012 206 . Incidence of gestational diabetes mellitus (GDM), corresponding to high blood sugar that develop during pregnancy but usually disappearing after giving birth, have also considerably increase.

The past decade, the incidence increases by 30% in young US women, while doubling for some population including Asian Indian 207 . In Europe, prevalence of GDM reaches 11% of total pregnant women. However, impact of this recent change on fetus development and long-term consequences remain poorly studied.

Yet, Barker and colleagues were the first to demonstrate in 1986 that an early (fetal) exposure to a nutrient stress increases the risk to develop diseases decades' later 208 . They first observed that English regions which were the most impacted by starvation and infant mortality in 20 th century were also the regions the most affected by CVD decades later 208 . Following this first observation, two large studies were led by Barker et al. to investigate this link and found concordantly a strong association between low birth weight, head circumferences or ponderal index, and death from coronary heart diseases and T2D decades later [209][210][211] . This observation was replicated in 3 others countries [211][212][213] and show that maternal undernutrition conditioned progeny to future environmental fitness and diseases susceptibility. These observations have opened the field of the developmental origins of health and diseases (DOHaD), which study how early exposures conditioned people to adapt, or mis-adapt, to CVD 224 as well as increased risk for neurologic disorders including depression, anxiety, autism, and cognitive delay [225][226][227][228] . Then, these results show that both restriction and excess of fetal growth is associated with increased ACD susceptibilities with similar outcomes(Figure 13), suggesting converging mechanisms. Several theories emerged to explain these links. The thrifty phenotype hypothesis, first formulated by Barker et al. in 1992 229 , stipulates that association between SGA and ACD risk results

INTRODUCTION

55 from irreversible alteration in the glucose-insulin metabolism during development. Maternal undernutrition led to a decreased insulin secretion by the progeny and to an increased peripheral insulin resistance 230 , leading to a greater glucose availability for brain and heart. If nutrients become abundant in postnatal life, the pancreatic Beta cells will defect and insulin resistance of peripheral tissue could then predispose to glucose intolerance and diabetes, as observed in SGA neonates with rapid catch-up growth who have more chance to develop insulin resistance and T2D later in life 231,232 .

A competitive hypothesis based on genetic influence was developed by Hattersley 233 . Indeed, the link between SGA and glucose intolerance/diabetes in adulthood could be explained in part by genetics factors, for example variants influencing insulin secretion, that can contribute to a decrease in birth size and glucose tolerance [234][235][236] . However, genetics variants associated to birth weight through GWAS explain only about 7% of the birth weight variation, still supporting an independent influence of prenatal exposure affecting birth weight and ACDs susceptibilities 237 .

Gluckman et al tried to generalize the Barker's hypothesis bringing an evolutionary point of view. They argue that these associations between prenatal exposure and ACD susceptibilities could reflect a (failed) predictive adaptation to future environment, i.e a way to have a fitness advantage on expected future environment 238 . To support this hypothesis, evidence of in utero adaptation to expected similar future environments exists [239][240][241][242] . Notably, it was shown that offspring from rat fed with HFD develop hypertension and endothelial dysfunction. . However, the endothelial dysfunction was prevented if offspring were kept on HFD during early life 239 . In human, poor in utero environment induces morphological and physiological changes like fat deposition, which promote future survival in deleterious environment 240 . However, if the structural/functional choice made during development ended up inappropriate in regard to the future environment, there is a mismatch between tissue adaption and reality, which increase the disease susceptibility. Several observations have been made in this sense, notably when in utero growth restricted individuals like SGA have postnatal environment favoring overconsumption, leading to further glucose intolerance, insulin resistance, and reduced lifespan in human or animal models 238,241,242 .

IV.1.a.ii.

Maternal hyperglycemia

Gestational Diabetes Mellitus (GDM) or maternal hyperglycemia during pregnancy is a current common complication (prevalence was about 17% in 2013 243,244 ) . GDM is strongly associated with increased birth weight [245][246][247][248] with persistent elevated glycemia (HbA1c ≥ 5.6%) at 3-month pregnancy leading to LGA in 26% of case 248 . The association is even more pronounced for type 1 diabetes mothers where the occurrence of LGA is about 56% in a Lille hospital retrospective study 249 . The in utero exposure to hyperglycemia is associated to increase childhood cardiometabolic risk in offspring including higher rates of impaired glucose tolerance, obesity, and higher blood pressure. Importantly,

INTRODUCTION

56 these associations are independent of BMI before pregnancy, being born large for gestational age, and childhood obesity, highlighting the direct effect of the in utero exposure to hyperglycemia 250 . Similarly, a previous study has shown in Pima Indian cohort, that T2D mothers lead to 45% of T2D in their offspring at age 20-24years old, while only 1.4% for non-diabetic mothers 251 . The risk persists even after correcting for paternal diabetes, age of diabetic onset, and offspring BMI. Another study showed that offspring exposed to T2D during gestation have a higher risk to develop T2D than their siblings born before maternal T2D onset 252 .

IV.1.a.iii.

Maternal Obesity

In western countries, obesity prevalence is estimated around 30%, while 40 % of women are overweight during pregnancy 253,254 . Growing evidence show that this deleterious metabolic status has long-term consequences on offspring especially on adiposity, cardiovascular and metabolic risk.

Maternal pre-pregnancy obesity is associated with significant increase risk of LGA 255,256 as well as with a 3-fold risk of childhood obesity 257 , while maternal over weight gain during pregnancy is associated with increase BMI [258][259][260] . Furthermore, these evidence are associated with higher blood pressure, adverse lipid profile, insulin resistance and higher inflammatory markers in childhood even if those cardiometabolic risks can be partially attributed to the increase BMI of the child [260][261][262][263][264] . Finally, a followup study on 37,709 individual has shown that higher maternal BMI detected at first prenatal visit is associated with increased risk of premature all-cause mortality and hospitalization for CVD 265 .

IV.1.a.iv. Glucocorticoids

Glucocorticoids exposure during development has been shown to be associated with an increased CVD risk and insulin resistance 266 . Glucocorticoids are key factors within the hypothalamicpituitary-adrenal (HPA) axis contributing to stress response. Glucocorticoids have a key role in fetal development, particularly during the 3 rd semester, where maternal glucocorticoids secretion regulates fetal growth, brain development, and organ maturation, allowing the fetus to prepare for extra uterine life. Glucocorticoids treatment during pregnancy reduces birth weight and lead to SGA associated ACDs risk as well as higher blood pressure in adolescent and altered neurological functions 267,268 .

Furthermore, adult born SGA have altered control of cortisol expression and increased activity of the hypothalamic-pituitary-adrenal suggesting a programming of the HPA axis activity and regulation 269,270 .

The altered cortisol level is associated with adverse metabolic profile (higher glucose, blood pressure, and dyslipidemia) in adult 270 .

IV weight gain has been associated to later obesity, cardiovascular diseases, while poor weight gain was associated with metabolic syndrome, impaired glucose tolerance and T2D later 231,272-277 . Breast feeding could also be associated to decrease risk of later metabolic disorders, but results are controversial because of important confounding factors [278][279][280] . The first 1000 days of life, are a critical period for tissue development especially for brain 281,282 . It is then a period of vulnerability, where deleterious exposure, including poor nutrition, exposure to toxicants and microbiota imbalance can have long lasting consequence on adult 283 . Improved nutritional income during these days improve cognitive function and school results. At the contrary, deficit in iodine during this period, which is a critical nutrient for brain development, impact future cognitive function even if iodine deficit is moderate 284 . Another important early life factor seems to be the microbiota colonization 285 . Main gut bacterial colonization occur in the first year of life driving by breastfeeding and other maternal transfer and is critical for immune system development 286,287 . Exploding evidence have highlighted the role of microbiota imbalance ("dysbiosis") on immune related diseases, including inflammatory bowel disease but also on metabolic disorder including obesity and T2D, and neurological disorders, including depression and anxiety, suggesting a role of early microbiota dysregulation in the programming of chronic diseases [286][287][288] .

In light of the impact of early development, clinical initiatives have been launched to better understand and inform on this critical period. In Lille, the program "1000 jours pour la santé" held by Laurent Storme aims to promote fundamental, clinical and technical research to better identify the critical early life factors influencing ACD development and better prevent them 289 .

IV.1.b. Animal models to understand the physiological mechanisms

Several animal models have been developed to validate the influence of perinatal environment on future ACDs risk and better understand the biological mechanisms behind.

In rats, global maternal undernutrition, or specific protein restriction, result in reduce birthweight 290 , increased blood pressure 291 , and impaired glucose tolerance 292 in the offspring in adulthood 293,294 . These results were reproduced in Guinea pig and sheep 295,296 . Putative mechanism behind the long term programming of glucose tolerance have been investigated in protein restricted pregnant rat model 293 . Such diet appears to reduce fetal pancreatic islet expansion leading at birth to reduce endocrine and Beta cells mass as well as reduced insulin secretion. Further studies have shown

INTRODUCTION

58 that islet cells exposed to this in utero restricted environment have a decrease of replication rate in vitro , and Beta cells an even lower proliferation rate 297,298 . Interestingly, the alteration of insulin secretion and islet expansion are still apparent after 7 days of culture in normal metabolic environment, demonstrating the programming of these cells 294,299 . Long term consequences of such alteration appear influenced by several parameters, including the sex of the offspring, the developmental window targeted by the exposure, and the postnatal nutrition 300,301 . If the proteinrestricted diet is present just during pregnancy, only female have reduced insulin response at 3 months old 294 . If restriction remains during lactation, plasma insulin is reduced even in adulthood and insulin response is greatly reduced in both sex 301 . Interestingly, such alterations are not associated with glucose intolerance, but at contrary, with greater glucose tolerances 301 . Further studies have shown that this appearing contradiction can be explained in part by the fact that peripheral tissue like liver, adipose tissue and muscle express more insulin receptors to compensate [302][303][304][305] . These evidence confirm the impact of maternal malnutrition on future cardiometabolic health with programming of metabolic circuits aspecially the insulin pathway. With the example of the endogeneous pancreas, these animal models studies also show that both tissue structure alteration (decrease Beta cells mass) and cell intrinsic factor (reduce Beta cell proliferation and insulin secretion ability) can mediate the long term consequences of early exposure to detrimental environment.

Further studies have assess the role of glucocorticoids signaling in these programming of metabolic risk 306,307 . Fetal exposure to glucocorticoid lead to decrease birth weight and increase blood pressure in sheep 308,309 , while maternal dexamethasone intake (a synthetic glucocorticoid) in rats lead to a reduced progeny birth weight as well as to hypertension and glucose intolerance with possible insulin resistance in adulthood 310,311 . Furthermore, it was shown that maternal undernutrition increases maternal glucocorticoid secretion 312 , while adrenalectomy, abolished effect of maternal low protein intake in the offspring, highlighting the key role of stress related glucocorticoid signaling in fetal programming of metabolic circuits 313 .

Interestingly, in both maternal low protein and caloric restricted diets rat models, impairment in glucose tolerance appear only following subsequent adverse life events or mis-adapted environment. This effect was notably demonstrated in the context of rapid catch up growth/ high food consumption in childhood leading to obesity 242 or during aging 314,315 . Indeed, offspring of rat fed with low protein diet show impairment in glucose tolerance only at around 15 months, and diabetes few months after 316 . Mechanistically, this can be explained because of the age dependent development of insulin resistance in peripheral tissue including adipocytes and skeletal muscle. This insulin resistance seems to develop from a molecular defect downstream of the insulin receptor which impaired PI3K kinase pathway activation 317 . These evidence show that in utero detrimental exposure like protein

INTRODUCTION

59 restriction can predispose individual to have ACDs following future adverse life events, further supporting a reduced cellular plasticity to adapt to future environment.

Not restricted to peripheral tissue, exposure to maternal undernutrition appear also to program behavior related to the central nervous system. Indeed, Delahaye et al have previously shown that rat maternal undernutrition can durably program hypothalamic appetite regulatory system through a drastic decreased of leptin surge involved in the development of this system and a reduced responsiveness of anorexigenic POMC neurons 318,319 . In an opposite way, offspring of obese rat show an amplified and prolonged neonatal leptin surge and lead to a long term leptin resistance, which could explain the programming of hyperphagia and obesity in its animals, but also in human 320 .

To remain on the other side of the exposure spectrum, the impact of over exposure to nutrients have also been shown to predispose to metabolic disorders. In rat, mild diabetic mothers lead to macrosomic progeny with increase pancreatic islets development and Beta cells mass due to hyperplasia and hypertrophy, while declare a glucose impaired tolerance later 321 . Similarly, maternal overeating lead to glucose intolerance on the offspring at 3month old 322 , while maternal high fat diet lead to hypertension, leptin, and insulin resistance, as well as fat accumulation [323][324][325] . Furthermore, a recent study has shown that parental HFD or High sugar diet program inflammatory and oxidative parameters in reproductive tissue of rats offspring, highlighting putative mechanism of transgenerational transmission 326 . Interestingly, some of those impacts were sex specific with, for example, female offspring being more affected by hypertension 327 . Together, these findings strongly show that mother diets or metabolic status impact future offspring metabolic health with impact on central and peripheral tissues.

Thus, several in utero and early life factors can influence future adult disease risk, influencing metabolic parameters in early life. However, the lifelong molecular and cellular mechanism behind remain poorly understood.

IV.1.c. Epigenetics memory of early exposure

There is several decades between the fetal exposure and the associated ACDs onset, Evidence for an epigenetic programming of the metabolic syndrome have been shown in SGA.

suggesting
A global DNA methylation alteration was observed in adult offspring exposed to prenatal famine in Dutch Hunger Winter 337 , with a decrease DNA methylation targeting the IGF2 gene 338 . An increase methylation and decrease expression of proopiomelanocortin (POMC) in cord blood, precursors of many metabolic hormones, have been associated with lower birth weight and with a higher triglyceride and insulin blood level during childhood, exposing a predictive epigenetic biomarker of future metabolic condition 339 . Abnormal birth weight is also associated with several durable epigenetics modifications in energy homeostasis genes, including DNA hyper methylation and reduce expressions of ATG2B, NKX6.1, and SLC13A5, related respectively to autophagy, Beta cells development and lipid metabolism; and hypo methylation and increase expression of GPR120 gene, regulating free fatty acid 340 . In rat, maternal dietary restriction led to decrease promoter methylation of glucocorticoid receptor (GR) and peroxisomal proliferator-activated receptor (PPAR), involved in stress response and lipid metabolism, and are associated to an increase expression of these genes in the offspring liver 341 .

Epigenetics change have also been shown in kidney and adrenal gland from in utero diet restricted offspring respectively in p53 and in angiotensin II type 1b receptor genes, both playing a role in hypertension programming 342,343 .

Epigenetics programming also occurs in LGA or related in utero excess nutrient exposure. In whole cord blood of LGA neonates, DNA hypermethylation of the FGFR2 gene have been observed 344 .

In placental, an hypermethylation has been found in repetitive elements LINE-1 and AluYb8 and was associated with the methylation of polycomb group targeted genes as well as developmentally related transcription factor binding sites 345 . Interestingly, similarly to SGA from Dutch Hunger Winter, GDM induced LGA display a change in IGF2 methylation pattern in cord blood and placental tissue [346][347][348] .

IGF2 change in methylation was shown to be associated with neonatal adiposity 338 . The common The expression of leptin, the major regulator of food intake and body mass, found altered in obese peoples 355 , can also be epigenetically programmed in utero, as its methylation status in cord blood offspring is associated with maternal glucose intolerance, GDM, and maternal obesity [356][357][358] . In mice, a global DNA methylation was shown in liver, muscle, and adipose tissue of offspring of mother fed with high fat diet (HFD) 359 . In primates, obesity and maternal HFD even prior to pregnancy and obese mother were shown to modify the chromatin structure of fetal liver through histone modification and were associated with dysregulated fetal lipid accumulation 360,361 . In female hypothalamus, perinatal maternal high-fat diet environment induced decreased melanocortin 4 receptor ( Mc4r) and increase H3K27ac in its promoter and were associated with increased food intake and obesity in offspring 362 .

Other prenatal stress, not dietary related, can also lead to long-term epigenetics change. In mouse brains, prenatal stress (mother subjected to daily physical constraint) induces important long term epigenetics alterations including aberrant DNA methylation and persistent DNMT expression while program for hyperactivity and for altered social interaction in adulthood 363 . These alterations were corrected upon the administration of an histone deacetylate inhibitor (valproic acid) and by antipsychotic agent with DNA demethylation activity (clozapine), highlighting a causative effect of DNA methylation alteration in cognitive disorders 363 . Such epigenetics programming was also shown in both human and animal models for others neurological disorders including anxiety, depression, attention deficit, and autism 364 . For AD, mouse models of AD exposed to prenatal or early life stress accelerate the impaired cognitive function including deficit in object location memory and impaired spatial learning 365,366 . Furthermore, chronic early life stress in these mice increases defective Abeta levels in middle age and correlates with reduce cognitive flexibility, while short treatment with glucocorticoids receptor antagonist rescues cognitive deficit and Abeta load 367 .

INTRODUCTION 62

Then, different in utero stress can led to durable epigenetics change. Even if some targeted approaches have shown correlation between epigenetics alteration and subsequent gene expression alteration, as the study observing hypermethylation of lipoprotein lipase promoter associated with reduction of its expression in pancreas 351 , the impact of the global epigenetics memory on transcription and signaling pathway remain poorly studied. Yet, this is critical to have a comprehensive view of the tissue dysfunction. Different integrative genomics analysis should be performed to understand more clearly the cellular and molecular signaling affected by these epigenomics alterations. Dr Fabien

Delahaye and colleagues have worked on that these last years and I had the opportunity to continue along this effort during this thesis.

IV.2. HSPCs model to study early influences

To study the influence of early exposure on regulatory landscape and cell signaling, Dr Fabien Delahaye and colleagues focused on the specific model of human hematopoietic stem and progenitors cells (HSPCs).

HSPCs contain hematopoetic stem cells (HSC) and more differentiated progenitors including the multipotent progenitors (MPP), and three main lineage progenitors the erythroid (MEPs), lymphoid (CLPs) and myeloid (GMPs) progenitors (Figure 14). [370][371][372] . In basal state, macrophages promote the retention of hematopoietic stem cells by regulating CXCL12 production in the bone marrow and megakaryocytes localized with HSC promoting their quiescence through CXCL4 and TGF-β1 production [370][371][372] . Under hematopoietic demand, depletion of macrophage mediated CXCL12 production allow HSC mobilization, while FGF1 production by megakaryocytes under stress promote HSC expansion 371 . HSPCs are a relevant model to study influences of early environment on ACDs risk. They are easily isolable at birth from cord blood while being able to self-renew throughout life, conserving epigenetic memory of past exposure. Furthermore, the hematopoietic system play an important role in ACDs development as I will develop in next sections.

INTRODUCTION 64

Cord blood HSPC are defined through CD34 cell surface marker. CD34+ cells represent around 0.4% of cord blood mononuclear cells (MNC) with high variability between samples typically from 0.2% to 1.4% 373 . By contrast, less than 0.01% of CD34+ cells are found in adult peripheral MNC and around 1.5% in bone marrow MNC. Among the CD34+ population, cells negative for CD38 cell surface marker phenotype are the HSCs with the greater long-term repopulation ability 373 and can be used for transplantation 374,375 . They represent around 0.05% of whole cord blood MNC.

Cord blood HSC are more responsive to stimulation than adult bone marrow HSC. Indeed, their quiescent form have a greater proliferative response to cytokines with lower dependence on stromal cells than bone marrow or adult blood HSC 376,377 . They also give rise to relatively different progeny compared to adult HSC. Notably, they give rise to less NK cells, produce a specific T cell progenitors (with the phenotype CD3-/CD8-), and different number of B cell subpopulation 377 . Several cytokines can stimulate HSC to proliferate notably SCF, Flt3, IL-11, IL-3, IL-6, GM-CSF while others can influence their differentiation, notably M-CSF, G-CSF, Epo, and Tpo. 377,378 .

IV.2.b. Stem cells epigenetic memory

HSPCs, as for others stem cells, are present throughout life and thus have the ability to conserve putative cell memory of past exposure. Evidence of stem cells epigenetics programming exist.

For example, it was recently shown that stem cells of follicle hairs can reprogram following wound damages to repair the epidermis through long-term epigenetics memories 379 . Adipose derived mesenchymal stem cells (MSCs) are functionally reprogrammed in obesity which lead to loss of stemness capacity and multipotency, change in their metabolism, and reduced immunomodulation and angiogenic capacity 380,381 . Dynamic regulation of DNA methylation plays an important role in orchestrating stem cell function 382 . Decreased expression of DNMT1, the enzyme maintaining DNA methylation pattern throughout division, reduces cell renewal and induces premature differentiation of epidermal progenitor cells, leading finally to tissue loss 383 . In HSC, reduced DNMT1 activity lead to defect in self renewal but also in decreased differentiation potential mirroring the defect observed in aging [383][384][385] . Then, the stem cells epigenetic memory appears as an important player in regulating stem cells function with important consequences on tissue regeneration capacities and tissue long-term function.

IV.2.c. Hematopoiesis and ACDs

The choice to study HSPC is also relevant because of its role in ACDs. [387][388][389][390] . Notably, the loss of TET2 leads to profound increase of HSC self-renewal while responsible for a myeloid bias differentiation [388][389][390] . TET1 loss leads to increase HSC self-renewal but was associated with lineage bias toward B cell production 391 . Ablation Together, these studies have shown the importance of DNA methylation and chromatin accessibility remodeling in the control of hematopoiesis and emphasize the putative impact of their alterations on hematopoietic function.

IV.2.c.ii. Importance in aging and diseases

Defective hematopoiesis, chronic inflammation, and oxidative stress are key interconnected mechanism observed in aging and contributing to ACD risk. Clonal hematopoiesis, defined as a clonal expansion of dysfunctional immune cells occurring during normal aging, doubles the risk of coronary heart diseases in human 394 . Such clonality directly affects the heterogeneity and plasticity of HSC niches driving hematopoietic dysfunction and increasing ACDs susceptibilities [START_REF] Jaiswal | Age-Related Clonal Hematopoiesis Associated with Adverse Outcomes[END_REF][START_REF] Jaiswal | Clonal Hematopoiesis and Risk of Atherosclerotic Cardiovascular Disease[END_REF] . Indeed, a relation between HSC clonal expansion and bias toward proliferation was observed in aging 395,396 . Furthermore, myeloid biased HSCs concomitant to an increase in myeloid cells in blood are observed in aging and strongly associated with CVD risk 395,397,398 . This increased myeloid cells, such as macrophages are known to worsen chronic inflammation by increasing levels of inflammatory cytokines 394,399 . Considering the hematopoeitic compartment, this disturbed homeostasis (balance between differentiation and proliferation) can have deleterious consequences on regulation of inflammation and therefore was shown to contribute to the inflamm-aging phenotype, a chronic low-grade inflammation observed in aging [START_REF] Jaiswal | Clonal Hematopoiesis and Risk of Atherosclerotic Cardiovascular Disease[END_REF]395,400 . Alone, the dysregulated inflammation is a major contributors to the vicious circle of obesity, T2D and CVD development 395,401,402 . Together, these evidence emphasize the role HSC heterogeneity or plasticity alteration in ACDs programming, highlighting the relevance of the hematopoietic system as a model to study the early programming of ACDs.

IV.2.c.iii. Evidence of early programming of hematopoiesis

Several detrimental exposures can affect hematopoietic compartment plasticity and exposes to diseases risk. For example, short term hyperglycemic spikes, as observed in prediabetic or T2D patient, increase myeloid cells production in bone marrow, which accelerates atherosclerosis 403 . It was also shown in a mouse models that hyperglycemic environment lead to a reduced HSCs mobilization capacity in response to G-CSF 404 . However, few studies have been done in the context of in utero exposure. In human, the concentration of circulating CD34+ cells (HSPCs) was shown to be associated with extreme fetal growth [405][406][407] , suggesting impact on HSPCs mobilization. More recently, a study led INTRODUCTION 67 by Kamimae-Lanning et al have assess the impact of maternal obesity and/or high fat diet on the fetal hematopoieitic system development 408 . They collected HSPCs from fetal liver to compare their expansion and repopulation ability depending on the nutritional exposure. They showed that HSPCs from fetus exposed to HFD, have a restricted physiological expansion and repopulating capacity while having an increased differentiation shifted toward myeloid lineage. These alterations are associated with changes in the expression of several genes involved in metabolism, immune and inflammatory processes, as well as stress response, and of key genes involved in self-renewal and HSC maintenance like Egr-1 and Bmi1. This evidence support the early influence onf the hematopoetics system, however, if such alterations are conserved after birth remain to be explored.

IV.2.d. First study of early epigenetics programming of HSPCs

To answer these questions, Dr Fabien Delahaye et al have performed epigenomics analysis on HSPCs collected from neonates exposed to extreme fetal growth (both restricted growth, i.e. SGA, and overgrowth, i.e. LGA) and from appropriately grown neonates (CTRL).

They collected HSPCs from 60 samples (20 SGA, 20 LGA, and 20 CTRL) isolated from cord blood thanks to the CD34+ cell surface markers and performed on DNAs genome wide CpG methylation assay using the HELP tagging method O409 . They compared either SGA or LGA samples to appropriately grown neonates (CTRL) samples. They showed that both SGA and LGA neonates present a global increase of DNA methylation close to genes regulating stem cells function 349 . They also found an interesting sexual dimorphism, with female LGA neonates being more affected by DNA hyper methylation than male. To study putative influence of these epigenetics alterations on gene expression, they integrated these results with histone marks profiling of CD34+ cells. They evaluated the putative regulatory landscape of HSPC defining promoter, enhancer, as well as heterochromatin region using these histone marks profiling. They observed an enrichment for differentially methylated CpGs in promoter and active enhancer regions suggesting putative transcriptional consequences associated to this epigenetics alteration 349,410 . Together, these results have highlighted evidence of epigenetics memory/programming of HSPC both in SGA and LGA.

IV.3. Remaining challenges

This previous study by demonstrating extreme fetal growth associated epigenetics programming of HSPCs rised important biological questions i) What is the impact of these methylation changes on gene expression; ii) What is the consequences on HSPC homestasis and function?

O the HELP (HpaII tiny fragment Enrichment by Ligation-mediated PCR) Tagging method is an enzymatic method based on the DNA cutting by the restriction enzyme HpaII of unmethylated CCGG site

INTRODUCTION

68 Indeed, the relationship between change in DNA methylation and change in gene expression remained mostly uncovered. Evidence suggest that hypermethylation of CpG islands close to promoter lead to transcriptional repression 18,19 , but the effect of the methylation respond to far more complex model largely influenced by the genomic context 411 . Then, integrate epigenomics and transcriptomic data is still necessary to assess the impact of epigenetics alterations on gene expression. This is integration will help us better understand the mechanism behind early programming of ACDs. Such integrative approaches have shown their interest in animal model. Notably, a study has shown that intrauterine growth restriction induce persistent DNA methylation alterations in pancreatic islet of rat with concordant changes in expression of nearby genes prediposing to T2D 412 . However, in human, such studies are lacking. This is why I dedicated part of my thesis works to the integration of epigenomic and transcriptomic data from exposed and control neonates HSPCs.

Hematopoiesis is finely regulated by epigenetics mechanisms, then epigenetics alteration observed in HSPCs exposed to extreme fetal growth could have a direct impact on HSPCs heterogeinety, or plasticity/differentiation capacity, and explain future tissue dysfunction and diseases susceptibility. Challenging exposure, like infection, tissue damaged, or blood loss can lead to durable change in tissue composition and function as well as regeneration capacity, impacting risk of developing future diseases 413,414 . Such impact on stem cells plasticity was already observed following detrimental exposure but also occur 'naturally' altered in aging. Indeed, a stochastic epigenetics alterations is observed in aging 382 , mostly reflecting imperfect tissue maintenance of epigenetics marks, creating an epigenetics mosaicism between cells of a same individual, aspecially for stem cells wich self-renew across life. This phenomenon have the ability to restrict stem cells plasticity and associated function but also lead to clonal expansion of defective stem cells 415 . Ultimately, that can lead to stem cells exhaustions, an other hallmarks of aging, but also tissue dysfunction and development of ACDs 385,415 .

Recent studies have shown that HSC differentiation is a continuous process rather than have discrete steps of differention as previously stated (Figure 15).

IV.4. Large for gestational age model

LGA are characterized by an excessive fetal growth leading to a birth weight and ponderal index over the 90th percentile, and have an increase susceptibilities to develop ACDs including obesity 221 , Type 2 diabetes 223 and cardiovascular diseases 224 . These increase susceptibilities are associated with an increased risk of impaired glucose tolerance and insulin resistance during childhood suggesting an early cellular reprogramming in these neonates [419][420][421][422][423] . Then, it is critical to understand others genetics influences as well as the role of these genes on brain and AD pathogenesis.

V.2. Brain complexity

The brain is a complex tissue displaying diverse cell types and complex cell to cell interactions.

It can be broadly described as a neuronal network supported by glial cells, working in concert to efficiently convey and process information under the form of electrochemical pulses, giving rise to appropriate behavior and body homeostasis. Glial cells include astrocytes, oligodendrocytes as well Q In a recent study, APOE related variants was able to explain 23% of all AD cases 39 INTRODUCTION 71 as microglia, bringing structural, metabolic and immune supports. Information are transfer across neurons thanks to "all or nothing" electrochemical pulse, called 'action potential', generated by neurons and transferred through their axon to synapses, where the information can be transmitted to other neurons through chemical signal. There is excitatory and inhibitory signal, governed mostly by Glutamatergic and GABAergic neurons, which respectively excite and inhibit downstream neurons through Glutamate and GABA neurotransmitters. The sum of excitatory and inhibitory signals transmitted to a neuron will be integrated to give rise or not to a new action potential transmit to downstream neurons. Furthermore, neurons are not only a passive information relay, but they are also able to retain information through biochemical and structural changes and therefore adapt their excitability and synapses connectivity. This feature allows plasticity of the neural network, and thus continuous adaptation and learning. That's how the brain allow complex treatment and modeling of the information giving rise to learning, analytical thinking, and other complex behaviors.

Even if all neurons have similar structures with dendrites and cellular body receipting and integrating electrochemical signal from others and one axon transmitting signal to others neurons, there are different neuronal subtypes depending on the neurotransmitter they produce/release at synapses. Most of the neurons in brain are glutamatergic (around 40%), i.e. they produce glutamate as main neurotransmitter, but others can be GABAergic (producing GABA), dopaminergic (producing dopamine) or cholinergic (producing acetylcholine). This diversity allow specific neural system, complex interactions and independent cognitive process. Glutamatergic synapses are mainly excitatory, i.e. they produce depolarization inpost synaptic neurons and are linked to many other neurotransmitter pathways, with glutamate receptors being found in more than 90% of all neurons 432 .

GABAergic neurons are the counter side of glutamatergic neurons producing mainly inhibitory signal,

i.e. their principal role is to reduce excitability of postsynaptic neurons. Dopaminergic and cholinergic neurons take part of independent neurocircuits in brain involved in specific cognitive process.

Cholinergic system is mainly involved in memory and learning in hippocampus 432 , while dopaminergic neurons are mainly involved in the rewarding system 433 .

Glial cells have also an important diversity in term of structure and functions than neurons.

They include astrocytes, microglia, and oligodendrocytes. Astrocytes have generally a star shape and synapses. They also give metabolic and trophic support to neurons producing neurotrophic factor and insulin like growth factor-1 436 . Like for neurons and astrocytes, they derive from NPCs. Besides these glial cells, there are also other cell types in brain that form the vascular system, maintaining the blood brain barriers including the vascular endothelial cells and pericytes. All of these non-neuronal cell types are affected in AD, but their contribution to AD-associated neurodegeneration remains incompletely understood 437 . Overall this cellular heterogeneity and tight cell-cell interactions allow integrity of the brain and its complex functions including synaptic plasticity and learning. A drawback of this complex and plastic interactions is that deregulation of one of this cellular interaction can be defective for the whole system. This also highlights the importance to take into account this cellular heterogeneity and interactions when modeling influence of genetics risk on brain functions and on AD pathogenesis.

V.3. How Alzeihmer's disease alter brain function ?

In AD brain, there is a progressive neuronal loss (neurodegeneration) characterized by toxic amyloid deposition in synapses and tau tangles accumulation in neurons. This leads to neuronal and synaptic loss, coupled with inflammation triggered by the proteotoxicity, leading to progressive memory loss and cognitive function impairment. The early mechanisms driving this defective accumulation is not fully understood, but key pathophysiological mechanisms are related to Abeta oligomers and tau tangles accumulation, as well as the associated neuroinflammation.

V.3.a. Amyloid beta accumulation

Probably the most important mechanism of AD development is defective Amyloid Precursors protein (APP) processing. Amyloid beta (Abeta) are byproduct of the APP cleavage in synapse. APP is a transmembrane protein playing essential role in nervous system development, synaptogenesis as well as modulation of synapse plasticity, and thus in learning and memory 438 . In normal APP processing, APP are cleaved by catalytic enzymes namely Alpha-secretase, Beta-secretase and Gamma-secretase following 2 different pathway. First processing pathway involve the Alpha-secretase and are non amyloidogenic. In the second pathway, APP are subsequently cleaved by Beta-secretase and the Gamma-secretase which lead to Abeta peptide formation. Abeta structure have the particularity to

INTRODUCTION

73 oligomerize which in high concentration are toxic for synapses and neurons activity producing ROS production, inflammation, and synapse loss [439][440][441][442] . Most specifically, the suboptimal cleaving by Gamma-secretase can produce elongated form of Abeta (Abeta42) are particularly important in AD development. Ab42 is found enriched in early onset AD patients, who carrier defective mutation of APP or catalytic subunits of the Gamma-secretase PSEN1 and PSEN2 [443][444][445] . Abeta42 have a particular structure and conformation decreasing its solubility, leading to significant increase oligomerization, and fibril (Abeta plaque) formation. More than being toxic for synapse, Abeta plaque appear to be also a major catalyzer of soluble Abeta oligomers, which can spread across the brain 446,447 .While Abeta have important role in synapse signaling and neurotransmission regulation at low concentration, their increase concentration and aggregation into soluble oligomers are deleterious for synapse. Indeed, Abeta oligomers bind to several synapse receptors, notably in post synaptic regions, impairing synapse conformation and composition, as well as neurotransmission. Local increase of Abeta concentration can be beneficial for synaptic plasticity regulation, but become detrimental when the Abeta accumulation is systemic like in AD 442,448 . Abeta level is not strikingly correlated to AD severity, but rather a prognostic of future AD onset. Some healthy elderly have amyloid plaque without have dementia or significant AD symptom 448 . Ab deposition start decades before first symptoms and are not always associated to AD 449 . Then, Abeta accumulation appear to be necessary but not sufficient to explain AD pathogenesis. Further diseases development are likely to be driven by the pathological Tau hyperphosphorylation and aggregation, as well as the induced neuroinflammation.

V.3.b. Tau tangles formation

The to the imbalanced regulation of protein kinases and phosphatases notably the glycogen synthase kinase-3β (GSK-3β) and protein phosphatase 2A (PP2A) 455,456 . Then, understand the critical mechanism leading to Phospho-Tau accumulation appear crucial to better understand AD development.

V.3.c. Neuro inflammation

In addition to amyloid plaques and NFTs, neuroinflammation also plays an important role in AD. Like for tau tangles, this chronic inflammatory state in brain is strongly correlates with AD severity 457 . Neuroinflammation relies on microglial and astrocytes activation in response to neurotoxic agent. They allow phagocytosis of toxic product and release of cytotoxic factors to clean injured sites.

Astrocytes and microglia play a major role in preventing Abeta mediated neurotoxicity. Indeed they are both involved in Abeta clearance through their internalization and degradation capacity. They notably express high levels of the Receptor for Advanced Glycosylation End (RAGE) which recognizes Abeta to degrade it through the endolysosomal process. However, after a certain concentration and specific AD related conditions, glial cells are overburdened, their benefical role switch off and they may even show a pernicious role in Abeta processing, contributing to the Abeta fibril formation and chronic neuroinflammation 107 . Microglial can remain long time in its activated form specially if neurotoxic agent is still present as for Abeta deposits, producing proinflammatory cytokines and toxic molecules which can worsen neurodegeneration and brain damage. As exposed previously, the pathological neuroinflammation can be mediated by the microglial activation in response to APP processing 458 or Abeta deposits accumulation 459 but others studies suggest others factors influencing neuroinflammation in AD like obesity and traumatic brain injuries 460 . GWAS studies have identified several genetics risk of AD falling genes express in immune cells and related to microglial functions including TREM2, SCIMP, MS4A3, HLA-DRA, HLA-DRB1 confirming important role of microglia in AD 39,[461][462][463] . Futhermore, the low grade systemic inflammation seen in normal aging or associated to obesity, could also play an important role in the priming of microglial activation through proinflammatory cytokines production and diffusion through the blood brain barrier 460,464,465 . Both inflammaging condition, and peripheral chronic inflammatory diseases are associated with accelerated neuroinflammation and higher AD risk 464,466,467 . Then, neuroinflammation as well as microglial and astrocytes activation appear to be important actors in AD pathogenesis. shown that repeated exposure to chemical agent including several heavy metals (including aluminum, arsenic, and mercury), particulate air, and some pesticides contribute to AD development 493 . Sleep deprivation appear also an environmental factors influencing Abeta accumulation, and could then increase AD risk 494,495 . On the other hand, cognitive ability and educational attainment appear protective factors against AD development 39 . These evidences of environmental influence highlight the importance to consider environmental exposure for AD prevention.

INTRODUCTION

Other pathological conditions can also contribute to AD risk. Having had a stroke increase by 50% the risk to have AD 496 . T2D, which increase stroke probability, increase also risk of developing AD 497 . Systemic inflammation induced by obesity or rheumatoid arthritis, or low grade chronic inflammation observed in normal aging (inflamm-aging), increase risk of AD development, and further support importance of inflammation in AD 465,[498][499][500][501] . To support that, anti-inflammatory treatment TNFalpha inhibitor reduces AD onset in arthritis rheumatoid patients 502 . In addition to inflamm-aging, several others aging related conditions have shown to contribute to AD development: Increase oxydative stress 503 , DNA damage 504,505 , but also increase senenescence 506 are others age related factors contributing to AD. Notably, senolytic drug-mediated removal of senescent Abeta-associated oligodendrocytes progenitors have been shown to improves cognitive functions while reducing neuroinflammation and amyloid beta load in an AD mouse model 506 .

INTRODUCTION 77

In other conditions linked to aging, the somatic mutation take attention these recent years, because somatic mutation in neurons was shown increase with age and sensitive to AD development [START_REF] Lodato | Aging and neurodegeneration are associated with increased mutations in single human neurons[END_REF][START_REF] Park | Brain somatic mutations observed in Alzheimer's disease associated with aging and dysregulation of tau phosphorylation[END_REF] . In a recent study, putative deleterious somatic mutation was differentially found in 27% of AD brains compared age matching healthy brain, with specific enrichment for genes contributing to tau related AD pathogenesis, including PIN1 [START_REF] Park | Brain somatic mutations observed in Alzheimer's disease associated with aging and dysregulation of tau phosphorylation[END_REF] . These results reconciling the strong genetics effect in AD susceptibilities and the late onset and sporadic feature of AD.

Epigenetic alteration is also a hallmark of aging and could have important role in AD. In aging brain, a DNA hypomethylation has been shown in the APP promoter [START_REF] Nativio | An integrated multi-omics approach identifies epigenetic alterations associated with Alzheimer's disease[END_REF] . In AD brains, H3K27ac and H3K9ac marks correlate with upregulation of chromatin and transcription related genes that contributes to Abeta42-driven neurodegeneration [START_REF] Nativio | An integrated multi-omics approach identifies epigenetic alterations associated with Alzheimer's disease[END_REF] . H3K9me3, mediating heterochromatin condensation, are also enriched in AD brains and lead to downregulation of associated genes involved in synaptic transmission and plasticity [START_REF] Lee | Epigenome signatures landscaped by histone H3K9me3 are associated with the synaptic dysfunction in Alzheimer's disease[END_REF] . AD synaptic dysfunctions are strikingly associated with miRNAs expression profile alteration. The best characterized miRNA biomarker is probably miR-125b upregulation in AD [START_REF] Cogswell | Identification of miRNA Changes in Alzheimer's Disease Brain and CSF Yields Putative Biomarkers and Insights into Disease Pathways[END_REF] and influencing the essential synaptic glycoprotein synapsin-2 (Syn-2)

contributing to APP processing and Abeta degradation 507,508 , as well as tau Hyper-phosphorylation 509 .

Too few studies have been made to appreciate the putative causal role of this epigenetics alteration in AD pathogenesis but require more attention regarding importance of epigenetics in others age related diseases. In any case, the epigenetic mechanisms identified in AD related features could be critical targets to better consider in AD diagnosis or management.

Together, these evidences of environmental, epigenetics, or age related factor give us insights about the critical non-genetics influences in AD pathogenesis and highlight important biological processes involved in AD, including inflammation/immune system deregulation, senescence, somatic mutation emergence and epigenetics alteration, which should be consider when modeling AD.

V.5. Genes involved in AD

The genetics influence in AD is high. The heritability is estimated at 60-80 % of all AD cases based on parental diagnosis linkage 510 . The early onset AD (EOAD, before 65 years old), representing 10% of all AD onset, have even more heritability ranging between 92% to 100% 444 . The known mutations involved in EOAD have high penetrance and are located in genes contributing to APP processing/ Abeta formation (PSEN1, PSEN2, and APP itself), but explain only 5-10% of EOAD case. The 90-95% remaining are still unexplained suggesting non-mendelian genetics or epigenetics transmission 444 .

To explain influence of genetics in late onset sporadic AD, GWAS are used allowing to identify loci associated to AD but also genes and biological process potentially implicated in AD 35,39,39,511,512 . In

INTRODUCTION

78 2019, a meta-analysis have identified 29 risk loci, implicating 215 potential causative genes and shown the important role of genes express in immune related cell types and regulating lipid related processes and processing of APP 39 . Then, two subsequent studies increasing meta-cohort size identified respectively 37 and 38 risk locis still highlighting strong role of microglia, immune cells and protein catabolism while discovering new genes candidates including CCDC6, TSPAN14, NCK2 and SPRED2 [START_REF] Schwartzentruber | Genome-wide meta-analysis, finemapping, and integrative prioritization implicate new Alzheimer's disease risk genes[END_REF]511 .

The most recent meta analysis led by Bellenguez et al, found 75 risk loci associated with AD, of which 42 newly identified 35 . Pathways enrichment analysis on genes associated to these variants further validate importance of amyloid and tau pathways as well as the role of microglia related process in AD pathogenesis. Importantly, the vast majority of risk loci fall in noncoding regions mostly in intronic or intergenic region 39 . Only 2% are in exonic region, and 1% as non-synonymous mutation. Those noncoding variants are enriched for active regulatory regions and tissue specific eQTLs, highlighting their role in regulating transcriptional activity, but ask further consideration before assigning them to a gene 39 . It is for this reason that gene prioritization analysis have done integrating several tissue specific regulatory information to prioritize genes in order to link risk loci to putative causal gene. In the last meta-analysis, gene prioritization analysis have highlighted 31 newly associated genes involved in putative new AD associated processes including tumor necrosis factor alpha pathway. Furthermore, they construct a genetics risk score integrating all this newly identified risk loci to predict AD onset, and show that this score was able to predict 1.6 to 1.9 fold increase risk of AD from the lowest to the highest decile. Together these GWAS meta-analysis highlight around 40-80 putative genes involved in AD with important role in APP processing, tau related process, lipids metabolism, endocytosis and immune system process including microglial activation.

The first genetics risk locus is APOE, well characterized since its discovery in 1993 513 . Its allelic version ε4 (APOE4) is present in 14% of the total population 431 , while found in between 40 to 80% of AD cases 514 . It increase by 3-to 15-fold the AD risk depending of the zygotic status 431 . APOE is an apolipoprotein able to transport lipids within or between cells and is involved in the clearance of Abeta by glial cells 515 . Furthermore, The APOE4 isoform have been shown to promote the lysosomal cholesterol accumulation in glial cells while impairing extracellular matrix homeostasis [START_REF] Tcw | Cholesterol and matrisome pathways dysregulated in astrocytes and microglia[END_REF] . It has also been shown to promote accumulation of extracellular and intraneural Abeta 516 .

INTRODUCTION 79

Others important risk loci have been robustly associated to AD including CLU, TREM2, and PTK2B, involved in previously mentioned AD related process. CLU is an extracellular chaperone inhibiting amyloid fibrils formation by sequestering the oligomeric forms 520,521 . TREM2 is a membrane receptor express in microglial recognizing Abeta42, which mediate its uptake and degradation by microglial 461,522 . For PTK2B, no obvious link with AD process was identified and need then further studies to its role in pathogenesis. This remark can be done for a majority of newly AD associated genes offering new perspective on our AD understanding.

V. Abeta management by microglia (APOE 515,516 , CLU 520,522,523 , TREM2 461 ), but some genes identified through GWAS do not directly links with Abeta pathway, indicating either independent mechanisms, or upstream non-direct regulators of Abeta pathway 39 . This is the case for BIN1 associated variants, which were significantly associated with total Tau level and phospho-Tau in the cereobrospinal fluid

but not with Abeta level 524 . BIN1, similarly to other AD risk genes including PICALM, CD2AP, CD33, EPHA, RIN3, MEF2C, and PTK2B, are involved in endosomal/membrane trafficking, suggesting important role of this biological process in AD pathogenesis in an Abeta independent manner [525][526][527][528] While BIN1 is the 2 nd most associated genetics risk locus, little is known about its role in AD.

Main variant associated to BIN1 increase by 17% the risk to have AD and are found across 40% of the population 35 . Because AD associated variants fall directly in BIN1 intronic region or colocalize with cis-eQTLs regulating BIN1 expression [START_REF] Schwartzentruber | Genome-wide meta-analysis, finemapping, and integrative prioritization implicate new Alzheimer's disease risk genes[END_REF] , it appears evident than AD associated variants impact BIN1 gene expression directly, suggesting important role of this gene in AD development. BIN1 is ubiquitously express in the body, with highest expression in skeletal muscle and the brain 529 . BIN1 gene is composed of at least 20 exons subject to intense splice events, having then several isoforms and tissue specific pattern of expression. Isoform 1 to 7 are specifically express in the brain, isoform 8 in skeletal muscles, while isoforms 9 and 10 are ubiquitous [530][531][532][533][534][535][536] .

BIN1 is an adaptator protein of the Bin/Amphiphysin/Rvs (BAR) family regulating lipid membrane dynamics 537,538 . All isoforms are able to induce or sense membrane curvature through their

INTRODUCTION 80
BAR domain, giving them a wide range of cellular functions in the control of cell membrane curving, shaping and remodeling 539 . However, only the neuronal isoforms have the CLAP domain mediating interaction with clathrin and AP2, two important protein involved in clathrin-mediated endocytosis 535,538,540,541 . Then, this information suggest specific role for BIN1 regulating endocytosis in brain.

Relatively few studies have been made since its discovery to decipher the role of BIN1 in AD.

In brain, BIN1 is express mostly in oligodendrocytes, glutamatergic neurons, microglia and GABAergic neurons 530,542,543 . In neuron specifically, BIN1 have been shown to negatively modulate endocytic flux 517 , and we recently found that BIN1 isoform 1 was able to regulate early endosome maturation and trafficking 517,544 . It has also been shown to participate to neuronal excitability by interacting with L-type voltage-gated calcium channels (LVGCCs) 545 . Recently, it have been shown regulating presynaptic neurotransmitter release with a role in memory consolidation 542,546 . In microglia, BIN1 could be involved in exosomes secretion 547 and have a role in regulating inflammation 548 . In oligodendrocytes, BIN1 could have role in membrane remodeling contributing with the process of myelination 530 . However, studies of the role of BIN1 in non -neuronal cell types of the brain remain elusive.

Role of BIN1 in AD remain unclear. In AD brains, BIN expression has controversially been shown to increase and decrease 543,549,550 . Indeed, one study have shown an increase total mRNA expression in frontal cortex of AD brains while others have found a decrease of mRNA in AD brains from multiple datasets and brain region 543 . To explain this putative contradiction, another study have shown that the protein level of the neuronal specific BIN1 isoform 1 was decrease in AD brain, but compensate by an increase of the ubiquitously express BIN1 isoform 9 551 . Furthermore, we do not know if this expression change occur similarly in all brain cell types or show cell specific expression alteration. Then, further analysis on clinical cell type specific transcriptomic data is needed to really decipher the cell type specific expression of BIN1 in brain and its alteration in AD.

Nevertheless, some preliminary studies have tried to decipher role of BIN1 in AD development.

Studies related to role of BIN1 in Abeta accumulation appears inconclusive. In primary works, researchers have studied the role of BIN1 in regulated APP processing and show that a BIN1 knockdown in neuronal cultures can increase Abeta generation through β-secretase (BACE1) sequestrating in early endosomes 518,552 However, these in vivo models based on familial AD mutations or AD genes overexpression, failed to accurately mimic biology of AD, especially the sporadic form which not rely on severe AD gene mutation, but rather at a progressive development depending of several early cellular or molecular mechanisms. These shortcomings of current animal models are illustrated by the fact that all promising preclinical therapies failed when translated into clinics. It is then necessary to develop new models of AD, which better integrate human and sporadic AD specificities.

Here we used neural progenitors cells (NPCs) derived from human iPSC (hiPSC) to generate neuronal bidimensional culture and brain organoid that have their own set of advantages compared to animal models. First, human iPSC can be easily manipulated and edit genetically allowing assessing impact of risk loci or role of gene in AD. Furthermore, patient derived iPSC can be used directly to assess the effect of population-relevant genotype in cell activity. Second, iPSC can be reprogrammed in a variety of brain cell types including neurons, astrocytes, microglia and blood barrier endothelial cells, allowing deciphering role of a gene or variant in each specific cell type. Such human derived cellular model better model human susceptibilities, exemplified recently on the study APOE4 AD risk allele [START_REF] Tcw | Cholesterol and matrisome pathways dysregulated in astrocytes and microglia[END_REF] . Indeed, authors of this study have shown that APOE4 have human specific effect on gene expression on microglia and astrocytes when compared to same cell types derived from APOE4 engineered mouse model. Finally, used of human derived brain organoid can recapitulate both cellular heterogeneity and interactions present in brain while kept human specific genetics influences 437 .

INTRODUCTION 83

We studied thanks to these models effect of the loss of function of one or two copies of the BIN1 gene (BIN1+/-and BIN1-/-respectively) on neuronal functions in the context of brain relevant cellular heterogeneity and structure (the cerebral organoid model), and in the context of cell autonomous activity, using pure hiPSC derived neuronal culture (hiNs). For this 2 nd study model, we leveraged scRNA-seq to assess cellular heterogeneity and cell type specific effect of this BIN1 deletion in these two different models, and integrate results with AD brain scRNA-seq data to assess resemblance with AD related cellular activity and signaling.

RESULTS

84

RESULTS

I. EPIGENETICS PROGRAMMING of HSPCs

The prenatal period is a critical period of rapid growth and differentiation where tissue acquire their structure and functions. Environmental challenge, during this period, like over exposure to nutrient leading to LGA neonates, predisposes individual to develop age related and metabolic

diseases, but the molecular mechanisms remain unclear. Several durable epigenetics alterations are found in early exposed tissue suggesting epigenetics programming of these diseases. Notably, my PhD supervisors are previously found a global DNA hypermethylation in LGA HSPC targeting genes regulating stem cell function suggesting early alteration of the hematopoiesis. Alteration of the hematopoietic system play an important role in the development of ACDs, and can be program as early as in utero following nutrient stress with long term consequences on hematopoietic functions. Then, based on these evidence, we hypothesized that early environment leading to excessive fetal growth affect hematopoietic compartment plasticity through epigenetics remodeling, thus modifying core functions of hematopoiesis and linking excessive fetal growth to increase sensitivity to metabolic and age-related diseases later in life. To challenge this hypothesis, we performed integrative analysis of DNA methylome, transcriptome and chromatin accessibility at single-cell resolution in early exposed HSPC in order to validate the DNA epigenetics alterations observed previously and evaluate their impact on HSPCs functions and plasticity.

We conducted a first set of analysis highlighting an epigenetics programming of the quiescence signaling in LGA HSC that we published recently. As a first step, using DNA methylation and scATACseq data, we further characterized the epigenetic memory of LGA HSPCs and predict impact on HSPCs function. For that we increase our number of cord blood derived HSPC DNA methylation data adding 16 CTRL and 16 LGA samples. To better highlight the influence of CpG methylation on gene expression, we implemented a new strategy integrating tissue specific regulatory annotation to weight each CpGs and infer regulatory link between CpG and neighbor genes. We confirmed that the DNA hypermethylation in LGA HSPCs target stem cells and growth signaling pathway. We also performed scATAC-seq on 5 LGA and 6 CTRL samples to validate the epigenetics alteration at cell level with another epigenetics layer; the chromatin accessibility. It also allowed us to validate the regulatory potential of differentially methylated CpGs (DMCs). Thanks to this integration, we found that both DNA methylation change and chromatin rearrangement occur in LGA in HSC-specific open chromatin region.

As a second step, using scRNA-seq on 6 LGA and 7 CTRL samples, we assessed the impact on transcription at subpopulation level. We further integrated with the epigenomics data to decipher if direct link can been found between epigenetic alterations and gene expression change. We observed

RESULTS

85 a concordant decreased expression of genes of an epigenetically altered regulatory network governing by EGR1, KLF2 and KLF4 TFs regulating activation/differentiation of HSC. Finally, using both scRNA-seq data and in vitro differentiation assay, we deciphered the functional impact on HSPCs observing both a decreased HSC abundance in LGA neonates and decreased HSC-derived colonies, suggesting a reduced ability for HSC to remain undifferentiated.

To further validate these results and challenge the hypothesis that LGA HSCs have a decrease regulation of their differentiation, I performed another set of analysis leveraging supplemental information of already generated scRNA-seq data but also generated new one using gene silencing experiments, cytokines stimulation, and single-cell multi-omics assay. Notably, I tested using these others approaches the hypothesis that LGA HSCs have a differentiation bias in response to stimulation(i), and challenged our scRNA-seq and scATAC-seq results using the recent single-cell multimodal assay with new samples(ii). These complementary analyses confirm the HSC differentiation bias in LGA by using two independent approaches. They also validate the KLF2 influence of downstream targets genes based on gene silencing experiment and single-cell multimodal analysis, and support the role of KLF2 and EGR1 related regulatory network on regulating response to cytokines stimulation and HSC activation.

Introduction

Hematopoietic stem cells (HSC) are involved in essential processes such as inflammation, cardiovascular repair, and immunity throughout the entire lifespan [1,2]. Thus, alterations in HSC's ability to self-renew and to adequately produce differentiated progeny have been suggested to contribute to the onset and progression of age-related diseases such as cancer and cardiovascular diseases [3,4]. Systemic alterations or the action of various stressors like aging [5,6] 2 of 20 functions in humans are not well understood, in part due to the diversity of phenotypes and behaviors of HSCs [7].

In mice, a maternal high-fat diet during gestation limits fetal hematopoietic stem and progenitor cells (HSPC) expansion and ability to repopulate while inducing myeloidbiased differentiation [8]. In humans, a limited number of studies have been conducted. Fetal growth was shown to alter the number of circulating CD34+ HSCs [9,10]. We previously described a global increase of DNA methylation in cord blood-derived CD34+ HSPCs from large for gestational age (LGA) infants compared to neonates with normal birth weight [11]. Still, the functional impacts of these early epigenetic alterations remain to be elucidated. Such an effort is essential to determine how these epigenetic modifications could mediate the association between early-life exposures and the induction of persistent life-long functional changes within the hematopoietic system.

We conducted a multimodal analysis combining single-cell epigenomics, single-cell transcriptomics, and in vitro analyses to link the DNA methylation alterations observed in LGA neonates with functional alterations in human cord blood-derived HSPCs. We developed novel analytical approaches to improve the integration of epigenomic and transcriptomic data. We found that the DNA hyper-methylation observed in LGA HSPC is associated with an HSC-specific decreased chromatin accessibility and gene expression of key genes involved in the HSC quiescence signaling as well as an alteration of the HSC colony-forming capacity.

Results

Optimized Methylation Gene Set Analysis Reveals Association between LGA DNA Hypermethylation and Stem Cell Differentiation Pathways

To confirm the LGA-associated DNA hypermethylation we previously observed, we significantly increased the power of our analysis. We expanded our original study through additional patient inclusions, thereby doubling the size of our cohort [11]. Using the HELP-tagging assay (HpaII tiny fragment Enrichment by Ligation-mediated PCR), we generated genome-wide DNA methylation data on 16 CTRL and 16 LGA cord-blood derived human CD34+ HSPC samples. We independently retrieved in this new dataset the global DNA hypermethylation initially found in LGA compared to controls [11] (Figure 1A). Then, to increase our detection power, we pooled both datasets and detected a total of 4815 differentially methylated CpGs (DMC) with 4787 CpGs hypermethylated and 28 CpGs hypomethylated in LGA (n = 36) compared to CTRL (n = 34, p-value < 0.001 and |methylation difference| > 25%; Figure 1A, Supplemental Table S1). This new set of DMCs was then used throughout the following analysis.

As the functional interpretation is performed at the gene level, each CpG (or DMC) must be linked to a specific gene. Thus, our ability to adequately infer the regulatory effect of a CpG and its target gene will affect our ability to identify relevant pathways. Standard analytical approaches usually rely on the distance between CpG and transcription start site (TSS) of the targeted gene and often only consider the top candidate DMC per gene, not taking into account the cell specific genomic context. Therefore, we refined the CpGgene association to optimally assess the influence of DNA methylation changes on gene expression and enhance functional interpretation. We built a novel gene-methylation score considering (1) the distance between TSS and CpG; (2) the CpG overlap with expression quantitative trait loci (eQTL) annotation, as eQTL information allows us to identify tissue-specific genomic region links to gene expression changes; and (3) the regulatory annotation (e.g., Promoter, Enhancer) based on cell-specific histone marks [12] and on the Ensembl Regulatory database, as we know that the relationship between change in DNA methylation and change in gene expression will depend on a cell-specific genomic context (Figure 1B). We established 756,470 CpG-gene associations including 34% of them found through eQTL annotation. We then summarized the CpG information at the gene level, generating a gene-methylation score for each gene (n = 24,857, 3 of 20 Supplemental Table S2). We first confirmed that the gene-methylation score properly recapitulates the influence of key parameters in DMC analysis such as significance and effect size of the methylation change, number of DMCs per gene, and distance from TSS, as well as promoter and enhancer localization (Figure 1C). We also confirmed that while preserving key information from standard methylation metrics, the gene-methylation score presented a better association with DEGs than significance or methylation change alone. Thus, the gene-methylation score appears to be a better predictor of the methylation influence on gene expression (Figure 1C). We then used our gene-methylation score to perform pathway enrichment analysis and data integration, especially considering integration with gene expression data. Using the gene ontology (GO) reference database, we performed methylation geneset enrichment analysis (GSEA) based on the gene-methylation score. We found that change in DNA methylation in LGA HSPC samples targeted genes involved in signaling regulating fetal development as well as in key stem cell pathways such as Wnt signaling, cell fate specification, and cell fate commitment pathways (adjusted p-value < 0.01, Figure 1D) confirming previous findings [11].

Single-Cell Transcriptomic Analysis Confirms Alteration of Hyper-Methylated Genes in Pathways Regulating Stem Cell Differentiation among LGA HSCs

To identify genes altered in LGA and to obtain further biological insight into the functional consequences of the DNA methylation modifications observed in LGA, we performed a single-cell transcriptomic analysis comparing CTRL and LGA HSPCs.

To enable lineage-specific transcriptomic analysis, we created a hematopoietic reference map (i.e., hematomap) by integrating data generated from cord blood-derived CD34+ HSPC cells (n = 18520) from 7 control neonates (Figure 2A). Based on clusterspecific gene expression, we identified 18 distinct clusters representative of major lineages (Long-Term HSC, HSC, Multi-Potent progenitor, Lymphoid, Myeloid, and Erythroid) of the hematopoietic compartment (Figure 2B, Supplemental Figure S2). Each cluster was annotated using cell-type-specific markers. Markers were then ranked based on their expression fold change and the specificity of the cluster. Top cluster-specific markers were compared with published cell-type-specific genes [13-16] (Supplemental Table S3). Candidate cell subpopulations were distributed as follows: 1% LT-HSC (ID1); 24% HSC (AVP); 45% MPP/LMPP (CDK6); Lymphoid (CD99, LTB); 1% B cell (IGHM); 1% T cell (CD7); 14% Erythro-Mas (GATA1); <1%Mk/Er (PLEK, HBD); 8% Myeloid (MPO); <1% DC (CST3, CD83). To identify differentially expressed genes (DEG) between CTRL and LGA samples, we implemented the Hash Tag Oligonucleotide (HTO) multiplexing strategy [17] allowing simultaneous processing of CTRL and LGA samples. Multiplexing is a means to limit the influence of technique-driven batch effects at every stage of the analysis to improve the biological relevance of the finding. We generated multiplexed single-cell transcriptomic data from 6 LGA (n = 6861 cells) and 7 CTRL (n = 5823 cells) samples. In LGA samples, we observed a shift toward downregulated genes (Supplemental Figure S3) especially in the HSC subpopulation (n = 285 downregulated genes over 373 DEGs, adjusted p-value < 0.05 and log2FC < (-0.5), Figure 2C; Supplemental Table S4). Notably, the well-known EGR1, JUNB, and KLF2 genes were among the top affected genes. Using GO enrichment analysis, we found that downregulated genes were enriched in growthrelated pathways (e.g., regulation of growth) as well as in stress-related biological processes (e.g., response to temperature stimulus, cellular response to chemical stress; Figure 2D, adjusted p-value < 0.05).

To assess if these HSC-specific transcriptomic changes may be associated with epigenetic changes, we integrated bulk DNA methylation with single-cell gene expression data using the gene-methylation score. We found that DEGs, and particularly the downregulated genes, mostly showed high gene-methylation scores (Figure 3A,B). We then assessed the association between changes in DNA methylation and gene expression at the pathway level. We looked for enrichment for differentially methylated genes considering pathways that were identified based on DEGs. We found a significant overlap between GO terms enriched in LGA HSC downregulated genes and GO terms enriched in hypermethylated genes (10 out of 46; p-value < 0.05, hypergeometric test). The most coenriched term is "regulation of growth" including notably SOCS3, SIRT1 and SESN2 genes that are both downregulated and within the top 10% of hypermethylated genes (Figure 3C). These results suggest that the epigenetic change in LGA could lead to an HSCspecific alteration of the regulation of growth signaling.

DNA Methylation Changes Occurs in HSCs and DEGs Associated Open Chromatin Regions

To assess if the HSC-specific transcriptional alteration could be due to HSC-specific epigenetic change, we profiled chromatin accessibility at the single-cell level (i.e., singlecell ATAC-seq). We generated open chromatin data across 8733 cells in HSPCs from 6 CTRL and 5 LGA neonates. We first annotated subpopulations using the label transfer approach between ATAC-seq data and the lineage labels from the Hematomap (Figure 4A, Supplemental Figure S4A). To validate the relevance of our lineage annotation, we performed TF motif enrichment and observed that lineage-specific peaks were effectively associated with well-known lineage-specific TF (Supplemental Figure S4B).

We then integrated our bulk DNA methylation data with our single-cell ATAC-seq data to assess DMCs distribution within open chromatin regions (OCRs). Overall, 31% of the 211,479 peaks contain CpGs queried by our genome-wide methylation assay. We first observed a strong enrichment for DMCs in OCRs with 74% of them located in OCRs compare to only 34% of overall queried CpGs (p-value < 0.001, hypergeometric test). Such enrichment further supports the putative regulatory influence of our DMCs. By performing lineage-specific analysis, we observed DMCs enrichment in HSC-specific open chromatin region with a total of 11% of HSC-specific peaks containing DMCs (adjusted p-value < 0.001, Figure 4B), while no enrichment was observed for the other lineages. This result corroborates the HSC-specific transcriptional impact of the DNA methylation changes observed in LGA. Furthermore, we observed that DEGs in LGA HSC and especially down-regulated genes were enriched for OCRs containing DMCs (Figure 4C).

Not limiting our analysis to the regulatory role of DMCs within open chromatin regions, we then assessed the change in chromatin accessibility in LGA HSCs. We identified 278 open chromatin regions that significantly differ between LGA and CTRL HSCs, with 215 showing decreased and 63 showing increased accessibility (adjusted pvalue < 0.001 and |log2FC| > 0.25, Supplemental Figure S4C). By performing TF Motif analysis on regions with decreased accessibility, we identified that the motif of the transcriptionally downregulated TFs EGR1 and KLF2 are highly enriched (p-value < 1.10 -40 ) and among the top 6 enriched motifs (Figure 4D, Supplemental Figure S4D).

We then assessed the interaction between DNA methylation, gene expression, and chromatin accessibility. Regions with decreased accessibility were also strongly enriched in peaks including DMCs and peaks associated with DEGs (Figure 4E), with 3-fold and a 2.5-fold enrichment, respectively. Furthermore, these regions were strongly enriched for peaks containing both DMCs and associated with DEGs (23-fold enrichment) illustrating that early epigenetic programming is actually not limited to changes in DNA methylation but also involves chromatin rearrangement targeting altered genes.

EGR1, KLF2, and KLF4 Are Key Upstream Regulators Influenced by Early Epigenetic Programming in LGA

To further characterize the molecular mechanisms affected in LGA HSCs and identified master regulators, we leveraged the single-cell resolution of our approaches to perform a co-regulatory network analysis. This approach allowed us to model the influence of upstream transcription factors (TF) on expression changes of downstream target genes. We performed co-expression analysis to identify genes co-regulated by the same TF, i.e., regulons, and filter each regulon based on the presence of TF motif within a cis-regulatory region (SCENIC). We identified a total of 250 regulons but only considered for further analyses the 106 regulons identified based on high confidence cis-regulatory motif. These regulons only rely on associations for which the presence of the TF motif was experimentally validated. We then scored the regulons activity in each cell using gene expression profiles of the entire regulons (AUCell). We observed that lineage-specific regulons are associated with concordant lineage determining hematopoietic TFs such as GATA2, GATA3, MEIS1, TAL1, TCF3, EGR1, CEBPB, HOXB4, SPI1, and STAT1/3 further supporting our subpopulation annotation and the SCENIC approach (Supplemental Figure S5A, Supplemental Table S5) [18].

To identify TF associated with the changes in gene expression observed in LGA HSC, we compared the regulon activity between CTRL and LGA. We found seven regulons with a significant decrease in activity in the LGA HSC population (adjusted p-value < 0.001 and |activity score fold change| > 10%, Supplemental Table S6). No regulons were upregulated. These regulons were associated with ARID5A, EGR1, KLF2, KLF4, KLF10, FOSB, and JUN (Figure 5A). Among them, ARID5A, EGR1, KLF2, FOSB, and JUN were part of the 10 top active regulons in HSCs (Supplemental Table S7). Based on functional enrichment analysis using as reference GO:BP gene sets, and HSC signatures of quiescence or proliferative state [19], we showed that these regulons were enriched in genes regulating stress response, proliferation, and HSC differentiation (Figure 5B).

To further support the association between change in DNA methylation and change in gene expression previously identified at the gene level, we performed GSEA analysis to identify regulon enriched for both differentially methylated and differentially expressed genes. We found 9 regulons enriched in both hypermethylated and downregulated genes (adjusted p-value < 0.01 and NES < -1.6), including the differentially active regulons ARID5A, EGR1, FOSB, JUN, KLF2, and KLF4 (Figure 5C). We also found 9 regulons enriched in hypermethylated and upregulated genes (adjusted p-value < 0.01 and NES > 1.6) with key HSPC-specific regulons such as SPI1 promoting myeloid differentiation [20] and HOX family (HOXA9, HOXA10, HOXB4) promoting HSPC expansion (Figure 5C) [21-23].

To confirm the putative influence of methylation change on TF activity, we performed TF motif analysis considering the proximal regions surrounding each DMCs (±20 bp). We found significant enrichment for 23 TF motifs (adjusted p-value < 0.05, Figure 5D). Among them, we found EGR1 and several members of the Kruppel-like factors (KLF) family: KLF14, KLF5, KLF1, and KLF6. Furthermore, by taking advantage of our singlecell ATAC-seq data, we looked at the enrichment of the TF motif in open chromatin regions of HSC containing DMCs. We found a strong enrichment in EGR1, KLF2, and KLF4 motifs indicating that DNA methylation change occurred in active regions of the EGR1/KLF2/KLF4 TF network (Figure 5E).

Multimodal Co-Regulatory Network Recapitulating TF-Gene Interactions Influenced by Early Epigenetic Programming in LGA

Based on the integration of the DNA methylation, single-cell ATAC-seq, and singlecell RNA-seq data, we built a network recapitulating interaction between main TFs and downstream target genes within the principal regulons altered in LGA neonates: EGR1, KLF2, and KLF4 (Figure 6). EGR1, KLF2, and KLF4 regulons rely on highly interconnected (co-regulated) genes (Figure 6A). For each target gene, we confirmed the presence of a unique or shared upstream TF binding motif within the open chromatin regions. We observed a high concordance between the regulons and open chromatin motif analysis: 96%, 91%, and 95% of genes included in EGR1, KLF2, and KLF4 regulons, respectively, were associated with at least one peak containing the corresponding TF motif supporting the association between genes and TFs. We then looked for evidence of epigenetic modifications that may alter TF-target interactions. We annotated genes with associated open chromatin regions containing at least one DMC (middle area) or identified as differentially accessible between CTRL and LGA (inside area) (Figure 6B). Overall, 23% (n = 27) of genes targeted by these TFs networks have epigenetic alteration (DMCs or decrease accessibility) in open chromatin regions while 22% (n = 26 genes) appear downregulated in LGA. Finally, we highlight KLF2 as possible master regulators influenced by early programming. Indeed, we identified KLF2 as a hypermethylated and downregulated gene that interacts directly with EGR1 and KLF4 suggesting the downstream influence of KLF2 on these TFs. Conversely, KLF2 was not identified as part of EGR1 and KLF4 regulons suggesting that KLF2 is not a target of these TFs. This network also further validated JUNB and SOCS3 being highly epigenetically altered in cisregulatory regions (Figure 6C), as well as ID1, CDKN1A, IER2, IER3, and IER5 as key downstream altered targets of KLF2, EGR1, and/or KLF4, again highlighting how early programming alters signaling involved in the regulation of cell proliferation and differentiation.

In Vitro Analysis Confirms the Alteration of HSPCs Differentiation Capacities in LGA

Our integrative analyses highlighted epigenetic and transcriptomic alterations targeting signaling pathways involved in the regulation of HSC differentiation and proliferation. Thus, we decided to challenge HSPC differentiation and proliferation potential in vitro using colony-forming unit (CFU) assays. After 14 days of expansion, colonies from 4 CTRL and 4 LGA samples were classified into three categories: those derived from common myeloid progenitors (CFU_GEMM), erythroid progenitors (BFU-E), and granulocyte-macrophage progenitors (CFU_GM) based on the morphology of each colony. We observed a significant decrease in the number of common myeloid progenitor colonies in LGA samples (p-value < 0.05; Figure 7A) as well as striking differences in shape and size of more differentiated colonies (Figure 7B). CFU_GEMM colonies are the product of a non-committed hematopoietic progenitor able to differentiate in both erythroid and myeloid lineage. In our samples, only HSC and MPP have these features, suggesting that the decreased CFU GEMM proportion in LGA reflects either fewer HSC/MPP in starting cell subpopulation composition or a decreased proliferation and differentiation capacity of these cells.

To evaluate these two possibilities, we monitored cell population distribution across conditions at molecular resolution using our single-cell expression dataset. We observed a decrease in HSC cells (p-value = 0.015) and a trend toward increased MPP cells (p-value = 0.13, Figure 7C) in LGA compared to CTRL. Another way to look at population shift is to use pseudotime, i.e., a measure that reflects how far an individual cell is in a differentiation process. Indeed, cord-blood-derived CD34+ HSPCs represent a heterogeneous population of cells ranging from progenitors to progressively restricted cells of the erythroid, myeloid, or lymphoid lineages as confirmed by our single-cell transcriptomic analysis. To follow cell distribution through these levels of differentiation and assess the influence of the LGA environment we used the pseudotime tool from Monocle [24] Collecting the pseudotimes across our different cell populations, we observed a positive correlation between pseudotime and lineage differentiation as expected (r = 0.99, Pearson correlation, Figure 7D, Supplemental Figure S6A). We then compared the distribution of the pseudotime between LGA and CTRL using the least differentiated cells as roots, i.e., the long-term HSCs. At the population level, we observed an increase in pseudotime in LGA (p-value < 0.001, Figure 7E). Indeed, we observed a decrease in the number of cells presenting pseudotime associated with the HSC state in LGA samples (p-value < 0.05) and a shift toward cells presenting elevated pseudotime suggesting that LGA HSCs exit quiescence and differentiate more quickly compared to CTRL HSCs (Figure 7E). Altogether, our analysis supports the association between LGA exposure and cell growth signaling targeted by DNA methylation and gene expression changes with alteration of differentiation and proliferation capacities.

Discussion

Here, we interrogated three major layers of the regulatory landscape in cord-bloodderived CD34+ HPSCs, DNA methylation, chromatin conformation, and gene expression. We characterized, in-depth and at single-cell resolution, the functional consequences associated with early DNA methylation changes observed in LGA neonates. Through, the integration of multiple datasets and the development of novel analytical approaches, we addressed a very challenging aspect of functional (epi)genomics, the interpretation of DNA methylation changes. Focusing on HSPCs, we believe that we contributed to a better understanding of how early environment shapes the hematopoietic compartment development and long-term function.

We demonstrated in LGA neonates a correlated increase in DNA methylation and change in chromatin accessibility associated with decreased expression of downstream target genes under the influence of key HSC transcription factors EGR1, KLF2, and KLF4. EGR1, KLF2, and KLF4 are zinc-finger transcription factors involved in HSC quiescence signaling. EGR1 has a known role in regulating cell growth, development, and stress response in many tissues. In HSPC, EGR1 plays a role in the homeostasis of HSCs regulating proliferation [25]. EGR1 promotes quiescence and decreases through differentiation. Interestingly, EGR1 has also been shown to interact with epigenetic regulators forming a complex with DNMT3 and HDAC1 [26] suggesting a possible role in the epigenetic remodeling observed in LGA HSC. The KLF family is implicated in key stem cell functions. KLF4 is the most well-known factor in this family due to its role in reprogramming somatic cells into induced pluripotent stem cells [27]. KLF4 has been identified as a target for PU.1 transcription factor required for lineage commitment in HSPCs [28]. KLF2 and KLF4 promote self-renewal in embryonic stem cells [29] but no study has looked specifically at KLF2 and KLF2/KLF4 interactions in HSPCs. Our data suggest direct and indirect (shared downstream target) interactions between these three transcription factors in HSPCs. EGR1, KLF2, and KLF4 represent targets to be further explored in order to challenge causality. Still, our findings lead to a better understanding of how early exposure can affect long-term hematopoietic maintenance in humans via epigenetic programming of the EGR1, KLF2, and KLF4 signaling. Furthermore, these coordinated epigenetic and transcriptomic changes target genes regulating growth signaling, such as SOCS3, SIRT1, and SESN2 [30-32]. Alteration of growth signaling highlights the tight correlation between in utero environment and the epigenetic programming. Indeed, excessive fetal growth observed in LGA neonates results in part from gestational hyperglycemia, dyslipidemia, or over secretion of placental insulin-like growth factors [33-35]. Altogether, these results further illustrate how DNA methylation and chromatin accessibility are key co-epigenetics actors regulating TF activity. Such interplay was already observed in the context of lineage commitment [36,37], but not yet in the context of developmental programming of HSCs. This highlights the interest in considering both methylation and chromatin rearrangement in fetal programming studies to decipher putative epigenetic imprinting and functional consequences.

Interestingly, EGR1, KLF2, and KLF4 are not only involved in the regulation of proliferation and differentiation per se but are key factors of the immediate, early response involved in stimulation-related cell activation. EGR1 and KLF2 expression increase in response to extrinsic stimulation. Elevated EGR1 and KLF2 expression promote selfrenewal and quiescence in HSC [25,29]. Our transcriptomic data suggests that such activation may be occurring in our samples with the activation of stress-related signaling. The primary scope of our study was not to characterize the environmental exposure that would trigger such responses. However, one can speculate that the activation could result from stress due to cold exposure or handling time inherent to sample preparation. Still, the decreased activity observed in LGA suggests that LGA HSCs' capacities to respond to environmental challenges are diminished. This hypothesis fits with the concept of early programming in which disease susceptibility relies not only on early impairment of organ development but also on a decreased adaptability to further environmental challenges to trigger disease [38]. Indeed, fine-tuning HSC quiescence mechanisms is of crucial relevance for optimal hematopoiesis. Not responsive dormant HSC would lead to hematopoietic failure due to a lack of differentiated blood cells. Although highly responsive HSC would lead to exhaustion of the population and a lack of long-term maintenance of the hematopoietic system [39].

To validate findings from our integrative approach, we challenged HSPCs in vitro and found a significant decrease in the number of CFU-GEMM colonies, colonies containing both erythroid and myeloid cells. These colonies are likely to originate from HSC or MPP cells, as only these cells have this multi-potential. These alterations could result from the decreased differentiation and proliferation capacities of these CD34+ cells or a decrease in their initial proportion in LGA cord blood. Our data suggest that both are altered in LGA. Indeed, the cell population analysis at the transcriptomic level revealed a decrease in HSCs in LGA neonates but a tendency to an increase in MPPs. We also observed epigenomic and transcriptomic alterations in signaling pathways and transcription factors regulating differentiation and proliferations of HSCs. Yet, this loss of stemness capacities in HSC is likely to drive the decrease in HSC subpopulations observed in our data and the decreased colony-forming capacity.

These findings corroborate previous studies on the developmental programming of the hematopoietic system [9,10]. A reduction in self-renewal of HSPCs and increased differentiation in both lymphoid and myeloid lineages have been observed in a mouse model of maternal obesity [8]. These effects may drive long-term consequences in human health as illustrated by the study performed by Kotowski et al. in which the integrity of the hematopoietic system in neonates was associated with susceptibility to onset of hematopoietic pathologies [40].

Hematopoietic stem cell differentiation and self-renew rely on a synergic interplay between genetically encoded signaling, cell-intrinsic, and cell-extrinsic factors as well as epigenetic modifiers [41]. This interplay appears altered in LGA neonates. We here provide a comprehensive model recapitulating the functional influence of the epigenetic early programming on HSPCs fitness to later environmental exposure (Figure 7F). We also linked LGA-associated epigenetic modifications to gene expression and functional alterations through a novel integrative approach. In this regard, we identified targets to be further explored. We also brought a better understanding of how early exposure can affect long-term tissue maintenance via epigenetic programming of EGR1, KLF2, and KLF4 associated regulation of growth signaling.

Methods

See the Supplemental Methods for additional information.

Clinical Sample Collection

Cord blood samples were obtained from CTRL and LGA neonates. LGA were defined by birth weight and ponderal index values greater than the 90th percentile for gestational age and sex. Control infants had normal parameters (between 10th and 90th percentiles) for both birth weight and ponderal index. Maternal and infant characteristics are shown in Supplemental Table S8.

Isolation of CD34+ HSPCs

Mononuclear cells were separated using PrepaCyte-WBC following which CD34+ cells were obtained by positive immunomagnetic bead selection, using the AutoMACS Separator (Miltenyi Biotech, Cologne, Germany). Cells were cryopreserved in 10% dimethyl sulfoxide using controlled rate freezing upon analysis.

Genome-Wide DNA Methylation Assay

DNA methylation levels for >1.7 M CpGs were obtained using the HELP-tagging assay as previously described [42].

Single-Cell RNA Sequencing Libraries Preparation

After cell count and viability check, the cell suspension was loaded into the Chromium controller (10x Genomics, Pleasanton, California, US) and library was generated using the chromium single-cell v3 chemistry following manufacturer recommendations. Gene expression library was sequenced using 100 bp paired-end reads on the Illumina NovaSeq 6000 system (Illumina, San Diego, California, US).

Single-Cell ATAC Sequen1cing Libraries Preparation

After cell count and viability check, nuclei were isolated from cell suspension and incubated with transposase. Transposed nuclei were then loaded into the Chromium 10x Genomics controller and library was generated using the chromium single-cell ATAC v1.1 chemistry following manufacturer recommendations. Gene expression library was sequenced using 150 bp paired-end reads on the Illumina NovaSeq 6000 system.

HTO Protocol

After cell counting and viability check and prior to cell suspension loading on the Chromium controller, cell hashtag (HTO) staining (Biolegend, San Diego, California, US) was used following the cell-hashing protocol [17].

Colony Forming Unit Assay

To assess clonogenic progenitor frequencies, 3 × 10 3 CD34+ HSPC cells were plated in methylcellulose containing SCF, GM-CSF, IL-3, and EPO (H4434; STEMCELL Technologies, Vancouver, Canada). Colonies were scored 14 days later.

Data Processing and Statistical Analysis

For DNA methylation analysis, low-quality CpGs were filtered out based on detection rate and confidence score. 754,931 out of 1,709,224 CpGs were conserved for further analysis. Linear regression and statistical modeling using the LIMMA R package [43] were used to identify differentially methylated CpGs (DMC) including maternal age, sex, ethnicity, batch, and library complexity in the linear model. We assessed enrichment for biological pathways performing GSEA using the ClusterProfiler package [44]. We performed transcription factor (TF) motif enrichment analysis using the HOMER tool [45] considering a 20 bp region around the DMCs.

For single-cell RNAseq (scRNA-seq) analysis, data were preprocessed using the CellRanger count pipeline (10x Genomics). Data filtering, normalization, and integration as well as cluster identifications were performed using Seurat (v4) pipeline. Pseudo-bulk differential expression analysis between LGA and CTRL cells within each hematopoietic lineage was performed using DESeq2 R package including batches and sex of samples in the negative binomial model [46]. Over representation test was performed on differentially expressed genes (DEGs) using enrichGO and enrichKEGG of the ClusterProfiler Package. The SCENIC workflow [47] was used to identify co-regulated genes module associated to a TF (regulons) and to generate cell-specific activity scores for each regulon. Differentiation trajectory analysis and pseudotime attribution were conducted with Monocle [24].

For single-cell ATAC-seq, data were preprocessed using the CellRanger ATAC pipeline (10x Genomics). Data filtering, normalization, and integration as well as clustering were performed using the Signac pipeline. Cell type identification was based on scRNA-seq annotation using a label transfer approach. Peaks calling at lineage level was performed using the MACS2 tool. Peaks specific to each lineage or differentially accessible between LGA and Control were identified using the FindMarkers function with Logistic Regression (LR) models including cellular sequencing depth as a latent variable. TF motif enrichment on lineage or group-specific peaks was performed using the FindMotifs function. All peaks enrichment analysis was performed using hypergeometric tests. For final Gene Regulatory Network (GRN) construction, TF target interactions inferred with SCENIC were filtered out based on the presence of a corresponding TF motif in the peak associated with the target. Supplemental Table S9 contains information on the number of cells per sample.

Gene-Methylation Score

To compute the gene-methylation score, 2 steps were needed: (1) to generate a CpG score that reflects the association between CpG and gene, and ( 2) to concatenate CpGscores at the gene level.

(1) CpG-score CpGScore = (-log10(pcpg) × meth.change) × LinkWeight × RegWeight Where pcpg is the nominal p-value of the differential methylation analysis, and meth.change is the difference between the percentage of methylation in LGA and the percentage of methylation in CTRL. LinkWeight represents the confidence in CpG-gene association and RegWeight represents the estimated regulatory influence of the considered CpG based on CD34+ specific genomic annotation defined using CD34+ specific histone marks as previously described [11] and EnsRegScore refers to regulatory regions defined based on the Ensembl Regulatory build hg19 genome annotation [48].

(2) To concatenate CpG-Scores at gene level: gene-methylation score To summarize the CpG methylation change at the gene level, we aggregated the CpG-Scores into a methylation gene score by taking care to (i) alleviate the arbitrary number of CpGs per gene and (ii) interpret differently CpG influences located on the promoter of them in others genomic region.

The gene-methylation score is defined as: Gene-methylation score = ( ∑ 𝐶𝑝𝐺 𝑆𝑐𝑜𝑟𝑒 × 𝑊𝑒𝑖𝑔ℎ𝑡 𝑛_𝑐𝑝𝑔 )promoter + (∑ |𝐶𝑝𝐺 𝑆𝑐𝑜𝑟𝑒| × 𝑊𝑒𝑖𝑔ℎ𝑡 𝑛_𝑐𝑝𝑔 )other_regions

Where the WeightnCpG was optimized to alleviate the influence of the number of CpGs linked to a gene and defined as:

WeightnCpG = √ 1 ∑ 1 |𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶+1| 3,8
The code to perform the analyses in this manuscript is available at https://github.com/umr1283/LGA_HSPC_PAPER.git (last accession date : 29 th June 2022).

Supplementary Materials:

The following supporting information cand be downloaded at: www.mdpi.com/xxx/s1 , Figure S1 Funding: Support for this project was provided by the Roadmap Epigenomics Program, R01 HD063791 (Einstein/Greally). Support was also provided by Einstein's Center for Epigenomics, including the Epigenomics Shared Facility and Computational Epigenomics Group.

Institutional Review

RNA velocity analysis

RNA velocity analysis was performed using Velocito to produce the spliced and unspliced gene expression matrices 556 and scVelo to estimate the RNA velocity across cells through dynamical modelling 557 . We then used both RNA velocity and the pseudotime analysis (published results) to estimate the cell fate of the cell, i.e. toward which differentiation state the cell is going to. To calculate this cell fate, we used the cell transition matrix produced by scVelo. The transition matrix gives the probability for each cell to differentiate in other cells based on this newly synthetized mRNA (pre-mRNA). Then, by multiplying these probabilities by the pseudotime of others cells we get a prediction of the future pseudotime of the cells, i.e toward with differentiation state the cell go. By subtracting this predicted pseudotime by this actual pseudotime, we obtained its expected pseudotime shift, representing the intensity of the cell differentiation, i.e. its differentiation bias.

I.2.a.iii. Rapidly processed samples

To produce rapidly processed samples, we removed the HTO multiplexing part from the scRNAseq protocol. To pool samples together and allow cell multiplexing, we pool samples depending on their sex, because samples can then be demultiplex based on sex specific transcripts. Each cryopreserved CD34+ cells from each sample were thawed in a water bath at 37°C 1min before to be sex (1 male and 1 female by pool) and cell suspension was loaded on a Chromium 10x Genomics controller following the manufacturer protocol using the chromium single-cell v3 chemistry with single indexing. After library preparation, the pool was sequenced using 100pb paired-end reads on NOVAseq 6000 system following the manufacturer recommendations (Illumina). Gene expression matrices were generated using the CellRanger count pipeline.

I.2.a.iv.

Gene silencing

We used electroporation (nucleofection) to transfect siRNAs targeting KLF2 transcripts on cells.

This experiment require a large number of cells because induce significant cell loss during electroporation step (>50%). To obtain a maximum of cells, CD34+ cells were isolated from fresh CTRL cord blood and put in culture overnight in StemPro™ CD34+ Cell Medium (GIBCO™, Fisher Scientific).

Then, the Human CD34+ Cell Nucleofector™ Kit (Lonza) was used to perform the siRNAs

RESULTS

110

(how a gene are actively transcribed) for each gene in each cell. Using this approach, we were able to estimate the RNAs velocity for 12 684 cells and to infer a transcriptional dynamic (velocity vectors) across all cells (Figure 16A). Interestingly, while the late branches (erythroid, myeloid and lymphoid progenitors) have a transcriptional dynamic toward differentiation, the main transcriptional dynamics of HSC and MPP cells are toward stemness conservation, so from MPP to HSCs, further supporting that the main transcriptional program in our cells are related to the regulation of activation. Indeed, we observed that the genes the most actively expressed (bigger RNA velocity and variance across HSPCs) are genes related to control of activation like FOS, DUSP1, FOSB, ZFP36, HES1, and NFKBIA (Figure 16B).

Concordant with that, by looking at regulon's enrichment for the main contributors of this transcriptional dynamics model, we found the EGR1 regulon being the most enriched, while KLF2 and JUN are in the top5 (Figure 16C). We used this transcriptional dynamic to estimate toward which differentiation states the cells are transitioning and compared then LGA and CTRL cells dynamics (see method). We observed that LGA HSCs transition faster toward more differentiated cells compared to CTRL HSCs, while such differences doesn't appear significant in the others lineages (Figure 16D). These results confirms that LGA HSCs have a differentiation bias compared to CTRL HSC, supporting published transcriptional and functional alterations.

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112 differentially expressed genes comparing HTO versus rapidly processed samples (adjusted p-value<0.05 and |log2FC|>0.5), including 1075 upregulated in HTO samples enriched for pathways regulating stress response like Foxo, NF-kappa B, and MAPK signaling, but also biological process related to stress or extrinsic stimulation (Figure 17B). These results confirms that the HTO protocol trigger a stress on our cells. Then, we performed a DEG analysis at subpopulation level to know if this response was subpopulation specific. The results of the DEGs analysis at subpopulation level showed that the main cellular response to HTO protocol comes from the HSC subpopulation (Figure 18C). The upregulated genes on HSCs were similar to the one found regarding all HSPCs, confirming that the main transcriptional response to HTO protocol is in HSCs (Figure 17D). Futhermore, by using SCENIC to infer TF regulons activity across each cells and comparing regulon activity between HTO prepared and rapidly process cells, we observed that HTO prepared HSCs have a strong increased activity for FOSB, ARID5A, EGR1, JUN, KLF2, FOS, JUND and KLF4, further supporting the role of these regulons in the stress/stimulation response(Figure 17E). Based on G2/M gene expression signature, we observed also that the HSC and MPP cells prepared with HTO are more committed in the mitosis process (G2/M phase) than non HTO-prepared cells (Figure 17F). These results confirm that the HTO-based protocol trigger a cellular activation, especially in HSC, leading to entry in proliferation and differentiation process. The fact that HSC respond more to the environment compared to other HSPCs fit with the

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113 fact that HSC activate quickly in the bone marrow niche in response to blood loss or other hematopoietics challenge to ensure blood homeostasis or respond to infection 371 . Thanks to these rapidly processed samples, we can study if the LGA specific transcriptional and functional alterations reflect a difference of cellular response to the stimulating/challenging environment during cells preparation. We processed 6 LGA and 7 CTRL samples with this quick cell manipulation condiditions. We annotated cells subpopulation based on the hematopoietics reference map (hematomap) established in the published results. We compared transcriptome within each subpopulation and subpopulation distribution similarly to the HTO processed samples. Based on these samples, we did not observe significant gene expression change across lineage between LGA and CTRL samples (Figure 18A). Concordantly, we did not observed significant subpopulation proportion change between LGA and CTRL (Figure 18B). These results suggest that rapidly processed samples LGA and CTRL samples are relatively similar in term of gene expression and HSPC composition when not challenged by stressful/stimulating environment and thus that both gene expression and subpopulation shift observed in HTO processed samples reflect a cellular intrinsic alteration of the response to stress/stimulation in LGA. These results are concordant with the fact that transcriptional alteration observed in LGA samples target the immediate early response genes, which are expressed specifically in HTO-prepared samples.

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115 Validate the EGR1/KLF2/KLF4 regulatory network and its role in HSC activation

Our published results have highlighted a gene regulatory network, govern by EGR1, KLF2 and KLF4 TFs, being epigenetically and transcriptionally altered in LGA. These regulatory networks were inferred based on co-expression analysis of TFs and putative downstream target genes using our scRNA-seq data (SCENIC), and further filtered thanks to scATAC-seq data observing if accessible regions close to a gene have the specific TF motif. However, this method assume that the closest gene of an accessible region is the gene regulated by this accessible region. Regarding that DNA topology/ folding this assumption can be false in certain case because of DNA 3D conformation. Then, to validate the relevance of the inferred regulatory network of KLF2, EGR1 and KLF4, we used another strategy to associate accessible region with gene expression. This strategy leveraged the single cell multimodal assay assessing both chromatin accessibility and gene expression in the same cells. The main advantage of this analysis is that we can directly correlate the chromatin accessibility of an open chromatin region (peak) to the neighbor genes expression and therefore link a peak to a gene more precisely. We compared also stimulated versus unstimulated cells to highlight if this regulatory network is stimulation dependant. To do that we used IL6, CSF, Tpo and Flt3 cytokines known to

RESULTS

116 activate HSCs and allow their expansions. We recovered 4354 cells from unstimulated and stimulated condition (2001 for unstimulated, 2353 for stimulated), and annotated them thanks to the hematomap (Figure 19A).

Based on lineage subpopulation analysis, we observed a net decrease of HSC in the stimulated conditions compared to unstimulated conditions (Figure 19B). This change is accompagnied by a net increase of MPP/LMPP cells as well as DC progenitors. These results show that cytokines stimulation activate HSC toward differentiation process. By performing lineage specific differential expression analysis comparing stimulated to unstimulated HSC, we observed 581 DEGs, including 239 upregulated and 342 downregulated genes (Figure 19C). Importantly, we observed that the upregulated genes are strongly enriched for regulons of JUN, FOSB, ARID5A, FOS, and JUNB being the top5 most enriched regulons, while EGR1 and KLF2 regulons are also found significantly enriched (Figure 19D). These results support that the cell response observed in HTO prepared cells are similar to a response to physiological cytokines and confirms that the regulons altered in LGA HSC are regulons regulating stimulation response. These new data further support that the cellular response to stimulation is altered in LGA HSCs. To validate more precisely the gene regulatory network altered in LGA and the impact of methylation, we infer peak-genes links for all the genes taking part of the previously identified regulons (n=2476 genes). Using the correlation between peak accessibility and neighbors' gene expression, we identified 4281 peak-gene links across 1487 genes representing 60% of the interrogating genes (Figure 20A). These gene expression associated peaks are candidate cis-regulatory elements (CREs; e.g. enhancer) able to regulate associated gene expression. Considering genes associated to EGR1, KLF2 and KLF4 regulons (n=123 genes), we identified candidate CREs for 78 genes (63% of the genes).

Interestingly, we found ZFP36L2 associated to a lot of candidat CREs ( 26) suggesting tight regulation (Figure 20B). This gene is an RNA-binding protein which is known to promote cell quiescence and to regulate erythroid differentiation 566 . Interestingly, it has also a role in promoting mRNA decay of immediate early genes (IEGs) 567 , highlighting its role in regulating response to stimulation. Then, we focused on CREs containing EGR1, KLF2, or KLF4 TF motif. We observed that 46% of genes in EGR1

regulon have an EGR1 motif on a CREs (p-value < 0.05, over-representation test), while 45% for KLF2

(p-value < 0.01, over-representation test), and only 38% for KLF4 (non-significant, over representation test) (figure 20C). Even if this analysis was not able to validate every inferred TF-gene regulations, it

shows that the EGR1 and KLF2 regulons inferred with previous methods are enriched for candidate CREs containing the corresponding TF motif, validating partially TF influence on these genes. However, these results were not able to validate the influence of KLF4 on inferred regulon. Together, these results highlight the interest of using single-cell multimodal data to find, or validate, TF-gene regulatory interaction based on correlation between chromatin accessibility and gene expression.

Then, to validate the putative influence of LGA associated methylation on gene expression, we integrate this newly identified regulatory information with methylation data. Overall, 2% of the ~750k queried CpGs fall in CREs, while 6% of the 4815 DMCs, showing 2.5 fold enrichment for DMCs in candidate regulatory elements (p-value<0.0001, over-representation test), further supporting putative DMCs impact on gene expression. Critically, these DMCs associated CREs are strongly enriched for EGR1/KLF2/KLF4 TF motifs with 91% of these CREs (198/216) having at least one of this TF motif (p-value < 0.0001, over-representation test; Figure 20D). These results further confirm that the DNA hyper-methylation in LGA target the EGR1/KLF2/KLF4 gene regulatory network.

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118

Gene silencing-based validation

We found a correlation between hypermethylation, chromatin rearrangement, and decrease expression of EGR1/KLF2/KLF4 related transcriptional network associated with a reduce HSC ability to stay quiescent in response to challenging environment in LGA samples. To assess the causality between this network downregulation and the differentiation bias in LGA samples; we performed gene silencing experiments, using siRNAs targeting KLF2. We chose to target KLF2 because being the most upstream R https://support.10xgenomics.com/single-cell-multiome-atacgex/software/pipelines/latest/algorithms/feature-linkage

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119 regulators based on co-regulatory network analysis results (SCENIC). This gene silencing experiment have also the potential to further validate the KLF2 influence on the downstream target genes (regulon) infer thanks to both scRNA-seq and scATAC-seq data. To perform this experiment, we used a fresh sample of CD34+ cells and transfect cells either with siRNAs targeting KLF2 transcripts (siKLF2 condition) or with negative control (siCTRL condition) and let the cells over-night in incubation on CD34+ cells optimized medium with stimulating cytokines (see Method). We first confirmed that siKLF2 indeed reduce expression of KLF2 through RT-qPCR (Figure 21A). We then performed scRNA-seq and recover 1533 and 2116 good quality cells from siCTRL and siKLF2 respectively. We annotated our cells based on the hematomap (Figure 21B-C) and found that the overnight incubation with media supplemented with cytokines lead to increase differentiated cells, because only 4% of cells were annotated as HSC compare to the 24% in our previous unincubated samples (Figure 21D). To observe if KLF2 silencing led to differentiation bias as observed in LGA samples, we looked at subpopulation distribution between siKLF2 and siCTRL samples. We observed a slight reduction of HSC and MPP cells in siKLF2 compared to siCTRL (Figure 21E). Even if the difference appears significiant using a chisquared test under the assumption that siCTRL distribution is the ground truth, our sample size is very limited.

This experiment should be reproduce at least 3 times to further validate the statistical signifiance.

To validate the role of KLF2 regulating the downstream genes identified with previous analysis, we performed differential expression analysis on siKLF2 compared to siCTRL HSCs focusing on genes from the KLF2 regulon (n= 89). We decided to focus the test on HSC cells specifically because we have previously shown that KLF2 alterations mainly target HSCs. We further confirm that KLF2 is specifically active in HSC, by measuring the KLF2 regulons activity score using AUCell algorithm (Figure 21F). We observed only few DEGs between siKLF2 and siCTRL HSC passing adjusted p-value threshold 0.05 (n=4), but 15/89 (16%) genes at nominal p-value (Figure 21G). This relative weak result can be explained by the relative low number of HSCs and by the transfection efficiency (transfection efficiency was estimated being around 50% based on preliminary analysis).

To go beyond this relative finding, we performed an unsupervised differential expression analysis and gene set enrichment analysis (GSEA) to see if these transcriptional alterations were specific to the KLF2 regulon. We observed 4 regulons significantly enriched in downregulated genes (adjusted p-value <0.1), with KLF2 regulon being the second most enriched regulon (Figure 21H).

Interestingly, the first most enriched regulon was STAT3, suggesting close link between KLF2 and STAT3 activity. These results confirm previous inferred data using both co-expression and chromatin accessibility and highlight putative regulatory role of KLF2 on STAT3 signaling. Here we validated this DNA methylation alteration using an independent cohort and integrate all data to gain in discovery power.

One main challenge of DNA methylation analysis is to link with putative functional alteration.

Indeed, functional enrichment analysis is performed at gene level and not at CpGs level. CpGs are heterogeneously distributed across the genome, which introduce a bias when linking to gene. Some tools have been developed trying to overcome this issue correcting for DNA methylation data specific bias [568][569][570] . Recently, Maksimovic et al have developed GOmeth and GOregion to better take into account methylation data specific bias allowing to perform functional enrichment analysis weighting each CpGs methylation differences according to the gene specific context 570 .

Here, we weighted each CpG methylation change with tissue specific eQTL data, and histones profiles, as well as global regulatory annotation while correcting for CpG bias to obtain a score by gene reflecting the probability that the methylation change affect expression of them. We showed that this score better predict gene expression change than classical methods highlighting the predictive power of this method. Thanks to this approach, we predicted the DNA methylation change affecting genes regulating fetal growth and organ development, as well as stem cells pathway including notably Wnt signaling pathway and genes regulating cell fate commitment. Wnt signaling is a key pathway regulating stem cells differentiation for organogenesis as well as tissue regeneration [571][572][573] . DNA methylation of these pathways has been found to alter stem cells differentiation capacity in adipocytes

RESULTS

122 derived stem cells 574,575 . Then, these results further confirm putative impact of DNA methylation alteration in LGA HSPCs on regulation of their differentiation.

Even if different epigenetics layer (methylation, histone marks, chromatin conformation) coexist in cells, the interactions between them are not always clear. Recently, a clear link between DNA methylation and chromatin accessibility have been found in Arabidopsis model, where Zhong et al showed that ablation of DNA methylation durably affect chromatin accessibility 576 .

Here, integrating bulk DNA methylation with scATAC-seq data, we observed in our LGA model that the increased DNA methylation observed is associated with a decrease chromatin accessibility,

showing in human cells this link between DNA methylation and chromatin accessibility. We found also that the most enriched TF motifs in DMCs regions are also those found in decrease accessibility regions further supporting the interplay between DNA methylation and chromatin accessibility. The TFs motifs enriched in epigenetically altered regions include TFs of the SP family like SP2 and SP3 encoding for zinc finger proteins having key role in Wnt signaling mediated embryogenesis 572 . They also include the zinc finger protein EGR1, which regulate hematopoetic stem cells proliferation and response to extrinsic stimuli 577,578 , as well as the Kruppel family TFs KLF2 and KLF4, which are key role in stem cells self renewal and regulation of differentiation [579][580][581] . Interestingly, within HSPC, LGA epigenetics alterations seems to affect specifically HSC. Indeed, we found that both DMCs and chromatin rearrangement are enriched in HSC specific open chromatin regions. These results highlight the interest to study epigenetics layers at single-cell level rather than bulk and suggest transcriptional consequences on HSC specifically. Together, we found in LGA a coordinated increase DNA methylation and decrease chromatin accessibility in candidat regulatory regions of TFs known to regulate proliferation and differentiation of stem cells suggesting impact on expression of these genes and regulation of differentiation in these cells.

I.3.b. Decipher the impact on gene expression

Single-cell transcriptomic data can both highlight transcriptomically distinct subpopulation and gene expression change between two conditions within the subpopulations. Several previous scRNAseq studies have decipher the different subpopulations present in cord blood HSPC highlighting the transcriptomics and functional heterogeneity within phenotypically defined population 122,582-584 . Here we identified 7 distinct subpopulations in HSPCs recapitulating the different lineage found in the cord blood hematopoietic compartment, from LT-HSC to restricted erythroid, myeloid and lymphoid progenitors. Notably, we identified like a recent study on developmental hematopoiesis 585 the oligopotential erythro-myeloid progenitors (EMP) connecting HSC/MPPs to erythroid, megakaryocytes, and

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123 mast cells. This oligo-potential progenitor appears under the governance of GATA2 and TAL1 TF based on markers expression and regulons analysis.

To decipher LGA specific transcriptomic change in these subpopulations, we performed pseudobulk differential expression analysis allowing to find robust gene expression difference in LGA compared to CTRL samples. We observed that the main expression changes were in HSC, with a significant number of genes (285) being downregulated. These downregulated genes are enriched for genes negatively regulating cell growth signaling and proliferation, and response to stress or stimulation, suggesting decrease ability of LGA HSC to control differentiation and proliferation in response to stimulation. By integrating these results with the epigenomics change discuss in previous section, we observed that these downregulated genes were enriched for hyper-methylated genomics regions as well as regions with decrease accessibility highlighting the putative epigenetics influence on gene expression. Together, these results suggest that the epigenetics memory of fetal overgrowth Here, combining co-regulatory network analysis of scRNA-seq data (using SCENIC tool), and TF motif accessibility based on scATAC-seq data, we identified EGR1, KLF2 and KLF4 are putative TF upstream regulator affected by the epigenetics change. We confirmed that the regulons of these TFs were enriched for hypermethylated and decrease accessibility genomics regions, supporting the role of the epigenetics change on TF activity and targeted downstream genes. Furthermore, we found that these

TFs have direct and indirects interactions. Indirect, because having co-downstream targets genes, and direct because regulating each other's. These interactions suggesting a common transcriptional program. Indeed, as exposed before, these TFs are all known to negatively regulate proliferation or differentiation, thus promoting HSC quiescence 577,586-589 . These evidence were confirmed in our data because their downstream genes are enriched for markers of HSC quiescence as well as for biological process regulating differentiation, proliferation, and response to stimulation.

Using single-cell multimodal (ATAC+ gene expression) data, we further confirm putative influence of EGR1 and KLF2, but not KLF4 on these downstream genes as well as their role in regulating HSC stimulation/activation. We further test the relevance of this regulatory network using gene silencing

RESULTS

124 experiments targeting KLF2. We found that the KLF2 knock-down was able to reduce expression of genes associated to the KLF2 regulon, but also to reduce HSC/MPP proportion further validating the KLF2 regulatory network and role in regulating differentiation of HSC. Taken together, we observed that HSCs exposed to fetal overgrowth have specific epigenetics alteration associated with a transcriptional downregulation of genes under the control of TFs, mostly EGR1 and KLF2, known to regulate differentiation and proliferation of HSCs. These results further suggest epigenetics programming of transcriptional activity in LGA, and consequences on HSC cell fates.

Interestingly, EGR1 was recently found as a key actor in shaping the brain DNA methylome by recruiting the DNA demethylase TET1 to regulate activation of downstream genes in response to life experience 590 . In our study, we observed that in LGA, EGR1 appears downregulated, with a decrease activity on downstream genes, while its putative binding regions are enriched for DNA hypermethylation. In light of its role in regulating DNA methylation, these results suggest then a direct link between EGR1 activity and the remodeling of DNA methylation observed in LGA.

Several evidence in our data show that the LGA response to stimulation/stresses is altered. The genes or regulons differentially expressed in LGA are strongly enriched for genes of the immediate response to external stimuli / stress. Notably, EGR1, SOCS3, JUNB, JUN, FOSB, DUSP2, IER2, and IER5 which are downregulated in LGA compared to control HSC, and known to regulate response to stress or stimulation taking part or the immediate early response 578 . The immediate early response genes (IEGs) are genes able to rapidly be transcribed, within minutes, following a stimulation. Our scRNA library preparation protocol require relatively long cells manipulation and incubation time on challenging environments (cold temperature, centrifugation...), due to sample multiplexing strategy (HTO) and are thus likely to trigger a cellular response through gene expression. These manipulation dependent gene expression was demonstrated in a previous study, aspecially for cryopreserved cells, as this is the case for our cells 591 . By processing samples with reduces cell preparation time, we confirmed that the HTO based cell preparation protocol triggers a cell response to stress / stimulation.

To validate the physiologically relevance of the HTO stimulation, we characterized the HSPC response to physiological cytokines, notably IL-6, known to activate HSC through the JAK/STAT pathway 592-594 , and found that cytokine stimulation activates similar regulons (JUN, FOSB, EGR1, STAT3...) that the the one with the HTO protocol supporting that the cellular response is physiological. Together, we showed that LGA functional changes are likely to represent an epigenetics programming of their response to stimulation/growth signaling. This observation fit with the concept that early exposure can have durable consequences on the ability of cells to respond to future environment as observed previously in others tissues like pancreas, adipose tissue, and muscle in the context of aging 314,316,317,595,596 .

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125 I.3.a. Assess the impact on HSPCs plasticity HSC are mainly quiescent and located in the bone marrow niche. The exit of quiescence toward proliferation and differentiation is tightly regulated by HSC molecular response to cytokines and cellcell interactions [597][598][599][600] . Several studies have found that this balance between HSC quiescence and differentiation is altered in aging 400 . Recently, Sureshchandra et al have shown in primate that HSC and progenitors ability can be altered as early as in utero. Indeed, they showed that maternal high fat western style diet was able to alter HSPC expansion and repopulation ability while reducing lymphoid potential 601 .

Here we found an alteration of the control of the balance between quiescence and differentiation in HSCs of neonates exposed to excessive fetal growth. We found that LGA was associated with a decrease proportion of HSC while an increase of more differentiated cells (MPPs), as well as a decreased number of HSC/MPP derived colonies in LGA compared to CTRL HSPCs. These results support a bias toward differentiation in LGA HSPCs concordant with the epigenetics and transcriptomics alterations observed. RNA velocity analysis confirms this hypothesis showing that LGA HSCs have a bigger probability to differentiate compared to CTRL. Furthermore, rapidly processed LGA samples do not show significant differences with CTRL samples supporting that the LGA functional differences observed in HTO processed samples are an intrinsic alteration of the response to challenging/stimulating environment rather than a basal change.

Altogether, these results highlight that LGA specific epigenetics and transcriptional alterations of genes network promoting HSC quiescence, is associated with an HSC differentiation bias within HSPCs compartment, suggesting an epigenetics programming of the control of HSC differentiation in LGA.

Bokeska et al have recently demonstrated such epigenetics programming on HSC. Indeed they showed that HSC early exposed to inflammatory challenges have durable epigenetics alterations and a decrease in vivo self renewal ability 602 . They also showed that these environmental challenges accelerate the cellular and molecular aging of HSCs with lifelong defect on tissue maintenance and regeneration. Our results highlight also a putative long-term effect of fetal overgrowth on the hematopoietic system and related chronic disorders. These results also corroborate with the findings of Sureshchandra et al in primates showing the role of early exposure to maternal high fat western style in programming hematopoiesis and related inflammatory status, with impair fetal bone marrow development and HSPC functions driving an hyperinflammatory phenotypes 601 . These evidence in light of our own results further support the key role of early detrimental exposure in future HSPCs dysfunctions with putative consequences on ACDs susceptibilities.

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126

II. BIN1 AD GENETICS RISK STUDY

Alzeihmer's disease (AD), responsible of 70% of dementia and affecting more than 20% of elderly people, is the 7th leading cause of death worldwide. While several genetics, environmental and age related mechanisms have been identified contributing to AD, lot of unresolved questions regarding the cause of its development remain, so that no efficient treatments is yet available to prevent or cure this disease. AD is estimated having a 70% heritability suggesting strong genetics influences. Variant E4 of the APOE gene was found as the main genetics risk factor of AD, but not explain every diseases onset, as 1 third of AD patient do not have this variant. Then, it is critical to understand others genetics influences as well as the role of these gene on AD development. BIN1 locus is the 2 nd most associated genetics risk but its role in AD development remain poorly understood.

Here we studied the role of BIN1 by deleting it in human iPSC derived cerebral organoid and neuronal culture models, leveraging scRNA-seq to identified in an unsupervised way the main influences on brain cell types. By conducting differential expression analysis between BIN1 knock out (KO) and wild type (WT) cells within each cell types identified we found that the transcriptional alterations in BIN1 KO mainly alter glutamatergic neurons and highlight pathways related to electrical activity, calcium related pathway, and synaptic transmission in both bi-dimensional and tridimensional cellular models. Comparing to scRNA-seq clinical data, we found similar transcriptional change in glutamatergic neurons of AD brains, suggesting similar functional alteration in AD. We then validated this functional alteration of neuronal electrical activity in 4-6 weeks bidimensional cultures using multielectrode arrays, showing reduced frequency of spikes by burst while an increase amplitude, indicating neuronal hyperexcitability, but also a temporal disorganization of spikes in the neural networks, suggesting impaired capacity of BIN1 deleted cells to generate organized patterns of electrical activity.

Leveraging single-cell transcriptomics signature in cerebral organoid, we found that proportion of neurons expressing genes signature of sustained electrical activity was significantly increased in BIN1 KO glutamatergic neurons from cerebral organoid, suggesting durable functional alterations of this neuronal hyperexcitability. Finally, we found that BIN1 was able to regulate the L-type voltage-gated calcium channel (LVGCC) Cav1.2, through direct interaction, and pharmacological inhibition of Cav1.2 partially rescued BIN1 KO mediated spikes desynchronization. LVGCC are known to regulate synchronous firing and its internalization prevent neurons hyperexcitability, bringing a mechanistic explanation to the role of BIN1 in regulating calcium signaling related neuronal hyperexcitability and neural network synchronization.

Introduction

The Bridging Integrator 1 (BIN1) is the second most associated genetic determinant with the risk of late-onset Alzheimer's disease (LOAD), after the Apolipoprotein E (APOE) gene 1-4 , and it is only since the report of its association with AD more than ten years ago that its role in brain functions started to be investigated. In the adult human brain, BIN1 is mainly expressed by oligodendrocytes, microglial cells, glutamatergic and GABAergic neurons 5-7 and its expression is reduced in the brains of AD patients compared to healthy individuals 7-9 . How this reduced expression of BIN1 may affect AD pathogenesis remains poorly understood.

Changes in BIN1 expression have been controversially associated with amyloid precursor protein (APP) processing towards the production of amyloid-beta (Aβ) peptides in cellular models 10,11

19

.

Despite these advances, no consensus has been reached on the role of BIN1 in AD pathogenesis and even its physiological functions in human brain cells remain mostly unknown. Therefore, rather than developing an Ab/Tau-based hypothesis as in most previous reports, we decided to first develop an agnostic approach to capture a BIN1dependent molecular landscape in cerebral organoids and neural cells derived from hiPSC underexpressing this gene.

4

Results

Transcriptional alterations in BIN1 KO hiNs highlight pathways related to electrical activity and synaptic transmission

To unbiasedly study possible changes in gene expression in human neural cells in function of BIN1 expression, we generated BIN1 wild-type (WT), and knockout (KO) cerebral organoids (COs) 20,21 . After 6.5 months of culture, COs were composed of all the major neural cell types identified by the expression of MAP2, GFAP and NESTIN, and we did not observe any gross differences in size or morphology of COs between genotypes (Fig. 1A). Western blot analyses confirmed the absence of BIN1 protein in BIN1 KO COs (Fig. 1B). Using snRNAseq, we recovered the transcriptional profile of 4398 nuclei that were grouped into 7 major cell clusters based on the expression of cell type markers (Fig. 1C-D). As observed in the human brain

7

, BIN1 expression in COs was mainly detected in oligodendrocytes and glutamatergic neurons (Fig. 1D). Notably, we observed a significant reduction in the proportion of glutamatergic neurons in BIN1 KO compared to WT COs (Fig. 1E), suggesting their selective loss or reduced differentiation. Using Wilcoxon test after sctransform normalization and variance stabilization of molecular count data

22

, we detected 124, 75, 4 and 1 differently expressed genes (DEGs; |log2FC| >0.25 and FDR <0.05) respectively in glutamatergic neurons, astrocytes, NPCs and oligodendrocytes, when comparing gene expression in single cell populations of BIN1 KO and WT COs (Fig. 1F; Sup. Table 1). Gene ontology (GO) term enrichment analysis for DEGs identified in BIN1 KO glutamatergic neurons revealed a significant enrichment for several terms associated with synaptic transmission, calcium binding and ion channels (Fig. 1G; Sup. Table 2). In BIN1 KO astrocytes, we found enrichment for GO terms associated with neuronal differentiation (Sup. Table 2). In addition, since BIN1 is expressed at very low level in WT COs astrocytes and we noticed several DEGs regulated by neuronal activity, such as APBA1, GRIN2B, NPAS3

and RORA (Sup. Table 1) 23 , changes in astrocytes are likely secondary to neuronal modifications/dysfunctions. Accordingly, we observed 65 DEGs in glutamatergic neurons of BIN1 heterozygous (HET) compared to WT COs but only 6 DEGs in astrocytes (Sup. Fig. 1).

Similar transcriptional alterations were observed in BIN1 KO hiNs generated in bidimensional cultures (Sup. Fig. 2).

5

We next aimed at evaluating the cell-autonomous effect of BIN1 deletion in glutamatergic neurons and, for this purpose, we generated BIN1 WT or KO pure glutamatergic neuronal cultures by direct lineage-reprogramming of human NPCs (hNPCs) using doxycycline-inducible expression of ASCL1 (see online methods). After validation that ASCL1 expression efficiently reprogrammed hNPCs into highly pure neurons (hereafter ASCL1-hiNs; Fig. 1H), we added exogenous human cerebral cortex astrocytes to support functional neuronal maturation and synaptic connectivity

24

. After 4 weeks of differentiation and snRNA-seq analyses (n=3114 from 2 independent culture batches), we observed that ASCL1-hiNs (~70% of all the cells; see Online Methods for a full description of the cellular populations) were composed of glutamatergic neurons (~92%) with a small proportion of GABAergic neurons (~2%) or of cells co-expressing low levels of markers of both neuronal subtypes (~6%). We detected 675 DEGs (|log2FC| >0.25 and FDR <0.05) in BIN1 KO compared to WT glutamatergic neurons, and only 1 DEG in GABAergic neurons (Fig. 1K; Sup.

Table 3). As observed in COs (Fig. 1G) and spontaneously differentiated hiNs (Sup. Fig. 2), GO term enrichment analysis revealed a significant enrichment for terms associated with synaptic transmission, ion channel activity and calcium signaling pathways (Fig. 1L; Sup.

Table 4). Noteworthy, exogenously added human astrocytes co-cultured with BIN1 WT and KO hiNs showed a low number of DEGs (25 in Astro-I and 18 in Astro-II; Sup. Table 3), likely again reflecting an astrocyte reaction to primary changes in hiNs in response to BIN1 deletion.

Altogether, results obtained from 2D and 3D models indicate that BIN1 loss-offunction leads to specific transcriptional changes associated with functional properties of glutamatergic neurons.

Molecular alterations in BIN1 KO organoids and hiNs resemble those observed in the brains of AD patients

We then sought to evaluate whether molecular alterations in our neural models may recapitulate some of those observed in the brain of AD cases. For this purpose, we used a publicly available snRNA-seq dataset generated from the entorhinal cortex (EC) and superior frontal gyrus (SFG) of AD patients at different Braak stages

9

. We first observed a progressive and significant decrease in BIN1 mRNA levels in glutamatergic neurons (Fig. 2A), suggesting that reduced BIN1 expression in this cell type may be a common feature occurring in the AD 6 pathology progression. We then compared DEGs identified in BIN1 KO glutamatergic neurons (either from COs or ASCL1-hiNs) with those identified in the same cell subtype of AD brains (Sup. Table 5). Remarkably, DEGs identified in BIN1 KO glutamatergic neurons (either from COs or ASCL1-hiNs) showed a statistically significant overlap with DEGs detected in this cell population in AD brains at different Braak stages (Fig. 2B). In astrocytes, however, a similar significant overlap could only be observed between COs and AD brains (Fig. 2B). GO analysis based on DEG overlap between BIN1 KO ASCL1-hiNs and AD brain glutamatergic neurons indicated significant enrichment for pathways associated with glutamate receptor activity and gated channel activity (Fig. 2C). Similarly, DEG overlap between BIN1 KO COs and AD brain glutamatergic neurons was significantly enriched for genes associated with glutamate receptor activity, gated channel activity and calcium ion binding (Fig. 2D; Sup. Table 6). No significant enrichment was observed for DEG overlap between BIN1 KO COs and AD brain astrocytes (data not shown). Altogether, these observations suggest that BIN1 loss-of-function is sufficient to elicit gene expression alterations in glutamatergic neurons in part similar of those observed in AD brains and associated with functional properties of glutamatergic neurons.

We finally investigated if AD-like biochemical modifications may occur in our different models by measuring the levels of phosphorylated TAU, APP, APP CTF-β and Aβ peptides.

We detected an increase in the intracellular levels of phospho-TAU (Ser202, Thr205) in BIN1

KO compared to WT cultures both in 2D and 3D cultures (Fig. 2E-H). In agreement with our previous observations in cerebral organoids (Lambert et al., 2022), we did not detect any significant differences neither in the concentrations of soluble Aβ (1-x) or Aβ (1-42), nor in the intracellular levels of full-length APP and APP CTF-β in BIN1 KO compared to WT hiN cultures in 2D (Sup. Fig. 3). Altogether, these results indicate that BIN1 underexpression may be sufficient to induce AD-related Tau hyperphosphorylation in glutamatergic neurons.

Number of synaptic contacts is decreased in BIN KO organoids

Since synapse loss is also an early marker of AD development, we then assessed whether BIN1 deletion may affect synaptic connectivity in our different models. Using immunohistochemistry experiments, we did not find any significant differences in the number of putative synaptic contacts (% SYP assigned) in BIN1 KO compared to WT ASCL1-hiNs, both at 4 and 6 weeks of differentiation (Fig. 3A-D). We also studied glutamatergic 7 synapses functionally using real-time imaging of ASCL1-hiNs expressing glutamate sensor iGLUSnFr 25

. Like our observations based on immunocytochemistry, we did not detect differences neither in the number of glutamatergic synapses (active spots) nor in the frequency of events (change in fluorescence levels in active spots) in BIN1 KO compared to WT ASCL1-hiNs (Sup. Fig. 4; Sup. Movies 1 and 2). In contrast, BIN1 KO COs showed a significant reduction in the number of synaptic contacts (Fig. 3H), mainly due to a reduction in the number of post-synaptic spots expressing HOMER1 (Fig. 3E-F). Thus, our data indicate that long term BIN1 underexpression may affect synaptic connectivity, even if not detectable at short term in 2D culture.

BIN1 null deletion modifies electrical activity pattern in ASCL1-hiNs

Although we cannot exclude that the latter observations may be linked to a difference between 2D and 3D cultures per se, we postulated that the decrease in synaptic contacts after long-term BIN1 deletion may be a consequence of synapse down-scaling resulting from chronically increased neuronal excitability due to deregulation of functional properties of glutamatergic neurons 41,42 .

To directly address this possibility, we used multi-electrode arrays (MEA) to record and quantify multi-unit activity (MUA) in ASCL1-hiNs cultured in a microfluidic device, which guides neurites into microchannels that are positioned over recording electrodes (Sup. Fig. 5). As observed in dissociated cultures of cortical cells

, 2D

cultures of ASCL1-hiNs cells exhibited a diverse range of spontaneous activity patterns, including regular discharges, population bursts and period activity (Sup. Fig. 4). In this respect, we found a conspicuous change in the temporal organization of MUA after BIN1 deletion, mainly characterized by an increased number of spike bursts at 4 weeks (Sup. Fig. 4). These alterations may result from compensatory adjustments in neuronal connectivity, intrinsic membrane properties or both. To disentangle these possibilities, we used waveform-based spike sorting to examine the functional consequences of BIN1 deletion at the single neuronal level (Fig. 4). We identified a similar number of single units per recording electrode between genotypes (WT: 4.92±2.34; KO: 5.27±2.45), indicating that BIN1 deletion does not impair the expression neither the density of active units within culture microchannels. However, we observed reduced single-unit activity (SUA) frequency (Fig. 4B) and increased SUA amplitude (Fig. 4C) in BIN1 KO compared to WT ASCL1-hiNs.

Interestingly, we could not detect significant changes in the number of bursts per neuron 8 (WT: 11.01±6.71; KO: 10.36±8.59), although the burst duration and the number of spikes within a burst were significantly decreased in BIN1 KO compared to WT ASCL1-hiNs (Fig. 4D-E), demonstrating the pertinence of performing spike sorting in MEA data. With this approach, we demonstrate the temporal disorganization observed in BIN1 KO hiNs networks (Fig. 4F) by computing the array-wide spike detection rate (ASDR), which reveals the strength of the synchronized population activity, and the autocorrelograms of SUAs, which allows the apprehension of synchronized periodicity. Both methods revealed striking modifications in the temporal organization of SUAs in BIN1 KO compared to WT ASCL1-hiNs (Fig. 4G-I). While most spikes of BIN1 WT neurons occurred at periodic intervals of about 8-10 s, the spikes of BIN1 KO neurons were randomly distributed, suggesting that BIN1 deletion in neurons impairs the capacity of these cells to generate organized patterns of electrical activity. Accordingly, the percentage of spikes occurring outside of bursts was significantly higher in BIN1 KO than in WT ASCL1-hiNs (Fig. 4J).

Acute MEA recordings in 5-month-old COs also revealed a significant increase in spike frequency in BIN1 KO compared to WT COs (Fig. 5A-B), but these experiments represent a very narrow time shot of COs differentiation. Therefore, to evaluate chronic alterations in neuronal electrical activity in this system, we developed an original approach based on the expression of activity-related genes (ARGs)

27

. While neurons stimulated with brief patterns of electrical activity transcribe rapid primary response genes (rPRGs) or early response genes (ERGs), those stimulated with sustained patterns of electrical activity express delayed primary response genes (dPRGs), secondary response genes (SRGs) and late response genes (LRGs) (Fig. 5C) 28,29 . Using Cell-ID

30

, we analyzed the enrichment for these gene signatures (Sup. Table 7) in our COs at single-cell resolution. As expected, we observed that ARG signatures were predominantly enriched in neurons (Fig. 5D). Quantification of the proportion of neurons significantly enriched for specific signatures (p adj <0.05) revealed a significantly higher proportion of glutamatergic neurons enriched for dPRGs, SRGs and LRGs in BIN1 KO compared to WT COs (Fig. 5E). Enrichments for SRGs and LRGs were specific for this cell type and could not be observed either in GABAergic neurons (Fig. 5E) or in BIN1 HET glutamatergic neurons (Sup. Fig. 6). Thus, BIN1 deletion leads to alterations in neuronal electrical activity before observable changes in synaptic connectivity, suggesting that functional changes in BIN1 KO ASCL1-hiNs are likely a consequence of altered cell-intrinsic properties.

9

BIN1 regulates neuronal Ca 2+ dynamics through LVGCCs Since we found significant enrichment for several terms associated with calcium binding and ion channels, we postulated that actors of these pathways may be responsible for such altered cell-intrinsic properties. To probe whether Ca 2+ dynamics was altered in BIN1 KO ASCL1-hiNs, we performed calcium imaging in 4-week-old cultures (Sup. Movies 3 and 4). We observed a significant increase in the frequency of Ca . First, we performed proximity ligation assay (PLA) to probe a possible interaction between BIN1 and Cav1 .2 or Cav 1.3 , the two LVGCCs expressed in ASCL1-hiNs (Sup. Fig. 7). We observed a widespread BIN1-Cav 1.2 PLA signal (Fig. 6G) and, to a lesser extent, a BIN1-Cav 1.3 one in neurons (Sup. Fig. 7). Next, we quantified neuronal LVGCC protein level and observed an increase in total Cav 1.2 levels in BIN1 KO compared to WT ASCL1-hiNs (Fig. 6H-I). Protein levels of neither Cav 1.3 , nor the members of the Cav 2 family (Cav 2.1 , Cav 2.2 and Cav 2.3 ) were increased in the same cultures (Sup. Fig. 7), suggesting a specific regulation of Cav 1.2 expression by BIN1.

Notably, LVGCCs are key regulators of the synchronous firing pattern in neurons 33 and one of the homeostatic mechanisms protecting neurons from hyperexcitability involves activity-dependent internalization of those channels

34

. Thus, to evaluate whether BIN1 deletion may impair this mechanism, we stimulated ASCL-hiNs with KCl 65nM for 30 min and collected total and endosomal proteins for analysis. We confirmed an increase in the global level of Cav 1.2 in BIN1 KO ASCL1-hiNs that was independent of KCl treatment (Fig. 6J).

However, Cav 1.2 expression in the endosomal fraction was increased by 50% after KCl treatment in BIN1 WT, whereas this increase was only of 10% in BIN1 KO ASCL1-hiNs (Fig. 6K). This effect was specific for Cav 1.2 since both early endosome antigen 1 (EEA1) and Cav 1.3 expression increased in both BIN1 WT and KO ASCL1-hiNs at similar levels after KCl treatment (Fig. 6K).

10

These last observations prompted us to investigate whether the network dysfunctions observed in BIN1 KO ASCL1-hiNs may be related to the increased Cav 1.2 protein levels. For this purpose, we treated these cells with nifedipine, a specific antagonist of Cav 1.2 at a physiologically relevant concentration (50 nM) for 2 weeks. We observed a partial recovery of the oscillatory pattern of neuronal electrical activity observed in WT cells (Fig. 6L).

Interestingly, the percentage of spikes outside bursts was not affected by nifedipine treatment in BIN1 WT, but significantly decreased in BIN1 KO ASCL-hiNs (Fig. 6M), indicating a partial recovery of burst organization. To note, no difference in firing rates was observed whatever the models and conditions (Fig. 6N). Altogether, these data support the view that BIN1 contributes to the regulation of electrical activity through the regulation of Cav 1.2 expression/localization in human neurons.

11

Discussion

In this work, we show that the AD genetic risk factor BIN1, plays a critical role in the regulation of neuronal firing homeostasis in glutamatergic neurons. Complete deletion of BIN1 gene in these neurons is sufficient to alter the expression of the LVGCC Cav 1.2 , leading to altered calcium homeostasis and neural network dysfunctions in human neurons in vitro.

These functional changes are correlated with changes in the expression of genes involved in synaptic transmission and ion transport across the membrane, as well as increased Tau phosphorylation. In long-term neuronal cultures using COs, we confirm that BIN1 **p adj =0.0034; WT vs KO: *p adj =0.0124; Dunn's multiple comparison test). (N) Average firing rates.

Supplementary data

Sup.

Differentiation protocol for induced hiNPCs

We differentiated neurons from virus-transduced hiNPCs according to an adapted protocol (Zhang et al., 2013; Yang et al., 2017). Briefly, hiNPCs are first transfected with the Differentiation was allowed to continue for another 2 weeks prior to subjecting the cells to various experimental manipulations.

Human cortical astrocytes (Catalog # 1800) were sourced from ScienCell Research Laboratories, CA, USA. Maintenance and proliferation of astrocytes were done as per specifications mentioned on the datasheet from the provider which is available on their webpage.

This culture system was characterized using snRNA-seq showing that 70% of cells (n=3114 from 2 independent culture batches) expressed the pan-neuronal markers SOX11, SNAP25, DCX and RBFOX3, with 66% of cells co-expressing the glutamatergic neuron marker SLC17A6, less than 1.5% of cells co-expressing the GABAergic neuron markers DLX1, GAD1 and GAD2, and 5% of cells co-expressing low levels of markers of both neuronal subtypes.

Generation of Cerebral Organoids

Cerebral organoids (3D Cultures) were generated from wild-type, heterozygous and knockout hiPSCs using a 4-stage protocol (Lancaster et al., 2013) of the dye solution was added to 400 µL of fresh BrainPhys medium in each well of a 24-well cell imaging plate. Existing BrainPhys media from the wells of the plate was removed and kept aside while the calcium-indicator dye was incubated in fresh BrainPhys medium. After the 1-hour incubation, the medium which was kept aside was replaced to each well. The 2D cultures were then ready to be filmed using a Spinning Disk Microscope housed at the Institut Pasteur de Lille, Lille, France using the MetaMorph imaging software.

For filming the calcium activity, 1000 images were taken using a 20X long-distance objective, 10 ms exposure time and 200ms intervals. For each well, 5 random fields were chosen, and the cellular activity was, thus, recorded.

For cells transduced with iGluSnFR, these cells were directly filmed after 4 weeks of differentiation and 500 images were taken using a 20X long-distance objective, 10 ms exposure time and 200ms intervals. Up to 8 fields per well were filmed, each field containing at least one fluorescent transduced cell along with its processes.

Analyses of Calcium Transients

All live recordings of neuronal calcium transients were first converted into .avi format after background subtraction using the FIJI software. Following these, the videos were subsequently opened using the free software for data analyses of calcium imaging, CALciumIMagingAnalyzer (CALIMA) made available online by Fer Radstake (Eindhoven University of Technology, The Netherlands). Each video recording of a field of cells was first downscaled to 2X in terms of size with a 10X zoom and was checked for the frame average mode. Moreover, in this first detection stage, pre-set filter parameters were adjusted and applied to enable the detection of the maximum number of fluorescent cells in each field. In the analysis tab, detection of the average activity was checked and for pre-processing, a median of 3 was applied. All cells within the pre-set filter parameters are detected as regions of interest (ROIs) in the detection stage. Cell activity from all detected ROIs is then recorded. However, in the subsequent analysis stage, only cells showing spiking frequencies with a standard deviation of at least 2 or more were taken into consideration. Data in the form of detection spikes and the correlation (peak) are extracted and exported as CSV files.

Electrophysiological recordings in 2D cultures and cerebral organoids o C and 5% CO 2 . Signals were recorded for 3 min, at 12.5 kHz sampling rate, using AxIS Navigator software (Axion Biosystems).

Spikes were detected using a fixed amplitude threshold of 5.5 and 4.5 standard deviations (for the 2D and 3D cultures, respectively) of the high-pass filtered (>300 Hz) signal for positive-and negative-going signals. The detection included a dead time of 3 ms to account for the refractory period of action potentials. Quantification of the number of detected spikes (MUAs) and spike bursts (defined as at least 5 spikes within 50 ms) was performed using Multi-Channel Analyzer 2.16.0 software (Multi-Channel Systems).

Spike sorting and temporal structure of spontaneous activity Channels containing detected waveforms were manually processed offline for spike waveform separation and classification using Offline Sorter v3 (Plexon, USA). Briefly, we applied principal component analysis (PCA) to cluster spike waveforms of similar morphologies. Using this approach, we identified from 2 to 10 well-isolated units per channel, and therefore, we considered this single-unit activity (SUA). For each SUA, we computed the average firing rate, the signal-to-noise ratio, the peak-to-trough amplitude 34 and duration, the average power (square amplitude of the average waveform), the mode of the interspike interval distribution, and their firing patterns. It has been demonstrated that dissociated neuronal cultures can develop complex discharge structures (Wagenaar, 2006).

Here, we considered burst activity if the SUA presents periods of high-frequency discharges interspersed by regular or no discharges at all. Operationally, a burst must have at least 3 spikes within 100 ms and 200 ms intervals, for the interval between the first and the second, and the second and the third discharge, respectively. After the third spike, the maximal interval to consider a discharge part of the burst was 200 ms. Thus, we computed the SUA that presented bursts, the number of bursts (i.e., the burst frequency), the average burst duration, the number of spikes within each burst, the average burst frequency, and the inter-burst interval.

Two complementary approaches investigated the temporal structures of spike trains.

In the first one, we computed the array-wide spike detection rate (ASDR), which is the number of spikes detected per unit of time, summed over all electrodes in the array. This method is commonly used in the literature to demonstrate synchronous activity (aka, bursts) in MUA data (Wagenaar 2006). The second approach uses the autocorrelation function (i.e., the probability of finding two spikes at a given time interval) to calculate the

snRNA-seq Library Preparation

Nuclei isolation and Hash-tagging with oligonucleotides steps were realized on ice with pre-cold buffers and centrifugations at 4°C. 6.5-month-old BIN1 WT, HET, and KO organoids were processed as previously (Lambert et al., 2022). Fisherscientific). Isolated nuclei were loaded on a Chromium 10X genomics controller following the manufacturer protocol using the chromium single-cell v3 chemistry and single indexing and the adapted protocol by Biolegend for the HTO library preparation. The resulting libraries were pooled at equimolar proportions with a 9 for 1 ratio for Gene expression library and HTO library respectively. Finally, the pool was sequenced using 100pb paired-end reads on NOVAseq 6000 system following the manufacturer recommendations (Illumina).

snRNA-seq Dataset Preprocessing

Unique Molecular Index (UMI) Count Matrices for gene expression and for Hash Tag Oligonucleotide (HTO) libraries were generated using the CellRanger count (Feature Barcode) pipeline. Reads were aligned on the GRCh38-3.0.0 transcriptome reference (10x Genomics). Filtering for low quality cells according to the number of RNA, genes detected, and percentage of mitochondrial RNA was performed. For HTO sample, the HTO matrix was normalized using centered log-ratio (CLR) transformation and cells were assigned back to their sample of origin using HTODemux function of the Seurat R Package (v4) [10]. Then, normalizations of the gene expression matrix for cellular sequencing depth, mitochondrial 36 percentage and cell cycle phases using the variance stabilizing transformation (vst) based Seurat:SCTransform function were performed.

snRNA-seq datasets integration and annotation

To integrate the datasets from independent experiments, the harmony R package

Differential gene expression and GO enrichment analyses

Gene expression within each main cell type was compared between conditions of interest using Wilcoxon test on the SCTransform normalized gene expression matrix. GO enrichment analysis on the differentially expressed genes was performed using the gost function of the gprofiler2 R package (CRAN).

Activity-related genes (ARGs) signature enrichment analysis at single cell resolution

To study enrichment for activity-related genes (ARGs) signature across cerebral organoid cells, the CellID R package (https://github.com/RausellLab/CelliD) was used. ARGs

II.2. Conclusion

BIN1 is mainly express in brain in oligodendrocytes, glutamatergic neurons, microglia and GABAergic neurons 543 . Past studies in rat hippocampal neurons, in mice, or drosophila have shown by over-expressing specific human isoforms, a potential role in endosomal trafficking, neurons excitability and long-term memory, interacting with Tau in both cellular and mouse models [544][545][546] . Here we showed in human cerebral organoid and bidimensional neuronal models leveraging single cell transcriptomics assay that the main transcriptional alteration of BIN1 deletion targets glutamatergic neurons and are enriched for genes involved in synapse organization and calcium channel related activity. These results support that the main activity of BIN1 occur in this cell type and highlight its putative role in regulating calcium channel related synaptic activity.

Neuronal calcium homeostasis is disturb in aging and is an important early cellular defects in AD, leading to neuronal hyperexcitability, defective long term memory, and ultimately neuronal cell death 603,470,477,478,604,605 . Here, we found that BIN1 deletion was sufficient to alter glutamatergic neurons transcriptional activity similarly than in AD brain and to drive neuronal hyperexcitability and neural network synchronization dysfunction. We also found that BIN1 deletion promotes in these neurons a gene expression signature of sustained electrical activity, suggesting long term impact on neurons function including long term memory. We then further validated BIN1 role in calcium homeostasis observing increase in calcium spike duration in BIN1 deleted neurons, as observed in aging 606,607 . We found then that the role of BIN1 in calcium homeostasis could pass through its ability to regulate membrane expression of the L-Type voltage gated calcium channel (LVGCC) Cav1.2, because found interacting direclty with this channel, and because BIN1 KO neurons have an increase Cav1.2 channel expression while reduced internalization capacity in response to stimulation. Cav1.2 play a key role in neurons excitability and long term memory 603 , and its activity-dependant internalization allow calcium homeostasis and regulation of neuronal hyperexcitability 608 . Then, dysregulation of its expression at post-synaptic membrane could explained neuronal hyperexcitability observed in BIN1 KO neurons.

Interestingly, expression of LVGCC increases in the aging brain and correlate with a neuronal hyperactivity 607,608 . Then, BIN1 role in AD pathogeneisis could pass through its ability to regulate neuronal excitability and synchronization through the LVGCC Cav1. The use of single-cell genomics in our two models allowed to decipher the influence of heterogeneity on early molecular and cellular disease mechanisms.

In both models, we were able to identify known cellular subpopulation using cell type specific markers, but also to redefine or highlight new heterogeneity within the studied tissues. In HSPCs, we found the main subpopulations, from LT-HSC to lineage-restricted progenitors but also confirm the continuous landscape of the hematopoietic differentiation. To note, even if the clustering algorithm generate discrete subpopulation within HSPCs, the limits between each subpopulation are in fact not clear and rather represent an arbitrary choice to facilitate further studies at subpopulation level. These results support the new model of continuous hematopoietic differentiation build through previous scRNA-seq studies but also raise the intrinsic subjectivity of defining subpopulation in the context of differentiation. This implies that comparing different scRNA-seq studies require first to integrate data and/or to have a same reference. Tools have been designed to facilitate this task, notably used a same scRNA-seq dataset reference to annotate cells from different studies or to integrate both in the same reducted dimensional space 182,189 . Same conclusions can be made with my second model using iPSC derived neural tissues. We identified known brain related cell types but also highlighted the continuous neural progenitor cells (NPCs) differentiation.

Furthermore, scRNA-seq has allowed us to identify new cellular heterogeneity within tissue, notably in HSCs, were transcriptomically distinct cell subpopulations were identified, including one, having STAT1 and IRF1 markers, both known to promote megakaryopoiesis 610 (See supplemental Figure S2 of the first article). These results support a priming of the HSC subpopulation toward megakaryocytes differentiation concordant with previous findings 416,611 . To validate the relevance of this gain in resolution, new sorting strategies could be implemented to isolate these different HSC clusters and study their putative activities or differentiation abilities. This intra-HSC heterogeneity can also rely on the transcriptional plasticity of HSC allowing different cell's fate 417,612,613 .

Critically, the single-cell genomics analysis has allowed to decipher in our two models the cell type specific effect of disease epigenetics or genetics factor which would not have been discernable

with classical bulk genomics assay. Regarding the first model, we highlighted the HSC specific epigenetics and transcriptional programming associated with excessive fetal growth and highlighted a decrease ability to regulate their activation or response to stimulation. In the second model, we found that glutamatergic neurons were particularly affected by deletion of AD risk gene BIN1, with dysregulation of calcium related signaling and neuronal excitability. This is a new level of unsupervised discovery allowed by single-cell genomics. More than the unsupervised identification of molecular mechanisms involved in a disease similarly to bulk genomics, single cell genomics allow also the unsupervised identification of the cell types involved. Nevertheless, such validation is still limited by the ability of cells to be transfected, the availability of efficient siRNAs and by the stress induced by the transfection method. To prevent putative siRNAs related limitations, others methods could be used notably CRISPR based knock out or silencing of candidate genes, associated with scRNA-seq (an approach called perturb-seq 134 ). In the second model, rather than using a TF oriented approaches; we leveraged the cellular resolution to identify cell specific transcriptomic signature using CellID 614 and highlighted that BIN1 deletion leads to an increased proportion of glutamatergic neurons enriched for genes signature of sustained neuronal electrical activity, bringing functional insights on the BIN1 role in neuronal activity regulation.

II. Single-cell genomics to decipher influence of DNA methylation alterations on gene expression and cellular plasticity. to excessive fetal growth in normally exposed cells. To do that, one interesting approach would be to exposed HSPCs to LGA related condition like high glucose, high IGF1 or high Insulin, with or without genetic deletion of DNMT3A, the de novo DNA methylation writer active in HSC 616 . This manipulation will allow us to test if LGA related condition induce stable epigenetics and transcriptomics changes as for HSPCs from LGA neonates (i) and if the deletion of DNMT3A prevent these changes(ii), which would confirm the epigenetics remodeling and impact on gene expression. Another approach could be to specifically change the DNA methylation of some key LGA affected regions using epigenome editing methods like dCas9-Dnmt3a allowing targeted de novo DNA methylation 617 and assess the impact on chromatin accessibility and gene expression.

We also linked the epigenetic programming with functional alteration of HSC, most specifically with differentiation ability/cellular plasticity. We found that DNA hypermethylation targets expression LGA associated hematopoiesis alteration in ACD risk [START_REF] Jaiswal | Age-Related Clonal Hematopoiesis Associated with Adverse Outcomes[END_REF][START_REF] Jaiswal | Clonal Hematopoiesis and Risk of Atherosclerotic Cardiovascular Disease[END_REF][START_REF] Fuster | TET2-Loss-of-Function-Driven Clonal Hematopoiesis Exacerbates Experimental Insulin Resistance in Aging and Obesity[END_REF]398 . To really challenge this hypothesis, we could conduct an experiment with (immunodeficient) animal model transplanted for LGA-exposed or control HSC, and follow metabolic status in order to assess ACD risk.

An important finding is that the transcriptomics and functional changes do not seems detectable without added environmental challenges. It is still interesting to note that the chromatin accessibility changes observed in LGA samples are not dependent of future exposure. Indeed, chromatin accessibility was assessed thanks to single nucleus ATAC-seq that was independent of scRNA-seq assay and not require sustained exposure of cells in non-physiological environment. This consideration suggests that the decreased chromatin accessibility, as well as the increased DNA methylation are stably altered in LGA, but the transcriptional effect of such epigenetics change occurs only in challenging/stimulating condition. While this environmental challenge was not physiological, equivalent cellular response was observed using cytokines promoting HSC activation. The environment dependent functional alterations is rising interesting considerations. First, it highlights the importance of later environmental exposition in disease onset. Second, it reinforce the need of care when interpreting data as it can be directly impacted by the protocol used to measure them. The dependency of the measured biological element to the measurement/monitor echoes the fundamental problem of the measure in physics but is often neglected in biology and should probably be better considered.

An important remaining question is if the epigenetics programming could be reverse in life. To answer this question, experiments on animal model of excessive fetal growth or transplanted for HSC exposed to LGA-like conditions can be performed with assessment of the epigenetics landscape of HSC at different time points according to different diets or medications. Human longitudinal study could also be performed to assess if LGA HSC epigenetics alteration remain in time but the difficulties/invasiveness to isolate HSCs from bone marrow will certainly ask derivative strategy to follow these epigenetics biomarkers. Use of peripheral blood cells as a proxy of HSPC to assess such epigenetics alteration could be evaluated.

Finally, a parallel question is the relevance of such epigenetics programming in others stem cells compartment, notably mesenchymal stem cells (MSCs). MSCs are mainly located in bone marrow but can be found in nearly all tissues and give rise to numerous cells of the body notably adipocytes which are key players in the context of obesity and related ACD. To decipher if these stem cells are also epigenetically altered in LGA, MSCs can be isolated from umbilical cord 618 and have also the advantage to be easily isolable after birth from diverse tissue including blood, dermis and dental pulp 619 , facilitating longitudinal studies.

III.

Single-cell genomics to understand impact of genetics risk in heterogeneous and difficult to access tissue

One of the main contribution of single-cell genomics in the AD related study was the ability to decipher cellular and molecular mechanisms in heterogeneous and not accessible tissue like the brain.

Indeed, before such assay, study of cellular mechanisms in brain models was restricted to cell imaging using immunophenotypic markers or electrochemical measurements, or cell subpopulation sorting using FACS but this required predefined hypothesis and cellular targets as well as consequent experimental procedures. Here, we were able to unsupervisely assess influence of an AD risk gene in several brain cell types and biological processes in one assay. Still, some challenges remain. First, our cellular models (bidimensional neuronal culture and tridimensional cerebral organoid) were composed only of neural progenitor cells-derived brain cell types, which excludes notably microglia, a cell type expressing BIN1 and known to play an important role in AD neuro-inflammation. Then, to decipher BIN1 role in microglia, it would be interesting to add microglia in our cellular models. To do so, it is possible to reprogrammed iPSC into hematopoetic progenitors and differentiate them into microglia [START_REF] Tcw | Cholesterol and matrisome pathways dysregulated in astrocytes and microglia[END_REF] .

Another possibility would be to use a conditional model of BIN1 deletion in mice allowing specific loss of function of BIN1 in microglia. Second, even if this study has highlighted BIN1 role in regulating calcium signaling, it cannot confirmed the influence of the AD risk variants on BIN1. Indeed, the BIN1 related risk variants do not lead to BIN1 deletion because they are not located in coding region. Some cues indicate that these variants could reduce expression of BIN1 in neurons 550 , however little is known about the influence of these variants on gene expression regulation. To decipher the real effect of genetics variants on AD, several strategies could be used: first, assess the impact of AD risk haplotype on brain model using iPSC from careers of theses AD risk variants. It will enable to assess the impact of the whole haplotype associated with AD risk variants on these cells. However, it could not decipher the influence of specific variants. The second strategy could be to genetically modified control iPSC by adding the AD specific risk genetics variant to see if the variant alone can trigger AD related effect.

Such studies have been led for APOE risk variants and in this case it appeared that the influence of such genetics risk was dependant of the whole haplotype [START_REF] Tcw | Cholesterol and matrisome pathways dysregulated in astrocytes and microglia[END_REF] , highlighting the importance of the risk variant interactions with individual-specific (epi)genetics background.

Finally

  , I would like to thanks Prof Susan Ozanne and Dr Pilib Ó Broin to accept being reporter of this thesis, and Pr. Didier Vieau and Dr Marco Antonio Mendoza to be reviewers for my PhD Defense. Remerciements/ Acknowledgement 11 Thank you also Dr Delphine Eberle to accept my special invitation for my PhD defense. Another thankyou to Dr Pilib O Broin and Pr. Didier Vieau to have followed me and give feedback during these 3 years for my CSI. approaches to understand adult chronic diseases etiology I.1

Figure 2 :

 2 Figure 2 : Different type of epigenetics mechanisms. Source B

I. 3

 3 .b. High throughput sequencing for unsupervised discovery I.3.b.i. History of DNA sequencing

INTRODUCTION 24 Figure 3 :

 243 Figure 3 : Revelation of DNA sequence using the Sanger method. Radioactively labelled gel electrophoresis of the four reactions containing either d dATPs, ddTTPs, ddGTPs, or ddCTPs blocking DNA elongation when integrated. Source C

INTRODUCTION 25 sequence

 25 allowing hybridization on the flow cell and sample indexing. These fragments are hybridized on a flow cell coated with billions of oligonucleotides primers and amplified by polymerase chain reaction (PCR) generating clusters of copy of a same DNA fragment. Similarly to what is done by Sanger

Figure 4 :

 4 Figure 4: Workflow of sequencing by synthesis method . Reprinted from "Next Generation Sequencing (Illumina)", by BioRender, June 2020 D

Figure 5 : 32 I

 532 Figure 5 : Advantage of integrate multi-omics layer to characteri ze molecular network involved in a phenotype. Reprinted from Hossain et al, Frontiers in Plant Science , 2015 E

  First methods developed used fusion protein composed of nonspecific DNA cutting domain coupled with specific DNA sequence recognizing peptides like Zinc finger nucleases (ZFNs) or transcription activator-like effectors (TALEs) allowing targeting DNA cutting. Because they are based on specific fusion protein design, these methods are relatively complex to set up requiring significant molecular biology skills and time. In the past 10 years, a new genome editing tool emerged, bypassing this limitation using only a small RNA (called sgRNA for single guide RNA) to guide the DNA breaking by endonuclease. This tool is called CRISPR-cas9 and became rapidly the gold standard method. This method is based on CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats), a genetics element used by bacteria to fight against viruses. CRISPR technics allow targeting of specific DNA element thanks to a DNA endonuclease enzyme, most of the time Cas9, guided by an easily customizable sgRNA. Cas9 can be catalytically active allowing DSB but also partially or completely inactived (dead Cas9, or dCas9) depending on the DNA modification desired. CRISPR based system allows specific modification of INTRODUCTION 33

Figure 6 :

 6 Figure 6 : Principle of major genome editing methods. Reprinted from Adli, Nature Communications, 2018 F

F

  https://www.nature.com/articles/s41467-018-04252-2/figures/3 INTRODUCTION 34 studies have shown ability of such system to edit specific histone acetylation or methylation but also edit DNA methylation. However, these methods still lack robustness requiring further development.

Figure 7 :

 7 Figure 7 : Differents application of CRISP R based systems. Reprinted from Adli, Nature Communications, 2018 G

INTRODUCTION 36 Figure 8 :

 368 Figure 8: sources of cellular heterogeneity within a tissue. Reprinted from Sun et al, Acta Pharmacologica Sinica, 2015 H

INTRODUCTION 37 dysregulation

 37 of this equilibrium is associated with the metabolic syndrome J . It was shown that an increase of adipocytes sizes, types, as well as increase in number of lymphocytes and macrophages infiltration contributes to the metabolic syndrome, associating a low-grade inflammatory state and peripheral insulin resistance 104 . For AD, change in brain cellular composition has been observed. Indeed, it was recently shown through scRNA-seq analysis that a new type of microglia appears in AD brain compared to normal brain and was shown to allow Abeta clearance once activated 105 . It was difficult to assess a loss or a gain of cellular heterogeneity prior to the emergence of single-cell genomics, as cell type identification relied on known cell surface markers.

38 III. 1 .

 381 HistoryEven if we can speak about single-cell "genomics" only recently with the rise of high-density microarray and high-throughput sequencing, technologies allowing analysis of samples at single-cell resolution exist since decades. In combination with staining methods microscope allows exploration and analyze of cells and tissues at single-cell resolution. After some centuries, modern technics combining molecular labelling and computational analysis have enable single-cells analysis in a more quantitative way. Fluorescence activated cell sorting (FACS), immunofluorescence, and RNA fluorescence in situ hybridization (FISH) as well as fluorescent fusion proteins are methods allowing quantification of gene expression at cellular level. However, these methods are limited in scale either on number of cells or in the number of targets explored simultaneously. The first massive parallel sequencer was commercialized in 2006 allowing the sequencing of the whole genome in a day. The genomics field then emerged and soon enables single-cell approaches.In 2009 was published the first whole transcriptome at single-cell resolution 110 . Few years after, different protocols arised to isolate cells including well-based, FACS-based, and droplet-based assays. All these protocols are based on a similar process: a cell isolation (i), RNA retrotranscription adding cell specific barcodes (ii), and finally cDNA library amplification and preparation for Illumina related sequencing (iii).

Figure 9 :

 9 Figure 9: Single-cell RNA-sequencing workflow. Source K

Figure 11 :

 11 Figure 11 : Droplet based single -cell ATAC-sequencing workflow. Adapted from Satpathy et al, nature biotechnology, 2019 M

INTRODUCTION 50 Figure 12 :

 5012 Figure 12 : Classical single -cell data analysis pipeline

Figure 13 :

 13 Figure 13: U shaped association between birth weight and ACD risk

  .1.a.v. Early life exposure Not restricted to in utero exposure, evidence have also shown the influence of early postnatal life when differentiation and maturation of the tissues and cells are still intense. Psychological stress INTRODUCTION 57 during childhood increases the risk of ACD and are linked to macrophage pro-inflammatory tendencies 271 . Early life nutrition and weight gain have also an important role in the programming of ACDs. Fast

INTRODUCTION 61 epigenetics

 61 signature between LGA and SGA was further demonstrated by Delahaye et al focusing on CD34+ cord blood progenitors identifying several DNA hypermethylation targeting stem cells and metabolic pathways 349 , that I will further develop later.Epigenetics alterations could also program insulin resistance as a global DNA methylation pattern in cord blood was shown associated with insulin sensitivity in childhood 350 . GDM induces hypermethylation and decreases expression of lipoprotein lipase gene in placenta and was associated with 5 years offspring body fat composition 351 . DNA methylation alteration persists throughout life as observed in blood leukocytes of in utero exposed children and adult, on genes known to contribute to T2D and pancreatic Beta cells function[352][353][354] .

Figure 14 :

 14 Figure 14 : Classical hematopoetic hierarchy and main regulators. Adapte d from Cheng et al, Protein & Cell, 2020 N

  of DNMT3A impairs HSC differentiation while promoting HSC expansion and is associated with substantial changes in CpG island methylation with upregulation of HSC multipotency genes including GATA3, RUNX1, PBX1, and CDKN1A, while downregulation of differentiation factors including FLK2, SPI1, and MEF2C. Further studies have further highlighted the important role of DNMT1, TET1 and TET2 in regulating methylation of HSC differentiation program 385 . Together, these evidence show the crucial role of DNA methylation in the control of HSC differentiation. Further studies have investigated other component of the regulatory landscape of hematopoiesis, including the chromatin dynamics and the TF activities. In the first study leveraging single-cell ATAC-seq to understand the chromatin changes governing hematopoiesis, Buenrostro et al have demonstrated that the regulatory landscape of HSPC is governed by modulation of lineage specifying TF motif accessibility 392 . Notably, they showed critical chromatin changes of GATA2 and MESP1 motif accessibility in HSC, respectively involved in erythroid and lymphoid lineage differentiation. Such rearrangement were also observed in more restricted hematopoeitic progenitors notably in TCF4 for the lymphoid-primed multipotential progenitors (LMPPs), STAT1 accessiblity for peripheral dendritic cells, and CEBPE for GMP differentiation. In a recent study assessing the impact of DNMT3A and TET2 mutations on chromatin landscape using single-cell sequencing, Izzo et al have shown that these mutations disrupt hematopoietic differentiation landscape, with opposite effects on erythroid and myeloid progenitors distribution 393 . They further show that Tet2 or Dnmt3a knockout (KO) induced opposite DNA methylation changes (hypermethylation for Tet2 KO and Hypomethyation for Dnmt3a KO) but both occur in same CpG rich accessible regions. These regions are enriched for erythroid TF motif, including Tal1 and Klf1, reconciling the opposite lineage priming (erythroid primed for Dnmt3a KO, and myeloid primed for Tet2 KO) observed in these two mutants. This opposite methylation change is associated with a shift in TF motif accessibility with decrease accessibility in Tet2 KO HSC while an increase accessibility for Dnmt3a KO HSC as demonstrated using single nuclei ATAC-INTRODUCTION 66 seq. These effects were studied in mice but were further validated in DNMT3A-mutated human samples supporting the role of the DNA methylation mediated chromatin change in the erythroid skew observed in these mutants. These results strongly support the crucial role of DNA methylation in remodeling chromatin accessibility and controlling activity of lineage defining TFs for appropriate HSC differentiation and multipotency.

INTRODUCTION 69 Figure 15 :

 6915 Figure 15 : Classical hematopoetic hierarchy (A) versus revised view of h ematopoesis based on single-cell RNA-sequencing evidences (B). Reprinted from Zhang et al, Stem Cell Research & Therapy , 2022 P

  bring metabolic and structural support to the neurons. They derive from same progenitors cells than neurons (neural progenitor cells; NPCs) and represent around 20-40% of glial cells. They have many function including neurotransmitter recycling, provisions of nutrient to neurons, control of extracellular ion balance, but also brain repair processes and immune clearance in response to injury, neurotoxic agents, or infection 434 . Microglia are resident macrophage cells in the brain involved in maintenance of brain homeostasis, pruning of synapses and active immune defense. For a long time INTRODUCTION 72 thought to derive from hematopoietic stem cells like others macrophages, they in fact derived from erythro-myeloid precursors in the yolk sac, having characteristic gene expression profile compared to others myeloid cells 435 . They are primary involved in the tight regulation of brain homeostasis, being very sensitive to any pathological change and allowing clearance of unnecessary or neurotoxic components including plaques, damaged or unnecessary neurons and synapse, and infectious agents435 . Oligodendrocytes mainly provide support and insulation of neuronal axons, producing the myelin sheath which wrap around axons allowing fast electrochemical action potential transmission to

  other important pathophysiological mechanism recognized in AD is the accumulation of pathological Tau conformation. Tau is encoding by the gene MAPT, and are a microtubule binding protein stabilizing axonal microtubule and playing important role in intracellular transport notably mitochondrial and neurotransmitter transport to synaptic regions 450 . Tau is also found in postsynaptic region, in dendritic spines, where it can play important role in glutamate receptor scaffolding regulation. Once phosphorylated, Tau loose affinity to microtubule generating free Tau able to twist around each other's in paired helical fragments and form neurofribrillary tangles (NFTs). Loss of microtubule affinity disrupt cytoskeleton and intracellular transport, impacting notably the transport of mitochondria and glutamate receptors to post synaptic region, reducing ATP production, calcium buffering and thus neurotransmitter containing vesicles release. Furthermore, pathological tau can accumulate in dendritic spines, where they can directly affect synaptic transportation and post synaptic excitability 451 . The progressive formation of NFTs next aggregates into paired helical filaments, and spread through the peripheric part of stroma. Finally, NFT lead to proteotoxicity and neuronal loss, defect in axonal transport, mitochondrial damage and stress, and microglial activation / INTRODUCTION 74 inflammation 451 . This NFT accumulation are good indicator of neurodegeneration and strongly correlate with AD cognitive defects 452 . However, the causal mechanism leading to Tau hyperphosphorylation and aggregation remain not well understood. Abeta oligomers have been shown to promote Tau pathological conformation 453,454 but this element is not sufficient to explain Tau hyperphosphorylation as Ab accumulation have found in healthy elderly people without tau associated defects. Others factors have been identified promoting Tau hyperphosphorylation related

  density and presynaptic protein cluster formation, while increase synaptic vesicles. Furthermore, these alterations in synaptic transmission in the center of memory consolidation (hippocampus) are concordantly associated with defective spatial learning and memory ability of Bin1 null mice. This evidence support a new role of BIN1 in synaptic function with putative effect on AD relevant coginitive process.Taken together, these previous studies highlight the broad range of possible roles of BIN1 in brain/neurons but fails to define a clear mechanism behind BIN1 related AD susceptibility. All these previous studies are biased toward a certain hypothesis, which exclude discovery of putative unrecognized role. Furthermore, they mostly consider study of neurons, in 2D culture or non-human INTRODUCTION 82 model, which could miss the real BIN1 influence in human brain and associated cellular heterogeneity and interaction. Finally, studies overexpressing BIN1 can exaggerate BIN1 activity failing to highlight patho physiologically relevant mechanism. The inconsistency of the results exposed above, notably in the controversial role of BIN1 in APP processing, further highlight the need to study BIN1 in an unsupervised way. Then, to decipher the role of BIN1 in AD related brain function while being liberated of previous biased, we implemented a new modeling approach, integrating different human relevant brain models, unsupervised investigation and functional characterization. This approach leveraged single-cell transcriptomics to investigate the cellular effect of BIN1 deletion on both neuronal culture and cerebral organoid derived from human induced pluripotent stem cells (iPSC).V.7. iPSC derived neuronal modelsTransgenic mice have been largely used since decades to model AD, and have allowed clarifying several pathophysiological mechanisms. First mouse model was developed in 1995 based on the overexpression of human APP harboring familial AD mutation. Since then, varieties of mouse AD models have been developed allowing demystify or consolidate role of Abeta deposit and pathological Tau conformation in neurodegeneration but also the important role of microglia in pathological Tau spreading or the role of astrocyte activation and neuro-inflammation in synaptic dysfunction 555 .

  can result in alteration of HSC destiny, and ultimately hematopoietic functions. The early mechanisms that control their long-term Citation: Pelletier, A.; Carrier, A.; Zhao, Y.M.; Canouil, M.; Derhourhi, M.; Durand, E.; Berberian-Ferrato, L.; Greally, J.; Hughes, F.; Froguel, P.; et al. Epigenetic and Transcriptomic Programming of HSC Quiescence Signaling in Large for Gestational Age Neonates. Int. J. Mol. Sci. 2022, 23, x. https://doi.org/10.3390

  Copyright: © 2022 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/license s/by/4.0/).

Figure 1 .

 1 Figure 1. LGA is associated with DNA hypermethylation targeting key stem cell signaling pathways. (A) Overview of study design (B) Volcano plot of DNA methylation score differences for LGA compared to CTRL in cohort 1, cohort 2, and cohort 1 + 2. Differentially methylated loci with p-value < 0.001 and |methylation difference| > 25% are shown in red. (C) Summary of calculation for the gene-methylation score. (D) Validation of the gene-methylation score. Gene-methylation score distribution. Bar plot of the association between gene-methylation score and genomic or methylation-related features. Bar plots representing the significance of the difference in genemethylation score of DEGs compared to non-DEGs considering different metrics. eQTR, region with expression quantitative traits loci; DMC, differentially methylated CpGs. (E) Network representation of GO Biological Process enriched in hypermethylated genes. Significantly enriched GO terms were identified using GSEA based on the gene-methylation score. Edges represent interactions (gene overlap) between pathways.

Figure 2 .

 2 Figure 2. Lineage-specific transcriptomic analysis. (A) Hematomap, UMAP representation of distinct HSPC lineages. (B) Dot plot representing key markers used to annotate cell populations. LT-HSC, long-term hematopoietic stem cell; HSC, hematopoietic stem cell; MPP, multipotent

Figure 3 .

 3 Figure 3. Association between changes in DNA methylation and in gene expression. (A) Dot plot representing the correlation between DNA methylation and gene expression changes. Differentially expressed genes with adjusted p-value < 0.05 and |log2FC| > 0.5 are shown in red. (B) Boxplots representing gene-methylation score distribution associated with non-DEG, up-regulated, and down-regulated genes (Wilcoxon test). (C) Network representation of significantly enriched pathways identified through GO GSEA analysis based on DEG identified comparing LGA vs. CTRL. Nodes are color-coded based on enrichment for differentially methylated genes using the genemethylation score. Edges represent interactions between pathways.

Figure 4 .

 4 Figure 4. Chromatin accessibility analysis. (A) UMAP representing HSPCs lineage based on chromatin accessibility. Annotations are based on the Hematomap using the transfer label approach. (B) Dot plot representing enrichment for DMC within lineage-specific peaks. (C) Bar plots representing enrichment for peaks containing CpG or peaks containing DMC associated to DEGs, up-regulated and down-regulated genes (* p-value < 0.05; ** p-value < 0.01, *** p-value < 0.001, hypergeometric test). (D) Dot plot representing enrichment for transcription factor motif within Down peaks identified comparing chromatin accessibility between LGA and CTRL. Dots are colorcoded based on percentage of peaks with motif and y-axis represents the significance of the enrichment. (E) Bar plots representing enrichment analysis considering accessible, down, and up peaks. Enrichment is performed using peaks in expressed genes (expressed), peaks with CpGs (CpGs), peaks in DEG (DEGs), peaks with DMC (DMCs), and peaks in DEG with DMC (DMCs_DEGs) as reference gene sets (** p-value < 0.01, *** p-value < 0.001, hypergeometric test).

Figure 5 .

 5 Figure 5. Epigenetic programming of HSC-specific regulons altered in LGA neonates. Regulons and TF target information were obtained through the SCENIC workflow. (A) Boxplots representing regulon activity score in CTRL and LGA HSC lineage. Barplot representing the change in regulon activity and significance comparing LGA vs. CTRL. Only significantly affected regulons are represented (adjusted p-value < 0.001 and |activity score fold change| > 10%). (B) Heatmap representing association between altered regulons and selected gene sets annotation. (C) Volcano plot representing enrichment in the change in expression and DNA methylation in regulons. Regulons enriched considering both expression and methylation (adjusted p-value < 0.01 and NES > 1.6) are in red. (D) Dot plot representing enrichment for TF binding motifs using HOMER considering a ±20 bp region around DMCs. Dots are color-coded based on the significance of the enrichment and y-axis represent the number of regions with binding motif among DMCs. (E) Dot plot representing enrichment for TF binding motifs using HOMER considering peaks with DMCs. Dots are color-coded based on the fold-enrichment and y-axis represents the significance of the enrichment.

Figure 6 .

 6 Figure 6. Network recapitulating interaction between the epigenomic and transcriptomic alterations in LGA. (A) Network representing interactions between target genes and transcription factors considering our top affected regulons ARID5A, EGR1, KLF2, KLF4, FOSB, and JUN. Each dot represents a gene within the network, the triangle represents a transcription factor, the arrow represents the interaction between the transcription factor and target genes, shapes are color-coded to reflect the change in gene-methylation score, and DEGs are labeled in red. Size of the shape represents the number of interactions. Only genes with two or more interactions are represented. (B) Tracks representing DNA methylation and chromatin accessibility for selected representative regions. Histogram representing change in DNA methylation at CpG level comparing LGA vs CTRL. Violin plot representing gene expression for selected genes (C) Network-based on the integration of DNA methylation, gene expression, and chromatin accessibility representing transcription factors and downstream target interactions within EGR1, KLF2, and KLF4 regulons. Only genes associated with peaks with TF motifs of interest are annotated. Donuts represent different levels of interactions. ***: significant change of peak accessibility (logistic regression) or gene expression (Wilcoxon test) in LGA compared to Control HSCs, adjusted p-value < 0.001.

Figure 7 .

 7 Figure 7. LGA is associated with decreased expansion capacity and an HSC shift toward more differentiated cells. (A) Bar plot representing colonies distribution after CFU assays. (B) Representative capture of colonies' morphological differences found in CTRL and LGA. (C) Boxplots representing the cell distribution across hematopoietic main lineages in CTRL and LGA. (D) UMAPs representing pseudotimes across lineages. (E) Box plots representing the cumulative percentage of cells per pseudotime in CTRL and LGA. Boxplots in the vignette represent overall pseudotime distribution in CTRL and LGA. Density plots correspond to cell populations distribution across pseudotimes. (F) Model recapitulating the influence of LGA on the hematopoietic compartment. (LT-HSC, long-term hematopoietic stem cell; HSC, hematopoietic stem cell; MPP, multipotent progenitor; LMPP, lymphoid-primed multipotent progenitors; Erythro-Mas, erythroid and mast precursor; Mk/Er, megakaryocyte and erythrocyte; DC, dendritic cell; CFU-GEMM, common myeloid progenitors; BFU-E, erythroid progenitors; CFU-GM, granulocytemacrophage progenitors.

  : Methylation data processing ; Figure S2: HSPC subpopulations analysis ; Figure S3: DEG analysis; Figure S4: ATAC-seq data processing ; Figure S5: TFs and pseudotime lineage specific characterization ; Supplementary Tables. Author Contributions: A.P., A.C., Y.M.Z., E.D., L.B.-F. and F.D. were responsible for conducting research and analyzing data. M.D. and M.C. provided feedback on the data analysis. A.P., A.C., F.D., A.B. and P.F. contributed to writing the manuscript. F.D., F.H. and J.G. were responsible for designing the study. All authors have read and agreed to the published version of the manuscript.

  of pre-heated medium. Cell suspensions were filtered with a MACS preseparation filter 30 μm and centrifuged 5min at 300g. Cell pellets were resuspended in Deionized Phosphate Buffer Saline 1X (DPBS, GIBCO™, Fisher Scientific 11590476) with 0.04% Bovine serum albumin (BSA) for counting on a Corning Cytosmart cell counter by Trypan blue (Trypan Blue solution, 11538886, Fisherscientific) counterstaining for viability check. Samples was pooled by 2 based on their

  electroporation following manufacturer recommendation. After centrifugate for 10min at 200g, the cell pellet was resuspended in 100 µL of nucleofector solution and 5 µL of 1 µM siRNAs solution was added (final concentration= 50 nM). The cells + siRNAs solution was transfered in a cuvette and put in the Nucleofector™ 2b Device (Lonza). The program U-008 was runned. After electroporation, 500 µL of pre-heated CD34+ Cell medium supplemented with stimulating cytokines (SCF,GM-CSF, Tpo and IL-6 ,STEMCELL Technologies) was added and cells was incubated in 24 well plaque overnight at 37°C in a humidified 5% CO2 incubator. Then, RNA isolation for RT-qPCR gene silencing validation, or scRNAseq protocol was performed.I.2.a.v. RT-qPCRRNA was isolated using the RNeasy Micro Kit (Qiagen). RNA was retro transcribed into cDNA using the Invitrogen™ SuperScript™ III Reverse Transcriptase. 1ng of cDNA was mix with 1X SYBR Green master mix (Thermofisher), and 1µL of 10µM reverse / forward primers mix and H2O qsp 20 µL, and incubated in the QuantStudio 7 Pro qPCR system (Thermofischer). Relative normalized expression change was determined using the RPLP0 housekeeping gene expression as reference gene. I.2.a.vi. Single-cell multimodal libraries preparation CD34+ cells where incubated 2 hours in StemPro™ CD34+ Cell Medium with or without stimulating cytokines (SCF,GM-CSF, Tpo and IL-6) at 37°C in a humidified 5% CO2 incubator. Then nuclei

Figure 17 :

 17 Figure 17 : HTO protocol effect. (A) volcano plot of differentially expressed genes (DEGs) in HTO versus non -HTO prepared cells for each individual. (B) MA plot of DEGs using pseudobulk analysis. (C) Functional enrichment of GO Biological Process and KEGG pathways enriched in upregulated genes in HTO prepared cells (D) Heatmap of the gene expression log2(FoldChange) comparing HTO vs non -HTO prepared samples for each lineage. (E) Number of DEGs by lineage. (F) Differential regulons activity between HTO

Figure 18 :

 18 Figure 18 : LGA vs CTRL comparison from rapidly processed samples. (A) volcano plot of differentially expressed genes (DEGs) in LGA versus CTRL (pseudobulk DESeq2 analysis) . (B) Subpopulation distribution in LGA and CTRL samples

Figure 20 :

 20 Figure 20: Single-cell multimodal analysis of cytokine stimulation. (A) UMAP representation of cells integrating both v ariability of gene expression and chromatin accessibility. (B) Subpopulation distribution in stimulated (grey bar) and unstimulated

Figure 20 :

 20 Figure 20: Validation of the EGR1-KLF2-KLF4 regulatory network epigenetics alteration. (A)Schema of peak gene linkage method. Reprinted from 10X genomics website support R . (B) Peak gene linkage in the ZFP36L2 locus. The score of the linkage correspond to the correlation between the peak accessibilty and the g ene expression. (C) Overrepresentation of EGR1, KLF2 and KLF4 regulons (x axis) in CREs containing EGR1, KLF2 or KLF4 motif (boxes). P-value of the enrichment are calculated using Fisher's exact test. Red point represent p-value < 0.05. (D) Venn diagram of the intersection between CREs containing DMCs (left) and CREs containing EGR1, KLF2 or KLF4 motif.

  cells in sample transfect with siRNAs targeting KLF2 (siKLF2) compared to sample exposed to siRNAs Control (siCTRL). (F) Activity score (AUCell) of KLF2 regulon in cells. (G) Differential expression analysis of HSC s exposed to siKLF2 compared to HSC exposed to siCTRL (wilcoxon test). Red point represent DEGs (adjusted p -value <0.05 and |log2FC|>0.25). (H) Regulons enrichment analysis using GSEA in differential expression results. NES: Normaliz ed Enrichment Score. NES<0 indicate enrichment in downregulated genes in siKLF2 HSCs. Red point represent regulons significantly enriched (adjusted p-value<0.1) I.3. Conclusion I.3.a. Characterize the epigenetic memory of LGA HSPCs Delahaye et al have previously shown that LGA HSPCs have a global DNA hypermethylation enriched for candidate cis regulatory elements and close to genes known to regulate metabolic function and stem cells properties 349 .

  impact a transcriptional program controlling differentiation and proliferation of HSC and response to stress/stimulation. Such mechanism of epigenetics memory was recently found on hair follicle bulge stem cells where previous exposure to wound damages was associated with durable chromatin accessibility change influencing stem cell's responses to future assaults 379 . Izzo et al recently demonstrated influences of DNA methylation on TF activity in the context of hematopoietic differentiation 393 . Researchers found TFs binding CpG rich DNA motifs, notably key erythroid TF Klf1 and Tal1, were affected by disruption of DNA methylation and driving cell fate change.

  ASCL1-hiNs, associated with changes in fluorescence dynamics indicative of longer times to reach the maximum intracellular Ca 2+ levels and to recover baseline levels (Fig. 6A-F).

Figure 1 :

 1 Figure 1: Transcriptional changes in BIN1 KO hiNs. (A) Immunohistochemistry for GFAP (red), MAP2 (green) and DAPI (blue) in 6.5-month-old BIN1 WT and KO COs. (B) Western blots showing the isoforms of BIN1 detected in WT and the absence of BIN protein in KO

Figure 2 :

 2 Figure 2: Similar molecular alterations in BIN1 KO hiNs and glutamatergic neurons of the AD brain. (A) Box plot representing BIN1 mRNA in expression through different Braak stages in entorhinal cortex (EC) and superior frontal gyrus (SFG) (***p adj <0.001; Wilcoxon test). (B) Dot plot representing the overlap between DEGs identified in glutamatergic neurons of the AD brain and BIN1 KO ASCL1-hiN cultures (left) or BIN1 KO COs (right). (C-D) Network representation of enriched GO terms in overlapping DEGs between AD brains and

  Figure 3: Similar synaptic density in BIN1 WT and KO ASCL1-hiNs. (A-B) Immunocytochemistry using the astrocyte marker GFAP, neuronal marker MAP2, presynaptic marker SYP and post-synaptic marker HOMER1 in BIN1 WT ASCL1-hiNs after 4 weeks of differentiation in a three-chamber microfluidic device. Scale bar = 200 μm.

Figure 4 : 20 Figure 5 :

 4205 Figure 4: Disorganization of neuronal activity in BIN1 KO ASCL1-hiNs. (A) Raster plots showing the decomposition of multi-unity activity (MUA, black lines) into single-unit activity

Figure 6 : 2 (

 62 Figure 6: Altered frequency of calcium transients in BIN1 KO ASCL1-hiNs. (A) Snapshot of a 4-week-old ASCL1-hiNs culture labeled with Oregon green BAPTA. (B) Representative

  (red), MAP2 (green) and DAPI (blue) in 6.5-month-old BIN1 WT and HET COs. (B) Western blots showing the decrease in BIN1 expression in HET COs. (C) UMAP representation of the different cell subtypes in COs identified using snRNA-seq. (D) Cell proportions in each subpopulation in WT and HET COs. (E) Volcano plot representing DEG comparing HET vs WT in astrocytes and glutamatergic neurons. DEGs with adjusted pvalue <0.05 and |log2FC| >0.25 are shown in red. Gene labels are shown for top 10 genes in terms of log2FoldChange and p-value. (F) Functional enrichment analysis of DEGs identified in BIN1 HET glutamatergic neurons. Bar plots representing the top 10 enriched gene ontology (GO) terms in biological processes (BP), cellular components (CC) and molecular function (MF) at p adj <0.01. (G) Venn diagram showing the overlap between DEGs identified in BIN1 HET and KO glutamatergic neurons. (H) Bar plots representing the top 10 enriched GO:BP for common DEGs. (I) Immunohistochemistry for HOMER1 (red), synaptophysin (SYP, green) in 6.5-month-old BIN1 WT and HET COs. (J) Quantification of the percentage of SYP assigned by HOMER1 spots in BIN1 WT and HET COs (***p=0.0002; Mann-Whitney test; n=3 COs per genotype). Sup. Figure 2: Transcriptional changes in spontaneously differentiated BIN1 KO hiNPCs. (A) UMAP representation of the different cell subtypes identified in 2D hiNPC cultures after 6 weeks of differentiation using snRNA-seq. (B) Proportion of cell subpopulations in both genotypes. (C) Dot plot representing expression of key markers used to annotate cell subtypes. (D) Volcano plot representing DEGs comparing BIN1 KO vs WT glutamatergic neurons. DEGs with adjusted p-value <0.05 and |log2FC| >0.25 are shown in red. (E) Functional enrichment analysis of DEGs identified in glutamatergic neurons. Bar plots representing the top 10 enriched GO terms in each category at p adj <0.01. 24 Online methods Maintenance of cells and generation of hiNPCs and hiNs hiPSCs modified for BIN1 in exon3 by CRISPR/Cas9 technology were sourced from Applied StemCell Inc. CA, USA. In addition to the BIN1 WT and KO hiPSCS, heterozygous (HET) iPSCS, harbouring a 1 bp insertion in one allele were also sourced Applied Stem Cells Inc. CA, USA.The parental cell line used for derivation of the cells was ASE 9109. The maintenance of these cells and the generation of hiNS, hiAs, and COs thereof, have been detailed in the publication by Lambert et al., 2022. All hiPSCs and their neuronal and glial cell derivatives including COs were maintained in media from Stemcell Technologies, protocols which have been elucidated on the webpage of Stemcell Technologies. In addition, the embryoid body method detailed by Stemcell Technologies was used for the induction of BIN1 WT and KO hiPSCs. Cell numbers and viability were recorded using a LUNA™ Automated Cell Counter (Logos Biosystems, South Korea).hiNs generated from ASCL1-transduced hiNPCs (protocol detailed in next section) were subjected to differentiation for 4 weeks. All differentiations were performed in tissue in 24well cell imaging plates (0030741005, Eppendorf) culture dishes pre-coated with Poly-Lornithine (P4957, Sigma-Aldrich) and Mouse Laminin (CC095, Sigma-Aldrich).

  represented about 15%, 8%, 4% of the cells, respectively. The first two cell populations likely represent two different states of astrocytes added to the cultures, whereas NPCs are likely cells that failed to reprogram into hiNs despite ASCL1 transduction.Culture of Induced Neurons (hiNs) in Microfluidic DevicesPreparation of Microfluidic Devices: Three-compartment microfluidic neuron culture devices were used in which the presynaptic and postsynaptic chambers are connected to 26 the synaptic chamber by respectively long and short micro-channels. Details of the microfluidic device design and fabrication have been previously described(Kilinc et al, 2020).The homemade devices were placed individually in Petri dishes for easy handling and UV sterilized for 30 min before coating for cell adhesion. The primary surface coating consisted of poly-L-lysine (Sigma-Aldrich) at 20 µg/mL in borate buffer (0.31% boric acid, 0.475% sodium tetraborate, pH 8.5). All coated devices were incubated overnight at 37°C, 5% CO2. After a wash with DPBS, devices were then coated with 20 μg/mL laminin in DPBS and incubated overnight at 37°C in 5% CO2. The following day, devices were carefully washed once with DPBS before cell plating.Cell Culture: In total, 30,000 NPCs resuspended in complete Neural Progenitor Medium (NPM, Stemcell Technologies) containing 10 µM of Y-27632 ROCK inhibitor were seeded per device, half at the entrance of the presynaptic somatic chamber and half at the entrance of the postsynaptic somatic chamber. Microfluidic devices were microscopically checked at the phase contrast to ensure the cells were correctly flowing into chambers.After a minimum of 5 minutes to allow the cells to attach, devices were filled with NPM (containing 10 µM of Y-27632 ROCK Inhibitor). Water was added to the Petri dishes to prevent media evaporation, and these were then incubated at 37°C in a humidified 5% CO2 incubator. The spontaneous neuronal differentiation of NPCs started 24 hours later, initiated by half medium change with complete BrainPhys Neuronal Medium. Induced neuron cultures were maintained for 4 to 6 weeks with half of the medium replaced biweekly with BrainPhys medium.For induced neuron culture from NPCs transduced for Ascl1, doxycycline (2 µg/mL) was added on the first day of half medium change to induce TetO gene expression. The following day, puromycin (1 µg/mL) was added to start cell selection. Two days after the puromycin selection, a total of 5,000 human cortical astrocytes (ScienCell Research Laboratories, CA, USA) were added per device. After 24 hours, Ara-C (2 µM) was added to stop their proliferation. Half of the medium was changed twice a week with complete BrainPhys medium + 2 µg/mL doxycycline for 14 days. After that, half medium change was performed only with BrainPhys medium. Four microfluidic devices were employed for each experimental condition (BIN1 KO vs performed. To assess the time-course effect, neuron cultures were stopped at 4 and 6 weeks.

  oscillation score and the oscillation period of every single unit(Muresan 2008:1333, J Neurophysiol). Briefly, the oscillation score was calculated as the averaged bin size of 200 ms). The oscillation period was calculated as the averaged time interval of the positive peaks of the autocorrelation function.

  SCTransform normalized matrices was merged and PCA was performed using Seurat::RunPCA default parameter. The 50 principal components (dimensions) of the PCA were corrected for batch effect using harmony::RunHarmony function. Then, the 30 first batch corrected dimensions were used as input for graph-based cell clustering and visualization tool. Seurat::FindNeighbors using default parameters and Seurat::FindClusters function using the Louvain algorithm were used to cluster cells according to their batch corrected transcriptomes similarities. To visualize the cells similarities in a 2-dimension space, the Seurat::RunUMAP function using default parameter was used. Cell clusters were then annotated based on cell type specific gene expression markers.

  Figure 2Glutamatergic Neurons Astrocytes

  2 internalization. Recently, Canter et al have shown that pharmacogenetic inhibition of neuronal hyperexcitability reduced AD pathogenesis in mouse including Abeta deposition 609 . Here, we found that calcium channel blocker nifedipine, a specific antagonist of Cav1.2, was able to partially rescue the neuronal hyperactivity mediated by BIN1 deletion, reducing disorganized spikes, and permitting better neuronal activity synchronization. Together, these results highlight the putative cellular mechanisms associating the BIN1 genetics risk with AD pathogenesis and suggests that blocking Cav1.2 through GENERAL DISCUSSION I. Single-cell genomics to identify putative early molecular and cellular mechanism of ACD development

  The cell resolution brings a new level of biological observations. It increases the number of individual biological observation enabling the study of gene co-expression across cells to identify associated biological module or molecular pathway. In the first model, we used SCENIC tools to infer the TFs and related gene regulatory network (GRN) affected by the epigenetics programming, highlighting the EGR1 and KLF2 related GRN being altered in HSC. This GRN inference is based on correlation, so interpretation of causality needs some caution. I used different strategy to ensure the relevance of this GRN, first integrating co-expression analysis with the chromatin information. Second with single-cell multiome ATAC+Gene expression allowing to correlate accessibility of an openchromatin region containing a particular TF motif with neighbor gene expression. This multi strategy has allowed us to validate the relevance of the infered TF regulations but also to filter out non-robust regulatory link notably for KLF4 regulon identified with SCENIC, which was not validated based on the single-cell multiome analysis. New tools have emerged to leverage the single-cell multiome data, notably SCENIC+ 195 which corrects putative bias of the previous version of SCENIC. It uses co-accessible cell type specific genomics region as candidate enhancer region to identify TF motif and subsequent GRN inference. Still, to validate the GRN inference, in vitro perturbation experiments are needed. Here, I used a gene silencing method to downregulate expression of TFs and see consequences on predicted downstream target genes. I was able to validate influence of KLF2 on downstream target genes.

  Single-cell genomics helped us to decipher cell specific influence of methylation on gene expression. Indeed, we demonstrated the cell type specific correlation between DNA methylation and gene expression changes confirming the importance to study epigenetics mechanism at cellular resolution. Role of DNA methylation in regulating transcription factors occupancy and subsequent gene expression has already been demonstrated in the context of NRF1 TF binding 615 , and also recently in the context of hematopoiesis 393 . However, transcriptional response to DNA methylation still appears context specific 411 . Then, to really confirm the causal role of DNA methylation in regulating gene expression, it would be interesting to mimic the DNA methylation changes observed in cells exposed

  of genes (mostly of the EGR1/KLF2 regulatory network) regulating cell growth and/or HSC entry in proliferation/differentiation. The critical role of DNA methylation in regulating HSC differentiation has been recently confirmed in mice models 393 . To confirm the impact of LGA-related DNA methylation change on HSC differentiation, scRNA-seq and in vitro or in vivo differentiation/expansion assay can be performed on LGA-related cellular models +/-DNMT3A exposed in previous paragraph. Such alteration of HSC differentiation occurs naturally in aging notably through the accumulation of somatic mutation and epigenetics alterations which lead to clonal expansion of defective HSC 394 . These alterations are associated with inflammaging and ACD risk, notably CVD, and recent studies have confirmed direct role of such defective hematopoiesis on ACD development, further highlighting the potential role of

  [START_REF] Park | Brain somatic mutations observed in Alzheimer's disease associated with aging and dysregulation of tau phosphorylation[END_REF]

  As forBisulfite-seq, ChIP-seq requires a large amount of DNA material (i.e. cells) to conserve enough material after treatment with formaldehyde, DNA fragmentation, and Immunoprecipation.

ATAC-seq is focused on profiling open chromatin regions, which are marks of active genomic regions. ATAC is for Assay for Transposase-Accessible Chromatin. ATAC-seq assay use a hyperactive mutant transposase Tn5 enzyme, which has the ability to cut and tag accessible DNAs, producing labeled DNA fragments which can then be amplified and sequenced.

  at one specific biological layer (genetics, RNA, DNA methylation, histone marks) limit considerably the understanding of biological or diseases mechanisms. Disease development relies on a complex biological system, necessitating the integration of multiple layers (multi-omics) to highlight interactions between them bringing substantial new insights in our understanding of the molecular mechanisms involved.To measure the influence of genetics variants on gene expression or other biological variables, quantitative trait loci (QTLs) studies are performed. Expression Quantitative Trait Loci (eQTLs) studies look for associations between genetics variant and expression levels of mRNAs in a tissue. They are mainly cis-eQTLs studies, i.e. considering genes in relatively close proximity of the genetic variant

	I.3.c.i. Quantitative trait loci (link genomics with other layers)

. Such epigenomics tools give us a new understanding of diseases development. However, to assess functional consequences of such epigenetics remodeling, integration with different omics layer is needed.

I.3.c. Multi-omics integration for functional characterization

INTRODUCTION

29

Studies focusing only (typically in a 1 Mb window). They allow to identify in some extent the 'proximal' regions able to regulate expression of genes, i.e. cis-regulatory elements (CREs) such as promoter and enhancer. To contrast, trans-eQTL studies looked at association between genetics variants and gene expression with more than 1 Mb distance or from different chromosome. They are performed to identify distant association, which can reflect indirect influence of a variant on gene expression, for example the impact of a TF variant on TF downstream target genes expression. However, these analyses require significant analytical resources to perform extremely large number of test (1.8M variant on classical array multiply by 20k human genes = 36 billions of test), increasing considerably false discovery rate and power discovery. Targeted or variable selection methods are thus required to effectively identify significant association. The tissue specific human eQTLs reference is the Genotype-Tissue Expression (GTEx) project, which aim to build a reference database of human tissue specific gene expression and regulation regrouping transcriptomic data and eQTLs analysis from nearly 1000 individuals across 54 healthy tissues. However they suffer from some bias of sampling because mostly based on postmortem tissue expression of aged or intoxicated individuals so others initiative have been led notably the Roadmap Epigenomics Project 27 , aiming to have more representative tissue wide datasets.

  We will see in the next parts how the transcriptomic and epigenomics cellular heterogeneity is important in health and diseases and how its consideration in genomics research can give new insight into disease development, mainly focusing on ACDs.

G https://www.nature.com/articles/s41467-018-04252-2/figures/3 INTRODUCTION 35 II. Importance of cellular heterogeneity II.1. Cellular heterogeneity in multicellular organism

  141

  INTRODUCTION54 future environments. Numerous others epidemiological, animal, and transversal studies succeeded to better understand this association and the mechanism behind the apparent early programming of Several evidence have shown that both extreme of fetal growth increase the risk of ACD. To show that, researchers have studied diseases rate in small for gestational age (SGA) or, large for gestational age (LGA), compared to appropriately grown neonates. SGA and LGA being defined respectively as neonates under the 10 th or over the 90 th percentile birthweight and ponderal index (PI= weight / height 3 ) adjusted for gestational age and sex. Epidemiological studies have found that in addition to coronary heart diseases, SGA are associated to an increased susceptibility to numerous diseases, including hypertension 214 , type 2 diabetes 215 , stroke216,217 , dyslipidemia 218 , and impaired neurodevelopment 219 . On the other sides of the birth weight spectrum, LGA have an increased risk of ACDs and related comorbidities including cancer 220 , obesity 221 , metabolic syndrome 222 , T2D 223 and

	ACDs.
	IV.1.a.	Epidemiological evidence
	IV.1.a.i.	Birth weight

  329 . Work out influences DNA methylation, but also histone acetylation and miRNAs expression330 . Six month aerobic exercise reshapes the whole genome DNA methylation in skeletal muscle and adipose tissue influencing lipogenesis, while miRNAs expression profiles allow discrimination between low and high responders to resistance exercise 330 . Chronic alcohol consumption leads to significant reductions in S-adenosylmethionine (SAM) levels, the metabolite substrate of DNMT enzyme, thereby contributing to DNA hypomethylation 331 . Smoking alters DNA methylation of numerous genes and disturb several miRNAs expressions 332 . In adult, impact of methyl donor deficient diets on methylation status and gene expression is partially reversible when the methyl donor is added back into the diet333,334 . Several durable epigenetics alterations have been shown in individual exposed to in utero stress, when the epigenome is established supporting important role of epigenetics in the early programming of ACDs335,336 .

that early exposure results in long term tissue development alteration and/or decreased cellular plasticity. Such programming mechanism often relies, at least in part, on epigenetics modifications. Indeed, epigenetics mechanisms play a key role in mediating the influence of environmental exposures at cellular and molecular level. Diet, living place, drug treatments, or unhealthy habits are environmental factors known to influence epigenetics status 328 . Dietary restriction protects from age associated DNA methylation and induces epigenetics reprogramming of INTRODUCTION 60 lipid metabolism

  HSC multipotency requires fine control of differentiation in order to give appropriate progeny and ensure cellular homeostasis. To ensure this control, it was recently demonstrated that epigenetics remodeling and TF activity act in concert. The role of DNA methylation in HSC differentiation has been pinpointed after the identification that the principal somatic mutations driving HSC clonal expansion and defective hematopoeisis in aging are in DNMT3A and TET2 genes, the writer and eraser of DNA methylation386 . Further functional studies have validated DNMT3A and TET2 roles in regulating HSC self renewal and differentiation in blood progeny

		INTRODUCTION
	IV.2.c.i.	Hematopoiesis regulation
		65

The hematopoietic system plays a critical role in processes like inflammation, angiogenesis, and cardiovascular repair throughout life, making its progressive alteration a candidate mechanism in the development of ACD.

  . Compared to SGA which have been intensively studied aspecially during hunger period, LGA have been less studied. Yet, LGA onset have increased during the 20-30 years, about 15-25% in developed country 424 and even larger in developing country425 . dyslipidemia, including high triglycerides and high density lipoprotein (HDL) are associated with increased birthweight and LGA risk, and was consistent across different populations 428 .

	Maternal dyslipidemia is also associated to over secretion of placental Insulin like Growth Factor 1
	(IGF1) highlighting a putative molecular pathway linking maternal dyslipidemia and excessive fetal
	growth 429 . Then, fetal overgrowth can be explained by overexposure to growth factor including
	glucose, insulin or IGF1. However, the long-term cellular and molecular consequences of this fetal
	exposures leading to LGA remains poorly understood. In these thesis works, I studied influence of this
	excessive fetal growth on HSPC regulatory landscape and plasticity, integrating both single-cell
	epigenomics and transcriptomics.
	However, the physiopathological consequences of being born LGA remain poorly understood. While
	being born LGA have also a genetic component, the recent raise of LGA clearly suggest that our modern
	environment contribute largely to this phenotype. Different in utero exposure can explain the fetal
	overgrowth. Maternal hyperglycemia, is associated with fetal hyperinsulinemia and increase by two

the risk to give birth to LGA neonate

245,426,427

. Maternal obesity increases also by two the risk of LGA 255 . P https://stemcellres.biomedcentral.com/articles/10.1186/s13287-022-02718-1 INTRODUCTION 70 Similarly, maternal V. Model 2: Alzheimer's Diseases (AD) susceptibility gene BIN1 study V.1. Alzeihmer's disease

Alzheimer's disease (AD) is a neurodegenerative disease responsible of 70% of dementia, affecting more than 20% of elderly people (> 75 years old). It is the 7 th leading cause of death worldwide and is the major causes of disability and dependency among older people globally

8,430

. It is characterized by progressive neuronal degeneration in the brain associated with memory and cognitive loss. Even if the definitive diagnosis can be performed only after brain autopsy, cognitive test and PET scan help to have a clinical diagnosis of the disease. First symptoms are short term memory loss and inability to acquire new information as a result of the reduced neuronal plasticity

7

. Cognitive and motor function progressively decline after years as the consequence of neurotoxic aggregates spreading across the brain. Many unresolved questions regarding the AD pathophysiological process remain, so that no efficient treatment is yet available to prevent or cure this disease. AD is estimated having a 70% heritability suggesting strong genetics influences. A major genetic risk is APOE4, found in ~60% of AD carrier, while in 15% of the global population but not explain every diseases onset, as at least 1 third of AD patient do not have this variant Q431

  Another important early molecular mechanism found altered in AD is the calcium homeostasis.Calcium homeostasis and calcium related intracellular signaling are key mechanisms to regulate neuronal activity. Indeed, Ca2+ participate to membrane depolarization and thus electrochemical signal transmission in neurons through plasma membrane receptors and voltage-dependent ion channels468 . One important process for neurons after its depolarization is its rapid repolarization to be mobilized again and synchronized with others neurons 468 . This is allow through delivery of Ca2+ in extracellular matrix through ATP dependant pumps or Na+/Ca2+ exchanger. Fine tune regulation of calcium channel and exchanger is crucial for neurons to have this appropriate equilibrium between neuronal excitability (depolarization) and fast repolarization / return to basal level. Neurons have important Ca2+ dependent signaling pathways to couple Ca2+ with their biochemical machinery.Notably, Ca2+ dependent kinases activation allow long term potentiation (LTP) of neurons, a key mechanism of memory and learning468,469 . Moreover, Ca2+ signal allow also to communicate between the synapse and the nucleus through gradient diffusion into nucleus, where Ca2+ can activate the TF CREB and control number of target genes. Then, calcium homeostasis and related signaling are crucial actors for appropriate neuronal activity and brains function including memory and learning.Nevertheless, both calcium homeostasis and calcium related pathway appear altered at early stage in AD interacting with the amyloidogenic pathway 470 . Indeed, several studies have found that basal Ca2+ level in neurons close to Abeta deposits are increase compared ton non pathological neurons471,472 . Mechanisms behind this phenomenon are unclear but predominant studies have found that Abeta oligomers could affect calcium homeostasis through its interactions with synaptic calcium channels, or by increasing Ca2+ permeability of the membrane[473][474][475][476] . But others evidence support an invert relationship, where calcium dyshomeostasis arise independently of Abeta but promote Abeta accumulation. Dreses-Werringloer et al have shown that a genetic variant in the Ca 2+ homeostasis modulator 1 (CALHM1), a voltage-gated ion channel which promotes Ca 2+ entry from the extracellular ambient, may increase the risk of AD while increase Abeta levels by interfering with Ca2+ permeability 477 . Furthermore, increase of Ca2+ can stimulate metabolism of APP[478][479][480] . Several studies have also shown that an increased calcium influx/signaling increase tau phosphorylation in neurons, further highlighting implication of calcium deregulation in AD pathogenesis479,[481][482][483] .One of the main consequences of disrupted Ca2+ signaling is alteration of synaptic plasticity and cell death 484 . Neurons near Abeta plaques have an increase activity (hyperexcitability) resulting from the reduced control of intracellular Ca2+ level485 . Concordant with a role of Abeta in calcium mediated cognitive functions, Abeta oligomers emergence is correlated with reduced LTP[486][487][488] , and INTRODUCTION 76 LTP defect can be reversed by antibody against Abeta 488 . Excess Ca2+ basal level can also activate mitochondrial released of cytochrome C inducing apoptosis cascade 484 . In support to this hypothesis, over expression of the antiapoptotic Bcl2 (reducing Ca2+ release by mitochondria), improve cognition in a mouse model of AD, before any sign of neurodegeneration 489 .What are the cellular mechanisms contributing to this calcium deregulation in AD remain unclear. Even if Abeta itself can alter calcium homeostaisis as seen previously, some studies have shown calcium deregulation before amyloid beta plaque detections[490][491][492] . Overall, these evidences shows the great importance of calcium dyshomeostasis and signaling in AD onset/progression.V.4. Non genetics factors of ADWhile genetics risk contribute to a major part of AD, it fails to explain around 30% of AD cases. Indeed, AD is a multifactorial diseases with sporadic onset, indicating that others factors influences their development including environmental, age related and epigenetics factors.

75 V.3.d. Calcium signaling and AD Different environmental exposure have been shown to modulate risk of AD. It was notably

  6. Understand early mechanism of AD through BIN1 gene function study Amyloid plaque, NFTs, neuroinflammation and calcium deregulation are central mechanism behind AD pathogenesis but the initial factor leading to this pathophysiological mechanism remain unclear. Even if amyloid plaques formation start decades before AD symptoms, suggesting critical causal mechanism, what predispose certain individuals to develop Abeta plaques mediated neurotoxicity remain to be determined. As shown in previous section, studies of genetics risk give us cues about what genes or biological process are crucially involved in AD development. GWAS studies clearly confirms the importance of gene regulating APP processing (ADAM10, PSEN1, PSEN2) and

  . However, in our previous work in drosophila model of AD, we found that human BIN1 deletion do not alter significantly APP processing while BIN1 dysregulation affect endocytosis and promote neurotoxicity544 . Furthermore, another study in AD mouse model have also found that reduction of BIN1 expression, do not affect amyloid pathology 553 .INTRODUCTION 81More evidence have been shown for role of BIN1 in tau pathology. BIN1 can interact with Tau and its expression correlate with Tau level and tau tangles pathology in AD brains549,551 . In a co-cultured model of tau propagation, Calafate et al have shown that knockdown or overexpression of neuronal specific BIN1 isoform 1 respectively promotes or inhibits Tau propagation in neurons 517 . They further show that loss of BIN1 increase endosomic flux, which increase Tau aggregate internalization and release into cytoplasm inducing Tau propagation. These results suggest that neuronal BIN1 have a protective role in preventing Tau pathology, and its downregulation as observed in AD brains could induce Tau pathology. Concordant with that, a recent study have shown that human BIN1 is able to recover human Tau induced cognitive defect in transgenic mice, preventing Tau mislocalization and somatic inclusions in the hippoccampus 546 . Authors further show that BIN1 can dynamically modulate its interaction with Tau through phosphorylation to compensate AD related Tau accumulation 546 .

	In another study regarding effect of overexpressing neuronal BIN1 isoform 1 in cultured rat
	hippocampal neurons, authors shows that upregulating BIN1 induced neuron hyperexcitability,
	increasing frequency of synaptic transmission, and increasing calcium transients showing neuronal
	hyperactivity

545

. They further suggested that over-expressed BIN1 could indirectly interact with L-type voltage-gated calcium channels (LVGCCs) through Tau. These calcium channels are involved in calcium transients and are known to be stabilized in membrane of cardiomyocytes thanks to BIN1

519,554

, which could then explain this BIN1 mediated neuronal hyperactivity. They finally show that Tau mediate the BIN1-LVGCC interaction being bound by both proteins, and that is reduction prevent network hyperexcitability operate by BIN1 overexpression

545

.

Role of BIN1 in neuronal excitability was further shown recently but regulating another pathway. Indeed, by conditionally deleting BIN1 in neurons, De Rossi et al have shown that BIN1 localize preferably in presynaptic sites of glutamatergic synapse in hippocampus, and that BIN1 are able to regulate the releases of neurotransmitter vesicles. Neuronal BIN1 deletion reduce synapses

Board Statement: :

  This study was approved by the Institutional Review Board of the Montefiore Medical Center and the Committee on Clinical Investigation at the Albert

					RESULTS
	I.2. Complementary works
	I.2.a. Materiels and methods
	I.2.a.i. Reagents, primers and siRNAs
	REAGENT		REFERENCE SUPPLIER
	Human CD34+ Cell Nucleofector™ Kit	VPA-1003	Lonza
	LKLF/KLF2 siRNA (h)		sc-35818	santa-cruz
	Egr-1 siRNA (h)		sc-29303	santa-cruz
	SiRNA Control-A		sc-37007	santa-cruz
	RNeasy Micro Kit (50)	74004 Qiagen
	Human Methylcellulose Complete Media HSC003	RnD systems
	Invitrogen™ SuperScript™ III Reverse	18080093 fisher
	Transcriptase			scientific
	dNTP mix (10 mM each)	18427013 thermofisher
	Random Primers		48190011 thermofisher
	primers EGR1 and KLF2	see primers table
	PRIMER_NAME	SEQUENCE		TARGET	PRODUCT
					SIZE
	pEGR1_forward	TTCAACCCTCAGGCGGACAC	EGR1	71
	pEGR1_reverse	GAGATGTCAGGAAAAGACTCTGCG EGR1	71
	pKLF2_forward	AGAGGGTCTCCCTCGATGAC	KLF2	100
	pKLF2_reverse	CTCGTCAAGGAGGATCGTGG	KLF2	100
	I.2.a.ii.
			107

  loss-offunction affects electrical activity and leads to synapse loss, transcriptional and biochemical alterations resembling those observed in the AD brain. These results suggest that misexpression of BIN1 in glutamatergic neurons may contribute to early stages of AD pathophysiology by dysregulating neuronal firing homeostasis through LVGCCs.

	42.	Loss of Ca Carvalho, L. I., Lambert, J.-C. & Costa, M. R. Analysis of modular gene co-expression 2+ homeostasis is an important feature of many neurological diseases and
	has been extensively described in AD 41,42 . Interestingly, DEGs identified in glutamatergic networks reveals molecular pathways underlying Alzheimer's disease and progressive
	neurons in our different cell culture models are enriched for calcium-related biological supranuclear palsy. medRxiv 2021.09.21.21263793 (2021)
	processes. This is also observed for DEGs detected both in glutamatergic neurons of BIN1 KO doi:10.1101/2021.09.21.21263793.
	COs and in AD brains. Thus, reduced expression of BIN1 in glutamatergic neurons may 43. Ou, Y. N. et al. Genetically determined blood pressure, antihypertensive medications,
	contribute to the breakdown of Ca and risk of Alzheimer's disease: a Mendelian randomization study. Alzheimer's Res. 2+ homeostasis in the AD brain, potentially contributing to
	neuronal circuit dysfunctions. Consistent with this hypothesis, we have previously shown a Ther. 13, 1-9 (2021).
	significant reduction in the expression of the transcript encoding for the neuron-specific 44. Wu, C. L. & Wen, S. H. A 10-year follow-up study of the association between calcium
	BIN1 isoform 1 in bulk RNA-sequencing data from a large number of AD patients channel blocker use and the risk of dementia in elderly hypertensive patients. Med. 7 and we
	show in this work that BIN1 expression is reduced in glutamatergic neurons of AD brains at (United States) 95, (2016).
	late Braak stages.
		Altogether, our results suggest that BIN1 misexpression in glutamatergic neurons may
	primarily undermine Ca	2+	homeostasis, leading to changes in neuronal electrical activity. In a
	later stage, gene expression and circuit-level alterations such as synapse loss would occur,
		Neuronal network dysfunctions are observed in AD patients at early stages of the
	disease and precede or coincide with cognitive decline likely because of altered neuronal electrical activity. A corollary to this model would be that . Under physiological conditions, 35-37
	early treatments aiming to restore Ca	2+	homeostasis and neuronal electrical activity may
	neuronal networks can maintain optimal output through regulation of synaptic and cell-
	intrinsic mechanisms have a beneficial impact in AD. Interestingly, a Mendelian randomization and a retrospective . Our results suggest that normal levels of BIN1 expression in 38
	population-based cohort study found evidence suggesting that Ca	2+	channel blockers are
	glutamatergic neurons are fundamental to regulate neuronal firing rate homeostasis.
	Indeed, BIN1 deletion in hiNs is sufficient to dysregulate network oscillations even without associated with a reduced risk of AD 43,44 . In the future, it would be interesting to study the
	impact of these drugs for AD onset/progress as a function of genetic variants in the BIN1
	impacting		the	number	of	functional	synaptic	contacts,	suggesting	that	the
	desynchronization observed in BIN1 KO hiNs circuits are a consequence of miscarried locus.
	homeostatic controls of neuronal activity.
		One key mechanism controlling neuronal spiking activity is the regulation of Ca	2+
	homeostasis	31,33,39 . Increased neuronal electrical activity induces the turnover of LVGCCs
	from the plasma membrane through endocytosis	34	and regulates the transcription of genes
	encoding for calcium-binding proteins and calcium-mediated signaling	40 , mechanisms
	aiming to restore local Ca	2+	signaling cascades and protect cells against aberrant Ca	2+	influx.
	We show that BIN1 interacts with Cav 1.2 in hiNs, similar to previous findings in cardiac T
	tubules	32	and in mouse hippocampal neurons	19	and provide evidence supporting a novel
	role for BIN1 in the regulation of activity-dependent internalization of Cav 1.2 in human
	neurons, thus linking BIN1 to firing homeostasis in human neurons through that LTVGCC.
											12 17 18

  . The first step was the Embryoid Body (EB) Formation Stage, where hiPCSc at 80%-90% confluency were detached from the Vitronectin XF substrate using Accutase (#AT-104, Innovative Cell Technologies). To form the EB, 9000 cells were plated per well in a 96-well round-bottom ultra-low attachment plate containing EB seeding medium (Stem Cell Technologies). After two days, the EBs were transferred to a 24-well ultra-low attachment plate containing Induction Medium (Stem Cell Technologies), where each well receives 1-2 EBs. This was the Induction Stage. Two days later, the EBs were ready for the Expansion Stage. The EBs were embedded in Matrigel (Corning) and transferred to a 24-well ultra-low adherent plate with Expansion

	The iGluSnFR construct was an adeno-associated viral vector (BS11-COG-AAV8) HOMER1 (160004, Synaptic Systems), Synaptophysin (101011, Synaptic Systems), and GFAP
	sourced from Vigene Biosciences, MD, USA. The viral construct was transduced at a MOI of (AB5804, Millipore). Cells were washed twice with PBS and incubated with the following
	5,000 at around 10 days of differentiation for the ASCL1-hiNs. Differentiation was allowed to secondary antibodies for 2h at room temperature: DyLight™ 405 Donkey Anti-Chicken (703-
	continue for a duration of 4 weeks prior to imaging. 475-155, Jackson ImmunoResearch), Alexa Fluor 594 Donkey Anti-Guinea Pig (706-585-148,
	Jackson ImmunoResearch), Alexa Fluor 488 Donkey Anti-Mouse (715-545-151, Jackson
	Immunocytochemistry and Immunohistochemistry ImmunoResearch) and Alexa Fluor 647 Donkey	Anti-Rabbit	(711-605-152,	Jackson
	Bidimensional (2D) cultures: All cells were fixed in 4% (w/v) paraformaldehyde ImmunoResearch). Cells were rinsed three times with PBS and microfluidic devices were
	(Electron Microscopy Sciences, Catalog # 15712) for 10 minutes in the imaging plates. mounted with 90% glycerol.
	Following, fixation, cells were washed thrice with PBS 0.1 M. Blocking solution (5% normal Samples were imaged with a LSM 880 confocal microscope with a 63X 1.4 NA
	donkey serum + 0.1% Triton X-100 in PBS 0.1 M) was added to fixed cells at room objective. Images were acquired at zoom 2 in z-stacks of 0.5 µm interval. Typically, 6 images
	temperature for 1 hour under shaking conditions. After the blocking step, primary were acquired per device from the synapse chamber near the postsynaptic chamber such
	antibodies were added to cells in the blocking solution and incubated overnight at 4°C. The the image contains multiple dendrites. Images were deconvoluted using the Huygens
	following day, cells were washed with PBS 0.1 M thrice for 10 mins. Each. Alexa Fluor®--software (Scientific Volume Imaging, Netherlands).
	conjugated secondary antibodies in blocking solution were then incubated with the cells for
	Medium (Stem 2 hours at room temperature under shaking conditions ensuring protection from light. Cell Technologies). After three days, the medium was replaced by
	Maturation Medium (Stem Cell Technologies) and the plate was placed in an orbital shaker Subsequently, 3 washes with 0.1 M PBS were done for 10 min each at room temperature
	(100 rpm speed). During this final Maturation Phase, 75% medium change was done on a under shaking conditions with protection from light. Hoechst 33258 solution was added tissue was sectioned at 20 μm using a Cryostar NX70 Cryostat (Thermo Scientific) and
	biweekly basis. Organoids were allowed to mature for a period of 6.5 months. during the second PBS wash. Cells were mounted with Aqua-Poly/Mount (Polysciences, Inc.) mounted slides were stored at -80°C until immunostaining was performed. For
	and imaged directly in the cell imaging plates. All images were acquired using an LSM 880 immunostaining, tissue sections were brought to room temperature and then rehydrated
	Viral Transductions Confocal Scanning Microscope housed at the Imaging Platform of the Pasteur Institute, Lille. with 3 washes with 0.1 M PBS, each for 5 mins. Slides were then washed once with PBS with
	Lentiviral constructs were produced by the Vect'UB platform within the TBM Core unit Duolink® Proximity Ligation Assays (PLA) was used to detect endogenous Protein-Protein 0.2% Triton X-100 for 15 mins. Tissue was blocked using 10% of donkey serum in PBS 0.1 M
	at University of Bordeaux, Bordeaux, France (CNRS UMS 3427, INSERM US 005). The Interactions. The following pairs of antibodies were used: anti-BIN1 (rabbit, 182562, abcam) for 1 h at room temperature. After blocking, primary antibodies were added to 0.2 % Triton
	lentiviral constructs used were TTA (ID # 571) and TetO-Ascl1-Puro (Addgene, Plasmid # and anti-Cav1.2 (mouse, 84814, abcam); or anti-BIN1 and anti-Cav1.3 (mouse, 85491, X-100 and 10% of donkey serum in PBS 0.1 M at appropriate dilutions and incubated
	97329). Lentiviral infections were done in NPCs at P3 or P4. The viral constructs were mouse). Other antibodies used for immunocytochemistry were: MAP2 (188006 and overnight at 4°C. The next day, slides were washed with PBS 0.1 M 3 times for 5 min each
	transduced at a multiplicity of infection (MOI) of 2.5. In brief, NPCs were plated at a 188004, Synaptic Systems), Beta III Tubulin (MAB1637, Sigma-Aldrich), GFAP (AB5804, with gentle shaking. Subsequently, slides were incubated with Alexa Fluor®-conjugated
	confluency of 1x106 cells per well of a 6-well plate. After 4 hours of plating the cells, Millipore; and 173006, Synaptic Systems). All Alexa Fluor®-tagged secondary antibodies secondary antibodies in 0.2 % Triton X-100 and 10% of donkey serum in PBS 0.1 M for 2 h at
	appropriate volumes of each lentiviral construct were mixed in complete Neural Progenitor were sourced from Jacskon ImmunoResearch Europe Ltd. room temperature in the dark. After secondary antibody incubation, slides were washed 3
	medium and 50 µl of the viral medium mix was then added to each well. We transduced the Microfluidic Devices: Cultured induced neurons were fixed in 4% paraformaldehyde in times with PBS for 5 min with gentle shanking. Nuclei were visualized by incubating the
	TTA construct at first in the NPCs. Following one passage, the TTA-transduced cells were PBS for 15 min at room temperature, washed three times with PBS, and permeabilized with tissue for 5 min with Hoechst 33258 stain in PBS 0.1 M. Sections were mounted using
	transduced with the construct for Ascl1. Cells having both viral constructs were then further 0.3% Triton X-100 in PBS for 5 min at room temperature. Cells were blocked in PBS
	expanded for 1 or 2 passages before being used for differentiation into hiNs. containing 5% normal donkey serum for 1 h at room temperature before overnight
	incubation at 4°C with the following primary antibodies: MAP2 (188006, Synaptic Systems);
				28 29

Cerebral Organoids: Cerebral organoids were fixed in 4% PFA (w/v) for 30 min at 4°C followed by three washes with PBS 0.1 M. Cerebral organoids were then placed in sucrose solution (30% w/v) overnight before being embedded in O.C.T (Tissue-Tek). Embedded aqueous mounting medium (Polysciences). Images were acquired using an LSM 880 Confocal Scanning Microscope in concert with the ZEISS ZEN imaging software housed at the Imaging Platform of the Pasteur Institute, Lille. Image acquisition was done at 40X for the 32

J The metabolic syndrome is an obesity, T2D, CVD risk associated medical condition regrouping at least 3 over the 5 following strinkingly correlated metabolic phenotypes: abdominal obesity, high blood pressure, high blood sugar, high serum triglycerides, and low serum high-density lipoprotein(HDL)

The second most associated risk loci is BIN1, encoding for an adaptator protein mostly involved in lipid membrane dynamics including endocytosis regulation, but have also a role in intracellular trafficking as well as cellular excitability[517][518][519] . Some studies have been made since its discovery to decipher its role in AD but no consensus are still emerged. I will further develop in next part our understanding of BIN1 and interest to better characterize its role in brain and AD.

Acknowledgements and financial support

The authors thank the BICeL platform of the Institut Biologie de Lille and the Vect'UB viral platform (INSERM US 005 -CNRS 3427 -TBMCore, Université de Bordeaux, France).

The authors thank Karine Blary at the IEMN Lille for the microfabrication work. The Maestro Pro multiwell microelectrode array was acquired with the "Prix Claude Pompidou pour la Recherche sur l'Alzheimer ( 2021)" to MRC. This work was co-funded by the European Union under the European Regional Development Fund (ERDF) and by the Hauts de France Regional Council (contract no.18006176), the MEL (contract_2016_ESR_05), and the French

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where isolated following 10X Genomics protocol of nuclei isolation for single-cell multiome ATAC + Gene expression sequencing 558 . Then, single-cell multimodal analysis was performed using Single Cell Multiome ATAC + Gene Expression Sequencing kit following manufacturer recommendations 559 . After library preparation, the Gene expression and ATAC libraries was sequenced separately following 10X Genomics recommandations. Feature-barcode matrices (of gene expression and peaks) were generated using the CellRanger ARC pipeline(10X Genomics).

I.2.a.vii. Data Processing and Statistical Analysis

Gene expression and peak counts matrices were filtered for low quality cells, and normalized following Seurat and Signac framework 560,561 as described in published method. Cells were annotated for hematopoietic lineage based on the hematomap by using the TransferLabel method of Seurat.

For rapidly processed samples, lineage specific pseudobulk differential expression analysis was performed using DESeq2 562 .For siRNAs and single-cell multiome datasets, lineage specific differential expression analysis was performed using the wilcoxon test on the SCTransform normalized matrices.

Functional enrichment analysis was performed using ClusterProfiler Package 563 . Regulons enrichment analysis in differentially expressed genes was performed using Fisher's exact test (phyper function) or the fgsea package 564 as described in the results. Regulon activity in cells was measured using the AUCell package 565 .

I.2.b. Results

I.2.b.i. Validation of the LGA HSC differentiation bias

We observed an HSC shift toward more differentiated cells in LGA suggesting a differentiation bias of LGA HSC. However, it was unclear if this HSC proportion decrease in LGA was linked to a differentiation bias of LGA HSC during the time of cells preparation. Indeed, after thawing, cells preparation takes ~2 hours to prepare according to the sample multiplexing protocol (HTO), and some cues suggest that cells are responding to a stress or a stimulation regarding that the most active regulons in HSCs are related to the immediate early response (ARID5A, EGR1, KLF2, FOSB, and JUN;

See Supplemental Table S7 of the article). To answer this question, I used two independent strategies: i) estimate the differentiation bias thanks to RNA velocity analysis, ii) compare the subpopulation shift with rapidly processed cells.

Estimate the differentiation bias thanks to RNA velocity analysis

To further validate the putative differentiation bias in LGA HSCs, we analyzed the dynamics of the transcriptional program in LGA and Control HSPCs thanks to the RNA velocity analysis (scvelo) 557 .

We can then estimate if this program leads toward differentiation or stemness conservation, and thus predict the cell fate of each cell. Velocity calculation relies on the ratio of pre-mRNA over mature mRNA

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Compare the subpopulation shift with rapidly processed cells

To tests if cells respond to a stress/stimulation during time of cells preparation, we processed new LGA and CTRL samples without the Antibody based multiplexing (HTO) protocol reducing considerably cell preparation/incubation time (see methods). We compared first if there is a transcriptional difference between both protocols, i.e. if HTO protocol indeed induce a cell response, and compared then the LGA vs CTRL samples in both condition.

To observe if the HTO multiplexing protocol led to a stress/stimulation response, we processed three same CTRL samples with the two protocols (n=3 samples representing a total of 6776 rapidly process cells and 1749 HTO processed cells). We identified a strong sample wide gene expression difference between the two protocols at both cell and sample level (Figure 17A-B). We identified 1518

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Declaration of interests

The authors declare no competing interests. Sup. Movies 1 and 2: Time-series of 1000 frames taken from BIN1 WT and KO ASCL1-hiNs transduced with iGLUSnFr after 2 weeks of differentiation and imaged 2 weeks later.

Author contributions

Videos are played at 100 fps.

Sup. Movies 3 and 4: Time-series of 1000 frames taken from BIN1 WT and KO ASCL1-hiNs after 4 weeks of differentiation and labeled with Oregon Green BAPTA and imaged.

Videos are played at 100 frames per second (fps).

Sup.

Quantification of Synaptic Connectivity

Synaptic connectivity was quantified as previously described (Kilinc et al, 2020). Briefly, images were analyzed with Imaris software (Bitplane, Zürich, Switzerland) by reconstructing Synaptophysin I and HOMER1 puncta in 3D. The volume and position information of all puncta were processed using a custom Matlab (MathWorks, Natick, MA) program. This program assigns each postsynaptic spot to the nearest presynaptic spot (within a distance threshold of 1hµm) and calculates the number of such assignments for all presynaptic puncta.

Immunoblotting

Samples from the 2D cultures or brain organoids were collected in RIPA buffer containing protease and phosphatase inhibitors (Complete mini, Roche Applied Science) and sonicated several times at 60%-70% for 10 seconds prior to use for the immunoblotting analyses. Protein quantification was performed using the BCA protein assay (ThermoFisher Scientific). 10 μg of protein from extracts were separated in NuPAGE 4-12% Bis-Tris Gel 1.0mm (NP0321BOX, Thermo Scientific) or 3-8% Tri-Acetate gel (EA03755BOX, Thermo Scientific) and transferred on to nitrocellulose membranes 0.2μm (#1704158, Bio-Rad).

Next, membranes were incubated in milk (5% in Tris-buffered saline with 0.1% Tween-20

(TBST)) or SuperBlock (37536, ThermoFisher Scientific) to block non-specific binding sites for 1 hour at room temperature, followed by several washes with TBST 0.1% or TNT 1x as washing buffers. Immunoblottings were carried out with primary antibodies overnight at 4°C under shaking condition. The membranes were washed three times in the washing buffer, followed by incubation with HRP-conjugated secondary antibodies for 2 hours at room temperature under shaking condition. The membranes were washed three times in washing buffer, and the immune reactivity was revealed using the ECL chemiluminescence system (SuperSignal, ThermoScientific) and imaged using the Amersham Imager 600 (GE Life Sciences). Optical densities of bands were quantified using the Gel Analyzer plugin in Fiji- Fractionation Kit (Invent Biotechnologies). Western blot was performed as described above.

AlphaLISA measurements

Cell culture media samples for AlphaLISA measurements were collected at the endof the 3rd and 4th weeks of differentiation of the ASCL1-hiNs. Alpha-LISA kits specific for human Aβ1-X (AL288C, PerkinElmer) and Aβ1-42 (AL276C, PerkinElmer) were used to measure the amount of Aβ1-X and Aβ1-42 respectively in culture media. The human Aβ analyte standard was diluted in the BrainPhys medium. For the assay, 2 µL of cell culture medium or standard solution was added to an Optiplate-384 microplate (PerkinElmer). 2hµL of 10X mixture including acceptor beads and biotinylated antibody was then added to the wells with culture media or standard solution. Following incubation at room temperature for an hour, 16hµL of 1.25X donor beads was added to respective wells and incubated at room temperature for 1 hour. Luminescence was measured using an EnVision-Alpha Reader (PerkinElmer) at 680-nm excitation and 615-nm emission wavelengths.

Calcium and iGluSnFR Imaging

Calcium imaging was performed in 2D cultures after 4-weeks (Ascl1-induced). Prior to imaging, the cells were incubated with Oregon Green™ 488 BAPTA-1 (OGB-1) acetoxymethyl (AM) (ThermoFisher Scientific) for 1 hour. A 2.5 mM stock solution of the calcium-indicator dye was prepared in Pluronic™ F-127 (20% solution in DMSO) (ThermoFisher Scientific). 1 µL

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172 pharmacological inhibitor could be a good strategy to fight against the neuronal hyperexcitability mediated by BIN1 defects and/or found in aging.