Je N'

La Nouvelle

Antonio Le Nouveau

Les Amagen

Les Joly

Les Rétaux

Gaëlle Recher

Ian Malcom

Keywords: Variability, Quantification, Morphogenesis, Cell lineage, Sea urchins, Rabbits

Pendant tout le temps de ma thèse, j'ai absorbé des savoirs et des passions d'un nombre incalculable de personnes. Avant tout, je remercie Nadine pour m'avoir pris dans son équipe en toute confiance. C'est une façon plutôt originale et remarquablement humaine d'intéragir avec les autres. Je suis heureux d'avoir pu rejoindre cette équipe exceptionnelle. Et exceptionnelle, c'est un faible mot. Louise d'abord, mon encadrante oursiniste, qui a dû quitter le navire à mon grand désarroi mais qui semble, de son côté, tout à fait satisfaite. Merci pour ton aide et tes conseils qui me servent encore de référence aujourd'hui. Prendre des notes, titrer correctement ses diapos et citer tout ce que j'écris (Louise Duloquin, 2010), c'est idiot mais il faut que ça rentre. Vasily, ou Basil maintenant, qui m'a accueilli par un "Si tu connais pas MatLab, ça sert à rien tu peux rentrer chez toi". Je te pardonne (même si tu t'en fous) ! je sais que derrière cette remarque acide se cache un intellectuel de bon goût et un philanthrope non-avoué. Merci à Monique, la Monique, incroyablement originale (promis je te fais un tutoriel MovIT rien que pour toi avant de partir). Je crois que c'est tout... Ha bah non, y a encore Gaëlle bien sûr, d'une aide cruciale et à répétition, reine des contrepèteries. Pense à saluer Fred et Patrick pour moi. Ah oui, et Adeline Boyreau-Tr... Tru..., bon la blonde quoi (désolé Guilhem). Heureuse, drôle, efficace, perspicace... et indiscrète (mais ça, ça reste entre nous). En plus de ton aide professionnelle, tu as apporté au labo un entrain singulier dont on se rappellera des générations durant (si si !).

Résumé en français

Les concepts fondamentaux de normalité, robustesse, résilience et variabilité Afin d'éviter toute ambigüité, je tiens avant tout à définir les termes que nous allons utiliser tout au long de ces travaux. Le terme variation sera défini comme la mesure expérimentale des différences entre deux populations. La variabilité représentera l'ensemble théorique des différences entre deux populations. Les concepts de robustesse et de résilience sont plus flous et aucun consensus entre les différentes disciplines scientifiques n'émerge réellement sur leurs significations. Dans cette thèse, nous prendrons le parti de définir la robustesse comme la capacité d'un individu à compenser entièrement une perturbation et à revenir à un état normal, alors que la résilience marquera la capacité d'un individu à s'adapter aux nouvelles conditions. Cette définition dépend fortement de ce que l'on entend par état normal, ce qui nous amène naturellement à chercher une définition statistique de la normalité de l'embryogénèse.

Le développement peut se décrire comme l'exploration d'un espace des possibles dans lequel une partie des embryons pourra se reproduire. L'espace des possibles n'est cependant pas directement superposable à l'espace de viabilité et certains embryons mourront ou ne seront pas fertiles. Pour pouvoir comprendre ce qu'est un développement normal, nous devons prendre en compte l'ensemble des états et des processus possibles, même s'ils mènent à la mort ou à des phénotypes inattendus. En effet, l'évolution des espèces est fortement liée à la variabilité phénotypique et le développement embryonnaire est une vii part importante de l'exploration des possibilités phénotypiques. Ainsi, le développement normal peut être défini comme l'ensemble des phénotypes viables et non-viables de la fécondation de l'oeuf à la mort de l'individu dans des conditions génétiques et environmentales connues.

Définir la normalité embryologique dépend donc de notre capacité à mesurer les variations. Longtemps considérées comme du bruit, les variations dans le vivant font parties intégrantes de notre concept d'évolution, et sont, au même titre que les traits phénotypiques eux-mêmes, des caractères sélectionnés au cours des générations. La mesure des variations s'obtient par comparaison des individus d'une population. La taille de l'échantillon, la moyenne des valeurs observées et leurs variations définissent les trois premiers moments mathématiques utilisés pour décrire une distribution empirique, et donnent une description pertinente des populations. L'approche statistique que nous suggérons définit un cadre expérimental permettant de comparer des cohortes d'individus à partir de la quantification des variations phénotypiques. Cependant, les variations phénotypiques intègrent l'ensemble des différences entre deux individus et ne sont pas toujours directement mesurables.

Pour palier ce problème, nous avons décomposé la variabilité phénotypique en deux sous-composants : la variabilité transmise et la variabilité acquise. La première définit l'ensemble des différences à t 0 , et la deuxième l'ensemble des différences résultant de l'histoire individuelle. Cette séparation théorique entre la variabilité acquise et transmise est réalisable expérimentalement avec des populations de jumeaux. En effet, au moment de leur génération, les jumeaux ont vécu la même histoire et héritent du même matériel.

En faisant l'hypothèse qu'au sein de la paire de jumeaux la variabilité transmise est négligeable, nous sommes en mesure de déterminer la variabilité acquise. A contrario, deux individus issus de paires différentes auront une variabilité transmise non nulle, nous permettant ainsi d'estimer la variabilité transmise. La même stratégie peut être utilisée pour mesurer les variabilités génétique et non-génétique, par exemple dans des populations de clones où les embryons sont supposés génétiquement identiques.

Questions et objectifs

Méthodologie

La chaîne de processus de BioEmergences (Workflow BioEmergences), prochainement publié dans Nature Communication (Fabrèges et al., in press), a pour objectif de répondre aux problèmes d'imagerie, de traitement, d'analyse et de visualisation des données à l'échelle cellulaire en 3D et dans le temps. La première étape concerne l'imagerie in vivo de la chromatine et des membranes d'embryons préparés pour l'imagerie 3D+temps. In fine, les embryons jumeaux, quelque soit leur phénotype, sont capables de converger vers une forme indiscernable du sauvage bien que plus petite. À l'échelle cellulaire cependant, nous pouvons montrer qu'il existe des différences fondamentales dans le lignage cellulaire, impliquant des changements de destiné et des tris cellulaires non-observés chez le sauvage. Cependant, le fait que ces processus ne soient pas observés chez le sauvage ne veut pas dire qu'ils n'opérent pas pour autant. Cela amène la question de la robustesse et de la résilience des jumeaux par rapport aux sauvages. Les jumeaux sont-ils différents des sauvages ? Peut-on observer des évènements de changement de destiné ou de tri cellulaire chez le sauvage ? La taille des embryons sauvages étant très variable, est-ce que des embryons jumeaux peuvent être aussi grands que des sauvages ? Est-ce que la Des expériences in silico réalisées sur les arbres de lignage cellulaire permettent de mesurer l'importance des divisions asymmétriques et leurs conséquences sur la variabilité.

En supprimant artificiellement les divisions asymmétriques chez le sauvage, nous montrons que la variation du nombre de cellules externes n'est pas sensible à la présence ou à l'absence de divisions asymmétriques. Par opposition, les cellules internes y sont très sensibles, suggérant que les divisions asymmétriques pourraient être un régulateur de la variabilité du nombre de cellules internes chez le lapin, aux conséquences évolutives importantes.

La variation du nombre de cellules chez les clones, quant à eux, n'est pas sensible à la présence ou l'absence de divisions asymmétriques, probablement car la variation du nombre de cellules internes est déjà très élevée (environ 80% autour de la moyenne) ce qui suggère un second mécanisme dominant.

La quantification du nombre de morts cellulaires au cours du temps répond à cette question et montre une forte pénalité des cellules internes chez le clones. Selon le moment de la mort cellulaire (en début d'imagerie avec peu de cellules, ou en fin d'imagerie avec de nombreuses cellules), les conséquences sont plus ou moins importantes, changeant drastiquement le nombre de cellules d'un embryon à l'autre. Les cellules internes chez le clones ont tendance à mourir plus souvent qu'attendu, alors que les cellules externes meurent moins souvent qu'attendu. Cette dernière observation suggère un rôle clé de l'origine environnementale du noyau. Nous suggérons à l'expérimentateur de sélectionner des cellules donneuses capables de s'adapter rapidement à un environnement changeant, maximisant ainsi les chances de survie des cellules embryonnaires.

Conclusions et Perspectives

Dans cette thèse, nous avons abordé la question de la normalité du développement em- The definition of concepts is an important issue in science, especially in biology, where metaphors and analogies are numerous and concepts are often not clearly defined. In order to avoid -or at least minimize -misunderstanding, I would like to state clearly how the concepts used in this thesis are defined in the field of developmental biology.

List of Tables

List of Movies

Variability and variation are often used interchangeably to describe differences between individuals within populations. Charles Darwin used exclusively the concept of variability in On the origins of species by means of natural selection (1859), while William Bateson preferred the concept of variation in Materials for the study of variation (1894).

In this work, however, we make a distinction between theoretical differences as a whole between individuals within a population -the concept of variability, and an actual quantification of differencesvariations -used to estimate variability.

Robustness and resilience are two other convoluted concepts. Depending on the reader's background, robustness and resilience may be equivalent or very different. In psychology, resilience is one's ability to start over or to recover from mental illness or trauma [START_REF] Werner | Vulnerable but invincible: a longitudinal study of resilient children and youth. Adams, cop[END_REF]American Psychological Association, 2014). In material physics, resilience describes the ability of a material to resume its shape after being deformed. It is defined in systems engineering as the ability of a system to change itself in order to continue functioning as before. According to the Resilience Alliance (http://www.resalliance.org), resilience is the ability of a system to cope with changes if perturbations keep the system below a certain threshold. A system exhibiting resilience may vary, but it keeps its function and structure [START_REF] Holling | Resilience and Stability of Ecological Systems[END_REF]Levin and Lubchenco, 2008, Figure 1.1A).

Although the Resilience Alliance suggests sticking with the definition initially proposed by [START_REF] Holling | Resilience and Stability of Ecological Systems[END_REF], it must be noted that this is not widely shared across fields within biology. Other studies define robustness as the ability of a system to return to its initial state after perturbations, and resilience as the ability of a system to maintain its function and structure despite changes [START_REF] De Visser | Perspective: Evolution and Detection of Genetic Robustness[END_REF][START_REF] Anderies | Aligning Key Concepts for Global Change Policy: Robustness, Resilience, and Sustainability[END_REF][START_REF] Prostova | Mutational robustness and resilience of a replicative cis -element of RNA virus: promiscuity, limitations, relevance[END_REF]. Robustness is depicted as a fail-safe system by [START_REF] Anderies | Aligning Key Concepts for Global Change Policy: Robustness, Resilience, and Sustainability[END_REF] -i.e., the system does not fail within a certain range of perturbations, and resilience as a safe-fail system -i.e., the system may change, but it ensures that the outcome is maintained.

The lack of consensus about the definitions of robustness and resilience may be due to the ambiguity of the underlying concepts of function, failure and expected outcome.

If organisms are not able to reproduce then they disappear. This trivial assessment is considered one of the most important properties of life by the scientific community [START_REF] Trifonov | Vocabulary of definitions of life suggests a definition[END_REF]. We assume that an individual needs to maintain its function, which is 1.1. FUNDAMENTAL CONCEPTS its viability and potential for reproduction. Therefore, the incapacity to reproduce (e.g., mutation, malformation or death) is a failure. In this work, we define robustness and resilience with statistical approaches. A robust mechanism leads to an organism that is statistically compatible with the expected outcome in chosen experimental conditions (Figure 1.1B pink). A resilient mechanism leads to an organism with the same function as the expected outcome, but statistically different from it (Figure 1.1B blue).

Finally, the expected outcomes are defined by normal development in specific experimental conditions. This naturally leads us to a statistical definition of normality in embryogenesis.

Normal development

We assume here that the development of an organism encompasses all its changes at all levels throughout its life cycle from oocyte fertilization to death. Implicitly, the scientific community assumes that normal development leads to survival and fertility. However, although most embryos develop to an adult and fertile stage in laboratory conditions, some of them fail to do so. These so-called failures are part of the dynamic of the system but are rarely considered. The source of these failures is the intrinsic characteristic of living systems to vary from one to another and to explore a specific space of possibilities. Most of the time, this exploration leads to a viable organism, although robustness and resilience mechanisms may operate to ensure the survival and fertility of the individual (Figure 1.1, pink and blue). Sometimes, though, the exploration of the space of possibilities takes the organism outside its domain of viability (Figure 1.1,red). In order to investigate the normality of the development we have to take into account all the possible states and developmental paths, even if they lead to death or unexpected phenotypes. Indeed, the evolution of species is tightly linked to phenotypic variability and embryonic development is a major part of the exploration of phenotypic possibilities. Thus, normal development may be defined as all the viable and non-viable phenotypes from oocyte fertilization to death in defined genetic and environmental conditions.

Variability and evolution

I used to think that evolution emerges from variations and natural selection. A principal component analysis of life definitions proposed in the literature gave the Darwinian definition "Life is reproduction with variation" [START_REF] Trifonov | Vocabulary of definitions of life suggests a definition[END_REF]. The concept of variation is clearly stated as being of the highest importance. In a variable population, individuals are by definition different, leading to higher and lower levels of fitness. Even the slightest difference will give better or worse chances of surviving and reproducing. Silent mutations are often given as an example of variations with no phenotypic consequences. However, we now know that silent mutations may interfere with RNA splicing [START_REF] Goldsmith | Silent" nucleotide substitution in a beta+-thalassemia globin gene activates splice site in coding sequence RNA[END_REF][START_REF] Chao | A silent mutation induces exon skipping in the phenylalanine hydroxylase gene in phenylketonuria[END_REF] or DNA transcription and RNA translation [START_REF] Deana | Silent mutations in the Escherichia coli ompA leader peptide region strongly affect transcription and translation in vivo[END_REF]. Any difference between two individuals inevitably leads to a difference in fitness.

Thus, natural selection is inevitable. Although variability seems to be regulated by organisms by means of robustness mechanisms, the behavior of particles at the atomic scale is not deterministic but probabilistic. These universal laws have major consequences: individuals are inevitably unique. Thus, as for natural selection, variability is inevitable.

Consequently, evolution is inevitable as well.

On the contrary, life depends on evolution. Organisms have a certain degree of freedom to regulate and control variability. The highest fitness in a specific environment is the result of millions of years of evolution. Size, color, behavior and other phenotypes have been selected to increase the fitness of individuals in their environment. Variations in phenotypic traits may not contribute to higher/lower fitness during individual lifespans, however, they may contribute to the survival of offspring in future environments. Therefore, variations in phenotypic traits and phenotypic traits themselves are subject to natural selection. Some biological features are largely conserved in the phylogenetic tree (e.g., DNA molecules, ribosomes or polymerase). Robustness mechanisms tend to decrease variability in populations. By contrast, other mechanisms must exist to increase variability and create the possibility for adaptation to environmental changes. Plasticity of processes, for instance, may be conserved through evolution to increase variation and maintainwith robustness -the level of variability in populations.

Comparing populations

Differences between individuals may be observed at the macroscopic scale (e.g., embryo size, viability and developmental path), the mesoscopic scale (e.g., cell lineage, cell compartments and organ development) and the microscopic scale (e.g., metabolism and DNA polymorphism). Thanks to recent advances in microscopy and computer science, it is now possible to quantify variations at different levels by the observation of chosen parameters. The empirical mean calculated from the value of measured parameters is useful to describe the phenotype of individuals in populations under defined genetic and environmental conditions. One must keep in mind though, that an empirical mean is a statistical estimation of a mean based on a finite number of observations. An empirical mean cannot be compared with other empirical means as is. Replicas of experiments, for instance, performed under the same defined conditions, often give different estimations of the mean. Although these measurements are mathematically different, they may not reflect significative biological differences and empirical means alone are insufficient to characterize the population.

Every empirical distribution can be described by mathematical moments. The sample size, the mean and the variance are the moments of order 0, 1 and 2 respectively. The moments of orders 3 and 4 are the skewness (asymmetry) and the kurtosis (flatness) respectively, and moments of higher orders exist to fully characterize the empirical data.

The higher the number of orders we use, the closer we get to the empirical data. By contrast, data are simplified by the use of lower numbers of orders. To compare empirical distributions, we have to compromise between a low number of orders (a higher chance of concluding that two populations are different when they are not) and a high number of orders (a lower chance of concluding that two populations are different when they are).

Using the first three orders is a good compromise. Sample size and the estimation of mean and variance are used to define a confidence interval. This predicts that the mean of the population has a certain chance -e.g., 95% or 99% -of being contained in the interval. Two replicas of the same experiment are likely to exhibit overlapping confidence intervals. Although this does not prove that populations are identical, it gives a fair comparison of populations based on a risk of error arbitrarily chosen by researchersconventionally below 5%. The statistical approach proposed here defines an experimental framework to compare cohorts of individuals from the quantification of the phenotypic variability.

Phenotypic variability and quantification

The state of the system at any level of observation and at any time results from a historical process. The entire organism and its tissues regulate cellular mechanisms, cell shapes and behaviors (top-down causality). Sub-cellular processes, through the regulation of gene expression and molecular interactions underlie cell shape changes and behaviors (bottomup causality). Consequently, every phenotype results from multiscale integration and cross-causalities, making the phenotype of the individual unique and therefore different from the phenotype of other individuals. The sum of these differences at every level of observation defines the phenotypic variability. This definition may be too general to be used for the quantification of variations, as it includes every theoretical difference, some of which are not measurable. A quantification of variability may require us to separate the measurable from the non-measurable and to divide the phenotypic variability into sub-categories.

The sub-categories, however, are difficult to establish and depend heavily on hypotheses and a priori. In 1859, Darwin stated that "Any variation which is not inherited is unimportant for us" [START_REF] Darwin | On the origins of species by means of natural selection[END_REF], Chapter 1). Darwin did not hypothesize about the laws governing inheritance and admitted that "no one can say why the same peculiarity in different individuals of the same species, and in individuals of different species, is sometimes inherited and sometimes not so" (Darwin, 1859, Chapter 1). The synthesis of Darwinism and genetics led to a semantic shift which gradually affirmed that the DNA sequence alone can explain inheritance and evolution, thus variations, relegating environmental sensitivity to noise (West-Eberhand, 2003). Consequently, the first naive decomposition assumed that phenotypic variability (V p ) was composed of genetic variability (V ge ) and environmental variability (V en ) (Equation (1.1)). This definition ignored epigenetics and other internal regulation mechanisms that are part of the developmental process. [START_REF] Danchin | Inclusive heritability: Combining genetic and non-genetic information to study animal behavior and culture[END_REF] proposed to integrate all known mechanisms into the definition of variability, so that the phenotypic variability is made up of transmitted (V t ), non-transmitted (V nt ), genetic (V ge ), non-genetic (V nge ), epigenetic (V epi ), environmental (V env ), social (V soc ), ecological (V eco ) and non-genetic parental (V par ) variabilities and their respective covariances (Equations (1.2a), (1.2b) and (1.2c)). Besides the fact that these parameters are difficult to reconcile in one equation, scientific methodology requires hypotheses to be falsifiable, which is difficult with this supposedly exhaustive definition without stating strong hypotheses on the relationship between the listed components.

Here, we propose to decompose the phenotypic variability into two exhaustive components: transmitted variability (V t ) and acquired variability (V a ). In addition, our model does not assume the relationship between the two components (Equation (1.3)) which is quite unknown.

V p = V ge + V en (1.1) V p = V t + V nt + V t × V nt (1.2a) V t = V ge + V nge + V ge × V nge (1.2b) V nge = V epi × V par × V eco × V soc (1.2c) V p = f (V t , V a ) (1.3)

Transmitted and acquired variability

The decomposition of phenotypic variability into transmitted and acquired variability is exhaustive. Every property that is not inherited is necessarily acquired through life.

In this definition, transmitted variability is fixed and refers to observable variations at The comparison proposed here can be extended to other experimental conditions.

For instance, one can use genetically identical individuals (e.g., clones) to measure the respective influence of genetic and non-genetic variability. It is not always possible to quantify the influence of the components of variability. Because it requires the generation of homogenized populations (twins or clones for instance), model organisms do not always conform with such experiments. However, a first approach consisting in the quantification of phenotypic variability in a normal population can surprisingly lead to new biological insights, as illustrated by the case of the precision of body proportions in Drosophila melanogaster.

Precision of body proportions in Drosophila melanogaster

During early development of the fly Drosophila melanogaster, morphogenesis is controlled by the sequential expression of transcription factors forming reproducible patterns and sharp boundaries. The French flag model of morphogen gradient [START_REF] Wolpert | Positional information and the spatial pattern of cellular differentiation[END_REF][START_REF] Wolpert | Positional information and the spatial pattern of cellular differentiation[END_REF] proposed that the local concentration of proteins is sufficient to determine cell fate along the antero-posterior axis. During its early embryonic phase, the fruit fly embryo is a single large syncytium. Before cellularization occurs, ARN and proteins synthesized in the syncytium diffuse across the embryo and form exponential decay gradients from their production site. The flag model predicts that genes are expressed if activators are above a certain threshold and inhibitors below another threshold. In particular, the maternal morphogen Bicoid (Bcd), expressed at the anterior pole and diffusing through the embryo, activates among other things the expression of hunchback (hb) in the anterior half of the embryo.

The model was widely accepted in the community, but debate about its validity has recently been revived. Quantitative approaches [START_REF] Houchmandzadeh | Establishment of developmental precision and proportions in the early Drosophila embryo[END_REF] suggest that the interindividual variability of the Bcd gradient was too large to produce alone the sharp boundary of hb expression. The morphogen gradient model relies on the diffusion of morphogens, which depends heavily on external factors (e.g., temperature) and internal Figure 1.1: Two different visions for robustness and resilience landscape. A: Resilience as defined by [START_REF] Holling | Resilience and Stability of Ecological Systems[END_REF]. Through resilience (left), the system (blue dot) remains in a stable area (red). If the system moves away from the stable area, resilience operates and the system is pushed back (blue arrow) to the stable area. If non-resilient (right), the system may diverge after moving away from the stable area. B: Robustness and resilience in a schematic phenotypic tree. Each branch refers to a developmental path over time (from left to right). The phenotypic path may diverge from the normal one (green) and lead to death (red), a different and viable phenotypic path (resilient, blue) or return to normal through a transient state (robustness, pink). Note that robustness may easily be mistaken for resilience if the operator does not observe the phenomenon at the appropriate timescale. factors (e.g., protease concentration and the copy number of mRNA). In addition, embryo size varies, but the relative position of hb expression is invariably at 50% of the anteroposterior axis. Based on the morphogen gradient model alone, hb expression should switch from on to off in a variable position along the antero-posterior axis. [START_REF] Houchmandzadeh | Establishment of developmental precision and proportions in the early Drosophila embryo[END_REF] proposed a mechanism involving a second morphogen, staufen, supported by in situ observations and in silico simulation [START_REF] Gregor | Diffusion and scaling during early embryonic pattern formation[END_REF][START_REF] Adomako-Ankomah | Growth factor-mediated mesodermal cell guidance and skeletogenesis during sea urchin gastrulation[END_REF]. Simultaneously, Olivier Crauk and Nathalie Dostatni suggested that the observed variations of the Bcd gradient were artefactual and resulted from experimental variation.

With the help of a reporter gene activated by Bcd only, they showed that it was possible to reproduce the exact same pattern as hb expression and that Bcd alone was sufficient to form a sharp boundary at the right position.

This debate illustrates the difficulty in quantifying interindividual variation [START_REF] Magnasco | Pulsatile cell-autonomous contractility drives compaction in the mouse embryo[END_REF]. The quantification in these articles was done by looking at immunostained fixed embryos. This methodology is a succession of non-linear operations introducing experimental variation. In addition, it only shows a snapshot of embryos and cannot reveal the spatial and temporal dynamics of processes.

In vivo approaches led to a major breakthrough towards the understanding of gene expression variation and regulation (Gregor et al., 2007b,a;[START_REF] Lucas | Live Imaging of Bicoid-Dependent Transcription in Drosophila Embryos[END_REF]. As predicted by Crauk and Dostatni (2005), Gregor and collaborators quantified in vivo a Bcd concentration variation half the size of that measured by [START_REF] Houchmandzadeh | Establishment of developmental precision and proportions in the early Drosophila embryo[END_REF].

In addition, access to the system dynamic revealed new mechanisms of regulation (control of transcriptional activity length rather than an on/off system with diffusion for instance, [START_REF] Lucas | Live Imaging of Bicoid-Dependent Transcription in Drosophila Embryos[END_REF], and led to a paradigm shift which encouraged authors to revisit classical embryology dogmas. In the context of the quantitative analysis of embryonic variability, the example of the body proportions in Drosophila melanogaster illustrates that the quantification of the system dynamic is essential to understand the mechanisms underlying embryonic development and to assess its variability. In order to characterize variations, we need to access 3D+time information. 3D+time reconstruction of cells of developing embryos offers an appropriate level of observation that integrates sub-cellular information and macroscopic influences. Using this information and the lesson learned from Drosophila melanogaster, we investigate phenotypical variability in animal morphogenesis in two animal models: the sea urchin and the rabbit.

Sea urchin morphogenesis

In this section, we review the normal development of sea urchin larvae from spawning to metamorphosis. Then, we explore the robustness and resilience capacity of individuals in experimental conditions with special focus on the mechanical generation of twins. Finally, we discuss the sea urchin as a model organism in the emergence of an integrative biology through the multiscale observation of biological processes and their multilevel reconstruction.

Publication information

Section 1.2 has been adapted from a recent publication [START_REF] Fabrèges | Phenotypic Variations and Resilience in Sea Urchin Morphogenesis[END_REF]. To

The Sea Urchin in the History of Science

Echinoderms are widely distributed in the world and many of them are easily accessible.

For more than 150 years, over 30 species of sea urchins have served as models for embryological studies (Figure 1.3). In 1847, the fertilization and embryonic development of sea urchin species explored by Alphonse Derbès, Adolphe Dufossé and Karl Ernst von Baër led to the first published works (for a review see [START_REF] Briggs | In the beginning animal fertilization and sea urchin development[END_REF] Boveri's work on fertilization and early development (for a review see Baltzer, 1964) showed that the embryo develops correctly if cells inherit a complete set of chromosomes that segregate symmetrically, although haploid and triploid embryos may form larvae in rare cases.

The accessibility, small size and transparency of sea urchin eggs, as well as their fast development, are major reasons for choosing them for studies of fertilization and embryogenesis. In the late 20 th century, the sea urchin was the animal model behind two fundamental discoveries. In 1983, the discovery by Tim Hunt and collaborators [START_REF] Evans | Cyclin: a protein specified by maternal mRNA in sea urchin eggs that is destroyed at each cleavage division[END_REF] of a family of proteins called cyclins in sea urchin eggs opened the way to the deciphering of cell cycle regulation. Tim Hunt was awarded the Nobel prize in 2001 for his discoveries of key regulators of the cell cycle. At the same time, the question of gene regulation in embryonic development was central to the field of developmental biology. In 1996, Eric Davidson and collaborators [START_REF] Yuh | Modular cis-regulatory organization of Endo16, a gut-specific gene of the sea urchin embryo[END_REF] demonstrated the modular organization of the cis-regulatory sequences upstream of the DNA sequences encoding the Endo16 protein, specifically expressed in the sea urchin endoderm. Each module controls Endo16 expression during a specific period of embryonic development. The concept of modular organization of cis-regulatory sequences was the basis of a systemic approach to the gene regulatory networks (GRN) underlying development. In 2002, 25 scientists published the GRN of the endomesoderm specification in the sea urchin [START_REF] Davidson | Specification of cell fate in the sea urchin embryo: summary and some proposed mechanisms[END_REF], providing the first systemic description of the architecture of a gene regulatory network underlying early embryonic morphogenesis. More than 150 genes have been in-Figure 1.3: Phylogenetic tree displaying sea urchin species studied in scientific literature. The size of each species' name is proportional to that species' occurrence in studies.

Strongylocentrotus purpuratus (18.4%) and Paracentrotus lividus (12.2%) are the most studied sea urchin species. Data from NCBI taxonomy database and Google Scholar.

tegrated into the sea urchin GRN, now thought to be close to completion [START_REF] Peter | A gene regulatory network controlling the embryonic specification of endoderm[END_REF].

The next challenges in developmental biology include the integration of cellular, genetic and molecular dynamics in predictive and explanatory formal models. The latter should be based on the multiscale and multimodal microscopy observation of living specimens. Despite the relative transparency of embryos from a number of species and their small size and fast development, cilia-driven movements of the sea urchin blastula largely impair the possibilities of long-term time lapse imaging. However, this challenging issue should be solved, so that sea urchins will remain among the most insightful models in developmental biology for understanding the basis of morphogenesis, homeostasis, robustness, variation and evolution.

Early Development of Wild Type Sea Urchins

Overview

The typical development of a normal wild type sea urchin is depicted in Figure 1.4.

Spawning and Reproduction

Sea urchin are iteroparous and most of them follow seasonal reproduction. The reproductive season depends on the species, winter for Strongylocentrotus purpuratus, spring for Paracentrotus lividus, summer for Diadema antillarum and fall for Lytechinus variegatus.

The reproductive season may be extended, shortened, preponed or postponed depending on the environmental conditions. Some species (e.g., Sphaerechinus granularis) can spawn at any time of the year. Spawning is synchronized by temperature [START_REF] Pearse | Reproductive Patterns of Tropical Reef Animals: Three Species of Sea Urchins[END_REF][START_REF] Fujisawa | Differences in Temperature Dependence of Early Development of Sea Urchins with Different Growing Seasons[END_REF], lunar cycle [START_REF] Kennedy | Lunar synchronization of the monthly reproductive rhythm in the sea urchin Centrostephanus coronatus Verrill[END_REF][START_REF] Iliffe | Annual and lunar reproductive rhythms of the sea urchin, Diadema antillarum (Philippi) in Bermuda[END_REF] and chemical signaling [START_REF] Kaupp | Sperm chemotaxis in marine invertebrates -molecules and mechanisms[END_REF] so that mature Paracentrotus lividus may be found at any time of the year in different places [START_REF] Hörstadius | Exp. Embryol. echinoderms[END_REF]. Meanwhile, PMCs surround the gut, fuse to form a syncytium and start the spiculogenesis which shapes the pluteus larva (J-L). Pluteus larva shows three axes: left/right axis, oral/aboral axis and ventral/dorsal axis. In L, the light-green curve corresponds to the oral/ventral side, the green curve to the aboral/ventral side and the dark-green curve to the aboral/dorsal side. Pluteus larva is represented in lateral and dorsal view showing the complex skeleton organization. The secondary mesenchymal cells (SMCs) stay at the tip of the gut (I and J in red) and migrate into the blastocoel or over the ectoderm during mouth formation (K and L). Blue: ectoderm; Orange: future endoderm; Green: uncertainties of lineage between ectoderm and endoderm; Red: future mesoderm; Pink: germ line.

Female Gametes Spawning of oocytes is highly variable from one species to another.

The abundance of eggs typically depends on the gonad size and can vary from a million eggs (e.g., Paracentrotus lividus) to 20 million eggs (e.g., Echinus esculentus, [START_REF] Hörstadius | Exp. Embryol. echinoderms[END_REF]. The egg size varies from 80µm in Strongylocentrotus purpuratus to 160µm in Stongylocentrotus droebachiensis. The egg size within a batch of eggs may vary too (e.g., ±10% in Paracentrotus lividus, unpublished observation).

Male Gametes Sperm is released into the water and swims toward the oocytes, guided by chemotaxis [START_REF] Kaupp | Sperm chemotaxis in marine invertebrates -molecules and mechanisms[END_REF]. Among some 80 sperm-activating peptides (SAP) identified in the sea urchin Arbacia punctulata, a 14 amino-acid peptide named resact has been characterized as a chemoattractant for the spermatozoids [START_REF] Suzuki | A peptide associated with eggs causes a mobility shift in a major plasma membrane protein of spermatozoa[END_REF][START_REF] Kaupp | The signal flow and motor response controling chemotaxis of sea urchin sperm[END_REF]. Whatever the mechanisms favoring spermatozoa displacement toward the oocytes, their efficiency is limited and the fertilization rate is very low ( 5%) when gametes are more than 2m away [START_REF] Levitan | How distribution and abundance influence fertilization success in the sea urchin Strongylocentrotus franciscanus[END_REF] in laboratory conditions. The fertilization rate drops even lower when strong water current is applied. Thus, the fertilization rate is greatly improved by spawning synchrony and by maintaining a high density of individuals.

Studies in natura conclude that on average, the fertilization rate is close to 1%.

Fertilization In vitro fertilization typically shows a success rate of 80% to almost 100%, depending on the experimental conditions and gamete quality. The first contact between spermatozoa and oocytes leads to the oocyte membrane depolarization. It has been proposed that membrane depolarization is the first shield against polyspermy, although recent analysis suggests the implication of actomyosin contraction, as spermatozoa were found to enter the oocyte through actin-enriched sites [START_REF] Corsaro | Bicoid Determines Sharp and Precise Target Gene Expression in the Drosophila Embryo[END_REF]. Then a first striking consequence of fertilization is the cortical reaction. Cortical granules near the spermatozoa entry point fuse with the oocyte membrane and the fusion of cortical granules propagates. The content of cortical granules is released into the perivitelline space to form a layer between the hyalin and the zygote membrane. This reaction is the slow blocking process preventing the entrance of other spermatozoa.

CHAPTER 1. INTRODUCTION

Meiosis is completed in the ovary. Pronuclei meet and fuse and the first mitosis is triggered. The first cleavage happens 90 minutes (Paracentrotus lividus) to several hours (Strongylocentrotus droebachiensis) after fertilization. Timing of cleavages depends on temperature and may vary up to 5% between individuals in the same experimental conditions [START_REF] Stephens | Studies on the development of the sea urchin Strongylocentrotus droebachiensis. I. Ecology and normal development[END_REF].

The animal-vegetal axis is already established in unfertilized eggs through an asymmetrical distribution of cellular components [START_REF] Hörstadius | Exp. Embryol. echinoderms[END_REF][START_REF] Schroeder | Expressions of the prefertilization polar axis in sea urchin eggs[END_REF][START_REF] Di Carlo | Spatial distribution of two maternal messengers in Paracentrotus lividus during oogenesis and embryogenesis[END_REF][START_REF] Romancino | Bep4 protein is involved in patterning along the animal-vegetal axis in the Paracentrotus lividus embryo[END_REF][START_REF] Romancino | Maternal Paracentrotus lividus RNAs are differentially localized during the first cell division[END_REF]. Although the oral/aboral axis is also already established in unfertilized eggs [START_REF] Cameron | The oral-aboral axis of a sea urchin embryo is specified by first cleavage[END_REF][START_REF] Vlahou | Maternal mRNA encoding the orphan steroid receptor SpCOUP-TF is localized in sea urchin eggs[END_REF], it is labile until the third cleavage [START_REF] Cameron | Lineage and fate of each blastomere of the eight-cell sea urchin embryo[END_REF]. The first cleavage is hollow and parallel to the animal-vegetal axis forming two cells, described as being identical in terms of potentialities.

Cleavage Stages and Formation of the Blastula

Pre-hatching Development The first asymmetrical cleavage in terms of cell size and cell fate is the third one, perpendicular to the animal-vegetal axis, forming four animal cells and four vegetal cells. Animal cells give eight mesomeres equally distributed (Figure 1.4D-E), and the vegetal cells divide asymmetrically to form four macromeres (about 1.7 times bigger than the mesomeres) and four micromeres (about one-third of the size of mesomeres). The 16-cell stage is therefore composed of three cell types: mesomeres, macromeres and micromeres, leading to the germ layers ectoderm, endoderm and mesoderm respectively. The blastomeres' fate may, however, vary, in particular at the boundary between layers, such as the ectoderm-endoderm boundary [START_REF] Logan | The allocation of early blastomeres to the ectoderm and endoderm is variable in the sea urchin embryo[END_REF] or the endoderm-mesoderm boundary, ill-defined at early stages.

At the next stage, the asymmetrical division of micromeres leads to the formation of four large micromeres and four small micromeres. Small micromeres are the precursors of the germ cells [START_REF] Pehrson | The fate of the small micromeres in sea urchin development[END_REF][START_REF] Yajima | Small micromeres contribute to the germline in the sea urchin[END_REF]. They divide once and stay quiescent until the end of gastrulation. Large micromeres divide three or four times, establishing a population of 32 cells (Paracentrotus lividus, Sphaerechinus granularis) or 64 cells (Lytechinus variegatus), depending on the species.

At the 32-cell stage, the embryo consists in a single-layer epithelium surrounding the blastocoel cavity. The major morphogenetic events observed at blastula stages are the compaction of the embryo with outside surface flattening, enhancement in cell-to-cell surface contact and adhesion strength, and increase in the blastocoel size.

The Swimming Blastula By the end of the blastulation period, the embryo hatches.

The vitelline envelope surrounding the blastula is digested by a specific protease [START_REF] Ishida | An enzyme dissolving the fertilization envelope of sea urchin eggs[END_REF][START_REF] Barrett | Handbook of Proteolytic Enzymes[END_REF], synthesized by animal cells [START_REF] Lepage | Spatial expression of the hatching enzyme gene in the sea urchin embryo[END_REF]. By the end of the cleavage period, each blastomere carries one motile cilium beating to generate a current. Freed from the vitelline envelope, the blastula uses this current to move in the medium.

Later during development, the animal plate cells bear an apical ciliary tuft, which is, however, not motile and functionally ill-defined [START_REF] Hörstadius | Exp. Embryol. echinoderms[END_REF][START_REF] Bisgrove | Development of serotonergic neurons in embryos of the sea urchin, Strongylocentrotus purpuratus[END_REF]. Recent work suggests a mechano-sensory role [START_REF] Jin | Glutathione transferase theta in apical ciliary tuft regulates mechanical reception and swimming behavior of Sea Urchin Embryos[END_REF].

Gastrulation Stages

The Early Gastrula Soon after hatching, the vegetal pole flattens (Figure 1.4H) to form the vegetal plate encompassing about 100 cells (Ettensohn, 1984). This event marks the onset of gastrulation and may happen a few minutes before hatching in some species (Sphaerechinus granularis). The embryo is regionalized and the map displays a ringshaped organization. The small micromeres (SMic), precursors of the germ cells [START_REF] Yajima | Small micromeres contribute to the germline in the sea urchin[END_REF][START_REF] Wessel | Cavitation in the mouse preimplantation embryo: Na/K-ATPase and the origin of nascent blastocoele fluid[END_REF], are located at the most vegetal region of the embryo.

SMic are surrounded by large micromeres (Figure 1.4F, pink), forming the external ring of the vegetal plate. Large micromeres undergo a first epithelium-to-mesenchyme transition (EMT) giving rise to the primary mesenchymal cells (PMCs, for a review see [START_REF] Wu | Ingression of primary mesenchyme cells of the sea urchin embryo: a precisely timed epithelial mesenchymal transition[END_REF]. Within this population, some cells called secondary mesenchymal cells (SMCs) undergo a second EMT during gastrulation.

The first EMT is a cell autonomous process and the ingression of PMCs is observed according to its intrinsic timing in ectopic and heterochronous transplantations [START_REF] Mcclay | Pattern formation during gastrulation in the sea urchin embryo[END_REF][START_REF] Peterson | Primary mesenchyme cell patterning during the early stages following ingression[END_REF] or in vitro cultures [START_REF] Okazaki | Spicule Formation by Isolated Micromeres of the Sea Urchin Embryo[END_REF]. Before PMC ingression, the sea urchin embryo is a single layer epithelium. On its apical side, cells are attached to the hyaline layer composed of hyalin fibrillar glycoprotein, laminin, echinonectin and collagens [START_REF] Spiegel | The hyaline layer is a collagen-containing extracellular matrix in sea urchin embryos and reaggregating cells[END_REF][START_REF] Spiegel | Fibronectin and laminin in the extracellular matrix and basement membrane of sea urchin embryos[END_REF][START_REF] Alliegro | Echinonectin: a new embryonic substrate adhesion protein[END_REF][START_REF] Matese | Regulated exocytosis and sequential construction of the extracellular matrix surrounding the sea urchin zygote[END_REF][START_REF] Wessel | A molecular analysis of hyalin -A substrate for cell adhesion in the hyaline layer of the sea urchin embryo[END_REF], and cilia pass through the hyaline layer towards the external environment. On the blastocoel side, cells lie on a basal lamina composed among other proteins of laminin, fibronectin and collagens (Wessel et al., 1984;[START_REF] Mccarthy | Laminin is structurally conserved in the sea urchin basal lamina[END_REF]. Cell-cell lateral adhesion depends on cadherin and specialized junctions [START_REF] Miller | Characterization of the role of cadherin in regulating cell adhesion during sea urchin development[END_REF]. Prior to ingression, the large micromeres lose their adhesion to the hyaline layer and cadherin cell surface expression, and reinforce their adhesion to the basal lamina [START_REF] Fink | Three cell recognition changes accompany the ingression of sea urchin primary mesenchyme cells[END_REF]. Meanwhile, PMCs become motile and move to the blastocoel through the basal lamina. Whether PMCs squeeze through the loose basal lamina or locally proteolyse it remains unclear.

The Mid Gastrula Several hours after PMC ingression, the vegetal plate cells invaginate by apical contraction (Ettensohn, 1984;[START_REF] Nakajima | The initial phase of gastrulation in sea urchins is accompanied by the formation of bottle cells[END_REF][START_REF] Kimberly | Bottle cells are required for the initiation of primary invagination in the sea urchin embryo[END_REF] to form the archenteron. Extension of the presumptive gut in the blastocoel toward the animal pole takes several hours. Endomesodermal tissues organized along the vegetal-animal axis include the presumptive hindgut, midgut and foregut, germ cells (SMic) and secondary mesenchymal cells (SMCs).

The elongation of the gut rudiment takes place in two steps. First, gut cells intercalate by means of convergent-extension movements. Then additional cells integrate the archenteron, consequently contributing to the extension of the forming gut. The gut elongation also serves to transport SMic to the animal pole [START_REF] Yajima | Autonomy in specification of primordial germ cells and their passive translocation in the sea urchin[END_REF]. Although no direct evidence has been provided, this translocation is thought to be passive.

By mid-gastrulation, SMCs extend filopodia towards the animal pole, exploring the inner surface of the oral plate where the mouth will open (Dan and [START_REF] Okazaki | Cyto-embryological studies of sea urchins. III. Role of the secondary mesenchyme cells in the formation of the primitive gut in sea urchin larvae[END_REF][START_REF] Hardin | The development of half and quarter eggs of Arbacia punctulata and of strongly centrifuged whole eggs[END_REF][START_REF] Miller | Dynamics of thin filopodia during sea urchin gastrulation[END_REF]. Meanwhile, PMCs adhere to the basal lamina and move, driven by a signal coming from the animal pole [START_REF] Ohshima | Skeleton formation of sea urchin larvae. V. Continuous observation of the process of matrix formation[END_REF][START_REF] Ettensohn | The regulation of primary mesenchyme cell migration in the sea urchin embryo: transplantations of cells and latex beads[END_REF]. PMCs stop slightly beyond the equator of the blastula to move back to the vegetal pole and settle in a ring shape around the archenteron, close to the ectodermendoderm boundary (Figure 1.4H, I). PMCs form two ventrolateral clusters, probably driven by VEGF signaling secreted by ectoderm-endoderm boundary cells adjacent to the cluster sites [START_REF] Duloquin | Localized VEGF signaling from ectoderm to mesenchyme cells controls morphogenesis of the sea urchin embryo skeleton[END_REF]. PMCs then fuse to form a syncytium [START_REF] Hodor | The dynamics and regulation of mesenchymal cell fusion in the sea urchin embryo[END_REF] which seems to exchange positional information with ectodermal cells by direct contact through long filopodia that are 200 to 400 nm thick and 5 to 30 µm long, up to 80 µm in rare cases [START_REF] Miller | Dynamics of thin filopodia during sea urchin gastrulation[END_REF].

PMCs destined to form the larval skeleton are maintained in vitro [START_REF] Ohshima | Skeleton formation of sea urchin larvae. V. Continuous observation of the process of matrix formation[END_REF] or in ectopic and heterochronous transplantation experiments [START_REF] Ettensohn | The regulation of primary mesenchyme cell migration in the sea urchin embryo: transplantations of cells and latex beads[END_REF][START_REF] Ransick | A complete second gut induced by transplanted micromeres in the sea urchin embryo[END_REF]. However, PMC organization is not strictly autonomous, since they depend on external signaling to correctly migrate in place and time [START_REF] Armstrong | Skeletal pattern is specified autonomously by the primary mesenchyme cells in sea urchin embryos[END_REF].

The differentiation of the precise number of PMCs seems to be a very robust process, as their ablation is compensated by the so-called trans-differentiation of SMCs [START_REF] Ettensohn | Cell lineage conversion in the sea urchin embryo[END_REF][START_REF] Ettensohn | Cell interactions and mesodermal cell fates in the sea urchin embryo[END_REF][START_REF] Ruffins | A clonal analysis of secondary mesenchyme cell fates in the sea urchin embryo[END_REF][START_REF] Kiyomoto | Skeletogenesis by transfated secondary mesenchyme cells is dependent on extracellular matrix -ectoderm interactions in Paracentrotus lividus sea urchin embryos[END_REF].

The endodermal and ectodermal lineages are not fully distinct yet [START_REF] Ruffins | A fate map of the vegetal plate of the sea urchin (Lytechinus variegatus) mesenchyme blastula[END_REF][START_REF] Logan | The allocation of early blastomeres to the ectoderm and endoderm is variable in the sea urchin embryo[END_REF][START_REF] Martins | Cells are added to the archenteron during and following secondary invagination in the sea urchin Lytechinus variegatus[END_REF][START_REF] Piston | Characterization of Involution during Sea Urchin Gastrulation Using Two-Photon Excited Photorelease and Confocal Microscopy[END_REF][START_REF] Ransick | Late specification of Veg1 lineages to endodermal fate in the sea urchin embryo[END_REF]. It has been shown, in archenteron ablation experiments, that both ectoderm and endoderm cells contribute to restoring a partial or even complete gut [START_REF] Logan | The allocation of early blastomeres to the ectoderm and endoderm is variable in the sea urchin embryo[END_REF].

The Late Gastrula and Early Larval Stages While PMCs are migrating to their final location, the forming ventrolateral clusters start to synthesize the larval skeleton. The PMCs' syncytium shapes two cavities in which cells secrete calcium carbonate, spicule matrix proteins and other compounds [START_REF] Wilt | Matrix and mineral in the sea urchin larval skeleton[END_REF][START_REF] Mozingo | Lectin uptake and incorporation into the calcitic spicule of sea urchin embryos[END_REF] forming several mineralized aggregates leading to two triradiate spicule rudiments [START_REF] Ohshima | Skeleton formation of sea urchin larvae. V. Continuous observation of the process of matrix formation[END_REF]. Aggregates are essentially made of 95% CaCO 3 , 5% MgCO 3 and less than 0.1% matrix proteins [START_REF] Wilt | Matrix and mineral in the sea urchin larval skeleton[END_REF]. For both rudiments, branches point to ventral, dorsal and animal sides.

Later, the two rudiments elongate and branch by the addition of material to the tips, leading to the complete larval skeleton [START_REF] Decker | Growth of linear spicules in cultured primary mesenchymal cells of sea urchin embryos is bidirectional[END_REF]. The skeleton that shapes the sea urchin larva is built in about two days by a small number of cells (32 cells in most species). Spiculogenesis as well as skeleton branching and elongation have been

shown to be cell autonomous [START_REF] Okazaki | Spicule Formation by Isolated Micromeres of the Sea Urchin Embryo[END_REF]. However, the precise shaping of the skeleton requires the correct location of PMCs, which depends on the ectodermal expression of growth factors [START_REF] Duloquin | Localized VEGF signaling from ectoderm to mesenchyme cells controls morphogenesis of the sea urchin embryo skeleton[END_REF][START_REF] Adomako-Ankomah | Growth factor-mediated mesodermal cell guidance and skeletogenesis during sea urchin gastrulation[END_REF].

Concurrently with spiculogenesis, the archenteron extends through the addition of endodermal cells. SMCs attached to the prospective mouth opening through their filopodia contribute to archenteron elongation and bending by pulling the apical plate (Dan and [START_REF] Okazaki | Cyto-embryological studies of sea urchins. III. Role of the secondary mesenchyme cells in the formation of the primitive gut in sea urchin larvae[END_REF][START_REF] Gustafson | Cellular mechanisms in morphogenesis of the sea urchin gastrula. The oral contact[END_REF][START_REF] Hardin | The role of secondary mesenchyme cells during sea urchin gastrulation studied by laser ablation[END_REF][START_REF] Latham | A putative role for carbohydrates in sea urchin gastrulation[END_REF].

Meanwhile, the apical plate invaginates (Figure 1.5A) through apical constriction, as observed earlier for the vegetal plate. Some SMCs escape from the tip of the gut and migrate through the blastocoel to their final destination. SMCs lead to at least four different cell types: 1) pigment cells; 2) blastocoelar cells; 3) coelomic pouches and 4) circumesophageal muscles. Pigment cells and blastocoelar cells come from the archenteron during its expansion (Figure 1.5B in red). Pigment cells display an intense protrusive activity and invade the ectoderm [START_REF] Young | Development of pigment in the larva of the sea urchin, Lytechinus variegatus[END_REF]Gibson and Burke, 1987). They show large vacuoles containing the red pigment echinochrome in proportion to sea water [START_REF] Bay | A simple new bioassay based on echinochrome synthesis by larval sea urchins[END_REF][START_REF] Ageenko | Expression of Pigment Cell-Specific Genes in the Ontogenesis of the Sea Urchin Strongylocentrotus intermedius[END_REF]. Blastocoelar cells extend thin filopodia across the blastocoel, creating a multicellular mesh between gut, skeleton, larval arms and ectoderm.

Blastocoelar cells play different roles in immunity [START_REF] Pancer | Origins of immunity: transcription factors and homologues of effector genes of the vertebrate immune system expressed in sea urchin coelomocytes[END_REF] such as aggregation at sites of injury to form cellular clots and exogenous compound clearance (including bacteria and allografts). After making contact with the apical plate, the archenteron continues to elongate, exerting increasing pressure on the ectoderm [START_REF] Gustafson | Studies on the cellular basis of morphogenesis in the sea urchin embryo[END_REF]. A first constriction of the archenteron forms between the coelom (Figure 1.5B, oral side) and the gut. As the archenteron continues to elongate, the coelom organizes on both side of the oral plate to form the two coelomic pouches (Figure 1.5C). Meanwhile, a second constriction forms between the presumptive oesophagus and stomach.

The mechanism underlying the fusion of the ectoderm and endoderm during the opening of the mouth remains ill-known, although it has been proposed that the tensions generated by archenteron growth, ectodermal invagination and the beginning of peristaltic contractions of the oesophagus may be sufficient to separate cells and trigger the mouth opening [START_REF] Gustafson | Studies on the cellular basis of morphogenesis in the sea urchin embryo[END_REF]. The progenitors of circumesophageal cells are found in coelomic pouches. They extend filopodia around the oesophagus to meet the contralateral coelomic pouch [START_REF] Burke | Development of the esophageal muscles in embryos of the sea urchin Strongylocentrotus purpuratus[END_REF]. Circumesophageal cells migrate out of the coelomic pouches and show autonomous contractions.

The last cell type to emerge during gastrulation is the primordial germ cells originating from the eight small micromeres located at the vegetal pole and born through the asymmetrical cleavage of micromeres (5th cleavage from 16 cells to 32 cells). During gastrulation, small micromeres stay at the tip of the archenteron [START_REF] Yajima | Autonomy in specification of primordial germ cells and their passive translocation in the sea urchin[END_REF]. Whether their displacement is active or passive is unclear, although the absence of detectable filopodia argues in favor of passive movement [START_REF] Yajima | Autonomy in specification of primordial germ cells and their passive translocation in the sea urchin[END_REF].

Small micromeres segregate into the coelomic pouches with a 5:3 ratio most of the time, although equal distribution may be observed [START_REF] Pehrson | The fate of the small micromeres in sea urchin development[END_REF]. Although proliferation along the small micromere lineage is not extensively documented, it has been shown that they divide at least once more in Strongylocentrotus purpuratus [START_REF] Pehrson | The fate of the small micromeres in sea urchin development[END_REF].

The Pluteus Larva The larval skeleton continues to grow, regulated by the PMCs' developmental clock and ectodermal cues. Skeletal rods grow to form the larval arms, meeting at the aboral extremity and shaping the larva from prism stage to pluteus (Ettensohn and Malinda, 1993, Figure 1.4L, dorsal view). Four arms are formed, two of which are close to the oral ectoderm and the two others on the aboral side. Oral and aboral ectoderms are separated by a band of differentiated cells forming the ciliary band. The ciliary band serves to bring food to the mouth and contributes to the larva movements. Pluteus larvae are autonomous, swim and feed as part of the zooplankton and their morphogenesis continues, in particular at the level of the skeleton. During their planktonic life, larvae will undergo metamorphosis through the development of a juvenile in one of the coelomic pouches, in principle the left one [START_REF] Duboc | Left-right asymmetry in the sea urchin embryo is regulated by nodal signaling on the right side[END_REF], although historical studies have shown that the right coelomic pouch can be active in vitro and in natura [START_REF] Ohshima | Skeleton formation of sea urchin larvae. V. Continuous observation of the process of matrix formation[END_REF].

Experimental Perturbation of the Sea Urchin Early Embryogenesis

Sea urchin development is often described as stereotypical, i.e., without significant differences between individuals or species [START_REF] Davidson | Specification of cell fate in the sea urchin embryo: summary and some proposed mechanisms[END_REF][START_REF] Mcclay | Evolutionary crossroads in developmental biology: sea urchins[END_REF][START_REF] Hinman | Developmental gene regulatory network evolution: Insights from comparative studies in echinoderms[END_REF]. This stereotypical description mainly concerns the first division axis, early asymmetries and early fate map. To my knowledge there is no precise quantification of phenotypic variation in normal sea urchin early development. However, a number of studies have pointed to significant interindividual variations and extensive cell plasticity (Khaner and [START_REF] Livingston | Range and stability of cell fate determination in isolated sea urchin blastomeres[END_REF][START_REF] Livingston | Range and stability of cell fate determination in isolated sea urchin blastomeres[END_REF][START_REF] Mcclay | Regulative capacity of the archenteron during gastrulation in the sea urchin[END_REF][START_REF] Logan | The allocation of early blastomeres to the ectoderm and endoderm is variable in the sea urchin embryo[END_REF]. In many cases, some of which will be described in this section, sea urchins are somehow able to overcome disturbances and form larvae with no obvious differences from normal ones.

Fractionation of Unfertilized Eggs

In the first part of the 20 th century, researchers were looking for cell components that are essential to good development. In 1932, Ethel Harvey proposed a simple and reproducible way to produce halves and quarters of sea urchin oocytes prior to fertilization. By means of strong centrifugation (10,000 x g for 30 minutes) in a sucrose gradient, sea urchin oocytes were separated into two halves with different contents [START_REF] Hardin | The development of half and quarter eggs of Arbacia punctulata and of strongly centrifuged whole eggs[END_REF]. Clear partitioning happened along the centrifugation axis with, starting from the centripetal end: lipid droplets, pronucleus, clear zone, mitochondria, yolk and pigment. The cytological content of oocyte halves or quarters and their development when activated by sperm were further analyzed 40 years later [START_REF] Anderson | A cytological study of the centrifuged whole, half, and quarter eggs of the sea urchin, Arbacia punctulata[END_REF]. Although Anderson did not reproduce the high rate of successful development observed by Harvey, both authors agreed that spermactivated halves and quarters, even those containing the male pronucleus only, were able to form pluteus larvae.

The centripetal half inherits the maternal pronucleus, but largely lacks food materials.

Despite this lack of resources, mitoses occur in time compared with control embryos.

According to Ethel Harvey and Anderson, these halves can form blastulae, gastrulae and normal plutei except for size and color.

The centrifugal half inherits food materials, but lacks the maternal pronucleus. Development is strongly affected as only a few embryos divide. Most of them replicate the male nucleus without cell division and die. In rare cases, embryos reach the blastula stage and even the pluteus stage with four well-developed arms. How this wide range of phenotypic variation is generated has not been investigated. We do not know where in the system's parameter space the embryo has a chance to develop properly. In any case, this centrifugation protocol reveals striking aspects of developmental plasticity.

Separated Fertilized Eggs: Twinning

Experimental twinning in sea urchin was introduced by Hans Driesch in 1891. He showed that blastomeres isolated from the 2-cell stage embryos were able to develop and form adult larvae. Although anatomical aberrations may be observed depending on the technique used [START_REF] Marcus | Developmental aberrations associated with twinning in laboratory-reared sea urchins[END_REF], blastomere isolation can lead to fertile adults [START_REF] Herrler | Proteins in the extraembryonic matrix of preimplantation rabbit embryos[END_REF][START_REF] Cameron | Twins raised from separated blastomeres develop into sexually mature Strongylocentrotus purpuratus[END_REF]. We present here a brief description of twin development from the 1-cell stage to the pluteus stage, focusing on developmental variation. Twins' Developmental Variation: An Ambiguous Fate Map Hans Driesch concluded from these experiments that "das Princip der organbildenden Keimbezirke [ist] widerlegt" (the principle of organ-forming territories is refuted) (Driesch, 1891, p.178) rather than acknowledging the regulative properties of the sea urchin embryo, as done later (for a review see [START_REF] Angerer | Regulative development of the sea urchin embryo: signalling cascades and morphogen gradients[END_REF]. The contemporary perspective would actually be that of resilience.

Twins' Development from 1-cell Stage to Hatching

Resilience is the ability to recover from injuries while robustness is the ability to compensate for variation without macroscopic phenotypic difference. Twins vary in terms of cell type, cell number, and cell organization (Figure 1.6E 1,2 ). These morphological types are likely to correlate with major changes in terms of embryonic fate map. Despite these striking differences, a high percentage of twins are resilient and form viable and fertile adults [START_REF] Herrler | Proteins in the extraembryonic matrix of preimplantation rabbit embryos[END_REF][START_REF] Cameron | Twins raised from separated blastomeres develop into sexually mature Strongylocentrotus purpuratus[END_REF]. Resilience in sea urchin has been observed and described in a number of experimental cases, including blastomere ablation and disaggregation [START_REF] Hardin | The development of half and quarter eggs of Arbacia punctulata and of strongly centrifuged whole eggs[END_REF][START_REF] Giudice | Restitution of whole larvae from disaggregated cells of sea urchin embryos[END_REF][START_REF] Anderson | A cytological study of the centrifuged whole, half, and quarter eggs of the sea urchin, Arbacia punctulata[END_REF][START_REF] Spiegel | The Reaggregation of Dissociated Embryonic Sea Urchin Cells[END_REF][START_REF] Ettensohn | Cell interactions and mesodermal cell fates in the sea urchin embryo[END_REF]McClay, 1986, 1988;[START_REF] Ransick | A complete second gut induced by transplanted micromeres in the sea urchin embryo[END_REF][START_REF] Mcclay | Regulative capacity of the archenteron during gastrulation in the sea urchin[END_REF][START_REF] Logan | The allocation of early blastomeres to the ectoderm and endoderm is variable in the sea urchin embryo[END_REF][START_REF] Davidson | Specification of cell fate in the sea urchin embryo: summary and some proposed mechanisms[END_REF].

Blastomere Ablation and Transfating

Ablation experiments are classic experimental protocols to investigate the role of cells or cell populations in development and the ability of the embryo to regulate. At the early gastrula stage (Figure 1.4H), [START_REF] Ettensohn | Cell lineage conversion in the sea urchin embryo[END_REF] removed the PMCs from the blastocoel and observed further development. Interestingly, archenteron invaginated and SMCs migrated into the blastocoel to form two ventrolateral cell clusters performing spiculogenesis. The same phenomenon was observed after ablation of the micromeres at the 16-cell stage [START_REF] Sweet | The role of micromere signaling in Notch activation and mesoderm specification during sea urchin embryogenesis[END_REF]. Although mouth opening and spiculogenesis happened with delay, this experiment revealed, once again, the embryonic potential for resilience. SMC transfating is not an isolated case. Transfating of endodermal cells to SMCs has been observed after ablation of the archenteron tip [START_REF] Mcclay | Regulative capacity of the archenteron during gastrulation in the sea urchin[END_REF].

Recent work has deciphered the mechanism by which SMC-to-PMC transfating may occur [START_REF] Ettensohn | Gene regulatory networks and developmental plasticity in the early sea urchin embryo: alternative deployment of the skeletogenic gene regulatory network[END_REF]. alx1 is a PMC-specific gene sufficient and necessary for PMC differentiation. Future SMCs normally repress alx1 expression by means of direct or indirect signaling from PMCs. In the absence of PMCs, this signal is abolished. Therefore, alx1 expression in SMCs is no longer repressed and transfating occurs. Interestingly, removal of both PMCs and archenteron tip lead to the same result, suggesting that transfating may successively occur from endodermal cells to SMCs, and from SMCs to

PMCs [START_REF] Mcclay | Regulative capacity of the archenteron during gastrulation in the sea urchin[END_REF].

Reaggregation of Blastomeres and Cell Sorting

The complete disaggregation followed by reaggregation of embryonic cells was first ex- Whether the underlying mechanism is active or passive is not clear. However, there is strong evidence that a hybrid mechanism operates with active cell-cell recognition and passive cell sorting [START_REF] Spiegel | Sorting out of sea urchin embryonic cells according to cell type[END_REF][START_REF] Takeichi | Cadherin cell adhesion receptors as a morphogenetic regulator[END_REF][START_REF] Ghersi | Migratory and invasive behavior of pigment cells in normal and animalized sea urchin embryos[END_REF].

Reaggregating cells have been shown to expand filopodia, to adhere to other cells and migrate, actively sorting out species-specific cells [START_REF] Spiegel | The Reaggregation of Dissociated Embryonic Sea Urchin Cells[END_REF]. Cell polarity is maintained through active processes and, within a few hours, cells reorient in the aggregate so that their apical surfaces face the outside [START_REF] Nelson | Cell polarity in sea urchin embryos: reorientation of cells occurs quickly in aggregates[END_REF]. Cell sorting has been correlated to the cell type, showing that aggregation is homotypic (i.e., cells of the same type aggregate together; [START_REF] Spiegel | Sorting out of sea urchin embryonic cells according to cell type[END_REF]. This observation has been confirmed in mice [START_REF] Takeichi | Cadherin cell adhesion receptors as a morphogenetic regulator[END_REF] and later in sea urchin [START_REF] Ghersi | Migratory and invasive behavior of pigment cells in normal and animalized sea urchin embryos[END_REF] and shown to rely on a cadherin-dependent mechanism.

Discussion and Perspectives

The remarkable regulative capacity of sea urchin showed by Driesch, Boveri, Harvey and Anderson has not yet been elucidated. The scientific community has surpisingly forgotten these studies which challenged (and continue to challenge) our understanding of embryogenesis. How can haploid or triploid eggs form plutei [START_REF] Boveri | Die Polarität von Ovocyte, Ei und Larve des Stronglyocentrotus lividus[END_REF][START_REF] Hardin | The development of half and quarter eggs of Arbacia punctulata and of strongly centrifuged whole eggs[END_REF][START_REF] Anderson | A cytological study of the centrifuged whole, half, and quarter eggs of the sea urchin, Arbacia punctulata[END_REF]? How can a fully disaggregated gastrula reorganize itself into a normal pluteus [START_REF] Giudice | Restitution of whole larvae from disaggregated cells of sea urchin embryos[END_REF]?

One of the reasons for this lack of interest may be the very low proportions of embryos able to form plutei, making these experiments tedious to reproduce. Boveri, Harvey and

Anderson reported that less than 1% of the haploids or triploids are able to form plutei.

Giudice observed that disaggregated gastrulae occasionally form a pluteus. In addition to the reproducibility issues, these experiments may be explained by experimental artefacts.

Centrifuged halves of sea urchin eggs, for instance, may be diploids in some rare cases.

However, Boveri clearly described triaster mitoses leading to normal-looking plutei and Driesch isolated blastomeres to confirm the correct development of twins.

These questions are still unanswered, although they challenge our concept of embryogenesis and deserve more attention. The case of sea urchin twins is a good candidate.

Driesch's experiment has been reproduced many times [START_REF] Marcus | Developmental aberrations associated with twinning in laboratory-reared sea urchins[END_REF][START_REF] Herrler | Proteins in the extraembryonic matrix of preimplantation rabbit embryos[END_REF][START_REF] Cameron | Twins raised from separated blastomeres develop into sexually mature Strongylocentrotus purpuratus[END_REF] The sea urchin has been a model organism for more than a century and dramatically changed our vision of embryology. A lot of questions are yet to be answered and hopefully technical advances will support a systematic revisiting of historical studies.

Mammalian preimplantation embryogenesis

In this section, we review the development of rabbit embryos from oogenesis to implantation, with a brief comparison with other species. We then present the generation of clones through nuclear transfer and discuss the field of possibilities opened by this type of strategies.

Diversity of mammalian models

In 2011, the European Commission recorded 25 categories of animal models used in fundamental, medical and agronomic research 1 . More than 80% of the animals were mammals, of which the overwhelming majority were mice (Mus musculus, 75%), rats (Rattus norvegicus, 17%) and rabbits (Oryctolagus cuniculus, 4%). Interestingly, while mice are preferred for biological studies of a fundamental nature, rats and rabbits, as well as other mammals, are often used for medical research (human or veterinary medicine).

Although scientific studies may include a wide and diverse range of mammalian species, most of the fundamental research is conducted with mice (86%) and rats (11%). This means that we are not studying mammals in general (even less so humans) but rodents, and we extremely need to widen our knowledge to a broader range of species [START_REF] Duranthon | On the emerging role of rabbit as human disease model and the instrumental role of novel transgenic tools[END_REF].

Prototypic development

A prototypic development is the summary of many observations made on many individuals of one or multiple species. Common features are gathered into one virtual mammal embryo. A prototypic development leaves little or no room for variability, however; it highlights shared features and simplifies descriptions. One must bear in mind that prototypic developments do not refer to actual developments and cannot be generalized to other mammals without further investigations.

In this chapter we propose to describe the prototypic development of rabbit embryos (Figure 1.7). It should be noted that extensive work in embryology is generally intended to identify prototypic traits for given species and even for classes (e.g. mammals). We will now review the current representation of a prototypic development of the rabbit embryo.

When knowledge is lacking, we will refer to other mammals, including mice, although recent observations tend to consider mouse embryos as the exception in the phylogenetic tree. In the discussion chapter, we will return to the epistemological issue of defining a prototype through a qualitative estimation of similarities without systematic estimation of variability.

Mating

Rabbits follow seasonal reproduction in spring and early summer. Every year, the female can have from three to five litters of three to twelve kits each. In theory, with a life expectancy of 9 years, rabbits can have more than 500 offspring during their life. In natura, rabbits have a life expectancy of two years and an average of 75% of their offspring die before sexual maturity. Thus, four to thirty kits in a female's life is more common.

Female gametes

In most animal models, oocytes are formed during the prenatal development. In rabbits, as in cats, a large number of oogonia are formed by mitosis during fetal development from primordial germ cells [START_REF] Chrétien | Etude de l'origine, de la migration et de la multiplication des cellules germinales chez l'embryon de lapin[END_REF] and stop until birth. During the first 20 days of post-partum development, oogonia enter meiosis and form primary oocytes, blocked in metaphase I (Peters et al., 1965, Figure 1.8A). Then, folliculogenesis starts and surrounding cells develop into follicular cells (Figure 1.8B). Besides the nourishing role, follicular cells shape into an ovarian follicle, a roughly spheroid cellular aggregation ready to ovulate which contains a single primary oocyte surrounded by cumulus cells. The latter make an important contribution to oocyte maturation and meiosis regulation in mice, rats, cows and sheep [START_REF] Buccione | Interactions between somatic cells and germ cells throughout mammalian oogenesis[END_REF]. of the rabbit, spermatogonia undergo five mitoses with differentiation to generate spermatocytes. On average, each spermatogonium undergoes an asymmetrical division to maintain the pool of spermatogonia and to produce 16 primary spermatocytes [START_REF] Swierstea | Citology and Kinetics of Spermatogenesis in the Rabbit[END_REF]. The latter undergo meiosis so that, in theory, one spermatogonium produces 64 spermatozoa, although degeneration may be observed. Interestingly, the number of spermatozoa produced from one spermatogonium is highly variable between species, varying from sixteen in ducks to 96 in rats.

Fertilization Spermatozoa are highly differentiated haploid cells, but not ready for fertilization yet. Chemotaxis and chemokinesis enable the spermatozoa to swim toward oocytes via ovarian follicle factors [START_REF] Ralt | Chemotaxis and chemokinesis of human spermatozoa to follicular factors[END_REF]. During their journey, spermatozoa are capacitated, probably through rapid changes in osmolarity [START_REF] Brackett | Capacitation of Rabbit Spermatozoa in vitro[END_REF]. Capacitation is an essential event to trigger the acrosome reaction (AR) after egg recognition.

The acrosome is a capping structure over the anterior half of the head of sperm that contains enzymes for the fusion of gametes.

When the secondary oocyte is ejected from the ovary, it moves towards the infundibulum of the uterine tube and meets the spermatozoa in the ampulla, the site of fertilization (Figure 1.8D). As in many species, fertilization induces dramatic changes and is tightly regulated. The local concentration of divalent ions (Ca ++ and Mg ++ ) and the high pH induce the AR. The acrosome membrane is degraded. This releases sperm lysins which digest the outer layer containing cumulus cells. The sperm penetrates through the outer layer and meets the zona pellucida (ZP). The ZP is a thick extracellular coat composed of four glycoproteins: ZP1, ZP2, ZP3 and ZP42 [START_REF] Stetson | Rabbit zona pellucida composition: A molecular, proteomic and phylogenetic approach[END_REF] arranged in a complex network of fibrils. The ZP3 glycoprotein is known to be a species-specific receptor for spermatozoa in mice and hamsters [START_REF] Wassarman | The multifunctional zona pellucida and mammalian fertilization[END_REF]. Sperm lysins digest the ZP and a burst of actin polymerisation extends the sperm membrane to the egg plasma membrane. The membranes fuse and the male genome is injected into the cytoplasm (Figure 1.7A). In the meantime, cortical granules located underneath the egg plasma membrane fuse to it and release their content into the perivitelline space. The perivitelline space increases and the conformation of ZP3 changes so that it is not recognized by other spermatozoa. The sperm centriole forms an aster which extends throughout the cytoplasm and brings pronuclei close together, where chromosomes are arranged in a mitotic spindle (Figure 1.7C). During migration, the sperm pronucleus fragmentates into small vesicles and its DNA decondenses (Figure 1.7B). The centriole of the sperm divides and forms two centrioles to generate a mitotic spindle.3

Morula stage and cell lineage segregation

The cleavage stage corresponds to the first embryonic divisions with preservation of the total cell volume. The first three cleavages are followed by the cell compaction of the embryo and the segregation of inner cell mass (ICM) and trophectoderm (TE) lineage (Figure 1.7D-G). These mechanisms are intimately correlated. Little is known about the rabbit's early development, but mice have been intensively studied over past decades.

Compaction Compaction is a essential morphogenetic event which packs cells into a tighter tissue and forms the blastocyst [START_REF] Johnson | The effect of prolonged decompaction on the development of the preimplantation mouse embryo[END_REF][START_REF] Larue | E-cadherin null mutant embryos fail to form a trophectoderm epithelium[END_REF]Stephenson et al., 2010, Figure 1.7E-F). More specifically, 8-cell stage embryos compact by enlarging their cell-cell contacts, allowing them to establish the first epithelium-like structure. As the process depends on cadherin adhesion molecules Cdh-1 [START_REF] Kemler | Surface antigen in early differentiation[END_REF][START_REF] Hyafil | A cell surface glycoprotein involved in the compaction of embryonal carcinoma cells and cleavage stage embryos[END_REF][START_REF] Peyriéras | Uvomorulin: a nonintegral membrane protein of early mouse embryo[END_REF][START_REF] Stephenson | Disorganized epithelial polarity and excess trophectoderm cell fate in preimplantation embryos lacking E-cadherin[END_REF], it was proposed that Cdh-1 drives compaction to increase contact surfaces and to minimize surface energy [START_REF] Goel | Simulation of cellular compaction and internalization in mammalian embryo development as driven by minimization of surface energy[END_REF][START_REF] Foty | The differential adhesion hypothesis: A direct evaluation[END_REF]. 60% of blastomeres in mouse embryos extend filopodia and may initiate or help compaction [START_REF] Fierro-González | Cadherin-dependent filopodia control preimplantation embryo compaction[END_REF] by drawing cells tighter together. A recent study suggests that compaction is initiated earlier in the development by the apical contractions of the acto-myosin network (Maître et al., 2015).

The molecular timing of compaction is unknown and the mechanisms are still elusive.

However, we know that compaction structures the inside and outside of embryos and is a necessary process for lineage segregation and the formation of a viable blastocyst with pluripotent ICM and extraembryonic progenitors. The first lineage segregation forms the inner cells and the outer cells. Cell position correlates with cell fate, although the timing of specification remains elusive. Four models have been proposed to explain how inner cells and outer cells segregate into two cell fates (Figure 1.9).

Model 1: Prepatterning Model

The first model of development is a direct transfer of knowledge from other species. It involves spatial organization of cell-fate determinants in the egg prior to fertilization. In this model, cleavages are asymmetrical and cell lineage segregation depends on differential inheritance. However, contrary to Drosophila melanogaster or Xenopus laevis in which this model has been successfully demonstrated, no determinant has ever been found in mouse eggs, and hypotheses are based on the relative contribution of early blastomeres to ICM and TE [START_REF] Bischoff | Formation of the embryonic-abembryonic axis of the mouse blastocyst: relationships between orientation of early cleavage divisions and pattern of symmetric/asymmetric divisions[END_REF], the position of the sperm entry [START_REF] Piotrowska | Monoclonal antibodies specific for an acetylated form of alpha-tubulin recognize the antigen in cilia and flagella from a variety of organisms[END_REF], or the expression level of potential determinants at the 4-cell stage [START_REF] Jedrusik | Role of Cdx2 and cell polarity in cell allocation and specification of trophectoderm and inner cell mass in the mouse embryo[END_REF]. Contradictory studies have shown no cell specification before the 32-cell stage -based on the expression of about 50 genes [START_REF] Guo | Resolution of Cell Fate Decisions Revealed by Single-Cell Gene Expression Analysis from Zygote to Blastocyst[END_REF] -and explained the observed biases at the 4-cell stage by spatial constraints and random patterning [START_REF] Alarcón | Spatial alignment of the mouse blastocyst axis across the first cleavage plane is caused by mechanical constraint rather than developmental bias among blastomeres[END_REF].

Model 2: Inside-Outside Model While the prepatterning model assumes that the regulative ability of embryos only plays a minor role in the specification process, the inside-outside model considers embryonic regulation as the major mechanism for lineage segregation. The capacity of isolated embryonic cells to form a complete blastocyst has been demonstrated at the 2-cell stage in rabbits [START_REF] Seidel | Die Entwicklungsfähigkeiten isolierter Furchungszellen aus dem Ei des Kaninchens Oryctolagus cuniculus[END_REF] and at the 4-cell stage and 8-cell stage in mice [START_REF] Tarkowski | Development of blastomeres of mouse eggs isolated at the 4-and 8-cell stage[END_REF]. Blastomeres can adapt their fate based on their local microenvironment (position or cell contact). The insideoutside model proposes that inner cells form the ICM and outer cells form the TE. The suggested mechanism is that outer cells become polarized because they are facing the external environment. Then, polarization segregates outer cells to TE and non-polarized Figure 1.9: Possible models for lineage divergence and patterning of the mouse preimplantation embryo. Asymmetry between cells is generated by differential inheritance of determinants localized in the zygote (prepatterning), differences in cell position (inside-outside model), differential inheritance of the apical domain (cell polarity model) or stochastic processes (stochastic patterning). Depending on the way the asymmetry is generated, distinct subsequent processes are necessary for the robust formation of the patterned blastocyst. From [START_REF] Wennekamp | Stochastic processes in the development of pluripotency in vivo[END_REF] inner cells to ICM. This model may be confirmed by the ability of the ICM alone to generate TE cells [START_REF] Romancino | Maternal Paracentrotus lividus RNAs are differentially localized during the first cell division[END_REF][START_REF] Eckert | Relative contribution of cell contact pattern, specific PKC isoforms and gap junctional communication in tight junction assembly in the mouse early embryo[END_REF], although recent observations contradict this hypothesis [START_REF] Szczepanska | Isolated mouse inner cell mass is unable to reconstruct trophectoderm[END_REF].

Model 3: Polarity Model By contrast with the inside-outside model, the polarity model proposes that outer cells form the TE because they are polarized. Alternatively, the inner cells form the ICM because they are apolar. During metaphases, the mitotic spindle can be oriented parallel to the surface of the embryo -one daughter towards the inside and one daughter towards the outside -or perpendicular to it. The orientation of the mitotic spindle is crucial to determining the polarity of the daughter cells. Cells inheriting the apical domain are polarized and stay in the TE. Cells inheriting the basolateral domain are apolar and constitute the ICM. This is consistent with isolated blastomeres at the 8-cell stage which often divide into a polar and an apolar cell and sometimes into two polar cells [START_REF] Johnson | The foundation of two distinct cell lineages within the mouse morula[END_REF]. In addition, asymmetrical divisions have only been observed in the outer cells in mice.

Model 4: Stochastic Model Gene expression has been proved to be highly variable in bacteria [START_REF] Elowitz | Stochastic gene expression in a single cell[END_REF][START_REF] Taniguchi | coli Proteome and Transcriptome with Single-Molecule Sensitivity in Single Cells[END_REF][START_REF] So | General properties of transcriptional time series in Escherichia coli[END_REF], yeast [START_REF] Blake | Noise in eukaryotic gene expression[END_REF][START_REF] Carey | Promoter Sequence Determines the Relationship between Expression Level and Noise[END_REF] and metazoans [START_REF] Wernet | Stochastic spineless expression creates the retinal mosaic for colour vision[END_REF][START_REF] Boettiger | Synchronous and stochastic patterns of gene activation in the Drosophila embryo[END_REF] including mice [START_REF] Ohnishi | Cell-to-cell expression variability followed by signal reinforcement progressively segregates early mouse lineages[END_REF]. These observations led to a new model of embryo patterning at various stages involving stochasticity [START_REF] Dietrich | Stochastic patterning in the mouse pre-implantation embryo[END_REF][START_REF] Yamanaka | FGF signal-dependent segregation of primitive endoderm and epiblast in the mouse blastocyst[END_REF]. Cell fate may be progressively segregated by the interplay of variable gene expression which eventually leads to one destiny predominating over the others. Combined with cell sorting and gene expression regulation, this model is consistent with previous observations. The fate of each cell relies on stochastic events, thus it cannot be individually predicted. However, the output at the embryo level of observation can be statistically predicted. Interestingly, the stochastic model suggests that cell lineage segregation is driven by the stochasticity of gene expression. In this model, variations are not noise, but a necessary force to generate diversity. In my thesis work, I favor this model as it is consistent with all available observations and can explain by itself why no consensus has been found yet. Indeed, because experimentation on mammal embryos is expensive and difficult, the number of replicas is low and results are very sensitive to experimental noise and biological stochasticity.

Maternal to zygotic transition

The first steps of rabbit embryonic development rely exclusively on maternal factors. Fertilization is controlled by several mechanisms to avoid spontaneous parthenogenetic activation of unfertilized eggs [START_REF] Hashimoto | Parthenogenetic activation of oocytes in c-mos-deficient mice[END_REF][START_REF] Miyagaki | Possible involvement of mitogen-and stress-activated protein kinase 1, MSK1, in metaphase-II arrest through phosphorylation of EMI2 in mouse oocytes[END_REF][START_REF] Hörmanseder | Modulation of cell cycle control during oocyte-to-embryo transitions[END_REF], to bind spermatozoa [START_REF] Wassarman | The multifunctional zona pellucida and mammalian fertilization[END_REF] or to control the first four cleavages in rabbits [START_REF] Henrion | Identification of maternal transcripts that progressively disappear during the cleavage period of rabbit embryos[END_REF] [START_REF] Flach | The transition from maternal to embryonic control in the 2-cell mouse embryo[END_REF], the rabbit can divide up to the 8 or 16-cell stage with maternal factors alone and MZT takes place very progressively at the early morula stage [START_REF] Magnasco | Pulsatile cell-autonomous contractility drives compaction in the mouse embryo[END_REF][START_REF] Léandri | Revealing the dynamics of gene expression during embryonic genome activation and first differentiation in the rabbit embryo with a dedicated array screening[END_REF]. The rabbit is a good animal model for studying MZT as it takes place slowly during the first two days of development. However, embryos are very fragile during this period and any experimental manipulation may alter the delicate embryonic stability and have dramatic consequences on further developmental steps.

Blastocyst stages and implantation

At the 32-cell stage (56 hours post-coitus, or hpc, in rabbits), the embryonic genome is fully activated and few or no maternal factors are still regulating development. Embryos

Cavitation and expansion

The specification of outer cells to TE cells polarizes them during and after compaction. Junctions between TE cells are reinforced by adherence junctions and tight junctions, forming a seal between cells [START_REF] Mclaren | Functional test of tight junctions in the mouse blastocyst[END_REF].

Aquaporins, molecules allowing fluid movement across cells, are produced and located in TE membranes [START_REF] Barcroft | Aquaporin proteins in murine trophectoderm mediate transepithelial water movements during cavitation[END_REF], along with Na/K ATPase, which regulates the ionic gradient that directs fluid movement from the outside to the inside [START_REF] Wessel | Cavitation in the mouse preimplantation embryo: Na/K-ATPase and the origin of nascent blastocoele fluid[END_REF].

The overall result is an increasing movement of water from the external environment to the internal environment [START_REF] Daniel | Early growth of rabbit trophoblast[END_REF], forming the blastocoele (Figure 1.7I light green). These molecular observations have mostly been made in mouse blastocysts. The mechanisms involved, though, are ubiquitous and observed in several tissues in various species, including rabbits [START_REF] Benos | Inhibition of gastrulation by the blastocoelic fluid from the sea urchin embryo[END_REF].

Cavitation is an essential step in embryonic development as it defines several aspects of embryos. The embryonic-abembryonic axis is defined by the relocation of ICM to one side of embryos (the embryonic side), although a previous study claims that early determinants may predict the blastocyst axis at the 4-cell stage in mice [START_REF] Plusa | The first cleavage of the mouse zygote predicts the blastocyst axis[END_REF]. In addition, the blastocoel offers a controlled environment in which the ICM develops into embryonic tissues. The role of the cavitation process in still elusive, but may serve for the second cell lineage segregation.

Second cell lineage segregation Early blastocysts display two tissues (ICM and TE).

The ICM segregates into two cell lineages: the primitive endoderm, or hypoblast (PrE, Figure 1.7J,K dark orange) located at the surface of the ICM, and the primitive ectoderm, or epiblast (EPI, Figure 1.7J,K orange), surrounded by the PrE (abembryonic side) and the TE (embryonic side). The EPI alone will form the fetus, and the PrE will form extraembryonic tissues. ICM cells of early blastocysts are not equivalent and do not share the same lineage potency (Rossant and Tam, 2009;[START_REF] Ohnishi | Cell-to-cell expression variability followed by signal reinforcement progressively segregates early mouse lineages[END_REF], as demonstrated by variations of gene expression. Along with signal reinforcement, these variations lead to a salt-and-pepper pattern of PrE and EPI progenitors.

The correct allocation of PrE to the surface of ICM (or respectively the correct allocation of EPI to the deep layers of ICM) is still elusive, although a recent study showed that cell movement may be guided by blastocoele expansion, cell death or other unelucidated mechanisms [START_REF] Plusa | Distinct sequential cell behaviours direct primitive endoderm formation in the mouse blastocyst[END_REF]. The timing of PrE and EPI segregation in the rabbit is not known yet. As it seems to rely on stochastic mechanisms in the mouse, the segregation is progressive. During the establishment of the two cell types, embryos continue to expand and eventually collapse and expand again several times before reaching the uterus, where they implant.

Collapse An intriguing feature of mammal embryo development is the repeated collapse from the blastocyst stage (80 hpc) to hatching and beyond. The blastocoele continues to grow and collapses quickly every few hours in mice [START_REF] Cole | Cinemicrographic observations on the trophoblast and zona pellucida of the mouse blastocyst[END_REF] and rabbits [START_REF] Lewis | Cinematographs of Living Developing Rabbit-Eggs[END_REF]. The ZP follows the expansion, although collapses are smoother (Figure 1.11). The mechanisms involved in embryo collapses are thought to be passive.

Cell junctions may not be tight enough to resist the increasing pressure of blastocoels which may leak between cell membranes and fall back to their original sizes. It has been suggested that major collapses are triggered by cell deaths. Somehow, blastocysts recover and slowly increase their volume and pressure, when they repeatedly collapse.

Interestingly, cell divisions do not break TE impermeability [START_REF] Niimura | Time-lapse videomicrographic analyses of contractions in mouse blastocysts[END_REF]. Collapses have been observed in vivo and seem to be a physiological feature rather than an in vitro culture artefact. Collapses are thought to drive embryo orientation and hatching through the constant pressure applied on the ZP (Figure 1.11a,b) and may guide PrE cells to their correct location [START_REF] Plusa | Distinct sequential cell behaviours direct primitive endoderm formation in the mouse blastocyst[END_REF]. -, volume of zona pellucida; -, volume of blastocyst. (a) indicates the beginning of partial rotations by the blastocyst, and (b) the initial penetration. From [START_REF] Cole | Cinemicrographic observations on the trophoblast and zona pellucida of the mouse blastocyst[END_REF]Cole, . 1979;;[START_REF] Herrler | Proteins in the extraembryonic matrix of preimplantation rabbit embryos[END_REF]. Both the embryo and the mother participate in synthetizing the new covering. At four days post-coitus (dpc), the ZP is completely digested and it is only one day later that embryos reach their implantation site in the uterine horn (Figure 1.8, antimesometrial side) and dramatically increase in diameter (0.27 mm at 4 dpc to 1.02 mm at 5 dpc) and cell number (1,100 cells at 4 dpc to 9,000 cells at 5 dpc, [START_REF] Daniel | Early growth of rabbit trophoblast[END_REF]. The term hatching referes to the exit of embryos from their ZP. In vitro, ZP are partially digested from the inside by embryo enzymes. Through a combination of thinner ZP and collapse pressures, embryos pierce their ZP in one weaker spot and hatch. Hatching per se does not exist in vivo as ZP are progressively digested by uterine enzymes and embryos. However, the second covering is also digested, between 5 dpc and 7 dpc, by uterine and blastocyst proteases [START_REF] Kirchner | Uterine protease activities and lysis of the blastocyst covering in the rabbit[END_REF], leading to the implantation of embryos in the uterine wall.

Hatching and implantation

Implantation is a complex and very elusive process in two or three phases [START_REF] Enders | Cytological aspects of trophoblast-uterine interaction in early implantation[END_REF]: apposition, adhesion and, in some species, invasion. Apposition refers to the first contact between blastocysts and the uterine wall. Then blastocysts attach more strongly, marking the adhesion phase. Finally, the TE acquires an invasive capacity used to penetrate the endometrium. The depth of penetration is highly variable depending on the species. Except for bats, mice, rats, primates and some others where embryos penetrate deep into the tissue, invasion is often superficial with little or no penetration [START_REF] Mcgowen | The evolution of embryo implantation[END_REF]. By the time of implantation (7 dpc), rabbit embryos are 5 mm in diameter with aproximatively 770,000 cells [START_REF] Daniel | Early growth of rabbit trophoblast[END_REF]. Three days later, the size of the embryos (17 mm) allows mesometrial implantation of placenta [START_REF] Niimura | Time-lapse videomicrographic analyses of contractions in mouse blastocysts[END_REF] and the embryos have almost 3 million cells [START_REF] Daniel | Early growth of rabbit trophoblast[END_REF].

Mammalian implantation is a fascinating process, but outside the scope of this thesis.

Generation of genetically identical individuals

As mentioned in the first section, variability is inevitable. Therefore, identical individuals do not exist. However, we can minimize embryo differences by twinning or cloning. In both scenarios, the goal is to increase reproducibility (in a very broad sense) or to study variability itself. Cloning is widely preferred to twinning for many reasons, some of which are listed below.

Although interest in cloning has progressively declined over the last 15 years (Ap-pendixFigure S2), the number of publications in 2014 is still high. Indeed, cloning is of great medical interest for the generation of stem cells or the production of drugs.

Economic and agronomic interests concern the cloning of livestock to keep high quality products available for breeding or to help to save endangered species from a more ecological perspective. Finally, clones offer a fascinating tool to study so-called genome reprogramming, epigenetics, stochasticity and variability in mammals.

Twinning

As achieved in a number of other animal species, twins in mammals are obtained by the isolation of blastomeres from 2-cell stage embryos, although twinning has been reported to be easier at the blastocyst stage in many species [START_REF] Katayama | Development of monozygotic twin mouse embryos from the time of blastomere separation at the two-cell stage to blastocyst[END_REF]. In mammals, embryos are manipulated in a Ca ++ -and Mg ++ -free medium to impair or weaken cell-cell adhesion, although separation performed without divalent ion depletion has been reported [START_REF] Morris | Developmental plasticity is bound by pluripotency and the fgf and wnt signaling pathways[END_REF]. Then, the ZP is nicked and blastomeres are squeezed out with a gentle flow of medium [START_REF] Katayama | Development of monozygotic twin mouse embryos from the time of blastomere separation at the two-cell stage to blastocyst[END_REF].

Studies involving twins in mammals often concern the mechanism by which monozygotic twins are generated, in particular in assisted reproductive technologies where 0.8%-5% of the deliveries involve monozygotic twins: far above the natural incidence (0.4%-0.45% of deliveries, [START_REF] Ménézo | Monozygotic twinning: is it related to apoptosis in the embryo?[END_REF]Aston et al., 2008). Rarely, studies focus on the twins' development itself. Twins have been used to make important breakthroughs in the study of transmitted and acquired variability, especially for epigenome and tran-scriptome variation [START_REF] Fraga | Epigenetic differences arise during the lifetime of monozygotic twins[END_REF][START_REF] Haque | Not really identical: Epigenetic differences in monozygotic twins and implications for twin studies in psychiatry[END_REF][START_REF] Roberts | Transcript profiling of individual twin blastomeres derived by splitting two-cell stage murine embryos[END_REF][START_REF] Morris | Developmental plasticity is bound by pluripotency and the fgf and wnt signaling pathways[END_REF]. However, single blastomeres isolated from 2-or 4-cell stages have a higher mortality rate and further manipulations are required to ensure their post-implantation survival. In addition, the number of similar individuals is low with a maximum of four isolated blastomeres per embryo in mice. In order to generate large numbers of similar individuals, cloning is necessary.

Somatic Cell Nuclear Transfer

As far as I know, the first case of successful somatic cell nuclear transfer (SCNT) was made in 1939 by Comandon and de Fonbrune in single-celled amoebae. It was only in 1952 that Briggs and King succeeded in obtaining normal tadpoles after nucleus transfer from blastula cells into enucleated eggs of frogs. The purpose of nuclear transfer was to determine the level of differentiation of nuclei and their ability to become totipotent again.

Although a lot was achieved in the last century, especially thanks to the prolific work of John Gurdon (Gurdon, 1962b,a;[START_REF] Gurdon | The developmental capacity of nuclei transplanted from keratinized skin cells of adult frogs[END_REF], these questions still remain open today. Dolly the sheep was the first viable mammal produced by SCNT [START_REF] Wilmut | Viable offspring derived from fetal and adult mammalian cells[END_REF] and challenged our understanding of nucleus differentiation in mammals. Later, several teams around the world succeeded in cloning organisms from other species, and our understanding of genome reprogramming and the subsequent steps in clone development is better, although still incomplete.

SCNT consists in transferring a nucleus taken from a somatic cell to another cell. In the case of embryonic cloning, the recipient is an unfertilized egg in which sperm entry is artificially triggered after SCNT to start the development. The very first development step of clones is the so-called genome reprogramming, when the epigenome state of the embryonic cell changes to a totipotent state that is essential for complete embryonic development. Although a lot of effort has been put into improving the technique, SCNT has a very low success rate in mammals, especially in rabbits where 30% of the clones reach the morula stage, of which 30% survive to the blastocyst stage and only 7% lead 1.4. THESIS to fertile kits when transferred into a surrogate mother [START_REF] Chesné | Cloned rabbits produced by nuclear transfer from adult somatic cells[END_REF]. The whole SCNT process from cloning to fertile adults has an overall success rate of less than 0.7%.

Although cloning is technically challenging and often fails, it is a groundbreaking technique which helps to portray the complex interactions between -omes and which has deeply changed our understanding of development, cell determination, specification and differentiation. The mechanisms underlying the development of clones are still elusive and a vast amount of work remains to be done.

Thesis

In the introduction of this work, we have seen that variability is an intrinsic property of living organisms. We have also seen that it must be quantified on spatial and temporal data to avoid unwanted oversimplification. The description of sea urchin early development and rabbit preimplantatory development has shown us the remarkable adaptative properties and phenotypic diversity of these embryos, making them unique and able to face various environments. These observations raise the question of developmental normality. More specifically, what outcomes can be expected from an embryo in specific conditions? Is it possible to predict the fate of developing embryos? Would it be possible to discover when, and in what part of the embryo, an abnormal behavior occurs? And finally, how can we link cell lineage variations to inherited variations and external differences? We address these questions through the study of sea urchin and rabbit cell lineage variations.

This thesis investigates the variability of embryonic development and its components, acquired and transmitted variability, to better define normal embryogenesis. In addition, we tackle the processes underlying developmental robustness and resilience. Within this paradigm, we have chosen an experimental approach and methodology that are phenomenology-driven, i.e., based on our observations of phenomena, to build hypotheses on the most precise quantitative descriptions. Because setting a methodology and vali-dating its relevance is a major goal of this thesis, we have carefully chosen the relevant animal models and spatial and temporal developmental sequences to investigate.

Taking into account technical constraints and biological insights, the choice of the sea urchin (Paracentrotus lividus and Sphaerechinus granularis) and rabbit embryos (Oryctolagus cuniculus) became evident. We had extensive laboratory experience in manipulating sea urchins, as well as in investigating morphogenetic processes in Deuterostome early embryogenesis. In addition, sea urchin and rabbit embryos are both quite transparent, making them good candidates for live imaging. Finally, both organisms offer an experimental background to discriminate between acquired and inherited variations, through sea urchin twinning and rabbit cloning.

It has been proposed that Oryctolagus cuniculus has embryonic characteristics close to those of humans in certain aspects, but little is known about the cell dynamics leading to blastocyst formation. In addition, rabbit cloning through nuclear transfer shows a particularly low success rate. This was hypothesized to result from early cell lineage abnormalities that a better understanding of early embryogenesis may solve. The present thesis aims to solve this issue.

Paracentrotus lividus and Sphaerechinus granularis gave us the opportunity to revisit early embryology experiments. Twinning leads to dramatic changes in early developmental stages that challenge our understanding of system resilience and robustness. In addition, the sea urchin research community has produced a considerable amount of data about molecular and genetic dynamics which are intended to be integrated into a multilevel description of sea urchin embryos. Our work to quantify and model cell dynamics from 3D+time imaging is a necessary step in this project.

The cellular level of organisation is the most appropriate integration level for microscopic and macroscopic processes. Our goal is to quantify the variations in cell behaviour, in small cohorts of specimens and under defined experimental conditions. We focus our investigations on accessible developmental stages encompassing cleavage stages and early steps of embryonic morphogenesis and cell differentiation. This work proposes a new standard representation of early embryonic development. The digital specimens delivered in this work are fully curated and annotated. We were able to identify specific cell processes -including cell movements, cell proliferation and cell death -and correlate them with embryonic regulation and developmental robustness.

Chapter 2 Methodology

Digitizing life Publication information

Section 2.1 corresponds to a publication that has been peer reviewed and is in press at the time of my thesis writing. It will be published in Nature Communications under the title: A workflow to process 3D+time microscopy images of developing organisms and reconstruct their cell lineage (Fabrèges et al., in press).

Introduction

Cells are the necessary level of integration of biological processes [START_REF] Jacob | The logic of life: a history of heredity[END_REF] and multicellular organization is best described by the cell lineage tree deployed in space and time. Thus, the quantitative investigation of cell behavior based on lineage branches annotated with relevant measurements at the individual cell level is the indispensable basis for reconstructing the multilevel dynamics of developing organisms [START_REF] Oates | Quantitative approaches in developmental biology[END_REF]. Accurate and precise data about cell positions, trajectories, divisions, nucleus and cell shapes can be derived from the automated processing of 3D+time images. Contributions in the field point to the necessary co-optimization of 4D multimodal imaging techniques and algorithmic image-processing workflows (Fernandez et al., 53 2010;[START_REF] Olivier | Cell lineage reconstruction of early zebrafish embryos using label-free nonlinear microscopy[END_REF][START_REF] Amat | Fast, accurate reconstruction of cell lineages from large-scale fluorescence microscopy data[END_REF]. Ideally, going from the microscopy data to the interactive visualization of the cell lineage tree and segmented shapes should be automated, easily manageable, and fast enough to allow a quantitative comparison of individuals [START_REF] Luengo-Oroz | Image analysis for understanding embryo development: A bridge from microscopy to biological insights[END_REF].

In recent years, decisive breakthroughs have been made in the microscopy imaging of living systems, thanks to progress in fluorescent protein engineering [START_REF] Mavrakis | Fluorescence imaging techniques for studying Drosophila embryo development[END_REF][START_REF] Chudakov | Fluorescent Proteins and Their Applications in Imaging Living Cells and Tissues[END_REF][START_REF] Buckingham | Tracing cells for tracking cell lineage and clonal behavior[END_REF] and microscopy imaging techniques, including multiphoton laser scanning microscopy (MLSM) and selective plane illumination microscopy (SPIM, [START_REF] Fischer | Microscopy in 3D: A biologist's toolbox[END_REF]. Concomitantly, image processing methods for cell segmentation, cell tracking, and the analysis of new types of quantitative data have diversified and improved [START_REF] Fernandez | Imaging plant growth in 4D: robust tissue reconstruction and lineaging at cell resolution[END_REF][START_REF] Olivier | Cell lineage reconstruction of early zebrafish embryos using label-free nonlinear microscopy[END_REF][START_REF] Amat | Fast, accurate reconstruction of cell lineages from large-scale fluorescence microscopy data[END_REF][START_REF] Meijering | Tracking in cell and developmental biology[END_REF][START_REF] Meijering | Methods for Cell and Particle Tracking[END_REF][START_REF] Khairy | The influence of cell interactions and tissue mass on differentiation of sea urchin mesomeres[END_REF]. The huge data flow produced by 3D+time imaging of live specimens has also greatly benefited from faster computer hardware and computing grid architectures able to cope with high-dimensional datasets [START_REF] Reuillon | a workflow engine specifically tailored for the distributed exploration of simulation models[END_REF]. Finally, computer-aided data analysis and visualization software have completed the toolbox of quantitative developmental biology [START_REF] Eliceiri | Biological imaging software tools[END_REF].

Successful applications are still rare, however, and producing an accurate cell lineage tree for any developing organism remains a difficult challenge. In 2006, the automated reconstruction of the nematode cell lineage from confocal images established the first standard [START_REF] Bao | Automated cell lineage tracing in Caenorhabditis elegans[END_REF], although it did not yield reliable results beyond the 194cell stage. Later, reconstructions were attempted on more complex organisms, such as the zebrafish embryo imaged by digital scanned laser light-sheet fluorescence microscopy (DSLM, [START_REF] Keller | Reconstruction of Zebrafish Early Embryonic Development by Scanned Light Sheet Microscopy[END_REF], or Drosophila imaged by MLSM during gastrulation [START_REF] Supatto | Quantitative imaging of collective cell migration during Drosophila gastrulation: multiphoton microscopy and computational analysis[END_REF], but they did not provide accurate long-term single-cell tracking either.

A concurrent work [START_REF] Olivier | Cell lineage reconstruction of early zebrafish embryos using label-free nonlinear microscopy[END_REF] on semi-automated cell lineage reconstruction from harmonic generation imaging of non-labeled zebrafish embryos provided six digital specimens with precise nucleus and membrane segmentation, but it was limited to the first 10 divisions of the egg cell. More recently, Amat et al., 2014 presented standalone software for the reconstruction of cell lineages from Drosophila embryos with fluorescently stained nuclei. Their method, also tested on developing mouse and zebrafish embryos, is well-suited to the low background and high temporal resolution of SPIM data. Among all the state-of-the-art algorithmic image-processing strategies, whether commercial or open-source software, theirs is the only one offering 3D+time cell tracking with detection of mitotic events to reconstruct the branching dynamics of cell lineage. Altogether, the growing number of solutions available today confirms that the automated reconstruction of cell trajectories and cell shapes, together with their interactive visualization, is at the cutting edge of developmental biology. Obviously, the performance achieved so far in terms of accuracy, scalability and ease of operation leaves plenty of room for improvement.

There are still a great number of methods to explore, and more to invent in the fields of image processing and machine vision.

We present here an original image-processing workflow, BioEmergences, in the form of standalone software. While optimized for MLSM data and fast cell movements in gastrulating zebrafish embryos, it generally performs well on 3D+time imaging data without heavy requirements in terms of spatial and temporal resolution or signal-to-noise ratio.

In addition to the reconstruction of the cell lineage branching process, the BioEmergences workflow includes segmentation algorithms for cell nucleus and membrane shapes. These are based on the subjective surface method, which can complete cell contours from heterogeneous fluorescent membrane staining [START_REF] Suri | Deformable models: theory and biomaterial applications[END_REF]. The standalone version of the workflow can be operated through a graphical user interface, and its output data is connected to Mov-IT, a custom-made interactive visualization program. Alternatively, our web service offers users customized assistance and fast processing on computer clusters or on the European Grid Infrastructure (EGI), together with the possibility to explore a large parameter space for the optimization of results (see Section 2.1.4 to request access).

We demonstrate the reconstruction and analysis of six digital embryos from three different species. All the data obtained, raw and reconstructed, is made available to the community. The BioEmergences workflow is compared with eight other software tools from four different providers on the basis of gold standard datasets obtained by manual validation and correction of cell lineages. It scores best in all three tested categories: nucleus center detection, linkage and mitosis detection. Thus, the combined BioEmergences/Mov-IT platform can contribute to the definition of standard procedures for the reconstruction of lineage trees from 4D in vivo data. The validation, annotation and analysis tools provided here support detailed, large-scale cell clonal analysis and characterization of cell behavior along the lineage tree. This leads the way to the creation of benchmarks for a new type of interdisciplinary and quantitative integrative biology. This allows the user to control data quality, measure the error rate, easily correct cell detection and tracking errors, and investigate the clonal history of cells and their behavior.

Results

Overview

The automated tracking of cells from 4D image datasets across whole living embryos involves a difficult trade-off between interdependent variables: signal-to-noise ratio, spatial and temporal resolution, imaging depth, and cell survival. Microscopy techniques for in vivo and in toto imaging of developing organisms are evolving rapidly. In particular, Figure 2.1: The BioEmergences reconstruction workflow. From top to bottom: successive steps starting from embryo preparation and leading to the reconstructed data, all readily available for display and analysis by the interactive visualization tool Mov-IT. Each processing step is described in greater detail in the Section 2.1.4. We propose two alternative nuclear center detection methods. Either output can be used for shape segmentation and/or cell tracking. the combination of two-photon excitation fluorescence (2PEF) for deep-tissue imaging over extended periods of time [START_REF] Squirrell | Long-term two-photon fluorescence imaging of mammalian embryos without compromising viability[END_REF][START_REF] Helmchen | Deep tissue two-photon microscopy[END_REF] with parallelized microscopy based on SPIM/DSLM is a promising approach [START_REF] Planchon | Rapid three-dimensional isotropic imaging of living cells using Bessel beam plane illumination[END_REF], although it is not yet widely available for routine time-lapse imaging in developmental biology.

We explored the coupling of two femtosecond lasers with two upright laser-scanning microscopes (Appendix Figure S3). Filtering and nucleus center detection algorithms based on multiscale image analysis [START_REF] Alvarez | Axioms and fundamental equations of image processing[END_REF] and partial differential equations (PDEs) are particularly useful for datasets that contain a high density of nuclei, as observed in the zebrafish by the end of gastrulation and beyond. Starting from raw images (Figure 2.4a), an initial image filtering step removes noise through geodesic mean curvature flow (GMCF, [START_REF] Caselles | Geodesic Active Contours[END_REF] relying on a nonlinear geometrical PDE that can simultaneously smooth and sharpen the image [START_REF] Krivá | 3D early embryogenesis image filtering by nonlinear partial differential equations[END_REF]. Next, the position of each nucleus is found by a flux-based level set center detection (FBLS) method (Figure 2.4b, Frolkovič et al., 2007). It identifies objects with humps in the image intensity function, and places nuclear centers at local maxima. This is done through a nonlinear advection-diffusion equation that moves each level set of image intensity by a constant normal velocity and curvature.

Alternatively, we also provide a module performing well with a low density of nuclei and highly contrasted images. Based on a difference of Gaussians (DoG) convolution filter, it is capable of simultaneously smoothing the image and keeping the most salient features.

In the web service implementation of the workflow, the parameters of the Gaussians are interactively selected upon visual inspection of the resulting centers.

In an optional step, both nucleus and membrane geometries are automatically found by shape segmentation (Figure 2.4c-e) using the subjective surface (SubSurf) technique initialized with the previously detected cell centers [START_REF] Sarti | Subjective surfaces: a method for completing missing boundaries[END_REF][START_REF] Zanella | Cells segmentation from 3-D confocal images of early zebrafish embryogenesis[END_REF][START_REF] Mikula | Segmentation of 3D cell membrane images by PDE methods and its applications[END_REF]. The numerical discretization of GMCF, FBLS and SubSurf is based on the co-volume method [START_REF] Corsaro | Bicoid Determines Sharp and Precise Target Gene Expression in the Drosophila Embryo[END_REF] and its parallel implementation [START_REF] Suri | Deformable models: theory and biomaterial applications[END_REF]. The SubSurf method was also used to segment the global volume of imaged embryonic tissue, useful to estimate the average cell density and its evolution over time (Figure 2.4f-h). Finally, our original cell-tracking algorithm inputs the list of approximate centers of cell nuclei and outputs their lineage in space and time following a three-step strategy. The first step initializes the lineage links by a nearest-neighbor heuristic method. The second step uses simulated annealing [START_REF] Kirkpatrick | Optimization by Simulated Annealing[END_REF], a variant of the Metropolis algorithm, to progressively enforce a set of constraints reflecting a priori biological knowledge. This is achieved by repeated random trials of link modification, and validation of possible changes according to a cost function, based on a weighted sum of contributing terms, and a temperature parameter. The last step uses of tracking errors (0.21%). False trajectories, in which tracking jumped from one cell to another between two time-steps were observed in a very small number of cases, too (1.03%). The performance on small organisms, sea urchin and ascidian, was close to the gold standards for the large majority of the images. Cell detection and tracking was occasionally poorer, depending on the image quality, essentially the signal-to-noise ratio, which degraded with imaging depth.

The relevance and usefulness of the BioEmergences reconstruction workflow are demonstrated by comparing its outputs with those obtained on the same dataset Dr1 using stateof-the-art commercial and open-source software. Eight image processing tools from four different providers were deemed suitable to handle gigabytes of time-lapse data. Their performance was tested on the detection of nuclear centers, links and cell divisions in several time intervals (Figure 2.5, Section 2.1.4and Appendix Table S1). Based on measurements of true positive rates, called sensitivity, and false positive rates, our methods produced the best results in every category. In particular, BioEmergences had the lowest rate of false linkage at 1% (Figure 2.5h, column e). It also obtained an average mitosis sensitivity of 67% against 13% for Amat et al. and 0% for the rest (Figure 2.5h, column c), meaning that the other tools were actually not designed to join lineage branches through the detection of divisions. Our software and the software of Amat et al. were the only ones allowing the linkage of one mother cell at time-step t to two daughter cells at time-step t + 1. In essence, mitosis detection is a special task that only two packages among the nine that we tested were able to solve. Typically, it is at the time of anaphase and telophase that an algorithm can detect two separate nuclei, and as soon as it does it must also link them back to their mother. If this is not accomplished, the lineage tree will lack a junction. Even when a mitotic event is missed, most methods are still able to correctly track the resulting two cells, but these cells will not be properly annotated as daughters (Appendix Movie S10).

Overall, our workflow reached an average lineage score of 96% (the product of center sensitivity and linkage sensitivity), while Amat et al. had 83% and all other tools remained below 50% (Figure 2.5h, column g). Behind these average values, there was The BioEmergences workflow presented here (red) was evaluated alongside Imaris (four shades of blue), Icy (yellow), Volocity (two shades of green) and Amat et al., 2014 (pink). Measurements were made by comparing the outcome of three reconstruction methods: nucleus detection, cell tracking (linkage) and mitosis detection, to a set of validated events (positions, trajectories, divisions) registered in a gold standard. This was done inside four time intervals centered on 4.36 hpf, 6.22 hpf, 8.08 hpf and 9.95 hpf. (a) Sensitivity of nuclear center detection, representing the rate of true positive (TP) centers. (b) Sensitivity of linkage, representing the rate of TP links (restricted to the subset of TP centers that possessed a validated link). (c) Sensitivity of mitosis detection, representing the rate of TP divisions (restricted to the subset of TP centers). (d-f ) Rates of false positive (FP) centers, links and divisions (only two software tools applicable to the latter). (g) Global lineage score, equal to the linkage sensitivity times the center detection sensitivity: all methods except BioEmergences deteriorate noticeably at later developmental stages. (h) Average rates calculated over the four time intervals: each column displays the mean heights of one of the previous seven charts. BioEmergences obtained the best results in every category: highest values in a-c and g (success rates), lowest values in d-f (failure rates). Formulas can be seen in Section 2.1.4, detailed values in Appendix Table S1. also a noticeable degradation of performance at later developmental stages in all systems except BioEmergences (Figure 2.5g).

Analysis of cell fate and proliferation in digital specimens. The cell lineage tree is the cornerstone of a detailed understanding of morphogenetic processes (Appendix Note S2 on in silico fate mapping). Digital specimens, such as the examples presented here, constitute a unique source of in-depth knowledge into embryonic patterning and the individuation of morphogenetic fields (Figure 2.6). In the zebrafish embryo, cells from the epiblast, hypoblast and epithelial enveloping layer were distinguished according to their position and behavior [START_REF] Kimmel | Stages of embryonic development of the zebrafish[END_REF], and selected in the digital specimen with the Mov-IT tool. The selected cell populations were tracked, either backward or forward, revealing their relative movements with resolution at the cellular level during gastrulation stages (Figure 2.6a,b and Appendix Movie S11). The more stereotyped cell lineage of the tunicate Phallusia mammillata allowed us to transpose the state-of-the-art fate map established at the 110-cell stage [START_REF] Lemaire | Unfolding a chordate developmental program, one cell at a time: Invariant cell lineages, short-range inductions and evolutionary plasticity in ascidians[END_REF]. Cells were marked in the digital specimen according to their presumptive fate (Figure 2.66c,d and Appendix Movie S12). The propagation of the 110-cell stage fate map along the cell lineage revealed the patterning of all the morphogenetic fields with a temporal resolution in the minute range. In the sea urchin embryo at cleavage stages, the cell membrane channel was used to mark cells according to their volume. This led to the identification of the three cell populations: micromeres, macromeres, and mesomeres, organized along the vegetal-animal axis, with further segregation of macromeres into Vg1 and Vg2 subtypes, and micromeres into small and large subtypes (Angerer and Angerer, 2000, Figure 2.6e,f and Appendix Movie S13). The propagation of colors along the cell lineage showed that, despite limited cell dispersion, the cellular organization in the sea urchin embryo varied from one embryo to another.

The Mov-IT interface was also designed for the fast import of processed data files to perform a systematic analysis of cell and tissue properties (Mov-IT tutorial). This is illustrated by the analysis of the evolution of cell proliferation and cell density in our sea urchin, ascidian and zebrafish specimens (Appendix Figure S5). Further statistical analysis and measurements are expected to contribute in a feedback loop to theoretical models and numerical simulations (Appendix Figure S6).

Discussion

The BioEmergences reconstruction pipeline, accessible through a simple graphical user interface, was designed to run as quickly and efficiently as possible from the acquisition of microscopy images to the display of cell lineage and cell segmentation aligned with raw data. While the standalone version was crafted for convenient processing of small datasets on a laptop computer, there is no size limit on the data that can be uploaded to the BioEmergences web service through iRODS, an open-source data management program, and the OpenMOLE engine [START_REF] Reuillon | a workflow engine specifically tailored for the distributed exploration of simulation models[END_REF] for data processing on the EGI.

The concept of augmented phenomenology, coined to describe the overlay of raw and reconstructed data required for validation, correction and analysis, is fully exploited using the custom-made Mov-IT software. This tool serves in particular to demonstrate that the reconstructed cell lineages meet the best standards of precision and accuracy. So far, cell lineage data can only be validated by visual inspection or by comparison with available gold standards, which are established manually and cross-checked by at least two experts. Corrected datasets are considered error-free as long as experts do not dispute this conclusion. Although gold standards themselves depend on what the eye can achieve, it is generally accepted that automated image-processing software is still on average less effective than human vision. Our cross-software comparison method based on a set of validated events (cell positions, temporal links, divisions) is a step in the direction of standardized validation and comparison protocols. All the results led to the conclusion that the BioEmergences workflow achieved the best performance on standard data produced by MLSM imaging with a temporal resolution chosen to explore either a small specimen (sea urchin or tunicate embryo) or up to one third of the zebrafish gastrula.

The software provided by Amat et al. was the next best, but its performance fell faster than BioEmergences with the increase in cell density and tissue thickness.

The cell-tracking pipeline delivered here should be useful for a large variety of model organisms that have adequate optical properties to allow the acquisition of 3D+time datasets, including a channel for stained nuclei. The requirements in terms of spatial and temporal resolution and signal-to-noise ratio are easily fulfilled with commercial microscopy setups, either MLSM or SPIM. However, the accuracy of cell lineage reconstruction depends to a great extent on the quality of the data. Biologists who try the automated processing of their time-lapse images for the first time might have to adjust their staining and imaging procedure to increase the quantity of information collected, especially across tissue depth. The image processing outcome also depends on the selection of algorithm parameters. This might require a few trials in the standalone version, if the default parameters do not already lead to satisfactory results. The web service version, on the other hand, offers the advantage of fast processing on a grid infrastructure, allowing the concurrent execution of hundreds of parameter combinations that can be easily explored by the user through Mov-IT, as done for the DoG center detection method.

The detection of cell nuclei is a critical step, as it is used not only for cell tracking but also for nucleus and membrane shape segmentation, which must be performed on validated nuclei to avoid major errors but is computationally expensive. Although the standalone software performs well on a few selected cells, full-scale shape segmentation can be achieved within a reasonable length of time only on a computing cluster such as the one accessible through the BioEmergences web service. Segmentation output can also be verified with Mov-IT, but its quantitative validation remains an issue [START_REF] Rizzi | Region-Based PDEs for Cells Counting and Segmentation in 3D+Time Images of Vertebrate Early Embryogenesis[END_REF].

The public availability of the BioEmergences platform and its application to the embryogenesis of model organisms is intended to open the path to in silico embryology based on digital specimens. We illustrate this ambition with fate-map studies. The possibility of constructing complete fate maps in digital specimens, as we achieved for the tunicate embryos, is a revolution in the field. Finally, while delivering here a standalone program, we also expect to initiate, through the web service option, a synergistic effort of the scientific community toward further validation, correction and annotation of digital specimens.

Methods

Embryo staining and mounting. Wild-type Danio rerio (zebrafish) embryos were injected at the one-cell stage with 200 pg H2B-mCherry and 200 pg eGFP-HRAS mRNA prepared from PCS2+ constructs [START_REF] Moriyoshi | Labeling neural cells using adenoviral gene transfer of membrane-targeted GFP[END_REF][START_REF] Shaner | A guide to choosing fluorescent proteins[END_REF]. Although mCherry was not as bright as eGFP and bleached more through imaging, this color combination was the best compromise for proper staining of cell membranes and further segmentation. Injected embryos were raised at 28.5°C for the next 3 hours. Embryos were mounted in a 3 cm Petri dish with a glass coverslip bottom, sealing a hole of 0.5 mm at the dish center, where a Teflon tore (ALPHAnov) with a hole of 780 µm received the dechorionated embryo. The embryo was maintained and properly oriented by infiltrating around it 0.5% low-melting-point agarose (Sigma) in embryo medium (Westerfield, 2000). Temperature control in the room resulted in about 26°C under the objective, slightly slowing down development with respect to the standard 28.5°C developmental table (Kimmel et al., 1995, Appendix Table S2). After the imaging procedure, the embryo morphology was checked under the dissecting binocular and the animal was raised for at least 24 hours to assess morphological defects. Embryo survival depended on total imaging duration, average laser power, and image acquisition frequency (time-step ∆t). The zebrafish datasets Dr1 and Dr2 were imaged through a standardized procedure with ∆t<150 s allowing up to 120 sections per time point with an average laser power of 80 mW delivered to the sample for more than 10 hours without detectable photodamage.

Lowering the laser power to less than 60 mW and increasing ∆t up to 3.5 min allowed us to image the embryos for more than 20 hours and then raise them to adulthood. These conditions were used to obtain up to 320 sections at a 400 Hz line scan rate (bidirectional scanning), or 200 Hz to improve the signal-to-noise ratio.

Oocytes from Phallusia mammillata (ascidian) were dechorionated and injected as described [START_REF] Sardet | Embryological Methods in Ascidians: The Villefranche-sur-Mer Protocols[END_REF] with 1 mg/ml H2B-eGFP mRNA prior to fertilization.

Membrane staining was achieved through continuous bathing in artificial sea-water containing FM 4-64 (Life Technologies) at a concentration of 1.6 µg/ml. Embryos were deposited in a hole made in 1% agarose in filtered sea water at the center of a 3 cm Petri dish.

Image acquisition.

Imaging was performed with Leica DM5000 and DM6000 upright microscopes SP5 MLSM, equipped with an Olympus 20/0.95NA W dipping lens objective or a Leica 20/1NA W dipping lens objective. Axial resolution at the sample surface (1.5 µm) was estimated by recording 3D images of 0.1 or 1 µm fluorescent polystyrene beads (Invitrogen) embedded in 1% agarose. For the zebrafish specimen Dr1, the field size was 700 × 700 m in x, y and 142 µm in z, with a voxel size of 1.37 × 1.37 × 1.37 µm and a time step of 67 sec. For the zebrafish Dr2, the field size was 775 × 775 µm in x, y and 164 µm in z, a cubic voxel of edge 1.51 µm and a time step 153 sec. For the ascidian Pm1, these dimensions were 384 × 384 µm in x, y, 165 µm in z, voxel 0.75 × 0.75 × 1.5 µm and time step 180 sec. For the ascidian Pm2: 353 × 353 µm in x, y, 188 µm in z, voxel 0.69 × 0.69 × 1.39 µm, and time step 180 sec. For the sea urchin Pl1: 280 × 280 µm in x, y, 86 µm in z, voxel 0.55 × 0.55 × 1.09 µm and time step 207 sec. For the sea urchin Pl2: 266 × 266 µm in x, y, 82 µm in z, voxel 0.52 × 0.52 × 1.05 µm and time step 180 sec. For two-color acquisition, simultaneous 2-photon excitation [START_REF] So | Two-Photon Excitation Fluorescence Micrscopy[END_REF] at two different wavelengths (1030 nm and 980 nm) was performed with pulsed laser beams (T-pulse 20, Amplitude Systèmes and Ti-Sapphire femtosecond oscillator Mai Tai HP, Newport Spectra physics, respectively). Details of the optical bench are provided in Appendix Figure S3. Raw-data movies were made with the Amira software (Mercury Computer Systems).

Image processing algorithms. Explanation of the parameters and their useful range, along with the specific values used to process datasets Dr1-2, Pm1-2, and Pl1-2, are all provided in Appendix Table S3. Filtering by geodesic mean curvature flow (GMCF) was based on the following partial differential equation (PDE, [START_REF] Krivá | 3D early embryogenesis image filtering by nonlinear partial differential equations[END_REF][START_REF] Bourgine | 4D embryogenesis image analysis using pde methods of image processing[END_REF][START_REF] Chen | Image denoising and segmentation via nonlinear diffusion[END_REF]:

∂ t u = |∇u| ∇• ( g(|∇G σ * u|) ∇u |∇u|
) .

(2.1)

It was accompanied by the initial condition u(0, x) = u 0 N (x), or u(0, x) = u 0 M (x), where u 0 N (x) and u 0 M (x) are the image intensities of the nuclei and the membranes respectively, depending on which channel was filtered. In the GMCF model, the mean curvature motion of the level sets of image intensity is influenced by the edge indicator function g(s) = 1/(1 + Ks 2 ), K ≥ 0, applied to the image intensity gradient pre-smoothed by convolution with a Gaussian kernel G σ of small variance σ. Details of the numerical method for solving Equation (2.1) and its (parallel) computer implementation are given in [START_REF] Krivá | 3D early embryogenesis image filtering by nonlinear partial differential equations[END_REF][START_REF] Bourgine | 4D embryogenesis image analysis using pde methods of image processing[END_REF] Center detection by flux-based level set (FBLS) defined the nuclei as the local maxima of a smoothed version of the original image. Our algorithm was based on the following PDE [START_REF] Frolkovič | Counting number of cells and cell segmentation using advection-diffusion equations[END_REF]:

∂ t u + δ|∇u| = µ|∇u| ∇• ( ∇u |∇u| ) .
(2.2)

where the initial condition was given by the intensity function of the filtered nucleus image

u f N . Equation (2.
2) represents the level-set formulation for the motion of isosurfaces of solution u by a normal velocity V = δ + µk, where δ and µ are constants and k is the mean curvature. Due to the shrinking and smoothing of all level sets, the function u f N was simplified, and we observed a decrease in the number of spatial positions of local maxima, which could be used as approximate nuclear center positions. Details of the numerical solution to Equation (2.2) can be found in [START_REF] Bourgine | 4D embryogenesis image analysis using pde methods of image processing[END_REF][START_REF] Frolkovič | Counting number of cells and cell segmentation using advection-diffusion equations[END_REF] Alternatively, smoothing and center detection could be achieved by difference of Gaussians (DoG), a convolution of the image with two Gaussians of different standard deviations, here 1.5-2.5 µm and 12-16 µm respectively. Their difference was calculated and the gray values above a threshold between 1 and 10% were selected. This allowed simultaneously to smooth the image and to keep the most significant objects. We ran multiple simulations combining different possible values of standard deviations and thresholds.

Optimal values were visually chosen with Mov-IT by interactively checking the detection results.

Nucleus and membrane segmentation extracted the shapes of cell nuclei and/or membranes by evolving an initial segmentation function based on the subjective surface (Sub-Surf) equation [START_REF] Sarti | Subjective surfaces: a method for completing missing boundaries[END_REF]:

∂ t u -w c ∇g • ∇u = w d g √ ϵ 2 + |∇u| 2 ∇• ⎛ ⎝ ∇u √ ϵ 2 + |∇u| 2 ⎞ ⎠ , (2.3) where g = g(|∇G σ * u f N |) for nucleus shapes, g = g(|∇G σ * u f M |)
for membrane shapes, and ϵ, w c , w d are parameters. A detailed description of the role of these parameters, with explicit and semi-implicit numerical schemes, along with a discussion of the computational results is given in [START_REF] Mikula | Segmentation of 3D cell membrane images by PDE methods and its applications[END_REF][START_REF] Zanella | Cells segmentation from 3-D confocal images of early zebrafish embryogenesis[END_REF]. The same Equation ( 2.3) is also used for the overall embryo shape segmentation [START_REF] Mikula | Segmentation of 3D cell membrane images by PDE methods and its applications[END_REF].

Our cell-tracking algorithm uses simulated annealing (SimAnn). It takes as input the list of approximate centers of cell nuclei detected at each time-step, and produces as output a lineage forest, a graph equal to the union of several disjoint trees, in which each cell present at the first time-step is the root of a lineage tree. A graph is composed of a set of edges, or links, each connecting a nucleus center at time t to a nucleus center at time t + 1. The algorithm was implemented by an automated three-stage process: First, edges between centers at consecutive time-steps were initialized using a nearestneighbor heuristic method. This created a set of links that were not necessarily biologically plausible.

Second, simulated annealing [START_REF] Kirkpatrick | Optimization by Simulated Annealing[END_REF], a variant of the Metropolis algorithm [START_REF] Metropolis | Equation of State Calculations by Fast Computing Machines[END_REF], was used to progressively enforce a set of predefined constraints summarizing together a certain number of biological requirements on the lineage forest, most notably: each cell beyond the first time-step should come from a single cell at a previous time-step; each cell should have a single mother (a corresponding center at t -1); no cell should have more than two daughters (corresponding centers at t + 1); no cell should disappear (there is no cell death at these developmental stages in the chosen species -an assumption that is invalid in other cases, such as mammalian preimplantation embryos); divisions should not occur too frequently; cell displacements should be bounded, and so on. At the start, a finite set of allowed link modifications and a cost function F measuring the departure from the constraints were defined. Then the algorithm operated on random link visits, and acceptance or rejection of a potential link change based on its cost. More precisely, F was the weighted sum of local contributions, each addressing one of the a priori biological requirements. The cost function weights were selected after some initial trials of the algorithm and visual inspection of the resulting lineages, then recorded in a configuration file and kept constant during computation.

This second stage consisted of repeatedly selecting and tentatively modifying a link at random from the list of permitted moves, then evaluating the resulting change through the cost function. In the case of cost decrease, the candidate change was systematically accepted, while in the case of cost increase, it was accepted with a probability proportional to exp(-∆F/T ), where T is a temperature parameter that was progressively lowered (annealed) over time so that fewer and fewer breaches to the constraints were accepted.

The temperature decrease schedule was linear with time. The cost function also included terms to penalize deformations, favor symmetry in the behaviour of sister cells, add noisecountering inertia, bound speed, and account for division times.

Third, the final goal was to minimize false-positive and false-negative errors in the nuclear detection steps. This was achieved by looking at the whole biological coherence of the lineage tree and identifying lineage gaps or cells that lived only a few time-steps. The algorithm could then delete the centers of short branches or introduce virtual centers to regain continuity. To find a best solution, it used simulated annealing again. A parallel implementation was written, which partitioned space-time into different cylinders that were run on different processors, then merged the results.

Validation protocol. Obtaining a complete gold standard annotated reference, even for small animals such as the ascidian and sea urchin embryos, was possible only with considerable human effort. The Phallusia and Paracentrotus datasets, Pm1-2 and Pl1-2, were almost completely checked and curated. Cell-tracking accuracy depended on the imaging depth, along which the signal-to-noise ratio degraded. Due to the decrease in image quality with depth, there remained a few errors for which there was no solution, even through manual expertise. We curated around 25,000 temporal links in Pl1, 22,000 in Pl2, 30,000 in Pm1 and 40,000 in Pm2. All divisions were also fully annotated. The four digital embryos produced were validated by two independent experts.

In the case of the zebrafish, however, a complete gold standard was almost impossible due to the number of cell samples: of the order of several million. Our gold standard for the Danio dataset Dr1 was obtained by a large-scale curation strategy: i) All detected nuclear centers were completely annotated inside 54 spatial boxes of average size 340 µm × 70 µm × 140 µm in x, y, z, over 10 time-steps starting at various sample times 20, 100, 200, 300, 350}. By this method, 25,123 links were manually checked, comprising 3,262 links for t 0 = 1 (from t = 1 to t = 10), 2,735 for t 0 = 20 (from t = 20 to t = 29), 3,005 for t 0 = 100, 8,736 for t 0 = 200, 5,474 for t 0 = 300, and 1,911 for t 0 = 350.This strategy ensured even sampling throughout the image data in both time and space (Figure 2.3). ii) As part of the investigation of the zebrafish fate map, several cell clones were manually validated and when required corrected along with the whole dataset, corresponding to 50,503 additional curated temporal links and the corresponding curated nuclei. iii) An entire layer of epithelial cells was completely curated at one specific time, t = 4. iv) In addition, a large number of links were randomly checked, bringing the total to 80,428. v) Finally, more than 600 mitoses were checked.

t 0 ∈ {1,

Accuracy estimation protocol.

Taking the consistent and representative gold standard of dataset Dr1 as a basis, we were able to automatically identify and count various error types in the reconstructed embryos (Appendix Table S1). This produced numbers of true positives (TP), false positives (FP) and false negatives (FN), which were used as a performance metric for BioEmergences and other software tools. The accuracy of a given processing workflow was evaluated on three outputs: nuclear center detection, linkage (tracking cells one time-step in the past), and mitosis detection.

By definition, the sum of TP and FN centers was equal to the total number of centers in the gold standard. This relationship did not hold, however, for TP and FN links or divisions, because counting those events was restricted to the subset of TP centers. For links, FN was equal to the sum of wrong links (WL) and missing links (ML). This is because WL, which connect a cell to a wrong target, contributed both to FP (by creating new links that do not exist) and to FN (by missing the correct links), while ML, which correspond to a cell without any link, contributed only to FN.

Then, for each output, three types of success and error percentages were measured: a sensitivity, equal to TP/(TP+FN), a false detection rate, equal to FP/(TP+FP), and a false negative rate, equal to FN/(TP+FN). Finally, a global lineage score was calculated as the product of center detection sensitivity and linkage sensitivity. The rationale for this formula is that linkage alone could appear successful even if many centers were missing, therefore it should be weighted by the actual proportion of detected centers.

Software performance and comparison. We identified eight state-of-the-art tools most relevant for our benchmark comparison: Icy (Spot Tracking, http://icy.bioima geanalysis.org), Imaris (Autoregressive Motion Expert, Autoregressive Motion, Brownian Motion, and Connected Component, http://www.bitplane.com/imaris/imaris), Volocity (Shortest Path and Trajectory Variation, http://cellularimaging.perkinel mer.com/downloads), and the last method published by [START_REF] Amat | Fast and robust optical flow for time-lapse microscopy using super-voxels[END_REF][START_REF] Amat | Fast, accurate reconstruction of cell lineages from large-scale fluorescence microscopy data[END_REF] For each software tool, if a command line mode was available, we ran several reconstructions using different sets of parameters and selected the most advantageous one. For example, in the case of Amat et al., we found that an optimal configuration was back-groundThreshold = 16 and persistanceSegmentationTau = 0. If the software provided only a visualization interface, we produced the best reconstruction by visual inspection based on a few parameter variations.

We used dataset Dr1 in input to all methods, preprocessed through our filtering algorithm (GMCF, 5 iterations). The nucleus detection and tracking outputs were compared with our gold standard data. To assess nucleus detection, we explored the neighborhood of validated centers by looking for other detected centers at a distance of 0.2 to 0.6 times the average internuclear distance. The number of correct links was estimated by inspecting the subset of detected nuclei labeled as true positives that also possessed a link in the gold standard, and counting the correct links from t to t -1. Mitosis detection was also assessed within the set of true positive nuclei. All measurements were made over four separate windows of 21 time-steps each: from t = 0 to t = 20 (corresponding to 4.36 ± 0.18 hpf), from t = 100 to t = 120 (6.22 hpf), from t = 200 to t = 220 (8.08 hpf), and from t = 300 to t = 320 (9.95 hpf). The final sensitivity, false detection and false negative rates were averaged over these four intervals. Detailed scores are shown in Appendix Table S1.

Datasets and software. The standalone BioEmergences Workflow, the visualization tool Mov-IT and the six in vivo 3D+time image datasets are available online at http: //bioemergences.iscpif.fr/bioemergences/openworkflow-datasets.php. For each specimen, we provide the 4D raw-data images and the corresponding reconstructed embryo. The parameters used to process these datasets are provided in Appendix Table S3.

Our BioEmergences platform is also available as a web service at http://bioemergen ces.iscpif.fr/workflow/, which offers users customized assistance in addition to fast processing. The online workflow architecture relies on iRODS, open-source data management software, and the OpenMOLE engine [START_REF] Reuillon | a workflow engine specifically tailored for the distributed exploration of simulation models[END_REF], a middleware platform facilitating the experimental exploration of complex systems models on a computing cluster, which leverages the power of the EGI. Interested users are invited to request access to these resources by sending an email to nadine.peyrieras@inaf.cnrs-gif.fr. of embryo staining; Y.K. co-supervised the design and implementation of the tracking method, and cowrote the manuscript; P.S. custom-designed the MLSM coupling and designed the imaging strategies; R.D. co-coordinated the project, co-designed cell tracking methods, and cowrote the manuscript; A.S. co-coordinated the project, and led several algorithm designs; K.M. co-coordinated the project, led several algorithm designs, and cowrote the manuscript; N.P. co-founded and co-coordinated the project, led the biological relevance, and co-wrote the manuscript; P.B. co-founded and co-coordinated the project, designed the formal and epistemological framework, and co-wrote the manuscript.

Acknowledgements

Imaging of mammals Publication information

Section 2.2 was written for publication in Cold Spring Harbor Protocols, but not yet submitted. It will be submitted under the title: In toto 3D imaging of pre-implantation mammalian embryogenesis: towards digital reconstruction (Fabrèges, Daniel, Duranthon, and Peyriéras).

Introduction

Embryonic development depends on tight spatial and temporal regulation at different scales. Deciphering the mechanisms underlying genetic, molecular and cellular interactions requires assessment of the topology, the temporality and the probability of events from quantitative data [START_REF] Megason | Digitizing life at the level of the cell: high-performance laser-scanning microscopy and image analysis for in toto imaging of development[END_REF][START_REF] Oates | Quantitative approaches in developmental biology[END_REF]. Assessing the cell behavior underlying morphogenesis is best achieved by direct observation through longterm time-lapse imaging [START_REF] Supatto | Advances in multiphoton microscopy for imaging embryos[END_REF]. Such a goal is especially challenging for mammalian species. As yet, long-term imaging of pre-implantation embryos to acquire data suitable for full cell lineage reconstruction has not been achieved. The acquisition of suitable biological data is a major bottleneck to taking advantage of state-of-the-art strategies to detect cell positions, segment cell nuclei and membranes, identify cell divisions in space and time and follow cell displacements as described for model organisms including C. elegans [START_REF] Bao | Automated cell lineage tracing in Caenorhabditis elegans[END_REF], D. melanogaster [START_REF] Supatto | Quantitative imaging of collective cell migration during Drosophila gastrulation: multiphoton microscopy and computational analysis[END_REF] and D. rerio [START_REF] Olivier | Cell lineage reconstruction of early zebrafish embryos using label-free nonlinear microscopy[END_REF]. Long-term in toto imaging of developing embryos requires a difficult imaging compromise in order to achieve the best possible signal-to-noise ratio and spatial and temporal resolutions without photobleaching or photodamage. Mammalian embryos have been shown to be especially sensitive to visible light [START_REF] Takeichi | Cadherin cell adhesion receptors as a morphogenetic regulator[END_REF][START_REF] Devgan | Impact of embryonic expression of enhanced green fluorescent protein on early mouse development[END_REF]. Nipkow disk confocal microscopy has been shown to be suitable for long-term time-lapse imaging of mouse embryos although with poor performances in depth [START_REF] Yamagata | Long-term, six-dimensional live-cell imaging for the mouse preimplantation embryo that does not affect full-term development[END_REF]. This limitation is overcome by multiphoton laser scanning microscopy imaging, although at the expense of spatial resolution [START_REF] Squirrell | Long-term two-photon fluorescence imaging of mammalian embryos without compromising viability[END_REF].

More recently, selective plane imaging microscopy was proposed as a valuable technique for in toto imaging of developing organisms [START_REF] Supatto | Advances in multiphoton microscopy for imaging embryos[END_REF]. However, mounting and maintaining physical-chemical conditions for mammalian embryos is not fully satisfactory. We propose a protocol for long-term 3D imaging of mammalian embryos fluorescently labeled by mRNA injection at the one-cell stage or transgenesis, from as early as the maternal-to-zygotic transition to hatching. We demonstrate that two-photon laser scanning microscopy imaging with an upright microscope, high numerical aperture dipping lens objectives and the use of a small chamber to control temperature and CO 2 is suitable for obtaining large 3D+time data sets of high quality. The quality is estimated by the tracking accuracy obtained with our automated cell lineage reconstruction strategies (Fabrèges et al., in press).

Materials

Mouse embryo harvesting and culture • M2 Medium containing BSA without phenol red (hereafter referred to as buffered medium).

• 0.5 mg.mL -1 hyaluronidase in M2 medium without phenol red (Appendix Note S3).

• EmbryoMax KSOM without phenol red (MR-020P-5F, Merck-Millipore, Germany) + 0.5% penicillin/streptomycin (#15140, Life technologies, USA; hereafter referred to as CO 2 medium).

• Mineral oil (M8410, Sigma-Aldrich, USA).

Rabbit embryo harvesting and culture

• D-PBS (#14190, Life Technologies, USA).

• M199 medium HEPES (Biochrom F0665, VWR, France) + 10% Fetal Bovine Serum, qualified, heat inactivated (#10500064, Life technologies, USA) + 0.5% penicillin/streptomycin (#15140, Life technologies, USA; hereafter referred to as buffered medium).

• M199 medium with phenol red (M4530, Sigma-Aldrich, USA) + 10% Fetal Bovine Serum, qualified, heat inactivated (#10500064, Life technologies, USA) + 0.5% penicillin/streptomycin (#15140, Life technologies, USA; hereafter referred to as CO 2 medium phenol red).

• M199 medium without phenol red (#11043023, Life technologies, USA) + 10% Fetal Bovine Serum, qualified, heat inactivated (#10500064, Life technologies, USA) + 0.5% penicillin/streptomycin (#15140, Life technologies, USA; hereafter referred to as CO 2 medium).

• Mineral oil (M8410, Sigma-Aldrich, USA).

mRNA preparation

• Skip mRNA preparation if an early-expressing fluorescent transgenic line is used.

• Linear pCS2+ vector with H2B-mcherry sequence transcribed through SP6 promoter.

• Linear pCS2+ vector with eGFP-Ras [START_REF] Moriyoshi | Labeling neural cells using adenoviral gene transfer of membrane-targeted GFP[END_REF] sequence transcribed through SP6 promoter.

• Ambion mMESSAGE mMACHINE SP6 kit (AM1340, Life technologies, USA). The kit includes all the buffers for in vitro transcription.

• NucleoSpin RNA Clean-up XS (#740903, Macherey-Nagel, Germany). The kit includes all the buffers.

Embryo mounting

• Non-toxic glue for high temperatures (rigid PVC-U glue, Tangit, Belgium).

• Custom-made mold (Figure 2.7).

• Plastic petri dish 30 mm diameter (#153066, Nunc, Denmark).

• Agarose 1% (EP-0010-05, Eurogentec, Belgium) in CO 2 medium. FBS or BSA in agarose solution is not recommended.

Two-photon imaging

• Two-photon SP5 laser scanning upright microscopes DM6000 and DM5000 (Leica, Germany) (see Figure 2.8).

• 20×/0.95 NA water immersion objective (XLUMPlanFl, Olympus, Japan) and 20×/1.00 NA water immersion objective (HCX APO #11507701, Leica, Germany).

• Infrared femtosecond t-pulse 20 ytterbium mode-locked laser (Amplitude Systemes, France); linear polarization; maximum power: 1.2W; wavelength λ 1 = 1030 nm; repetition rate: 50MHz; pulse duration<200 fs.

• Infrared femtosecond Mai Tai One Box Tunable Ultrafast Ti:Sapphire mode-locked laser (Spectra Physics/Newport, USA) used at fixed wavelength λ 2 = 980 nm; linear polarization; maximum power: 0.9W at 980 nm; repetition rate: 80 MHz; pulse width: 100 fs.

• Chosen wavelengths for the two-photon excitation of EGFP (980 nm) and mCherry (1030 nm) are compromises between excitation efficiency and phototoxicity.

• Polarized cubes (CVI/Melles Griot, USA) coupled to achromatic half-wave plate (CVI/Melles Griot, USA) placed on a motorized rotation stage to electronically control laser power. Communication with the motorized rotation stage is performed with custom-made software and a programmable microchip (Arduino Diecimila, Arduino, Italy).

• 45/45 beam splitter (broadband non-polarizing cube, Newport, USA) to transform two separate beams with distinct wavelengths into two dual-wavelength beams with equal optical power.

• A 525/50 nm band-pass filter (Semrock, USA) for eGFP fluorescence detection.

• A 610/75 nm band-pass filter (Semrock, USA) for mCherry fluorescence detection.

• A 680 nm short-pass filter (Semrock, USA) to remove infrared reflection.

• Photomultiplier tubes or hybrid detectors (Hamamatsu Photonics K.K., Japan, for Leica, Germany) for photon detection.

• Leica LAS-AF software for image acquisition.

• Water jacket chamber (H101-WJC-UP-LG, OKO-lab, Italy) for upright stands compatible with Leica SP5 galvo stage.

• Thermal regulation (H101-CRYO, OKO-lab, Italy) including a temperature controller and a heating/cooling water bath.

• 100% CO 2 for cell culture (M5120L47R0A001, Air Liquide Santé, France).

• Gas mixer (DGT-CO2BX-PLUS, OKO-lab, Italy) to produce 5% CO 2 from 100% CO 2 and air pump (OKO-AP, OKO-lab, Italy).

• Humidity module (H101-HM, OKO-lab, Italy) to inject CO 2 with high humidity.

We advise not to use the humidity module for long-term imaging (longer than 6 hours) as humidity produces water droplets on the objective which then fall into the medium and induce hypo-osmotic shock.

• Temperature control software (H101-CRYO, OKO-lab, Italy) and CO 2 control software (DGT-CO2/O2BX-PLUS-S, OKO-lab, Italy).

Procedure

Imaging chamber

• Put a small drop of PVC-U glue on the underside of a mounting mold.

• Place the mold with glue on a plastic surface and slide it to remove excess glue.

• Place the mold in the center of a 30 mm Petri dish.

• Wait for the glue to solidify (at least two hours).

• Place the imaging chamber in water for one hour to wash potential solvent. Replace the water with clean water overnight.

• Wash with 70% ethanol.

• Wash with clean water. Discard the water and let the imaging chamber dry.

• Imaging chambers can be re-used. Remove any residue of mineral oil from previous experiment with absolute ethanol. Clean with 70% ethanol and water before each use.

Medium preparation

• Place drops of around 20 µL of CO 2 medium in a plastic Petri dish. One drop will contain up to 20 embryos. For 40 embryos, prepare at least two drops or increase the drop volume to at least 40 µL.

• Cover drops with mineral oil. Keep at the optimal temperature (37°C for mice and 38.5°C for rabbits) + 5% CO 2 with a lid for at least 2 hours prior to use. This plastic Petri dish is referred to below as the microdrop dish.

• Buffered medium must be kept at the optimal temperature.

• CO 2 medium must be kept in a bottle at the optimal temperature + 5% CO 2 with bottle cap slightly unscrewed for at least 2 hours prior to use.

• Mineral oil must be kept in a bottle at the optimal temperature + 5% CO 2 with bottle cap slightly unscrewed at least 2 hours prior to use.

• Agarose solution must be made up fresh a few minutes prior to use.

Mounting

• Fill the imaging chamber mold (Figure 2.7A) with warm agarose solution. Immediately suck the agarose solution out to leave a thin coating.

• Let the agarose coating solidify for five minutes at room temperature.

• Use the imaging chamber within the next few hours.

Imaging

• Switch on the lasers at least one hour before imaging.

• Make sure the water jacket chamber is perfectly screwed onto the microscope stage.

• Complete the OKO-lab regulation system as shown in Figure 2.9A.

• Switch on the water bath, the temperature controller, the gas mixer and the air pump. Open the CO 2 cylinder (2 bar) and set the pressure regulator upstream of the gas mixer to 1 bar.

• Fill an imaging mold with 50 µL of medium.

• Place the Petri dish inside the water jacket chamber.

• Gently insert the temperature probe into the probe hole of the mold (Figure 2.7D).

• Touch the surface of the medium drop with the objective so as to form a column of medium between the mold and the objective. Make sure the temperature probe does not hinder objective movements.

• Gently add mineral oil to cover the objective. The medium must no longer be in contact with air.

• Place the chamber lid around the objective with an adapted ring to decrease gas diffusion. Proper installation is shown in Figure 2.9B,C.

• Run OKO-control 2010 (OKO-lab software). Once the water bath, temperature controller and gas mixer have been recognized by the software (green lights in the devices list), set the target temperature to 37°C (mouse embryos) or 38.5°C (rabbit embryos).

• Run temperature regulation.

• Wait for temperature and CO 2 to stabilize (Appendix Note S4).

• Next steps have to be performed very fast (not more than 10 minutes).

• Toggle off OKO-control 2010 regulation (but not data storage) by clicking on the Opening incubator icon.

• Remove the water jacket chamber lid and the objectives.

• Remove the imaging chamber, and clean the objective lens with ethanol 70%. Do not leave mineral oil around the lens as this will disturb imaging.

• Place the microdrop dish with embryos on a heating plate.

• Add 50 µL of warm and CO 2 equilibrated medium in the coated hole of a new imaging chamber.

• Place the embryos in the medium. Because the agarose solution solidifies with a meniscus shape, the embryos will tend to gather in the middle of the hole. To avoid embryos getting out of the field, we suggest placing them slightly off-center.

• Place the imaging chamber containing the embryos inside the water jacket chamber.

Make sure the imaging chamber does not move.

• As described above, gently insert the temperature probe into the probe hole, touch the surface of the medium drop with the clean objective and gently add warm and CO 2 equilibrated mineral oil to cover the objective. The medium must no longer be in contact with air.

• Place the chamber lid around the objective with an adapted ring to decrease gas diffusion.

• Toggle on OKO-control 2010 regulation by clicking on the Close incubator button.

The temperature inside the chamber and the CO 2 concentration is now controlled (Appendix Note S4) and stored.

• Use bright field to locate the embryos. Be careful not to break the medium column by moving the objective away. If this happens, you will no longer be able to image properly as mineral oil will cover the medium drop, and you will have to start again.

• The average power of each laser under the objective should not exceed 15 mW. The sum of laser powers should not exceed 20 mW, especially in the early stages.

• Each nucleus should be visible on at least 5 slices for better automatic cell detection, which means a maximum of 2 µm between two slices for rabbit and mouse embryos.

• Each mitosis event should be visible on at least 3 time steps for better automatic mitosis detection, which means a maximum of 15 minutes between stack acquisitions for rabbit and mouse embryos.

• Voxel size is usually between 0.5 µm×0.5 µm×1.0 µm and 0.9 µm×0.9 µm×1.8 µm (slice size: 512 px×512 px; stack size: between 50 slices and 150 slices).

• Whole stack acquisition takes about 2 minutes with a scanning speed of 700 lines per second and averaging twice. Increasing the time-step duration (between 5 minutes and 15 minutes) will help to prevent photodamage. In the case of mRNA microinjection, a longer time-step duration will limit photobleaching.

• It is highly recommended to image at least 100 µm above the embryo's surface to anticipate volume increase.

• Laser power should be linearly increased through depth.

• Start imaging.

Acknowledgments

This work is supported by a fellowship from the French Ministry of Higher Education and Research (MESR). This work was done in close collaboration with the BioEmergences platform, which provides microscopes, staff, data management and data analysis (Fabrèges et al., in press). We thank the laboratory of Magdalena Zernicka-Goetz (Gordon Institute, Cambridge University, United Kingdom) for providing us with the transgenic mouse line. We especially thank Agnieszka Jedrusik, with whom we significantly improved the imaging conditions. We are grateful to N&D and INAF members for comments and discussions. Mouse embryo harvesting was supervised by Monique Frain (N&D) in accordance with ethical guidelines. We also thank Adeline Boyreau (N&D) for the highly efficient mRNA transcription and Patrick Parra and Jean-Yves Tiercelin (Mechanics Staff of INAF) who were deeply involved in the conception and manufacture of the imaging molds. The optical path and microscopes were considerably improved from the initial set up (Fabrèges et al., in press) mostly thanks to Ludovic Leconte (Curie Institute, France) and Gaëlle Recher (N&D). The latter also provided inestimable help, discussions and comments especially about We also thank BDR members for the very interesting discussions we had and the sharing of their knowledge about mammalian imaging. Temperature probe measures the temperature in the mold (Figure 2.7D). Computer sets water bath temperature according to the measured temperature. Temperature target is reached after 30 minutes (Appendix Note S4). Gas mixer mixes 100% CO 2 with air to produce 5% CO 2 . This gas is warmed up. B: Water jacket chamber with imaging dish and temperature probe properly installed. For better view, water jacket chamber has been removed from the microscope. C: Schematic view of the final mounting. Objective dips in the embryo medium (blue) forming a column between the mold (grey) and the objective. To avoid evaporation, mineral oil (orange) surrounds the embryo medium. Embryos (black dots) lie on an agarose coating (white). The temperature probe (green) is placed 400 µm below embryos.

Chapter 3 Characterization and origins of phenotypic variability in sea urchin morphogenesis

The first time I went to the Sea Urchin Meeting at Woods Hole in 2011, I attended a presentation by a famous scientist of the sea urchin community. The speaker showed a picture of many sea urchin larvae and told us how wonderful the genome was to form these identical larvae, how the size was the same for all of them and how these larvae were beautifully able to reproduce the 4-arm shape without any defects. I was silently objecting as the presented larvae were obviously all different, the smallest was half the size of the biggest, some were symmetrical and others not, some were flecked with a lot of red pigment cells and some were completely transparent. I was even able to see dead larvae. How was it possible that a scientist working for decades on the sea urchin embryo was not able to see how larvae were all unique? Whether these larvae were alike or not is subjective. However, this internal debate illustrates the fact that after more than a century working on sea urchins, the scientific community does not know how much larvae are different.

93

Moreover, the speaker's assumption, that the observed likeness is the result of genome expression alone, is the nonsense of the never-ending nature and nurture debate. We know now that individuals are formed by constant cross-talk between inherited materials and individual history. Although we cannot quantify the influence of these components on the formation of invidiuals, we can quantify the influence of their variability. As proposed in the introduction of this work, we used the separation between transmitted variability and acquired variability to describe the phenotypic variability. We show that the transmitted variability has no measurable effects on the survival and developmental path of embryos.

Finally, we discuss evolution in a bottom-up and top-down context based on our observations of the sea urchin.

Measurement of transmitted and acquired variability

Comparison of individuals

As proposed in the introduction (Figure 1.2), features of the embryonic development are observed in several individuals and compared within and between twin pairs. The sea urchin offers a well-known model in which blastomeres are separated at the 2-cell stage (Driesch, 1891). Twins generated from one fertilization event share the same transmitted materials (e.g., genome, epigenome and transcriptome) and the same history events (e.g., fertilization, experimental conditions and manipulation). The resulting phenotypic variability depends solely on the acquired variability and can be directly quantified. We assume here that twins are identical and start to diverge as soon as they are separated.

This assumption does not take into account a possible asymmetrical repartition of cellular compounds or the DNA polymorphism induced by the first DNA replication, which are assumed to be negligible.

Generation of fluorescent twins

The method for generation of twins and fluorescent staining is explained in detail in Notes S5, S6 and S7. Embryos are microinjected with mRNAs encoding the fusion protein for chromatin staining (H2B-mCherry) and membrane staining (eGFP-Ras), before being rinsed once in Ca ++ -free and Mg ++ -free sea water, twice in Ca ++ -free sea water and then kept at 18°C until the 2-cell stage in Ca ++ -free and Mg ++ -reduced sea water. A gentle mechanical stimulation is then enough to separate the blastomeres, which are then maintained at 18°C in regular sea water. This protocol was adapted from [START_REF] Stephens | Autonomous expression of tissue-specific genes in dissociated sea urchin embryos[END_REF] In this section, twins refers to two blastomeres isolated from one individual, singletons refers to blastomeres isolated from different individuals and wild types (WT) refers to nondissociated individuals.

Multiple developmental paths and resilience

Three phenotypes by the 8-cell stage

The twin development from the 1-cell stage to the 4-cell stage cannot be distinguished from the WT. The third cleavage (from 4 cells to 8 cells) corresponds to the fourth cleavage in WT (from 8 cells to 16 cells) and shows asymmetrical divisions forming mesomeres, • The third phenotype (Figure 3.1C, F ) represents 12.3% (n=9) of my observations.

It is an aggregate of various numbers of cells of various sizes. Identification of cell types is difficult and the number of cells is not always a power of two.

Interestingly, the overall survival rate of singletons (66%, n=73) is noticeably identical for every phenotype (64%, 73% and 67% respectively), indicating that the stereotypical cell organization at the 8-cell stage and the 16-cell stage is not essential for survival.

Despite their unexpected organization, singletons are able to form plutei which have no distinguishable morphological characteristics from WTs except their size.

Transmitted variability versus acquired variability

We investigated the contribution of acquired variability to phenotypic variability. We tested the distribution of the developmental path followed by twins (Table 3.1). We used a χ 2 test of independence with the null hypothesis H 0 "the occurrences of twins' phenotypes are statistically independent". In order to be relevant, a χ 2 test of independence requires that no more than 20% of theoretical values (Table 3.1, red numbers) are less than 5 and no theoretical values are less than 1. To overcome these issues, we performed the test on data grouped by identical phenotype and different phenotype (Table 3.2). Surprisingly, the χ 2 test does not reject the null hypothesis (χ 2 = 3.168, dof= 1, p-value= 0.075), which means that the phenotype of one twin embryo cannot confidently predict the phenotype of its homozygotic sibling. This result shows that we cannot statistically demonstrate any influence of transmitted variability on phenotypic variability along the developmental path.

We performed the same analysis on the survival of larvae. We based our observations on the proper formation of four arms and a tripartite gut (Table 3.3). As for the phenotype distribution, we used a χ 2 test of independence with the null hypothesis H 0 "the occurrences of twins' survivals are statistically independent". We were not able to show any statistical correlation between the inheritance and the survival rate (χ 2 = 0.022, dof= 1, p-value= 0.88).

Finally, we measured the independence between the survival rate and the developmental path with a χ 2 test of independence (Table 3.4). The null hypothesis is that "the occurrences of phenotypes and survival are statistically independent". Although conditions are not fully satisfied (at least 20% of expected frequencies are less than 5), we

were not able to show statistical dependence between the phenotype and the survival rate (χ 2 = 0.186, dof= 2, p-value= 0.91) even when grouping phenotypes 2 and 3 together to satisfy the conditions of the χ 2 test of independence (χ 2 = 0.104, dof= 1, p-value= 0.75).

Altogether, these observations show that the variations in twins' development and the survivals of the twin embryos are not governed by the transmitted variability but by the acquired variability. In addition, the survival rate cannot be correlated with any developmental paths. These results were unexpected and show the large capability of embryos to adapt their development depending on the situation rather than inherited materials. The regulatory mechanism involved is still to be elucidated. However, it is clear how such regulatory mechanisms can be positively selected through the evolution filter, as they give a tremendous survival advantage over deterministic developments in case of unexpected perturbations. Sea urchin eggs are indeed exposed to viruses, bacteria, predators and various physical and chemical conditions. Although we expected regulatory mechanisms to vary from parents to offspring, the absence of correlations suggests that the regulatory mechanisms are highly conserved.

This conclusion raises a major question. If observed variations are not inheritable, how can evolution, as we know it, operate? Natural selection functions by increasing the proportion of good features which will then be transmitted to future generations. As Darwin said: "Any variation which is not inherited is unimportant for us" [START_REF] Darwin | On the origins of species by means of natural selection[END_REF] Chapter 1). In the case of observed sea urchins, the higher capacity to survive is not inheritable despite its obvious evolutionary advantage. As we stated before, embryos are exposed to a variable environment and sea urchins deal with it thanks to their remarkable regulative capacity. In return, embryological mechanisms rely on variable processes which generate diversity regardless of the features -good or not -carried by inheritable materials. Diversity may be the preferred option selected through evolution to maximize the chances that at least a subpopulation of offspring will be fit enough and survive the forthcoming environment.

The above analyses were performed on singletons and twins. Although twins have always been considered normal except for their size, the question of whether they are the same nature as wild type has never been investigated. To extrapolate our conclusions about twins to WT, several models of robustness, resilience and hybrid are proposed (Figure 3.2) and tested.

Changes of PMC lineage

If twin development follows the model of robustness proposed in Figure 3.2B, we would expect the cell number and cell size of larvae to be statistically similar to wild type.

However, twins develop with the same developmental clock as wild type, but with half their number of cells. In order to catch up to the normal number and size of cells, they must divide once more than wild-types and accumulate the resources to grow. PMCs are located in a ring shape around the forming gut in the vegetative side of embryos.

Embryos were mounted in a drop of sea water between two microscope slides. Embryos tend to spin around the Z-axis. In this position, the vegetative pole is downward, the apical pole is upward, and PMCs can be identified (Figure 3.3A-D). Surprisingly, we observed 32 PMCs in every embryo, including singletons. Because this observation is consistent with a supplementary round of divisions, we investigated the size of PMCs and were not able to show a significative difference of PMC diameters (Figure 3.3).

Our observations favor the second model of robustness and resilience (Figure 3 and 6.5 µm respectively. Although we were not able to show any statistical differences between diameters of wild types and singletons, the measurements do not correspond to predictions. The measurements may not reflect reality, especially because the shape of PMCs changes while forming the larval skeleton and may not be relevantly considered spherical. Moreover, singletons start gastrulation with a varying delay of 0 to 6 hours.

Comparing PMCs of singletons with PMCs of wild types may be an anachronism. To overcome these issues, we tried to image wild types and singletons at the cell-scale level during post-hatching processes. The results of these attempts are discussed infra.

We have seen in the introduction of this work that sea urchins show complex regulative mechanisms (see section 1.2.3.3). It has been shown that PMC-less embryos are able to compensate for the lack of PMCs by the so-called transfating of SMCs to PMCs at the cost of a loss of pigment cells in particular [START_REF] Ettensohn | Cell lineage conversion in the sea urchin embryo[END_REF][START_REF] Ettensohn | Mesodermal cell interactions in the sea urchin embryo: properties of skeletogenic secondary mesenchyme cells[END_REF]. Thus, we investigate the hypothesis that PMCs in singletons may partly come from SMCs through transfating mechanisms. We counted the number of pigment cells and observed a significantly decreased number of pigment cells in singleton larvae (µ = 24.18, σ = 12.08, n = 25) compared with wild-type larvae (µ = 41.06, σ = 12.79, n = 24). However, based on the null hypothesis that "progenitors of pigment cells do transfate into PMCs", we expected singletons to have less than half the number of pigment cells of wild-type embryos. Therefore, we compared the observed number of pigment cells in singletons with the expected number of pigment cells. This number was calculated from experimental measurements of the number of pigment cells in WT divided by two (Table 3.5). We performed a χ 2 test of independence (with Yates' correction) and rejected the null hypothesis (Yates' χ 2 = 12.972, dof= 2, Yates' p-value= 0.002). This conclusion states that the number of pigment cells in singletons is not significantly different from half of the number of pigment cells in wild types. [START_REF] Ettensohn | Cell lineage conversion in the sea urchin embryo[END_REF] showed that every type of SMCs may be affected by transfating. We have shown that progenitors of pigment cells are unlikely to transfate to PMCs in singletons. Table 3.5: Frequencies of the number of pigment cells in singletons of sea urchins. Observed frequencies were obtained from experimental measurements. Expected frequencies were obtained from experimental measurements of the number of pigment cells in wild types divided by two. A bin size of 17 was used to satisfy the χ 2 test of independence.

The observations we made tend to favor the robustness model (Figure 3.2B), although we cannot exclude the other models. In order to investigate what happens in greater detail, we aimed to acquire the development of singletons at the cell scale from the earliest stages to the formation of the mouth (24 hpf). We first observed the pre-hatching development in DIC microscopy to describe the overall behavior of cells. We then sought to characterize the development at the scale of individual cells using live fluorescent microscopy and the BioEmergences pipeline.

Cell-scale observation of pre-hatching development

Using DIC microscopy, we observed the mechanical process used by singletons to shape themselves into a normal-looking individual. Although DIC offers a good contrast, there is

no algorithm yet, to my knowledge, capable of recognizing and tracking cells in 3D+time.

The human eyes and brain, however, are able to recognize these shapes and describe cell organizations through time (Appendix Movie S14). Singleton pre-hatching developments are surprisingly regulative in shape. After the 8-cell stage and the 16-cell stage described above, most of the singletons, regardless of their phenotypes, converge to a flat shape with little or no 3D organization (Figure 3.4). DIC microscopy does not allow reliable identification of cells based on their size. However, we can assume that the vicinity network of singletons is preserved during the phenomenon of flattening, since the mechanical changes take less than 30 minutes, which is not enough time for cells to migrate or sort themselves out. Quickly, then, the embryos close themselves and form a spherical and sealed blastula comparable to wild types. The process of closing is erratic and singletons tend to reopen at each round of divisions. We clearly see late cells that were loosely attached to the others. Some of them rejoined the embryos; others detached and did not participate in the formation of larvae. Finally, embryos hatch with a delay of 2 hours ± 2 hours, probably due to the open-phenotype.

The mechanisms behind this flatness are still to be elucidated. Singletons were washed for a brief period of time in artificial sea water without Ca ++ (ASWCF) and with little or no Mg ++ (ASWCMF or ASW3.1). The treatment was intended to loosen the adhesion forces between blastomeres and make blastomere separation easier [START_REF] Le | Recycling of E-Cadherin: A Potential Mechanism for Regulating Cadherin Dynamics[END_REF]. The absence of adhesion proteins at embryo surfaces may partly explain the flatness, although embryos treated with ASWCF, ASWCMF and ASW3.1 without mechanical separation of blastomeres do not show the flat-phenotype. A second and non-exclusive hypothesis is that smaller spherical embryos exhibit higher curvature, which requires stronger attachments that the loose and slowly-recovering cells of singletons cannot achieve. As cells divide, adhesion forces increase and local curvature decreases, allowing individuals to close themselves. Erratic closing of singletons may be due to mitoses, which are known to briefly loosen cell adhesion and make cells more spherical. This hypothesis implies that the spherical shape of embryos is controlled at the cell scale (e.g., cell shape, polarity or adhesion protein locations). Therefore, we assumed that observing the development at the cell scale with membrane staining may be sufficient to quantify embryo-and cellshape changes, with the perspective of observing adhesion protein dynamics. The delay in hatching observed in singletons may also be due to the open-phenotype.

It has been shown that wild-type development is slowed down and stops right after hatching if sea water is replaced by blastocoelic fluid (Berg and Akin, 1971). Embryos resume their development if the outside medium is rinsed with sea water, even after a couple of days. Authors suggested that blastocoelic fluid may contain an inhibitor of gastrulation, later named ExoGastrula Inducing Proteins (EGIPs, [START_REF] Ishihara | The blastocoelic fluid of sea urchin embryo induces exogastrulation[END_REF].

Although EGIPs do not explain all the observations made by Berg and Akin (1971), they

show that there is a mechanism of compound concentrations in blastocoele involved in developmental speed and morphogenesis. In the light of this conclusion, the delay in singleton development may be due to the absence of blastocoele and the impossibility for embryos to concentrate blastocoelic fluid. The open-phenotype may naturally reproduce the Berg and Akin (1971) experiment. A direct observation at the cell scale will be sufficient to confirm or refute the correlation between time of closure and hatching delay.

The convergence of the three phenotypes towards a unique flat phenotype excludes the resilient model proposed in Figure 3.2C, although some of the embryos -of phenotype 1 -kept their spheroidal shape and avoided the flat stage. Previous observations cannot conclude on the outcome (robust or resilient) of singletons. We have shown, however, that the full closure of embryos relies on stochastic processes and greatly depends on individual history. Because the closing phenomenon is erratic and stochastic, it may, by definition, never happen. Thus, the inheritance-independent death rate we observed may be partly due to the inability of some embryos to close themselves. We refined the possible models of robustness and resilience based on these conclusions (Figure 3.5) and investigated them further with fluorescent imaging at the cell scale.

Fluorescent imaging of developing singletons was achieved in three steps:

• micro-injection of mRNA encoding fusion proteins H2B-mCherry (chromatin staining) and EGFP-Ras (membrane staining);

• separation of the two blastomeres with the twinning protocole described before;

• placing twins under a microscope for 10 hours.

After the 9th cleavage (around 512 cells in wild types), WTs hatch and swim (see Section 1.2 for details). Several attempts were made to immobilize sea urchin while imaging, from injection of alpha-tubulin antibodies [START_REF] Piotrowska | Monoclonal antibodies specific for an acetylated form of alpha-tubulin recognize the antigen in cilia and flagella from a variety of organisms[END_REF], bath of parsley extract [START_REF] Semenova | A synthetic derivative of plant allylpolyalkoxybenzenes induces selective loss of motile cilia in sea urchin embryos[END_REF] or a micro-drop of formaldehyde, to embedding in agarose or ultra-fast imaging. Unfortunately, these methods have dramatic effects on larval development or are not compatible with singleton development. Before imaging through the hatching period, I decided to focus on the pre-hatching period and let embryos develop freely under the microscope until they swam and left the field. We acquired 3 twin pairs of Paracentrotus lividus (Appendix Movies S15, S16 and S17) and 5 twin pairs of Sphaerechinus granularis (Appendix Movies S18, S19, S21, S20, S22). At the time of writing, the datasets are still being processed. However, experimental conditions and potential scientific impacts are discussed below.

Discussion

Convential microscopy gave us a quick overview of what happens during singleton development. A lot of questions remain to be solved. In this section, we discuss the relevance of cell-scale imaging and the expected results on cell behavior, robustness and resilience.

Cell fate changes or cell sorting

A robustness mechanism can be defined as a feedback loop that compensates for perturbation. Feedback loops display an intrinsic property named hysteresis that defines the time between the perturbation of a system and its regulation. Hysteresis is a well-known property of the refractory period in some neurons [START_REF] Das | Digital Signaling and Hysteresis Characterize Ras Activation in Lymphoid Cells[END_REF], the unemployment rate in a country or the temperature of a room. If hysteresis is too quick and the perturbation too small, regulatory mechanisms will not be observable, and variations will be compensated for immediately. In our example of room temperature, quick hysteresis would mean turning heaters on as soon as the temperature falls below the reference value, and turning them off as soon as the temperature rises above the reference value.

The overall consequence is that heaters will constantly be turned on and off, and nobody will be able to find out what regulates the room temperature. The same reasoning applies to wild-type embryos. They may exhibit small perturbations at every moment, but if the system hysteresis is too quick we may not be able to see any regulation.

By contrast, if we use the same analogy and suddenly reduce the room temperature by several degrees, operators will be able to measure the regulative effect of heaters despite the quick hysteresis. Singleton embryos exhibit three different phenotypes which are very different from wild types. However, the system is able to regulate these differences and reorganize cells into a viable blastula. Although we saw that this regulation may be quiet fast -less than an hour -, this is enough time to observe reorganization.

Cell positions in singletons are not comparable with cell positions in wild types. At the 16-cell stage, wild types show a ring of eight mesomeres forming the animal pole above four macromeres and four micromeres. This stereotypical organization implies that mesomeres and micromeres cannot be in contact. In addition, the vegetal plate is the place of PMC ingression and gut invagination, while the animal pole exhibits the apical tuft and, slightly to the side of it, the prospective mouth opening. Except for phenotype 1 bypassing the flat stage, singletons do not respect the spatial organization and micromeres do sometimes attach to mesomeres. Moreover, cells are not always assembled and may be scattered across the embryo. As described earlier (see section 1.2.3.4), disaggregation followed by reaggregation of wild types at the 16-cell stage leads to viable and fertile sea urchins. The same process is expected in singletons.

A close study of cell trajectories during pre-hatching development should reveal active/passive cell sorting and possible transfating. In addition, the previous work of Louise

Duloquin provides five digital specimens of Paracentrotus lividus (soon to be submitted for publication). They can be used to compare the spatial organization of singletons with that of wild types, and to reveal whether blastula are robust or resilient, according to whether they exhibit the same spatial organization as wild types or a different one respectively. During my thesis, I successfully obtained the pre-hatching development of several twin pairs. Due to technical reasons and the necessary time to set up experimental conditions, I obtained most of these recently. Consequently, the data are still to be curated and analyzed.

Peri-hatching period

Imaging made on singletons stopped when embryos left the field of view, i.e., when they started to swim. Further analyses of singleton development, in particular the future of PMCs and their lineages, need continuous observation despite blastula swimming. As many others before, we first tried to prevent blastula swimming by means of chemical products or mechanical constraints. However, these methods change biological behavior and are therefore not relevant in our experimental conditions.

Swimming is a major process in the life of sea urchin larvae. Blocking this mechanism is a direct modification of the morphogenesis and may have dramatic consequences on ex-perimental observations. Rather than trying to limit or avoid blastula swimming, I think we should try to be faster than the embryos. The highest rotation speed I recorded was 1.5 revolutions per minute for an embryo of 100 µm. In order to keep a low displacement of cells during acquisition (less than 3 µm), a volume must be acquired in less than four seconds. To reach this speed, each slice must be acquired in no more than 40 ms which, in our experimental conditions, corresponds to 12,800 Hz. Resonant scanning, the fastest method compatible with our 2-photon laser scanning microscope (2P-LSM), is limited to 8,000 Hz, far below the requirements.

Such a speed can be attained with alternative microscopy methods such as digital scanning light-sheet microscopy (DSLM). This uses a scanning laser ray to illuminate a plane, line by line. Meanwhile, a camera records the entire plane in one take with an exposure time of a few milliseconds. Because the laser ray comes from one side of specimens, DSLM and other similar techniques induce artefacts. To circumvent these issues without sacrifying imaging speed, it is possible to use dual illumination and dual detection [START_REF] Krzic | Multiview light-sheet microscope for rapid in toto imaging[END_REF]. Finally, embryos must be confined in a chamber to keep them in the field of view. They must not be able to escape from the chamber, but they must be able to swim freely. Thus, we can observe sea urchin development without any artificial constraints and over a long period of time. A low exposure of 10 ms is easily achievable with such systems without having to sacrifice the data quality.

Gastrulation and beyond

The measurements we obtained from the PMC count are in favor of a resilient mechanism in which SMCs transfate into PMCs. However, we have shown that the spatial resolution we used is too poor to be statistically significant. In addition, the number of pigment cells was compatible with our theoretical prediction in the absence of transfating. However, there are five types of SMCs and transfating in Paracentrotus lividus may concern nonpigmental cells. Whether or not transfating occurs in singletons is still to be elucidated.

Under the hypothesis that transfating occurs as described by Ettensohn and Mc-Clay (1988), late blastula should exhibit only 16 PMCs in singletons completed by 16

SMCs at mid-gastrula. A previous work has shown that PMCs are the only cells in the embryo that bind wheat germ agglutinin (WGA, DeSimone and [START_REF] Desimone | Wheat germ agglutinin binding to the micromeres and primary mesenchyme cells of sea urchin embryos[END_REF]. Other specific markers of PMCs have been found [START_REF] Carson | A monoclonal antibody inhibits calcium accumulation and skeleton formation in cultured embryonic cells of the sea urchin[END_REF][START_REF] Wessel | Sequential expression of germ-layer specific molecules in the sea urchin embryo[END_REF][START_REF] Anstrom | Localization and expression of msp130, a primary mesenchyme lineage-specific cell surface protein in the sea urchin embryo[END_REF], but as far as I can tell WGA is the only marker compatible with in vivo development. Once singletons form their blastocoel (around 10 hpf), one can micro-inject tagged-WGA into the blastocoel and observe the PMC ingression and the SMC conversion. Long-term imaging is not necessary to tackle this question.

The conventional observation of embryos is sufficient to observe the conversion processes.

However, in the context of this work, I would pay particular attention to quantitative and qualitative variations between singletons and between twins. If transfating is observed, and in the absence of contradictory results, gastrula and later stages in singletons must be considered resilient with respect to wild types, rather than robust. By contrast, if no transfating is observed, there must be a supplementary round of divisions, hence PMCs must be smaller. As WGA binds to membranes, a unique experimental protocol can be used to observe transfating, if any, and to measure PMC volume over time.

Finally, the fate of SMCs in singletons is still elusive. The conventional observation of fixed specimens is not a good option to characterize SMC cell lineage, since observing individual cell behavior over time is essential to understanding the mechanisms behind differentiation and transfating dynamics. SMCs are supposedly committed to their respective fate by the time of mouth opening (around 20 hpf). The observation of developing specimens, from pre-hatching stages to mouth opening, is the next step in our full understanding of cell behavior during cell fate specification.

Singletons have always been considered normal. This statement is almost always accompanied by a remark about their smaller size. By definition, the latter contradicts the former. However, biologists often accommodate themselves to these kinds of contraditions, especially when the point of contradiction is considered minor -in this case, the smaller size. We have shown here that the differences between singletons and wild types are cloning have yet to be elucidated. In order to understand more of these events, we focused on the preimplantatory development of normal rabbit embryos and compared them with clone developments.

In order to adapt to a variable environment, individuals must vary as well [START_REF] Darwin | On the origins of species by means of natural selection[END_REF]. As a result, the only individuals or groups of individuals to survive are the variable ones, making variability ubiquitous in living systems [START_REF] Raj | Nurture, or Chance: Stochastic Gene Expression and Its Consequences[END_REF].

To increase the reproducibility of experiments, attempts have been and still are being made to remove or minimize animal variability by means of inbreeding or experiment replication. This has been done at the expense of artefact generation, strong hypotheses and distance from wildness. However, reproducibility can be improved by taking into account interindividual and interspecific variability. In recent decades, several studies have focused on the quantification of variations in various populations such as bacteria [START_REF] Elowitz | Stochastic gene expression in a single cell[END_REF][START_REF] Taniguchi | coli Proteome and Transcriptome with Single-Molecule Sensitivity in Single Cells[END_REF][START_REF] So | General properties of transcriptional time series in Escherichia coli[END_REF], yeasts [START_REF] Blake | Noise in eukaryotic gene expression[END_REF][START_REF] Carey | Promoter Sequence Determines the Relationship between Expression Level and Noise[END_REF] or metazoans [START_REF] Wernet | Stochastic spineless expression creates the retinal mosaic for colour vision[END_REF][START_REF] Boettiger | Synchronous and stochastic patterns of gene activation in the Drosophila embryo[END_REF][START_REF] Ohnishi | Cell-to-cell expression variability followed by signal reinforcement progressively segregates early mouse lineages[END_REF]. However, studies of mammal variability are often limited to the single-cell scale or fixed embryos. Quantifications made at the cell scale offer a unique level of observation that integrates sub-cellular variations (e.g., thermal agitation or stochastic gene expression) and cues from the environment, tissues and embryos (e.g., mechanical constraints or environmental conditioning). However, in toto imaging of living embryos at the cell scale and over a long period of time are technically challenging, because embryos are highly sensitive to light [START_REF] Squirrell | Long-term two-photon fluorescence imaging of mammalian embryos without compromising viability[END_REF] and imaging parameters must remain steady. In addition, although automatic tools have recently been published [START_REF] Fernandez | Imaging plant growth in 4D: robust tissue reconstruction and lineaging at cell resolution[END_REF][START_REF] Amat | Fast, accurate reconstruction of cell lineages from large-scale fluorescence microscopy data[END_REF]Fabrèges et al., in press), the high volume of generated data is still a major obstacle to the widespread application of 3D+time embryo imaging.

Here we report the automatic digitization and quantification of preimplantatory development in three rabbit embryos and two rabbit clones. We highlight the major differences observed between the two populations and possibly unveil new regulatory mechanisms not visible in normal conditions. Finally, based on our conclusions, we suggest the investigation of a strategy to increase SNCT efficiency.

Imaging of five live specimens

Temporal rescaling

The number of cells over time (

Cell number variation

To compare wild-type embryos with clones, we pooled data from wt1, wt2 and wt3 together, and nt1 and nt2 together, and we measured the coefficient of variation (Figure 4.3).

The coefficient of variation (CV) expresses the variation in a population as a proportion of its mean value. It is calculated by dividing the standard deviation by the mean value and allows direct comparison of populations of various sampling sizes. Wild-type embryos were on average less variable than clones with a coefficient of variation for wild types (CV wt ) of 0.12 and for clones (CV nt ) of 0.26 (p-value<0.0001). Although variations in the outer cell number have different distributions over time, they are very similar (CV wt = 0.22 and CV nt = 0.24, p-value<0.01). Most of the differences measured between wild-type embryos and clones can be explained by the dramatic difference in inner cell variations (CV wt = 0.33 and CV nt = 0.84, p-value<0.0001). These observations suggest that the low survival rate in clones may be explained uniquely by higher variations in inner cells. To investigate further the mechanisms and cell behavior behind the variation Wild-type embryos showed a fairly constant ratio (µ = 0.208 ± 0.019). Clones showed a two-phased ratio (Appendix Figure S9). During the first phase (from the beginning of imaging to 72.6 hpa), the proportion of inner cells increased from 11.8% to 15.3%. The second phase (from 72.6 hpa to the end of imaging) is similar to the inner cell ratio of wild-type embryos (µ = 0.200 ± 0.016). The reduced proportion of inner cells in clones at the beginning of the imaging seems to be compensated for by 72.6 hpa, and the proportion of cells is normal until the end of imaging. This regulation mechanism was not observable in wild types, although it may operate to keep the inner cell ratio close to 20%.

Clones are, however, far from the inner cell ratio observed in wild types at the beginning of imaging. The regulation of the inner cell ratio may exhibit diverse amplitudes and speeds, which in return induces more variation in the inner cell number. The number of cells can be regulated by controlling cell proliferation, cell migration and cell death. We investigated these mechanisms through the symmetries in mitoses and cell death biases.

Asymmetrical divisions and regulation of the cell population size

Asymmetrical divisions of outer cells and the early divergence of inner cell mass and extraembryonic tissues have been extensively described in mouse embryos [START_REF] Johnson | The foundation of two distinct cell lineages within the mouse morula[END_REF][START_REF] Sutherland | Inner cell allocation in the mouse morula: The role of oriented division during fourth cleavage[END_REF][START_REF] Johnson | Lineage allocation and cell polarity during mouse embryogenesis[END_REF][START_REF] Morris | Developmental plasticity is bound by pluripotency and the fgf and wnt signaling pathways[END_REF]; for a review see [START_REF] Wennekamp | Stochastic processes in the development of pluripotency in vivo[END_REF]. After compaction at the 8-cell stage, some of the outer cells divide asymmetrically, generating the progenitors of the inner cell mass. Asymmetrical divisions have been observed to occur in waves between the 8-cell stage and the 16-cell stage and between the 16-cell stage and the 32-cell stage in mice. Because asymmetrical divisions are the only known direct mechanism to generate inner cells, we investigated this feature in rabbit embryos (Figure 4.4). We found a wave of asymmetrical divisions of outer cells (Figure 4.4A,C) in both wild-type embryos (72.5hpf ± 6. 22) and clones (70.5hpf ± 6.17). Although there is a slight increase of the number of asymmetrical divisions around 100 hpf in wild-type embryos and clones, we did not find any significant wave of asymmetrical divisions after 80 hpf or 80 hpa. This suggests that we observed the last wave of asymmetrical divisions in both clones and wild-type embryos.

The total number of asymmetrical divisions observed in outer cells is also very similar between wild-type embryos (6.67 ± 5.03) and clones (6.5 ± 4.95). This result validates the rescaling method we used, which was based on two morphogenetic events not related to asymmetrical divisions.

Contrary to outer cells, inner cells do not show any waves of asymmetrical divisions.

In inner cells of wild-type embryos (Figure 4.4B), 50% of the divisions at 70 hpf were asymmetrical. This proportion decreased to less than 10% 30 hours later. Clones showed very few asymmetrical divisions (Figure 4.4D). The total number of asymmetrical divisions per embryo observed in inner cells differs considerably between wild-type embryos (7 ± 3.61) and clones (2.5 ± 0.71) and cannot be explained by the lower number of mitoses in wild-type embryos (184 ± 107) than in clones (103 ± 74). Interestingly, the asymmetrical mitoses in clones were only observed after 75 hpa, when the inner cell ratio in clones 10 ng/µL 25 ng/µL 75 ng/µL No injection 96% (25) H2B-mCherry + EGFP-ras 83% (6) 71% ( 7) 25% (8)* H2B-EGFP --85% (20)* Table 4.1: Survival rates of blastocysts after micro-injection of none, one or two mRNA at different concentrations. Total number of embryos between parentheses. Asterisks mark image data quality suitable for analysis.

reached the value of the inner cell ratio in wild types. This result is consistent with a regulatory mechanism that compensates for the low ratio of inner cells. We suggest that the proportion of inner cells in rabbit embryos is maintained at around 20% on average by regulation of the number of asymmetrical divisions.

Cells away from the centroid of the forming inner cell mass were closer to the outer cells and tended to divide asymmetrically (Appendix Figure S10). Although the imaging we provide cannot fully answer the question, we suggest that asymmetrical divisions of inner cells may be regulated by extracellular cues such as space, nutrient accessibility or heterotypic adhesion. Previous studies in mice showed that isolated inner cell masses were not able to form trophectoderm derivatives [START_REF] Fleming | Trophectodermal processes regulate the expression of totipotency within the inner cell mass of the mouse expanding blastocyst[END_REF][START_REF] Eckert | Relative contribution of cell contact pattern, specific PKC isoforms and gap junctional communication in tight junction assembly in the mouse early embryo[END_REF][START_REF] Szczepanska | Isolated mouse inner cell mass is unable to reconstruct trophectoderm[END_REF] but were able to invade a population of outer cells if injected into the blastocoele [START_REF] Beddington | An assessment of the developmental potential of embryonic stem cells in the midgestation mouse embryo[END_REF], suggesting that cues to perform asymmetrical divisions may come from the outer cells. We cannot exclude annotation artefacts with absolute certainty. Because membranes were not stained, outer cells may have been annotated as inner cells, in which case asymmetrical divisions of inner cells may actually be asymmetrical divisions of outer cells. Both scenarios can be further investigated with membrane staining. However, no transgenic lines are available in rabbits with nuclear and membrane staining. In addition, we showed that in our conditions, the total mRNA concentration required for dual micro-injection is too high to maintain a reasonable survival rate (Table 4.1) or a data quality suitable for analysis.

Asymmetrical divisions of outer cells were observed with the same temporal distribution and the same amplitude in the five digital specimens. The behavior of inner cells is however more variable. Distributions are heterogeneous even within the population of Removing a robust feature ineluctably gives more weight to the variable features it counterbalances. On the contrary, removing a variable feature gives more weight to the robust features it counterbalances. Now, for organisms to evolve and survive, a certain degree of variation must be conserved. In addition to generating and controlling the size of cell populations, asymmetrical divisions may be a key regulator of interindividual variability.

Cell death

Temporal distributions of the CV nt were more fluctuating than CV wt (Figure 4.4H). The CV nt of the number of inner cells was high and stayed high during the whole imaging sequence. Two-thirds of the time the CV nt was over 80%. In addition, asymmetrical divisions had no visible effect (Figure 4.4I,J), strongly suggesting that a second mechanism is involved in the generation of cell number variations. This mechanism may have hidden the regulative effect of asymmetrical divisions. Because cell death was observed in our digital specimens, we investigated its effect on cell number variability (Figure 4.5).

Interestingly, the death ratios were highly variable from one individual to another: wt3 and nt1 respectively showed 39% and 33% of dead inner cells between the 60th and 70th hours post-fertilization, whereas wt1, wt2 and nt2 did not show any dying cells during the same period. By contrast, nt2 lost more than 27% of its cells between 90 and 100 hpf, and very few before that. We observed specimens with very high ratios of dying cells (wt3, nt1 and nt2), a low ratio of dying cells (wt1) and no dying cells at all (wt2).

Outer cells showed similar interindividual variations. However, intra-individual variations were lower. The death ratio was quite constant over time, except for wt3 and nt2, which both showed a peak of cell deaths between 70-80 hpf and 90-100 hpa respectively.

Considering the death ratios of outer cells exclusively, nt1 and nt2 are similar to wt1 and wt3 respectively. Although the developmental impact of dying cells may be estimated with the death ratio, it is noteworthy that the number of dead cells does not seem as variable as the death ratio. Between 60 and 70 hpf or hpa, two inner cells of wt3 died and only one cell of nt1 died. At this development timing, the total number of inner cells is low (average number of inner cells per time-step: 9, 9, 5, 3 and 7 for wt1, wt2, wt3, nt1 and nt2 respectively). Consequently, the impact of a cell dying early is more dramatic than later in the development.

Somatic cell nuclear transfer to produce live animals such as Dolly the sheep [START_REF] Wilmut | Viable offspring derived from fetal and adult mammalian cells[END_REF] relies on the assumption that the genome is somehow reprogrammed and epigenetic imprints erased, thus cells become totipotent and able to form all the adult tissues in time. Although successful cloning experiments have been realised before [START_REF] Wakayama | Mice cloned from embryonic stem cells[END_REF][START_REF] Hochedlinger | Monoclonal mice generated by nuclear transfer from mature B and T donor cells[END_REF][START_REF] Inoue | Generation of cloned mice by direct nuclear transfer from natural killer T cells[END_REF], nuclear shifting to a state compatible with normal development may be an exception [START_REF] Rideout | Nuclear Cloning and Epigenetic Reprogramming of the Genome[END_REF], as suggested by the very low survival rates [START_REF] Hochedlinger | Nuclear Transplantation, Embryonic Stem Cells, and the Potential for Cell Therapy[END_REF][START_REF] Yang | Nuclear reprogramming of cloned embryos and its implications for therapeutic cloning[END_REF]. As previously demonstrated in mice [START_REF] Gao | Somatic cell-like features of cloned mouse embryos prepared with cultured myoblast nuclei[END_REF], we expected clones to keep part of the donor identity. We found that clones showed a strong cell-type bias of cell death (Figure 4.5C). We measured a bias index defined as the observed death ratio per cell type divided by the proportion of cell types. A value of 1 is expected if death happened irrespective of cell type. The bias index showed that cell death was not correlated with cell type in wild-type embryos and every cell had an equal chance of dying (µ = 1.03±0.90 and µ = 1.06 ± 0.31 for inner and outer cells respectively). By contrast, clones showed a strong correlation between cell type and death. Inner cells died more than expected (µ = 2.32 ± 0.03), whereas outer cells died less (µ = 0.63 ± 0.26). It should be noted that the cumulus cells used as donor cells developed in an epithelial environment. The nuclei of the donor cells may have kept part of their epigenetic state, giving them a better chance of surviving in the trophectoderm than in the inner cell mass. Such regulations are tightly linked to the environment and random selection. The latter generates higher variations of cell death, which occurred at different timings and may be the main source of variability in cell numbers.

Discussion

Variability has been shown to be greater in clones than in wild-type embryos. Under our initial hypothesis, embryos expressing high variability, such as clones, explore a bigger space of possibilities than wild-type embryos. Although an exploration of the space of possibilities may be essential for individuals to survive in a changing environment, exploring too far will inevitably lead embryos outside the viable space. Despite a clear shift of the state of donor cell nuclei towards a state more similar to totipotent nuclei, embryos of clones may keep part of their initial state. A stressful environment, such as chemical factors and electroshocks, may force cells to explore other areas of the space of possibilities (Kupiec and Sonigo, 2000, p.118), leading some of them to so-called genome reprogramming. Cells then have the opportunity to evolve by increasing the proportion of the fittest cytoplasmic and nuclear states. As evolution and adaptation rely on stochastic processes, we argue that the greater variation in clones depends mostly on successful micro-evolution towards totipotent cells. In order to improve the success rate of cloning, we suggest helping cells to adapt to their new environment. The use of induced pluripotent stem cells (IPSCs) discards the cells that are not able to adapt to an environment supposely close to the embryonic environment, although artificially maintained through the use of factor cocktails. The IPSCs are already adapted to the environment defined by the factor cocktails prior to the nuclear transfer. Thus, the path to pluripotency is shortened and cloning success rate is improved. The success of this method relies partly on the a priori of what an embryonic environment is and how it should be artificially maintained to select the fittest IPSCs, and partly on the embryos' plasticity. Instead of, or in combination with, finding the best IPSCs, we suggest using donor cells extracted from a highly variable environment (e.g. skin, gut or endothelial cells). In order to survive, such cells have to be permissive or intrinsically adaptable. Permissive cells may not be suitable for cloning, as we want the cells not only to survive, but also to differentiate and evolve. To find the best candidate, we suggest quantifying interindividual variations of donor population (e.g., mRNA diversity) and selecting the population with the highest variability.

Methods

Animals and embryo culture

The experiment was performed in accordance with the International Guiding Principles Embryos were maintained at 38.5°C, 5% CO 2 in bM199-FBS without HEPES (M199-FCS) with phenol-red in a humid incubator until the morula stage. Embryos were then transported to Gif-sur-Yvette for imaging in a drop of bM199-FBS in a capillary. The temperature was maintained at 38°C during the transportation to Gif-sur-Yvette, were embryos were stored in the same conditions as in Jouy-en-Josas in M199-FBS.

Cloning

Rabbit oocytes without cumulus cells were enucleated according to [START_REF] Challah-Jacques | Production of Cloned Rabbits by Somatic Nuclear Transfer[END_REF]. Cumulus cells were introduced into the subzonal space of enucleated oocytes. Celloocyte pairs were electrostimulated to induce fusion. One hour later, fused embryos were activated by a second set of pulses followed by 1 h of incubation with 5 µg/mL cycloheximide and 2 mM 6-dimethylaminopyridine in M199-FBS. Then they were cultured in the same way as wild-type embryos.

Imaging

Digital reconstruction

Digital specimens were reconstructed with the BioEmergences Workflow as described in Fabrèges et al., in press and in Section 2.1 of this thesis. Nucleus centers were detected with the Difference of Gaussians algorithm, parameters were manually selected every 25 timesteps to best fit the raw data and interpolated over the whole imaging sequence.

Trackings were made with the Simulated Annealing algorithm. Cell lineages were manually curated with the software Mov-IT (Fabrèges et al., in press) until hatching was observed (wt1, wt3, nt1 and nt2) or until data quality was too poor to validate the cell lineage (wt2). Cell populations were annotated as inner cells or outer cells based on the nucleus positions at the last time-step. Annotations were then propagated backward and every mitosis was checked for asymmetrical divisions. Fragmenting nuclei were annotated as dying cells.

Analysis

Cell positions, temporal links and annotations were extracted to CSV files and analyzed with custom R scripts. Fertilization timing was estimated at 8 hours post-coitus as measured previously [START_REF] Pincus | The Comparative Behavior of Mammalian Eggs in vivo and in vitro[END_REF]. Volume of embryos was estimated with the volume of the hull. The first decrease of more than 10% of the hull volume was considered as the first blastocoele collapse. Division symmetries were determined based on the mother's identity and the identity of daughter cells prior to their next mitosis.

If daughter cells did not divide by the end of the imaging sequence, the identity at the last timestep was used. When embryos were pooled together, measures were resampled over time to have the same number of time points. The finest temporal resolution was preserved and data were linarly interpolated if necessary. P-values were measured with the Wilcoxon test.

Chapter 5

Discussion and perspectives

What is normal development?

We defined normal development as the space of possibilities in defined conditions, hence including viable and non-viable output. In this definition, the function of an individual is distinct from its normality. In the present work, we addressed the question of normality through the study of rabbit and sea urchin early development, and we proposed models of development for rabbit wild types, rabbit clones and sea urchin twins.

The definition of normality we used is based on an a priori on experimental conditions.

We are expecting experimental conditions to be identical -or very similar -from one experiment to another. The variations measured result from intrinsic and environmental variations as well as variations due to the observation itself. In order to specifically exclude the latter, we tried to minimize experiment influence as much as possible. Although environmental conditions were controlled (temperature, CO 2 and osmolarity), embryos

were not exposed to the same laser power, and imaging conditions in general were tuned for each experiment. Further studies should check whether interindividual variations can be correlated to experimental variations. For instance, micro-injection of mRNA is known to slow down development proportionally to the injected volume. Because the latter varies from one micro-injection to another, the variation in injected volume may be sufficient to explain the differences in developmental speed observed in sea urchins and rabbits.

131

In addition to experimental variations, we have to bear in mind that the observed developments may not be representative of natural development, i.e., under the ocean for sea urchins or in the mother's uterus for rabbits. Because developmental normality relies on the external conditions, 100% of death at early stages for instance, is perfectly normal if the temperature is above 45°C. Thus, our understanding of normal development depends on our understanding of the developmental conditions. Laboratory conditions have been chosen to maximize the number of developments leading to a consensual phenotype.

For instance, laboratory conditions for sea urchin development give more than 95% of symmetrical larvae, which is far above the estimated success rate in natura (less than 1%, [START_REF] Levitan | How distribution and abundance influence fertilization success in the sea urchin Strongylocentrotus franciscanus[END_REF]. Although experimental reproducibility is better in laboratory conditions, they are not representative of natural conditions and extrapolations of embryo dynamics to the natural environment must be made carefully. The difficulty in comparing laboratory conditions with natural conditions is even more striking for rabbit development. Laboratory conditions have been chosen to maximize the proportion of caviting embryos. By contrast with the sea urchin, where larva fertility can be assessed by raising embryos to the adult stage, blastocysts of rabbits cannot be reimplanted in a surrogate mother, presumably because in natural conditions, the change of the zona pellucida composition happening during the embryo journey through the genital tract is mandatory.

Thus, the output of in vitro rabbit culture is an embryo that cannot reproduce.

In fine, an observation made in our conditions will lead to the characterization of a space of possibilities that may differ from the natural conditions. Micro-injection, laser illumination or culture in an artificial medium are all examples of perturbations.

Quantifying the strength of these perturbations is difficult, especially because we use these perturbations to quantify embryogenesis (e.g., we use nuclear staining to reconstruct cell lineages). Future research would certainly gain in relevance by taking into account and minimizing these perturbations. I suggest getting closer to the natural conditions.

Sea urchins can easily be studied in natural sea water rather than artificial sea water.

Mammals can be cultured with uterine medium rather than artificial medium for instance, which gives better survival rate. Companies such as Life Technologies or Sigma Aldrich used to commercialize uterine medium of rabbits, but stopped for economic reasons, greatly impacting the success of the in vitro mammal culture. Although technically not accessible yet, intra-uterine imaging may be the key to understanding the development of mammalian embryos in their natural environment.

We have seen in this work all the difficulties inherent in describing embryogenesis in a defined environment. Getting closer to natural conditions may increase the experimental variations. Embryos may express different responses to environmental variations, revealing unknown features and hiding others expressed in laboratory conditions. Statistical definition of normal development in natural conditions is not possible with a couple of digital specimens; it requires cohorts of greater size.

High Throughput Data

Performing significant statistical analysis required to image, process and analyse larger cohorts. The methods and tools that have been published so far [START_REF] Fernandez | Imaging plant growth in 4D: robust tissue reconstruction and lineaging at cell resolution[END_REF][START_REF] Amat | Fast, accurate reconstruction of cell lineages from large-scale fluorescence microscopy data[END_REF]Fabrèges et al., in press) can be used for semi-automatic high throughput processing and processing is not the bottleneck. Imaging, however, requires human intervention to harvest, micro-inject and set the imaging conditions. In addition, a significant amount of time is often required for the preparation of data before processing and the number of microscopes is limited. In order to achieve statistically significant analysis, we need to overcome the bottlenecks for high throughput imaging without sacrificing data resolution and quality.

A conventional approach to the problem is to develop new tools and new methods to reduce human intervention and automate experimentation and imaging. Micro-injection of mRNA, for instance, can be replaced by the use of transgenic lines or by bathing embryos with vital dyes. Several vital dyes have recently been developed specifically to stain chromatine or membrane in living embryos. By contrast with transgenic lines which requires inbreeding, the use of vital dye is compatible with every genetic background, including wild-type genetic backgrounds that are closer to natural conditions. Their impact on embryo development is, however, largely unknown and must be tested. The use of label-free non-linear microscopy [START_REF] Yelin | Laser scanning third-harmonic generation microscopy in biology[END_REF][START_REF] Zipfel | Live tissue intrinsic emission microscopy using multiphoton-excited native fluorescence and second harmonic generation[END_REF][START_REF] Olivier | Cell lineage reconstruction of early zebrafish embryos using label-free nonlinear microscopy[END_REF] is also promising, although the total laser power received by the embryos is high and may impair development. By reducing the number of human intervention, these methods minimize experimental variation and make the imaging pipeline more fluid. However, they do not address the problem of the low proportion of analyzable data generated in our conditions.

In assistant has to be developed to adjust imaging conditions in real time. In order to increase the chances of obtaining analyzable data, patterns of interest must be identified and tracked, and laser power must be adjusted to optimize the signal-to-noise ratio in real time. In addition, because embryos may not survive during the entire imaging sequence, the artificial assistant must be able to recognize dead embryos to stop imaging them.

Although I developed a tool for the real-time feedback of laser power and embryo position during my thesis, the use of this tool is limited by the opacity of proprietary software of microscope companies, which do not allow external applications to interact with their own. Recent efforts have been made by LaVision BioTec to allow integration of external applications, however the integration is still experimental and not reliable for everyday experiments.

A complementary approach for high throughput imaging is crowdsourcing. Crowdsourcing consists in multiplying the number of microscopes all over the world and gathering the data in one place for further analysis. As far as I know, the BioEmergences workflow is the only imaging pipeline designed for crowdsourcing. Interestingly, crowdsourcing offers a unique opportunity to explore the space of possibilities of various species in diverse conditions. Gathering all the data in a common database would lead to large cohorts and would allow a statistical description of every species throughout its entire development. Developing methodologies to deal with such data and achieve interindividual and interspecies comparison would remain a major issue.

Interindividual and interspecies comparison

What is a population? And what is a species? Those are categories. Comparing individuals implies that they share features and processes. We know how to quantify some features and processes, and we know how to compare them. Would it be possible to compare every feature and every process to build a multidimensionnal space of possibilities in which every individual has its position?

The phylogenetic tree results from positioning individuals in such a space. The construction of the phylogenetic tree is, however, largely based on one quantitative dimension:

the DNA sequence. Comparisons are made on specific gene sequences. Assuming that gene sequences share the same history, it is possible to measure the distance between individuals and use this distance to build a binary tree. Depending on the a priori and the method used for determining genetic distance, branches may vary, and unless the true relationships among the taxa are known, there is no way to assess the accuracy of the methods. The reconstruction of a phylogenetic tree based solely on DNA suffers from several limitations, all of which are related to the permeability of phylum compartmentalization to DNA (hybrid speciation, horizontal transfer, endosymbiosis, etc.), or the approximation that the transmission of genetic changes does not depend on the time of generation.

The limitations of the use of genetic distance can be overcome by using informations at different levels of observation. Each of these observations alone cannot relevantly describe the relationship between individuals. Together, however, these observations have n-dimensions, one dimension per parameter, and can be used to form clusters of likeness.

Finally, algorithms of clustering, principal component analysis or more elaborate strategies are expected to reveal categories that might correspond to species and phyla as we know them, as well as relative distances, possible intersections and speciation. We answer the question "What is a species?" with clustering algorithms. A clustering algorithm accepts a parameter to discriminate between categories. For instance, algorithm can be run with a constraint on the number of categories, the maximal distance between points, or the point density. The use of a discrimination parameter allows to mathematically define what a species is by conventionally defining a threshold for this parameter (Figure 5.1).

The use of quantifications made at different levels of observation on features and processes integrates de facto every information, hence provides a level-free comparison of individuals, populations and species. This approach offers a new definition of species based on the development and genomic properties of individuals, a first step towards building an ontophylogenetic tree [START_REF] Kupiec | Ni dieu ni gène[END_REF]. The resulting categories define normal developments in defined genetic and environmental conditions. Then, the statistical representation of a category allows the construction of prototypes.

In silico experimentation

Embryos are intrinsically variable, which gives them an essential advantage for survival.

One consequence of variability is that each experiment is unique and measurements can only be made once. Moreover, the embryos studied now differ from the embryos studied in the past and from those that will be studied in the future. To address the question By contrast, a high value of ϵ correlates with a low number of categories (ultimately one category for living organisms). For instance, if the category species is set by consensus to ϵ = 0.3, then the sample here exhibits three different species. For more information about the OPTICS algorithm, see [START_REF] Ankerst | Optics: Ordering points to identify the clustering structure[END_REF]. Data were generated using R and the dbscan library [START_REF] Hahsler | Density Based Clustering of Applications with Noise (DBSCAN) and Related Algorithms[END_REF].

the mathematical objects form a cohort of validated and corrected gold standards that can be manipulated in silico. Laboratories all over the world can share the data to ask various biological questions, make complementary measurements and analyze the same embryos.

The cohort can grow and previous analyses can be extended to new observations, taking into account experiment metadata, such as temperature, laser power and laser wavelength, etc. Once embryos have been digitized, the scientific community is in position to agree (or disagree) on interpretations and conclusions. Moreover, future scientists will be able to reanalyze past data in the light of new knowledge without relying on the way the data were interpreted at the time. In particular, metadata can be used to measure micro-evolution by correlating changes of non-embryonic data (e.g., global warming) with changes in biological mechanisms. This is of major interest as observations made decades ago are used as references, although the evolutionary time of sub-cellular mechanisms in animals is utterly unknown. For instance, the recent characterization of two different species Ciona intestinalis and Ciona robusta instead of one single species Ciona intestinalis [START_REF] Brunetti | Morphological evidence that the molecularly determined Ciona intestinalis type A and type B are different species: Ciona robusta and Ciona intestinalis[END_REF] may be explained by an incomplete description by Linnaeus (1767), Roule (1884) and Millar (1953), or by gradual speciation.

In addition to providing an extraordinary opportunity to unify scientific efforts towards the same goal, digital specimens are the necessary step for in silico prediction. The information about animals, augmented by hypotheses, can be integrated into mathematical models and compared with digital specimens. Digitization of embryos results from a minimization of the differences between digital specimens and living specimens. In silico simulation generates virtual individuals that can be compared with the prototype, with digital specimens and, by extension, with living specimens. Rules and hypotheses used to generate virtual embryos can then be refined to minimize distances between the virtual specimen and the prototype, helping to improve our understanding of genetic and mechanical laws during embryogenesis. Finally, with minimal differences, we hope to be able to simulate life and predict the output(s) of experiments in chosen conditions. Besides the possibility of experimenting in a way not yet possible in our world, predictive biology may be the necessary step towards the unification of our interpretation of life, and the establishment of a general theory of living systems.

Figures

Figure In blue: all detected and tracked cells; in red: cells validated by visual inspection. Cell division synchrony was more prominent in the sea urchin. For the zebrafish embryos, analyzed here at late blastula stages, the interpretation of the increase in cell number was highlighted by the quantification of cell density and internuclear distance (see next row). Whereas the sea urchin and ascidian reconstructed specimens were extensively corrected by experts to define gold standards, we only validated and corrected subsets of the zebrafish clones. The characteristics of the validated cells (in red) were consistent with a linear progression of cycle length for most cells throughout the blastula and gastrula stages. However, the dispersion of cycle length around the average, besides suggesting a certain number of errors in the detection of cell divisions (especially at late developmental stages), also highlighted a greater diversity of behavior.

In this respect, the ascidian Phallusia mammillata showed the largest variety of specific proliferation rates along the lineages. (c) Average distance between pairs of neighboring cell nuclei through divisions as a function of hpf (error margin in gray). Cell neighborhoods are calculated by 3D Delaunay triangulation using the Computational Geometry Algorithms Library (CGAL, [START_REF] Pion | CGAL User Ref. Man[END_REF]. In all three species, this average distance decreased during early embryogenesis and converged to a minimal value around 10µm, corresponding to an average cell diameter. This observation fits with the plot of cell density, obtained by segmentation of the global imaged volume (Figure2.4h), which increased throughout gastrulation and plateaued at the end of gastrulation (10 hpf). It is also consistent with an estimate of the average proliferation rate during the same developmental period [START_REF] Kane | Mitotic domains in the early embryo of the zebrafish[END_REF]. (d) Average number of neighbors as a function of hpf.

In both the zebrafish and the ascidian, this value is near 12, i.e., in the range of perfect 3D hexagonal close packing [START_REF] Farhadifar | The Influence of Cell Mechanics, Cell-Cell Interactions, and Proliferation on Epithelial Packing[END_REF]. In the sea urchin, the observed value of about 10 neighbors is consistent with the presence of a blastocoel and the organization of its early embryo into a pseudostratified epithelium. (e) Dispersion of cells as a function of hpf. At each time-step, all possible pairs of neighboring cells are identified and tracked forward. Distances between neighbors are divided by the average internuclear distance.

The resulting average normalized distance increases over time, providing a quantitative evaluation of cell dispersion. Information about the global cohesiveness of the embryonic tissues indicates distinct developmental periods.

Figure S6: Formal and applied epistemology for the reconstruction of the multilevel dynamics of complex systems. This diagram summarizes our general methodology for the integrative modeling of the morphogenesis of living systems. Ultimately, imaging data revealing the characteristic scales of biological processes should lead to models coupling the different organization levels of developing embryos (molecular, cellular and tissular).

The methods and tools provided in this publication are specific to the cellular level of organization. The architecture of the workflow accessible through our webservice, however, is ready for the integration of new algorithms operating on other types of data.

The three bottom pictures illustrate our concepts with the biomechanical modeling of the sea urchin early embryogenesis (blastula stages): imaged live specimen (bottom-left panel), reconstructed specimen (bottom-center panel), and simulated specimen with the MecaGen platform (bottom-right panel, image courtesy of Julien [START_REF] Delile | Computational Modeling and Simulation of Animal Early Embryogenesis with the MecaGen Platform[END_REF]. We call phenomenological multilevel model, or reconstruction (middle-left panel and underlying triangle graph) the set of operations based on algorithmic methods to extract measurements from 3D+time images and provide the most accurate quantitative information relevant to the detected components: cell numbers, positions, shapes, interactions, trajectories, and so on. The design and implementation of image processing algorithms, data management, and analysis tools are key steps toward high-throughput 3D+time image analysis. This phenomenological reconstruction feeds a database of raw and reconstructed data used for statistical analysis. Quantitative results are then used to set the parameters of a theoretical model (top panel) and its derived numerical simulations, leading to a simulated multilevel model, or virtual morphogenesis (middle-right panel). This scheme creates a virtuous cycle of experimental validation of the models (top triangle graph) by testing both the biological system and the simulation. Measuring the differences (∆'s) between the multiscale raw data (imaged specimen), the phenomenological data (reconstructed specimen) and the virtual data (simulated specimen), constitutes a major challenge. So far, the ∆ between raw and reconstructed data is estimated by a manual procedure using our custom-made visualization interface Mov-IT. a wrong links, which connect a cell to a wrong target, contribute both to false positives (by creating new links that do not exist) and to false negatives (by missing the correct links) b missing links, which correspond to a cell without any link, contribute only to false negatives [START_REF] Kimmel | Stages of embryonic development of the zebrafish[END_REF][START_REF] Hotta | A web-based interactive developmental table for the ascidian Ciona intestinalis , including 3D real-image embryo reconstructions: I. From fertilized egg to hatching larva[END_REF][START_REF] Stephens | Studies on the development of the sea urchin Strongylocentrotus droebachiensis. I. Ecology and normal development[END_REF] a A more detailed explanation of the method, the meaning and choice of the parameters can be found in [START_REF] Frolkovič | Counting number of cells and cell segmentation using advection-diffusion equations[END_REF][START_REF] Bourgine | 4D embryogenesis image analysis using pde methods of image processing[END_REF]. for membranes embryo type if equal to 1, the method is tuned for small and packed cells (i.e. Dr1-2 datasets); if equal to 2, then it can be used for large cells (i.e. Pl1-2 and Pm1-2 datasets) c 1 or 2 1 for Dr1 and Dr2, 2 for the others a A more detailed explanation of the method, the meaning and choice of the parameters can be found in Bourgine et al., 2010; Mikula et al., 2011. b The result of the segmentation process is in the form of VTK files containing integer values between 0 and 255 in every voxel. The segmented object can be rendered as triangulated surface by choosing a suitable isosurface value in the resulting VTK file (usually 128, but other values should be tried to obtain the best fit with the raw data). By choosing a larger isosurface value, one can obtain smaller segmented objects; by choosing a smaller isosurface value, larger segmented objects.

Parameters for the DoG center detection algorithm

Name

c The choice of 1 or 2 for embryo type is related to the initial condition of the SubSurf segmentation process. The usual procedure is to construct a certain initial shape around the cell center, which is then evolved by the SubSurf method. This initial condition should be chosen according to the size of the cells and their expected shape. For different types of data and species, one can either try choices 1 or 2 or then customize the code of the procedure used to construct the initial condition. For user support, please contact: mikula@math.sk

Notes

Movie S2: Phallusia mammillata (Pm1), 3D raw data rendering. See full movie here: http://bioemergences.iscpif.fr/users/dimitri.fabreges/thesis/movies /movie2.mp4. Abstract: Variability is an intrinsic characteristic of biological systems, essential for evolution and embryogenesis. Considered as noise for centuries, it is only recently that scientists have started to systematically explore the stochasticity of biological processes. The present thesis addresses the following questions: What is a normal development? What is the extent and role of variability in developmental robustness and resilience? We addressed these issues in the rabbit (Oryctolagus cuniculus) and the sea urchin (Paracentrotus lividus and Sphaerechinus granularis). We also aimed to quantify the determinism and stochasticity of developmental variability by studying sea urchin twins and cloned rabbits. Variations in cell behavior were investigated through reconstruction of cell lineage from 3D+time imaging. We showed that sea urchin twins can follow three different developmental paths never observed in normal embryos, before converging to normal-looking blas-tula. Moreover, comparisons between and within pairs of twins revealed that phenotype and survival depend on individual history alone. Our quantitative observation of twin pairs raises a question for a future line of research: are twins robust or resilient? Rabbit preimplantation development was explored with five digital specimens (three wild types and two clones) from the 32-cell stage to hatching. We showed that inner and outer asymmetric divisions regulate the variation of inner cell numbers and may control inner cell mass size. In addition, the inner cell numbers in clones show higher variation than the outer cell numbers, which seems to be directly correlated to their cell death ratio. Our current hypothesis is that the potential to lead to viable clones requires plasticity of the donor's epigenetic state.

  d'établir une méthodologie nouvelle, nous avons soigné le choix des animaux modèles et la séquence de développement à observer. D'un point de vue pragmatique, le choix de l'oursin (Paracentrotus lividus et Sphaerechinus granularis) et du lapin (Oryctolagus cuniculus) est évident. L'oursin est étudié depuis plus de 150 ans et la communauté scientifique dispose d'un nombre considérable d'informations à son sujet. De plus, l'oursin et le lapin sont particulièrement transparents, faisant d'eux de bons candidats pour l'imagerie in vivo. Enfin, ces deux organismes rendent possible les expériences visant à discriminer les variations acquises des variations transmises par le biais des jumeaux d'oursins, et les variations génétiques des non-génétiques par le biais des clones de lapins. Le lapin est présenti pour avoir des caractéristiques embryonnaires proches de celles de l'humain sur certains aspects, mais la dynamique cellulaire menant à la formation d'un blastocyste reste méconnue. De plus, le clonage chez le lapin ne conduit que très rarement à des lapereaux viables et fertiles. Nous proposons l'hypothèse selon laquelle les clones seraient plus variables que les sauvages, diminuant leurs chances de se développer x R ÉSUM É EN FRANC ¸AIS correctement. Cette thèse vise aussi, en partie, à répondre à cette question par une meilleure compréhension du développement précoce. Les oursins quant à eux, offrent l'opportunité de revisiter les expériences embryologiques passées. La génération de jumeaux mène à d'important changement dans le développement précoce et remet en question notre compréhension de la résilience et de la robustesse. Cette thèse a pour objectif de comprendre la nature de ces changements et leurs conséquences sur le développement embryonnaire. De plus, la communauté scientifique a généré une grande quantité d'informations sur les dynamiques moléculaires et génétiques de l'oursin. Le travail présenté ici est une étape importante pour intégrer ces informations à des données d'imagerie 3D+temps. Le niveau cellulaire d'organisation est le niveau d'intégration le plus approprié pour comprendre les processus microscopiques et macroscopiques. Notre but est de quantifier les variations des comportements cellulaires dans de petites cohortes d'individus et dans des conditions expérimentales controlées. Nous nous sommes concentrés sur des étapes expérimentalement accessibles comprenant les étapes de clivages et les étapes précoces de morphogénèse et de différenciation cellulaire. Ce travail propose un nouveau standard de la représentation du développement embryonnaire précoce. Pour fournir des animaux numériques entièrement corrigés, validés et annotés, nous avons dû développer de nouveaux outils d'analyse et de visualisation.

L

  Figure i: Aperçu de la morphogénèse de l'oursin Paracentrotus lividus

.

  Figure ii: Aperçu de la morphogénèse du lapin Orycolagus cuniculus

  bryonnaire de l'oursin et du lapin. Nous avons défini la normalité comme l'ensemble des états possibles du développement d'un individu, viables et non-viables. La somme de ces états définit l'espace des possibles et caractérise la dynamique du système. Un individu seul ne représente qu'un point de l'espace, et il est nécessaire de prendre en compte la diversité et la variabilité du développement embryonnaire d'une cohorte d'embryons pour en apprécier toutes les dimensions. Les individus que nous avons décrits ont été observés dans des conditions de laboratoire. Nous avons cherché à minimiser les différences de traitement entre les individus afin de perturber au minimum les expériences et être en mesure de comparer les individus entre eux. Le corrolaire de cette homogénéisation des conditions expérimentales est l'appauvrissement de la diversité phénotypique des populations. En effet, les conditions naturelles sont très éloignées de nos conditions de laboratoire, qui ont été choisies pour maximiser le taux de bon développement, et ne sont donc pas représentatives du développement naturel. Il est impératif de garder à l'esprit que toutes nos observations et toutes nos conclusions concernent des embryons se développant en milieu contrôlé, c'est à dire en dehors du système naturel ayant sélectionnés leurs comportements. Bien que viables dans nos conditions, ces embryons n'auraient peut-être pas été capables de survivre en conditions naturelles où moins d'un pourcent des embryons survivent. De plus, les embryons ne survivant pas dans nos conditions de laboratoire sont peut-être ceux à même de survivre dans des conditions naturelles. Nos interprétations faites en laboratoire doivent être extrapolées au milieu naturel avec beaucoup de précautions et nous suggérons de rapprocher les futures études le plus possible des conditions naturelles. Cela implique davantage de classes phénotypiques et davantage de diversité. En conséquence, des comparaisons statistiquement significatives ne seront obtenues qu'avec des cohortes de plus grande taille. Les prochains défis techniques à relever sont ceux de l'imagerie et l'analyse haut-débit. Cela peut se faire par l'utilisation d'assistant artificiel, maximisant les conditions d'imageries afin de limiter les pertes (taux de survie, qualité d'image, etc). Une autre solution consisterait à valoriser le crowd sourcing en incitant les chercheurs à mettre leurs données à la disposition de tous. Le workflow BioEmergences . CONCLUSIONS ET PERSPECTIVES xvii est, à ma connaissance, le seul service d'imagerie et de reconstruction souhaitant valoriser le partage des données in vivo au sein de la communauté. L'accès à l'ensemble de ces ressources rend possible la comparaison intra-espèce et inter-espèce. L'ensemble des informations extraites de ces données peuvent être exploitées pour construire un espace multi-dimensionnel dans lequel chaque individu est représenté par un point. Une analyse en composante principale, un algorithme simple de partionnement de données ou des solutions plus complexes tel que l'utilisation de diagrammes de persistences, permettrait de regrouper les individus, statistiquement et sans a priori, en paquets et ainsi proposer une définition mathématique et consensuelle des espèces et des taxons. L'utilisation de lois mathématiques et statistiques pour décrire des espèces est une première étape nécessaire à la construction d'embryons in silico. L'expérimentation in silico représente une chance unique d'unifier les objets biologiques dans la communauté scientifique, de revisiter notre compréhension du vivant et d'avancer vers plus de biologie prédictive. La biologie prédictive est une étape obligatoire vers l'unification de notre interprétation des systèmes biologiques et l'établissement d'une théorie générale du vivant. 5 Discussion and perspectives 5.1 What is normal development? . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 High Throughput Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Interindividual and interspecies comparison . . . . . . . . . . . . . . . . . . 5.4 In silico experimentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Movies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . different visions for robustness and resilience landscape . . . . . . . . 1.2 The twin paradigm to distinguish between transmitted and acquired variability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Phylogenetic tree of the sea urchin species in literature . . . . . . . . . . . 1.4 Overview of the sea urchin larval development . . . . . . . . . . . . . . . . 1.5 Sea urchin mouth formation . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Early development of sea urchin twins . . . . . . . . . . . . . . . . . . . . 1.7 Overview of the preimplantation development of the rabbit embryo. . . . . 1.8 Oogenesis, ovulation and fertilization . . . . . . . . . . . . . . . . . . . . . 1.9 Possible models for lineage divergence and patterning of the mouse preimplantation embryo. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.10 A comparative overview of the maternal-to-zygotic transition (MZT) in several model organisms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.11 Time course of volume changes in an individual blastocyst related to the penetration of the zona . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 The BioEmergences reconstruction workflow. . . . . . . . . . . . . . . . . . 2.2 Reconstructing early embryogenesis from time-lapse optical sectioning. . . 2.3 Visualization and validation of the lineage tree reconstruction (zebrafish dataset Dr1). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Chain of PDE-based algorithms for the 3D segmentation of embryonic cells. 2.5 Comparative performance of nine software tools on zebrafish dataset Dr1. . 2.6 Automated fate map propagation. . . . . . . . . . . . . . . . . . . . . . . . 2.7 Custom-made mold for mammal imaging. . . . . . . . . . . . . . . . . . . . 2.8 Protocol pipeline from mammal acquisition setup to analysis. . . . . . . . . 2.9 OKO-lab regulation system and embryo mounting . . . . . . . . . . . . . . 3.1 The three phenotypes of sea urchin embryos. . . . . . . . . . . . . . . . . . 3.2 Models of sea urchin twin embryogenesis. . . . . . . . . . . . . . . . . . . . 3.3 Number and diameter of PMCs in sea urchin gastrula. . . . . . . . . . . . 3.4 Sea urchin singleton with flat cell organization. . . . . . . . . . . . . . . . 3.5 Refined models of sea urchin twin embryogenesis. . . . . . . . . . . . . . . 3.6 Biological questions and technical barriers to studying sea urchin singletons.113 4.1 Reconstruction of digital rabbit specimens from 3D + time imaging of nuclei118 xxiii xxiv LIST OF FIGURES 4.2 Temporal rescaling of digital rabbit specimens . . . . . . . . . . . . . . . . 119 4.3 Number of cells in wild-type embryos and clones . . . . . . . . . . . . . . . 120 4.4 Asymmetrical divisions in wild-type embryos and clones . . . . . . . . . . . 124 4.5 Proportion of cell deaths in rabbit embryos . . . . . . . . . . . . . . . . . . 127 5.1 Example of an algorithm for unsupervised clustering (OPTICS). . . . . . . 137 S2 Number of scientific publications concerning animal cloning from 1972 to 2010. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 S3 MLSM imaging setup. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 S4 Eye inspection of false positive events. . . . . . . . . . . . . . . . . . . . . 147 S5 Cell proliferation rate and cell dispersion along the lineage tree. . . . . . . 149 S6 Formal and applied epistemology for the reconstruction of the multilevel dynamics of complex systems. . . . . . . . . . . . . . . . . . . . . . . . . . 151 S7 OKO-lab heating, temperature feedback stability and CO 2 concentration over time in three typical experiments. . . . . . . . . . . . . . . . . . . . . 153 S8 Temporal rescaling methods. . . . . . . . . . . . . . . . . . . . . . . . . . . 155 S9 Distribution of the proportion of inner cells. . . . . . . . . . . . . . . . . . 157 S10 Euclidean distance between dividing inner cells and the centroid of the inner cell mass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

S1

  Danio rerio (Dr1), 3D raw data rendering. . . . . . . . . . . . . . . . . . . S2 Phallusia mammillata (Pm1), 3D raw data rendering. . . . . . . . . . . . . S3 Paracentrotus lividus (Pl1), 3D raw data rendering. . . . . . . . . . . . . . S4 Danio rerio (Dr1), reconstructed specimen. . . . . . . . . . . . . . . . . . . S5 Phallusia mammillata (Pm1), reconstructed specimen. . . . . . . . . . . . . S6 Paracentrotus lividus (Pl1), reconstructed specimen. . . . . . . . . . . . . . S7 Danio rerio (Dr1), validated cell clones. . . . . . . . . . . . . . . . . . . . . S8 Phallusia mammillata (Pm1), clonal history of selected cells. . . . . . . . . S9 Paracentrotus lividus (Pl1), clonal history of selected cells. . . . . . . . . . S10 Manual correction of a false negative mitosis in Paracentrotus lividus (Pl1). S11 Danio rerio (Dr1), fate map and relative movements of cell populations. . . S12 Phallusia mammillata (Pm1), fate mapping. . . . . . . . . . . . . . . . . . S13 Paracentrotus lividus (Pl1), distinct cell populations. . . . . . . . . . . . . S14 Pre-hatching development of a sea urchin singleton. . . . . . . . . . . . . . S15 Pre-hatching development of a fluorescent sea urchin twin pair (130830aF). S16 Pre-hatching development of a fluorescent sea urchin twin pair (130904aF). S17 Pre-hatching development of a fluorescent sea urchin twin pair (140629aR). S18 Pre-hatching development of a fluorescent sea urchin twin pair (130928fR). S19 Pre-hatching development of a fluorescent sea urchin twin pair (131017aF). S20 Pre-hatching development of a fluorescent sea urchin twin pair (131022aF). S21 Pre-hatching development of a fluorescent sea urchin twin pair (131022aZ). S22 Pre-hatching development of a fluorescent sea urchin twin pair (140930aR).In this section, we present fundamental concepts of embryogenesis and evolution. Then, we attempt to define what normality is in developmental biology and how variability, robustness and resilience are interdependant in the evolution of living organisms. Finally, we define variability and how it can be measured to decipher the mechanisms behind normality and abnormality. The concept of variability is illustrated by case studies about the regulation of body proportions in Drosophila melanogaster.

  t 0 (e.g., at the time of fertilization). It encompasses the differences due to inheritance mechanisms such as DNA polymorphism, maternal mRNA distribution or the size of the embryo. By definition, acquired variability is null at t 0 and increases or decreases during embryonic development. It results from the interaction of the organism with its environment. The response of the organism at all levels of organization depends on its unique characteristics and multilevel dynamics.Throughout the life of the individual, acquired and transmitted variability are combined into phenotypic variability. Consequently, measurements made on phenotypic traits reflect the complex combination of acquired and transmitted variability, making the distinction between the two generally impossible. A trivial solution of the problem is at t 0 , where transmitted variability can be directly quantified because acquired variability is still null. For the other cases where t>t 0 , it is possible to compare homozygotic twins to reduce or remove part of the phenotypic variability. Twins have the most similar inherited material and the most similar histories that can be expected. The comparison of individuals within twin pairs minimizes transmitted variability(Figure 1.2, red). Comparison between twin pairs of embryos brings transmitted variability back into the equation while keeping the same acquired variability (Figure1.2, green). These two comparisons uncouple acquired variability and transmitted variability, making it possible to measure the influence of acquired variability and determine the influence of transmitted variability on phenotypic variability.

Figure 1 . 2 :

 12 Figure 1.2: The twin paradigm to distinguish between transmitted and acquired variability. Twin pairs of embryos are observed and parameters are measured for each individual.Transmitted variability within twin pairs is null, so the phenotypic variability is the acquired variability and can be measured by intra-pair comparison (red). Comparison of the measurements between twin pairs (green) gives information about both transmitted and acquired variability.

  make the reading more fluid and to avoid repetition, two paragraphs have been removed (Defining Normal Development and Introducing Developmental Variability) and one figure has been modified (Figure 1.6).

Figure 1 . 4 :

 14 Figure 1.4: Overview of the typical development of a sea urchin (Paracentrotus lividus) from fertilization to pluteus larva. A-G: pre-hatching stages. The first two cleavages form four identical cells (C). The third cleavage is asymmetrical, forming four animal cells and four vegetal cells (D). The fourth cleavage generates three cell types in E:eight mesomeres (blue), four macromeres (orange) and four micromeres (pink). The next cleavage symmetrically divides mesomeres and macromeres (F). The micromeres divide asymmetrically in four large micromeres (pink) and four small micromeres (purple). Cells compact prior blastula hatching (G). The vitelline layer is digested giving the swimming blastula (not shown). H-L: primary mesenchymal cells ingress (PMCs, in red) into the blastocoel. PMCs form two clusters on both sides of the blastocoel (H). macromeres' progeny (orange) invaginates forming the archenteron (I). The tip of the gut fuses to the future ectoderm (J, blue) and completes its shaping in three parts by the pluteus stage. Meanwhile, PMCs surround the gut, fuse to form a syncytium and start the spiculogenesis which shapes the pluteus larva (J-L). Pluteus larva shows three axes: left/right axis, oral/aboral axis and ventral/dorsal axis. In L, the light-green curve corresponds to the oral/ventral side, the green curve to the aboral/ventral side and the dark-green curve to the aboral/dorsal side. Pluteus larva is represented in lateral and dorsal view showing the complex skeleton organization. The secondary mesenchymal cells (SMCs) stay at the tip of the gut (I and J in red) and migrate into the blastocoel or over the ectoderm during mouth formation (K and L). Blue: ectoderm; Orange: future endoderm; Green: uncertainties of lineage between ectoderm and endoderm; Red: future mesoderm; Pink: germ line.

Figure 1 . 5 :

 15 Figure1.5: Focusing on the prospective mouth region during mouth formation, secondary epithelial to mesenchymal transition and gut differentiation. A: invagination (black arrow) of the ectoderm (blue) coincides with the endoderm (orange) bending driven by filopodia of secondary mesenchymal cells (SMCs, not shown) at the tip of the archenteron. B: archenteron tip meets the ectodermal invagination. A first constriction of the gut (dashed line) separates the coelom (dark green, derived from SMCs) from the rest of the gut. Meanwhile, SMCs not involved in coelom formation (red) migrate into the blastocoel and differentiate in pigment cells or blastocoelar cells. C 1,2 : as ectoderm (blue) continues to invaginate and gut further elongates, the coelom (dark green) is pulled apart into two coelomic pouches. A second constriction of the gut (dashed line) appears between the oesophagus (light green) and the rest of the gut. A-C 1 : lateral view from the left. C 2 : animal view. Adapted from[START_REF] Gustafson | Studies on the cellular basis of morphogenesis in the sea urchin embryo[END_REF]

  To the best of my knowledge, the only description of early development of sea urchin twins was made by Hans Driesch himself (Driesch, 1891). The most striking difference compared with normal embryos is the discrepancy between cell differentiation and cell number. At the 1-cell stage, twin embryos have the cytoplasmic clock of 2-cell stage blastomeres. Therefore, at the 2cell stage, blastomeres have the cytoplasmic clock of normal 4-cell stage cells. In fine, asymmetrical divisions happen at the same age post-fertilization. The usual three different cell types are formed at the 8-cell stage rather than the 16-cell stage (Figure 1.6B-D).Hans Driesch reported that 75% of the twin embryos looked precisely like one half of normal ones (Figure1.6E 1 ) until the blastula stage. Interestingly, he observed that a few hours later, sea urchin twins formed perfectly-shaped blastulae (Figure1.6F). He did not describe how embryos were able to catch up by closing their half-sphere shape. 25% of the embryos followed a different developmental path and exhibit 8 cells of identical size (Figure1.6E 2 ). It was not clear for Hans Driesch whether these observations were experimental artefacts (e.g., 4-cell stage embryo aggregation) or the result of particular biological mechanisms (e.g., delayed asymmetrical division).

Figure 1 . 6 :

 16 Figure 1.6: Early development of sea urchin twins. A: first cleavage in whole embryo. B: mechanical isolation of 2-cell stage blastomeres. C: 2-cell stage of one blastomere corresponding to the 4-cell stage of a normal embryo. D: 4-cell stage of one blastomere corresponding to an 8-cell stage of a normal embryo. E 1 : 75% of the observed phenotypes are described by Driesch as half-embryos. E 2 : cell types cannot be distinguished or two 4-cell stage twins aggregate. F: a percentage of embryos from E 1 and/or E 2 are able to form a small swimming blastula.

  plored by Giovanni Giudice in 1962. Giovanni Giudice isolated cells from sea urchin blastula (Figure1.4G) and gastrula (Figure1.4I). Putting the cells back together in regular sea water was sufficient to lead to cell reaggregation and larva formation. It was, however, shown that dissociation at gastrula stage did not lead to such a high percentage of successful development. In addition, cells from different species never mixed, indicating that cell reaggregation is species-specific. More recent experiments have shown that disaggregation by the 16-cell stage (Figure1.4E) followed by reaggregation led to fertile larvae[START_REF] Herrler | Proteins in the extraembryonic matrix of preimplantation rabbit embryos[END_REF].

  and methods have been improved. As far as I know, the early development of twins has never been investigated in detail and questions remain open. How are half-sphere shaped embryos able to converge to a normal-looking pluteus? Is each twin composed of half the number of PMCs, SMCs and germ line cells? Does cell transfating operate to regulate cell populations in twins? According to the concept of "constancy of the wild type"[START_REF] Vlahou | Maternal mRNA encoding the orphan steroid receptor SpCOUP-TF is localized in sea urchin eggs[END_REF], we expect to observe greater variability between twins than between wild types. The increased variability may explain Driesch's observations of multiple developmental paths (Figure1.6E 1,2 ). Finally, twins offer the possibility to quantify acquired and transmitted variability in developing embryos, giving the opportunity to characterize whether the commitment to a developmental path or the survival of individuals in disturbed conditions is correlated to inherited material or stochastic processes.

1

  Commission Staff Working Paper -Report on the Statistics on the Number of Animals used for Experimental and other Scientific Purposes in the Member States of the European Union in the year 2011, SWD(2013) 497 final

  Figure 1.7: Overview of the preimplantation development of the rabbit embryo. A: The secondary oocyte is blocked in metaphase II with the first polar body trapped beneath the zona pellucida (transparent white). A spermatozoa binds to the zona pellucida which induces acrosome reaction and the fusion of the gamete membranes. B,C: the acrosome reaction reactivates oocyte meiosis which produces the second polar body. The female haploid genome (white) forms a pronucleus, while the sperm genome (black) fragments into multiple vesicles. The DNA decondenses and the male centriole captures pronuclei to form the metaphase plate. D,E: Cells divide by regular mitoses until the morula stage (8-cell stage). F: Compaction stage. Contacts between cells are reinforced and the cortical cytoplasm is re-organized. Individual cells are not visible in bright field and the surface of the embryo becomes smooth. G: So-called symmetry breaking generates inner (orange) and outer (blue) cells. The fate of cells is not determined and may change in the next steps, although this remains an open question. H, I: Cells continue to divide and form the trophectoderm, a thin monolayered epithelium (light blue), and the inner cell mass, a pack of non-polarized cells (orange). Molecular channels move external fluid into the embryo to form the blastocoele (light green). J: A second cell specification occurs, generating the hypoblast (dark orange), a cuboid epithelium derived from the inner cell mass facing the blastocoele, and the epiblast (orange), the progenitor of the fetus itself. K: Implantation of the embryo in the uterus wall of the mother (purple) while the epiblast forms the amniotic cavity (blue hatches).

Figure 1

 1 Figure 1.8: Oogenesis, ovulation and fertilization. A: The first 20 post-partum days are the setting for the first steps of oocyte meiosis to metaphase I. B: Folliculogenesis matures the oocyte. A zona pellucida is made around the oocyte and follicular cells develope while maintaining GAP junctions with the oocyte. The oocyte grows and the ovarian follicle is formed (yellow cells with black nuclei). C: Coitus induces an LH surge which opens the ovarian follicle to the outside of the ovary. The oocyte resumes its meiosis and, along with cumulus cells, migrates through the infundibulum and the ampulla. D: The oocyte blocked in metaphase II meets spermatozoa (black) in the ampulla where the fertilization takes place.

  Figure 1.10). Contrary to mouse MZT, which happens during the first cleavage (Flach

Figure 1 .

 1 Figure 1.10: A comparative overview of the maternal-to-zygotic transition (MZT) in several model organisms. Key embryonic stages for each model organism are depicted schematically above the corresponding cleavage cycle and time after fertilization. The red curves represent the degradation profiles of destabilized maternal transcripts in each species.The light-blue and dark-blue curves illustrate, respectively, the minor and major waves of zygotic genome activation. The last embryonic stage presented for each organism is the developmental point at which there is a major requirement for zygotic transcripts. From[START_REF] Tadros | The maternal-to-zygotic transition: a play in two acts[END_REF]

  Figure 1.11: Time course of volume changes in an individual blastocyst related to the penetration of the zona. The record began 98 h after the estimated time of ovulation.-, volume of zona pellucida; -, volume of blastocyst. (a) indicates the beginning of partial rotations by the blastocyst, and (b) the initial penetration. From[START_REF] Cole | Cinemicrographic observations on the trophoblast and zona pellucida of the mouse blastocyst[END_REF]

I

  invite the reader who wants to know more about implantation to read[START_REF] Mcgowen | The evolution of embryo implantation[END_REF] and the very complete review by[START_REF] Carson | Embryo implantation[END_REF]

  of the BioEmergences Workflow. The phenomenological reconstruction of embryonic cell lineage starts from multimodal 4D data, typically comprising at least one channel for the fluorescent signals emitted by cell nuclei, and possibly another one for cell membranes. While nuclear staining is essential for cell tracking, membrane staining is necessary to assess cell morphology and neighborhood topology. We deliver the first public version of the standalone BioEmergences workflow, with graphical user interface, capable of performing a succession of algorithmic steps on two-channel raw data (Figure 2.1 and Section 2.1.4). Image filtering, nucleus center detection, membrane shape segmentation, and cell-tracking methods were all designed and tuned to deal with the inherent noise and incompleteness of 4D imaging data generated by MLSM (Figure 2.2). The BioEmergences standalone pipeline produces reconstructed data that can be directly displayed by the interactive visualization software Mov-IT (Section 2.1.4). By superimposing reconstructed data on raw data, Mov-IT adds visual inferences to create an augmented phenomenology.

Figure 2 . 2 :

 22 Figure 2.2: Reconstructing early embryogenesis from time-lapse optical sectioning. (ad) Danio rerio dataset Dr1, animal pole (AP) view; (a,b) sphere stage, volume cut at 68 µm of the AP to visualize deep cells; (c,d) 1-somite stage (1s), volume cut at 76 µm of the AP, pcp: prechordal plate, ysl: yolk syncytial layer, nk: neural keel. (e-h) Phallusia mammillata dataset Pm1, vegetal view, circum-notochord side up; (e,f) gastrula stage; (g,h) tailbud stage. (i-l) Paracentrotus lividus dataset Pl1, lateral view, animal pole up; (i,j) 57-cell stage; (k,l) blastula stage. (a,c,e,g,i,k) 3D raw data visualization (with Amira software), hours post fertilization (hpf) indicated top right, developmental stage indicated bottom left, scale bars 50 µm.(a,c,i,k) Nuclear staining from H2B-mCherry mRNA injection (in orange), membrane staining from eGFP-HRAS mRNA injection (in green). (e,g) Nuclear staining from H2B-eGFP mRNA injection (in blue-green). (b,d,f,h,j,l) Reconstructed embryo visualized with the Mov-IT tool, corresponding to a,c,e,g,i,k respectively. Each cell is represented by a dot with a vector showing its path over the next time-steps (15 for the zebrafish data, 9 for the ascidian and sea urchin data). Color code indicates cell displacement orientation.

  Our methods are illustrated on 6-hour spatiotemporal sequences covering the cleavage and gastrulation periods in Danio rerio (Dr1 and Dr2) and Phallusia mammillata (Pm1 and Pm2), and the cleavage stages in Paracentrotus lividus (Pl1 and Pl2) (Figure 2.2, Appendix Movies S1, S2and S3). The ascidian and echinoderm embryos were imaged in toto, allowing the reconstruction of the complete cell lineage across entire specimens. The subvolume imaged in the developing zebrafish contained up to 9,500 cells, adding the difficulties of high nucleus density and noise increase along the imaging depth. The user can choose between a standalone version, easily deployed on a personal computer but limited in terms of scalability, and a web service with user support, designed to rapidly process large datasets through parallel implementation on our computer clusters and the EGI (Appendix Note S1 on computational speed). The latter is especially powerful for optimizing the choice of algorithm parameters under expert supervision. The comparison of the BioEmergences workflow with other available strategies demonstrates the high performance and usefulness of our tools.Image processing steps. The BioEmergences automated image processing workflow (Figure2.1) is delivered as a standalone code that can be executed from the command line and, in part, from a graphical user interface. It starts by filtering the images and detecting the cell centers from local maxima, then performs a tracking of the cells' trajectories, with optional segmentation of the shapes of nuclei and membranes, producing in the end a digital specimen in output (Section 2.1.4, Appendix Movies S4, S5 and S6).

Figure 2 .

 2 Figure 2.3: Visualization and validation of the lineage tree reconstruction (zebrafish dataset Dr1). Results from the zebrafish dataset Dr1. All screenshots taken from the Mov-IT visualization interface, then tagged. (a) Cell division illustrated by three snapshots; time in hours post fertilization (hpf) indicated top right; cell centers (cyan cubes) and cell paths (cyan lines) superimposed on two raw-data orthoslices showing the membranes (gray levels). (b) Flat representation of the cell lineage tree for three cell clones over 17 consecutive time-steps: each cell is represented by a series of cyan squares, linked according to the cell's clonal history; the cell dividing in Figure 2.3a is circled. (c,d) Nucleus center detection and 10-step tracking (short white lines) in a subpopulation of cells chosen at 11.61 hpf. Mov-IT visualization in checking mode displaying correct nuclei in green and false positives in red; (c) all detected nuclei; (d) validated nuclei only.

Figure 2 . 4 :

 24 Figure 2.4: Chain of PDE-based algorithms for the 3D segmentation of embryonic cells. Results from the zebrafish dataset Dr1. Scale bars in µm (100 in a,b,f,g; 10 in c,d,e). (a) Raw data image; single z section at 67 µm depth (orientation as in Figure 2.2), magnification in inset. (b) Nucleus center detection by the FBLS method, after filtering by GMCF; approximate centers (cyan cubes) superimposed on raw data (gray levels, same orthoslice as Figure 2.4a). Magnification in insets: left inset at depth 67 µm; right inset located one section deeper, showing that several centers not displayed at 67 µm were in fact correctly detected and visible below the chosen plane (white arrowheads); remaining centers can be found on other planes using the Mov-IT interactive visualization tool. (c) Nucleus segmentation by SubSurf method; left panels: an interphase nucleus; right panels: a metaphase nucleus; top panels: initial segmentation (pink contour) superimposed on raw data (gray levels); bottom panels: final segmentation (orange contours) superimposed on the same raw data. (d) Nucleus segmentation by SubSurf method; top panel: three nuclear contours superimposed on two raw-data orthoslices; bottom panel: 3D rendering of the segmented nuclei on a single orthoslice. (e) Membrane segmentation by SubSurf method; top panel: one cell membrane contour superimposed on two-raw data orthoslices; bottom panel: 3D rendering of the segmented membrane shape on a single orthoslice. (f ) 3D rendering of segmented cell shapes. (g) Embryo shape segmentation; local cell density increasing from blue to red (same orthoslice as Figure 2.4a). (h) Total cell number (blue curve) and cell density (brown curve) as a function of time; arrow indicates the end of gastrulation correlating with a plateau in cell density.

Figure 2 .

 2 Figure 2.5: Comparative performance of nine software tools on zebrafish dataset Dr1.

Figure 2 . 5 :

 25 Figure 2.5: Comparative performance of nine software tools on zebrafish dataset Dr1.The BioEmergences workflow presented here (red) was evaluated alongside Imaris (four shades of blue), Icy (yellow), Volocity (two shades of green) andAmat et al., 2014 (pink). Measurements were made by comparing the outcome of three reconstruction methods: nucleus detection, cell tracking (linkage) and mitosis detection, to a set of validated events (positions, trajectories, divisions) registered in a gold standard. This was done inside four time intervals centered on 4.36 hpf, 6.22 hpf, 8.08 hpf and 9.95 hpf. (a) Sensitivity of nuclear center detection, representing the rate of true positive (TP) centers. (b) Sensitivity of linkage, representing the rate of TP links (restricted to the subset of TP centers that possessed a validated link). (c) Sensitivity of mitosis detection, representing the rate of TP divisions (restricted to the subset of TP centers). (d-f ) Rates of false positive (FP) centers, links and divisions (only two software tools applicable to the latter). (g) Global lineage score, equal to the linkage sensitivity times the center detection sensitivity: all methods except BioEmergences deteriorate noticeably at later developmental stages. (h) Average rates calculated over the four time intervals: each column displays the mean heights of one of the previous seven charts. BioEmergences obtained the best results in every category: highest values in a-c and g (success rates), lowest values in d-f (failure rates). Formulas can be seen in Section 2.1.4, detailed values in Appendix TableS1.

Figure 2 .

 2 Figure 2.6: Automated fate map propagation. Cells were manually selected according to their identity or fate, using the interactive visualization tool Mov-IT. Developmental stages indicated bottom left, developmental times in hpf, top right. All scale bars 50 µm. (a,b) Three cell populations in Danio rerio specimen Dr1, animal pole view; (a) sphere stage, ventral (anterior) up, enveloping layer cells in cyan, epiblast cells in blue; (b) 1somite stage, same color code plus hypoblast cells in yellow. (c,d) Fate map in Phallusia mammillata specimen Pm1, vegetal view, circum notochord up, color code as in Lemaire, 2009; (c) gastrula stage; (d) tailbud stage with automated propagation along the cell lineage of the fate map shown in Figure 2.6c. (e,f ) Cell populations in Paracentrotus lividus specimen Pl1, lateral view, animal pole up; small micromeres in dark purple, large micromeres in light purple; mesomeres in cyan. (e) 32-cell stage, macromeres in red; (f) blastula stage, Veg1 cell population in red, Veg2 cell population in yellow.

  We thank Patrick Parra and Jean-Yves Tiercelin for custom-made high precision mechanics; Amparo Ruiz Vera and Odile Lecquyer for the management of the Embryomics and BioEmergences EC projects; Audrey Colin, Valérie Lavallée and Simon Agostini for fish care; Roger Tsien and Richard Harland for DNA constructs (mCherry and eGFP-HRAS, respectively); Pascal Abbas for technical assistance; the N&D and ISC-PIF staff for administrative support, and Dominique De Waleffe from DENALI SA for his contribution to the BioEmergences EC project's workflow interface. This work was supported by grants EC NEST Adventure 012916, EC NEST Measuring the Impossible 28892, ZF-Health EC project, ACI STIC santé, ANR BioSys Morphoscale, ANR-10-INSB-04 for N.P. and P.B., ARC 3628 for N.P., and APVV-0184-10 for K.M. 2.1.6 Author contributions E.F. coordinated the workflow development, the manuscript preparation, and performed the software comparison; T.S. developed the visualization interface Mov-IT and the BioEmergences web service; C.M. developed the cell tracking method and contributed to the workflow development; M.R. developed nucleus and membrane segmentation methods; O.D. developed filtering and nucleus center detection methods; D.F. contributed to the specification of the Mov-IT functionalities and organization of the BioEmergences database; B.R. developed the Gaussian-based nucleus center detection method; R.S. developed the embryo shape segmentation method and contributed to the workflow development; M.H. contributed to the workflow development, in both its standalone and web service versions; R.C. developed algorithm parallelization tools; G.R. coordinated the specification of the Mov-IT functionalities and organization of the BioEmergences database; B.L. worked on the reconstruction validation and standardization of error assessment; L.D. conducted imaging experiments, data validation and correction; I.C. and S.D. conducted imaging experiments, data validation and correction; J.K. provided engineering support to the computation; P.A., B.M. and A.B. conducted data validation and correction; J.Y.N. and P.C. provided support for data management, transfer and storage, and cluster management; P.V. contributed to defining the BioEmergences workflow concept; M.F. conducted data validation and correction; G.L. led the molecular biology

  Figure 2.8. The authors are indebted to members of the Unité Commune d'Expérimentation Animale (UCEA) responsible for the rabbit colonies.

Figure 2 .

 2 Figure 2.7: Custom-made mold for mammal imaging. A: Hole for embryo placing. B: Step to keep drops of medium. C: Step to avoid medium outflow. D: Hole to insert temperature probe. Dimensions in mm. Made of polymethyl methacrylate (PMMA).

Figure 2 .

 2 Figure2.8: Protocol pipeline from mammal acquisition setup to analysis. Laser 1 (Amplitude laser) is split (50-50) by a beam splitter (BS) and laser 2 (Spectra Physics/Newport laser) by a polarized cube (PC) following a half-wave plate (HWP) which enables modulation of power splitting. Each laser power for each microscope is then modulated by HWP followed by a PC. HWP are motorized and rotations are controlled with custommade software. The two lasers are combined before the scan head and scanned to the sample with a scanning mirror (SM). Light is focused into the sample with a 20× objective. Emission wavelengths are reflected by a dichroic mirror (DM) to the non descanned detection module. Remaining infrared light is cut by a short pass (SP) filter at 680 nm. Fluorescence is split by a DM and selectively filtered with pass-band filters (525/50 nm for EGFP fluorescence or 610/75 nm for mCherry fluorescence). Detection is achieved by photomultiplier tubes (PMT) or hybrid detectors (HyD). Photon energy is converted into current and signal, which is encoded in voxel intensity value. Acquisition software (LAS-AF) assembled 3D volume (stacks) at each time-step. Raw data are then automatically processed to detect and track cells over time and to reconstruct cell membranes. Users can then visualize, validate and analyze reconstructed data via a custom-made database interface(Fabrèges et al., in press).

Figure 2 . 9 :

 29 Figure 2.9: OKO-lab regulation system and embryo mounting. A: Diagrammatic view of the OKO-lab regulation system. Blue: cold fluid. Orange: warm fluid. Red: Hot fluid. Green: Data transfer. Water bath transports heated water to the water jacket chamber.Temperature probe measures the temperature in the mold (Figure2.7D). Computer sets water bath temperature according to the measured temperature. Temperature target is reached after 30 minutes (Appendix Note S4). Gas mixer mixes 100% CO 2 with air to produce 5% CO 2 . This gas is warmed up. B: Water jacket chamber with imaging dish and temperature probe properly installed. For better view, water jacket chamber has been removed from the microscope. C: Schematic view of the final mounting. Objective dips in the embryo medium (blue) forming a column between the mold (grey) and the objective. To avoid evaporation, mineral oil (orange) surrounds the embryo medium. Embryos (black dots) lie on an agarose coating (white). The temperature probe (green) is placed 400 µm below embryos.

  macromeres and micromeres. Contrary to Driesch, 1891, who obtained one phenotype and suspected a second one, I distinguished three twin phenotypes at the 8-cell stage and the 16-cell stage. The results below were obtained after pooling data from three different experiments made with Paracentrotus lividus: • The first phenotype (Figure 3.1A, D) represents 72.6% (n=53) of my observations and corresponds to the first phenotype observed by Driesch, who described a similar prevalence of 75%. It corresponds exactly to one half of a normal embryo in terms of cell number and cell positions. • The second phenotype (Figure 3.1B, E) represents 15.1% (n=11) of my observations. It corresponds to one half of a normal embryo in terms of cell number, but cell positions are shuffled.

Figure 3

 3 Figure 3.1: The three phenotypes of sea urchin embryos. A-C: Images of the three phenotypes of sea urchin (DIC microscopy). D-F: Schematic representation of the phenotypes. A, D: One half of a normal embryo, as described by Driesch, 1891. B, E: One half of a normal embryo with cells shuffled. C, F: Group of cells of various sizes with no clear identification possible. Field width = 100 µm.

CHAPTER 3 .Figure 3 . 2 :

 332 Figure 3.2: Models of sea urchin twin embryogenesis.

Figure 3 . 2 :

 32 Figure 3.2: Models of sea urchin twin embryogenesis. Developmental time from left to right. A: Initial model based on Driesch's observations (Driesch, 1891). B: Model of robustness where twin larvae are considered normal. C: Model of resilience where every phenotype is preserved until larval stage. D: Hybrid model depicting a partially robust development with resilience. Models in B-D take into account the three phenotypes observed in this work. Dashed circle: Hypothetical stages. Same color code as Figure 1.4

  .2B) in which singleton developments use robust mechanisms to compensate for the initial differences and converge toward normal larvae. Because singletons lack half of the normal weight, they may not have the same PMC volumes -despite the absence of statistical differences -and they may not be able to fully demonstrate their robustness mechanisms before incorporating matter. Theoretically, wild-type individuals form 32 PMCs from 4 large micromeres. Considering that divisions are symmetrical in terms of volume, except for the 4th cleavage where large micromeres inherit about 35% of their precursors' volume, the volume of PMCs must be close to 0.55% of the 1-cell stage volume and have a diameter of 15.6 µm. If PMCs of singletons divide once more, their volume should be close to 0.27% of the 1-cell stage volume and have a diameter of 12.6 µm. The measurements made in Figure 3.3E show an average diameter for wild types and singletons of 5.8

Figure 3

 3 Figure 3.3: Number and diameter of PMCs in sea urchin gastrula. A, B: Apical view of wild-type (A) and singleton (B) gastrula. C, D: Same as A and B respectively, PMCs were identified during the experiment and marked with red dots. E: Diameters of PMCs measured during the experiment for controls (blue, n=38 PMCs in 10 embryos), controls of treatment in which blastomeres were not mechanically separated (purple, n=56 PMCs in 12 embryos) and singletons (red, n=32 PMCs in 8 embryos). Scale bar = 50 µm.

Figure 3 . 4 :

 34 Figure 3.4: Sea urchin singleton with flat cell organization. The embryo shown here had around 50 cells and was finishing its seventh round of division (between 30 to 60 cells).

Figure 3 . 5 :

 35 Figure 3.5: Refined models of sea urchin twin embryogenesis. Developmental time from left to right. A: Robustness flat model. Most of the three singleton phenotypes at the 16-cell stage converge to a flat phenotype. Depending on stochastic events, embryos rejoin the wild-type phenotype or die. B: Resilience flat model. This model is similar to A, but favors resilience over robustness. Same color code as Figure 1.4.

  not minor. Their development follows different paths and uses qualitatively different mechanisms. The differences we observed during development do not necessarily give different results. We must keep in mind that singletons may be different from wild types on average, but individually compatible with normal development. Defects in sea urchin development may be rectified by metamorphosis. As far as I know, three to five cells are involved in the development of adult rudiments. As well as the flat stage of singletons, the adult rudiment may be the stage of convergence between wild types and singletons, making the singleton development, in fine, robust. Biological questions left opened by this work and technical barriers to further studies are summarized in Figure 3.6.

Figure 3 . 6 :

 36 Figure 3.6: Biological questions and technical barriers to studying sea urchin singletons. Developmental time from left to right. First row: Wild type development. Other rows: Singleton development. A: The mechanical rules explaining the flat phenotype from the three 16-cell stage phenotypes are unresolved. In addition, some embryos are able to bypass the flat phenotype. The reason for this behavior is still unknown. B: Although previous studies corroborate the hypothesis of cell sorting, we still have to figure out how scattered cells are able to reorganize themselves to form a supposely normal blastula. C: the PMC cell lineage must be elucidated to determine whether transfating occurs or PMCs divide once more than in normal embryos. D: Although singleton larvae are smaller than wild type on average, we still do not know whether singletons are in the range of normal development. E: Plutei are able to feed themselves and grow according to their needs, which may compensate for the difference in the quantity of matter between singletons and wild types. Metamorphosis is the onset of a new development formed by three to five cells in wild types. Singletons may not be different from wild types afterwards. Green: Biological questions and the relevant stage where they apply. Orange: Technical barriers to overcome before further studies. Swimming embryos is an issue for live imaging. Late stage study requires larvae feeding. In addition, imaging must last for several days to observe metamorphosis. Same color code as Figure 1.4.

  Digital specimens were obtained by the imaging of five developing embryos (wt1, wt2 and wt3: wild-type embryos; nt1 and nt2: clones) during preimplantation stages, i.e., from the 32-cell stage to hatching. Chromatine was stained with a fusion protein H2B-EGFP encoded by a micro-injected mRNA (Figure4.1, first row) and datasets were automatically analyzed to reconstruct the 3D cell lineages (Figure4.1, second row). Clones were obtained by nucleotransfer of cumulus cells from the mother. Cell lineages were curated manually and analyses were performed (Figure4.1, third row).

Figure 4 .

 4 2A) shows very different developing speeds from one embryo to another, making inter-individual comparisons difficult. To compare embryos over time, we normalized the embryo developmental speed. Temporal rescaling based on cell numbers gave the best result in terms of the number of cells only, but poor results in terms of embryo volumes (Appendix FigureS8A,C). Temporal rescaling based on embryo volumes gave poor results in terms of the number of cells only, but very good results in terms of the volume of embryos, as expected (Appendix FigureS8B,D). In order to reconcile both features and to minimize experimental artefacts, we based the temporal rescaling on morphogenetic events presumably non-related to the cell number or embryo volumes. We chose two fixed points for each embryo: 1) the fertilization timing (wild-type embryos) or the activation timing (clones); 2) and the timing of the first blastocoele collapse (Figure4.2B). The temporal rescaling we obtained indicates that clones were developing slower than wild-type embryos (12% and 17% slower for nt1 and nt2 respectively).

Figure 4 . 1 :

 41 Figure 4.1: Reconstruction of digital rabbit specimens from 3D + time imaging of nuclei. First row: Z-projection of imaging volume of five embryos 15 minutes before the first blastocoele collapse. Second row: Z-projection of reconstructed embryos at the same timestep. Each dot is a mathematical representation of a physical nucleus. Third row: Imaging sequences of the five embryos. Plain bar: imaged and analyzed sequences. Hatched bar: imaged sequences. Black diamond: hatching. White disc: first major collapse. Age based on fertilization timing (wild-type embryos) and activation timing (clones). wt1-3: Wild-type embryos in orange (wt1), red (wt2) and green (wt3). nt1,2: Cloned embryos in blue (nt1) and purple (nt2). Scale bar: 50 µm.

Figure 4 . 2 :

 42 Figure 4.2: Temporal rescaling of digital rabbit specimens based on fertilization or activation timing and timing of the first blastocoele collapse. A: Number of cells over time without temporal rescaling. B: Number of cells over time with temporal rescaling. Dashed line: time of the first blastocoele collapse. See Figure 4.1 for color code.

Figure 4 . 3 :

 43 Figure 4.3: Number of cells in wild-type embryos and clones. A: Total number of cells (black line), inner cells (orange line) and outer cells (blue line) in wild-type embryos. Standard deviation is shown as semi-transparent area for total cells (grey), inner cells (light orange) and outer cells (light blue). B: Number of cells in clones. Color code is the same as for A. C: Proportion of inner cells in wild-type embryos (green) and clones (purple). Dashed line: timing of the first blastocoele collapse.

  embryos. We investigated further the influence of the asymmetrical divisions on the population variability and used the digital specimens to perform in silico experiments (Figure4.4E-J). The CV wt of the number of outer cells was quite constant over the imaging sequence, whereas the CV wt of the number of inner cells varied in three phases(Figure 4.4E). First, the coefficient was high (between 36% at 66.9 hpf and 75.7 hpf to 51% at 73.2 hpf), then it decreased as low as 19.7% at 84.6 hpf, and finally quickly steadied to 31%. The role of asymmetrical divisions in interindividual variability was investigated by switching asymmetrical divisions of inner cells (Figure4.4F) or outer cells (Figure4.4G) to symmetrical divisions. In all conditions, the CV wt of the number of outer cells were not affected, suggesting that asymmetrical divisions were highly reproducible between individuals (Figure4.4A,C) or the relative change in the number of cells was insignificant.By contrast, the CV wt of the number of inner cells were dramatically affected. Removing asymmetrical divisions of inner cells specifically removed the increase in the last part of the curve. Consequently, the CV wt of the number of inner cells stayed as low as the CV wt of the number of outer cells. On the contrary, removing asymmetrical divisions of outer cells dramatically increased interindividual variation for the whole imaging sequence. Because the wave of asymmetrical divisions of outer cells is conserved between individuals, it increases the number of inner cells by the same amount and at the same period of time in every embryo. Consequently, the interindividual variability tends to decrease. However, asymmetrical divisions of inner cells are not conserved and are highly variable.

Figure 4 . 4 :

 44 Figure 4.4: Asymmetrical divisions in wild-type embryos and clones. A-D: Proportion of asymmetrical divisions in wild-type embryos (A, B) and clones (C, D) of outer cells (A, C) and inner cells (B, D). Light colors show partial data. Red curves show the gaussian fit for asymmetrical divisions of wild-type embryos and clones. E-J: Coefficient of variation of total cells (black), inner cells (orange) and outer cells (blue) over time. E-G: Wild-type embryos. H-J: Clones. E,H: Original data. F,I: Cell lineage data with asymmetrical divisions of inner cells replaced by symmetrical divisions. G,J: Cell lineage data with asymmetrical divisions of outer cells replaced by symmetrical divisions. Dashed line: timing of the first blastocoele collapse.

Figure 4 . 5 :

 45 Figure 4.5: Death ratio as the number of cell deaths observed every 10 hours divided by the number of cells in wild-type embryos (green) and clones (purple). Light colors show partial data. A: Death ratio of inner cells. B: Death ratio of outer cells. C: Measured death ratio divided by expected death ratio. The expected ratio corresponds to a condition were every cell has the same probability of dying, regardless of its position.

  for Biomedical Research Involving Animals, as promulgated by the Society for the Study of Reproduction, and with the European Convention on Animal Experimentation. Researchers involved in direct work with the animals possessed an animal experimentation license from the French veterinary services. Female New Zealand White rabbits were superovulated as described by Henrion et al., 1997 and mated with normal males (wild types) or vasectomized males (clones). In vivo one-cell stage embryos were recovered from oviducts flushed with PBS at 19 hours post-coitum (hpc). Embryos were micro-injected with a solution containing 75 ng/µL of H2B-EGFP mRNA at 38.5°C in M199 medium containing 10% FBS and 0.5% penicillin/streptomycin and 20mM HEPES (bM199-FBS).

  my experience, few experiments are successful because of technical problems (e.g., crash of acquisition software or power outage), bad adjustment of laser power (e.g., change of staining brightness during embryo development) or observation of a wrong region (e.g., embryo shifting out of the field or rotating). A technical problem is difficult to anticipate, especially if it concerns global system failure, but technical problems are rare and do not often impair experiments. Because an embryo changes during in vivo imaging, its fluorescence, dimension and position change too. Users have currently to stay close to the microscope to adjust parameters in real time, which is tedious for long-term imaging, or they have to anticipate embryo size, position and brightness changes. In three experiments out of four, the predictions are wrong and data cannot be properly analyze. An artifical

  Figure 5.1: Example of an algorithm for unsupervised clustering (OPTICS). The set of points represents a distribution of two arbitrary parameters x and y. The discrimination factor of the method (ϵ) varies from 0 to 1. Colors represent categories (population, species, class, etc.). Black points correspond to unclassified individuals. A low value of ϵ correlates with a high number of categories (ultimately one category per individual).By contrast, a high value of ϵ correlates with a low number of categories (ultimately one category for living organisms). For instance, if the category species is set by consensus to ϵ = 0.3, then the sample here exhibits three different species. For more information about the OPTICS algorithm, see[START_REF] Ankerst | Optics: Ordering points to identify the clustering structure[END_REF]. Data were generated using R and the dbscan library[START_REF] Hahsler | Density Based Clustering of Applications with Noise (DBSCAN) and Related Algorithms[END_REF].

Figure S4 :

 S4 Figure S4: Eye inspection of false positive events.

Figure S4 :

 S4 FigureS4: Eye inspection of false positive events. The automated comparison of the reconstructed data with the gold standard from zebrafish dataset Dr1 provided a list of false positive events that were verified with Mov-IT (Appendix Software S2). Visual inspection demonstrated the validity of the conclusions drawn from our comparison protocol (Fig-ure2.5 and Appendix TableS3). Three examples of false positives in the reconstructed data obtained with the software ofAmat et al., 2014 are presented here. (a,b) False positive nucleus detection creating a lineage branch. (a) Two approximate centers (pink and yellow) are found inside a single real nucleus, displayed at the intersection of three raw-data orthoslices (gray levels). In checking mode, Mov-IT extends detected centers with a short vertical white line to signal their presence across other z sections where they may not be visible. (b) Flat representation of the lineage tree with Mov-IT showing that this event is associated with a false positive division, since the two centers are interpreted as having the same mother cell. The false positive nucleus remained visible over the next time steps. (c,d) False positive cell division, displayed in a single raw-data orthoslice (gray levels). (c) The putative mother is labeled with a pink dot. Future trajectories over the next three timesteps are indicated by thin yellow lines. (d) A neighboring cell was incorrectly picked to be a daughter cell and colored in pink, too, since Mov-IT propagates colors assigned to selected cells along their lineage. (e,f) False positive nucleus detection ending a lineage branch. (e) Same display mode as (a) and, coincidentally, at the same time. (f) Flat representation of the lineage tree with Mov-IT showing that this particular false positive event actually originated earlier, then disappeared at 4.36 hpf.

Figure S5 :

 S5 Figure S5: Cell proliferation rate and cell dispersion along the lineage tree.

Figure S5 :

 S5 FigureS5: Cell proliferation rate and cell dispersion along the lineage tree. Left column: zebrafish dataset Dr1. Center column: ascidian dataset Pm1. Right column: sea urchin dataset Pl1.(a,b) The successive cell division cycles in the developing ascidian or sea urchin embryos were revealed by plotting (a) the total cell number and (b) the cell cycle length (time between two consecutive divisions) as functions of time, in hours post fertilization (hpf). In blue: all detected and tracked cells; in red: cells validated by visual inspection. Cell division synchrony was more prominent in the sea urchin. For the zebrafish embryos, analyzed here at late blastula stages, the interpretation of the increase in cell number was highlighted by the quantification of cell density and internuclear distance (see next row). Whereas the sea urchin and ascidian reconstructed specimens were extensively corrected by experts to define gold standards, we only validated and corrected subsets of the zebrafish clones. The characteristics of the validated cells (in red) were consistent with a linear progression of cycle length for most cells throughout the blastula and gastrula stages. However, the dispersion of cycle length around the average, besides suggesting a certain number of errors in the detection of cell divisions (especially at late developmental stages), also highlighted a greater diversity of behavior. In this respect, the ascidian Phallusia mammillata showed the largest variety of specific proliferation rates along the lineages. (c) Average distance between pairs of neighboring cell nuclei through divisions as a function of hpf (error margin in gray). Cell neighborhoods are calculated by 3D Delaunay triangulation using the Computational Geometry Algorithms Library (CGAL,[START_REF] Pion | CGAL User Ref. Man[END_REF]. In all three species, this average distance decreased during early embryogenesis and converged to a minimal value around 10µm, corresponding to an average cell diameter. This observation fits with the plot of cell density, obtained by segmentation of the global imaged volume (Figure2.4h), which increased throughout gastrulation and plateaued at the end of gastrulation (10 hpf). It is also consistent with an estimate of the average proliferation rate during the same developmental period[START_REF] Kane | Mitotic domains in the early embryo of the zebrafish[END_REF]. (d) Average number of neighbors as a function of hpf. In both the zebrafish and the ascidian, this value is near 12, i.e., in the range of perfect 3D hexagonal close packing[START_REF] Farhadifar | The Influence of Cell Mechanics, Cell-Cell Interactions, and Proliferation on Epithelial Packing[END_REF]. In the sea urchin, the observed value of about 10 neighbors is consistent with the presence of a blastocoel and the organization of its early embryo into a pseudostratified epithelium. (e) Dispersion of cells as a function of hpf. At each time-step, all possible pairs of neighboring cells are identified and tracked forward. Distances between neighbors are divided by the average internuclear distance. The resulting average normalized distance increases over time, providing a quantitative evaluation of cell dispersion. Information about the global cohesiveness of the embryonic tissues indicates distinct developmental periods.

Figure S7 :

 S7 FigureS7: OKO-lab heating, temperature feedback stability and CO 2 concentration over time in three typical experiments. Grey curve: water bath temperature. Blue curve: temperature in the probe hole (Figure2.7D). Orange curve: temperature in the medium (Figure2.7A). Green curve: CO 2 concentration of dry air injected into the imaging chamber. Red dash line: temperature set point. Green dash line: CO 2 concentration set point. A: Temperature stabilization from room temperature (22°C) to set point (37°C), and CO 2 concentration stabilization from atmospheric conditions (0.03%) to set point (5%). Temperature difference between probe hole and embryo medium reached a maximum of 0.5°C. A first plateau was rapidly reached after 10 minutes (29°C). After 45 minutes a temperature of 35°C was reached. Stable temperature (36.7°C) was reached after 1 hour and 15 minutes. CO 2 target concentration was reached in less than 30 minutes. B: The imaging chamber in A was replaced with a new one filled with room temperature medium and room temperature mineral oil. The water bath was already hot. The temperature of embryo medium in the imaging chamber rapidly reached a plateau at 35.2°C (20 minutes). The software over-compensated 20 minutes later and the temperature of the probe hole rapidly increased to 37.5°C. The embryo medium reached a maximum of 36.7°C during the same period of time. The temperature of the chamber slowly decreased to the same stable point as in A. C: The imaging chamber in B was replaced with a new one filled with warm embryo medium (37°C) and warm mineral oil (37°C). The curves are very similar to the one in B, but start warmer (32°C). D: Temperature stability in the imaging chamber during 19 hours and 30 minutes. Embryo medium temperature fluctuated between 36.5°C and 36.6°C.

Figure S8 :

 S8 Figure S8: Temporal rescaling methods.

Figure S8 :

 S8 Figure S8: Temporal rescaling methods. Temporal rescaling methods applied on the five digital specimens, wt1 (orange), wt2 (red), wt3 (green), nt1 (blue) and nt2 (purple). A-C: Number of cells over time. D-F: Hull volume over time. A,D: Rescaling method based on morphogenetic events, i.e., fertilization or activation and collapsing. B,E: Rescaling method based on the best exponential fit of the number of cells. C,F: Rescaling method based on the best exponential fit of the hull volume.

Figure S9 :

 S9 FigureS9: Distribution of the proportion of inner cells. A: Ratio in wild-type embryos before 72.6 hpf (light green) and after 72.6 hpf (green). B: Ratio in clones before 72.6 hpa (light purple) and after 72.6 hpa (purple).

Figure S10 :

 S10 Figure S10: Euclidean distance between dividing inner cells and the centroid of the inner cell mass

  the linear diffusion used to pre-smooth the image gradient (edge detector) in the "diffusivity" function g; a larger σ means more smoothing of the gradient inside the edge detector, thus the final result is less sensitive to steps; a small iter means less smoothing of the image; a larger iter means more smoothing Parameters for the FBLS center detection method a the FBLS center detection method; τ is restricted by the Courant-Friedrichs-Lewy (CFL) above this threshold are counted as cell centers at the current time step; a small threshold means that more centers (even inside one cell) are detected; a large threshold means that less centers are

  by cell: if n cells are being processed, SimAnn will perform n×p 4 tentative link modT is computed to make the probability of accepting a move (when the cost increases by an amount equal to the average of the 100 highest costs in the population) equal to p 5 . cost associated to the deformation of the tissue between times t and t + 1 (this deformation depends on the choice of links) cost penalizing the sudden appearance of a cell out of any former lineage

  Movie S6: Paracentrotus lividus (Pl1), reconstructed specimen.See full movie here: http://bioemergences.iscpif.fr/users/dimitri.fabreges/thesis/movies /movie6.mp4. Titre : Variabilité phénotypique dans la morphogénèse animale : oursins jumeaux et lapins clonés Résumé : La variabilité est une propriété intrinsèque aux systèmes biologiques, essentielle pour l'évolution et l'embryogénèse. Souvent considérée comme du bruit, ce n'est que récemment que l'aléatoire des processus biologiques a commencé à être systématiquement étudié. Cette thèse pose les questions suivantes : qu'est-ce qu'un développement normal ? Quel est l'étendue et le rôle de la variabilité dans la robustesse et la résilience du développement embryonaire ? Ces questions sont posées pour le lapin (Oryctolagus cuniculus) et l'oursin (Paracentrotus lividus et Sphaerechinus granularis). Nous nous sommes aussi intéressé à la quantification du déterminisme de la variabilité embryonnaire à l'aide d'oursins jumeaux et de lapins clonés. La mesure des comportements cellulaires est effectuée sur des lignages cellulaires obtenus à partir d'imagerie 3D+temps. Nous montrons que les oursins jumeaux peuvent se développer selon trois phénotypes différents, jamais observés chez le normal, avant de converger vers une blastula d'apparence normale. De plus, les comparaisons entre et au sein des pairs de jumeaux montrent que le phénotype et la survie ne dépend que de l'histoire individuelle des embryos. Nos mesures quantitatives des pairs de jumeaux amènent des questions ouvrant de nouveaux horizons de recherche : les jumeaux sont-ils robustes ou résilient ? Le développement pré-implantatoire des lapins a été étudié sur cinq embryons numériques (trois sauvages et deux clones), du stade 32-cellules à l'éclosion. Nous montrons que les divisions asymétriques internes et externes régulent la variation du nombre de cellules internes ainsi que la taille de la masse cellulaire. De plus, la variation du nombre de cellules internes est plus grande que pour les cellules externes, ce qui semble directement lié au taux de morts cellulaires. Notre hypothèse est que le potentiel de bon développement des clones est assuré par une grande plasticité épigénétique des cellules donneuses. Ce travail espère définir des méthodes et des concepts fondateurs pour une exploration quantitative et une modélisation multi-échelle de la morphogénèse animale. Mots clés : Variabilité, Quantification, Morphogénèse, Lignage Cellulaire, Oursins, Lapins Title: Variability in animal morphogenesis: sea urchin twins and cloned rabbits

  3.1 Contingency table of sea urchin phenotypes within twin pairs . . . . . . . . 3.2 Sea urchin phenotypes within twin pairs grouped by similarity . . . . . . . 3.3 Sea urchin survivals within twin pairs . . . . . . . . . . . . . . . . . . . . . 3.4 Sea urchin survivals and phenotypes between singletons grouped by similarity 3.5 Frequencies of the number of pigment cells in singletons of sea urchins . . .

4.1 Survival rates of blastocysts after injection of mRNA . . . . . . . . . . . . S1 Comparitive performance of BioEmergences and eight software tools on dataset Dr1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S2 Developmental table of the imaged embryos (3 species, 2 specimens for each species) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S3 Description of parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . xxv

Table 3

 3

	.1: Contingency table of sea urchin phenotypes within twin pairs. Embryo 1
	(columns) and embryo 2 (rows) were arbitrarly chosen for each pair. Black numbers
	indicate observed data. Red numbers between parentheses indicate theoretical data under
	H 0 .
		Identical phenotype Different phenotype
	Observed	25	10
	Expected	19.78	15.22
	Table 3.2: Sea urchin phenotypes within twin pairs grouped by similarity.

Table 3 .

 3 4: Sea urchin survivals and phenotypes between singletons grouped by similarity. Black numbers indicate observed data. Red numbers between parentheses indicate theoretical data under H 0 .

  the field size was 455.68×455.68 µm in x and y, and 174.24 µm in z, with a voxel size of 0.89 × 0.89 × 1.76 µm. For wt3 and nt1, the field size was 529.41 × 529.41 µm in x and y, and 241.27 µm in z, with a voxel size of 1.034 × 1.034 × 1.049 µm. For nt2, the field size was 412.67 × 412.67 µm in x and y, and 222.36 µm in z, with a voxel size of 0.806 × 0.806×0.797 µm. Volumes were acquired every 15 minutes. 2-photon excitation at 980 nm was performed with pulsed laser beams (Ti-Sapphire femtosecond oscillator Mai Tai HP,

	See Section 2.2 of this thesis for mounting information. Imaging was performed with
	Leica DM5000 and DM6000 upright microscopes SP5 MLSM, equipped with an Olympus
	20/0.95NA W dipping lens objective or a Leica 20/1NA W dipping lens objective. For wt1
	and wt2, Newport Spectra physics). Laser power was automatically modulated with a motorized
	half-wave plate, a polarized cube and real-time feedback to maintain a relatively constant
	signal-to-noise ratio. The emission signal was filtered with a 680 nm short-pass filter
	(Semrock, USA) to remove infrared reflection and a 525/50 nm band-pass filter (Semrock,
	USA). Photon detection was made with hybrid detectors (Hamamatsu Photonics K.K.,
	Japan, for Leica, Germany). OKO-lab H101 system was used for temperature control.
	OKO-lab DGT-CO2 system was used to control CO 2 concentration. Raw data movies
	were made with Fiji (Schindelin et al., 2012) and a custom-made plug-in (BioEmergences's
	Fiji Tools) to open VTK files available at http://www.bioemergences.eu/bioemergenc
	es/resources.php.

  S2: Number of scientific publications concerning animal cloning from 1972 to 2010. Number of scientific publications concerning animal cloning from 1972 to 2014. Louise Brown was the first human born after conception by in vitro fertilization in 1978, marking a gain of interest for cloning and gene therapy. In 1997[START_REF] Wilmut | Viable offspring derived from fetal and adult mammalian cells[END_REF], Dolly the sheep marked the onset of frenetic research on cloning and the embryonic mechanisms involved, closely followed by a gain of interest in stem cell research in 2003. Although the number of publications per year is constantly decreasing since 2003, the absolute value is still relatively high. Data from NCBI research engine.FigureS3: MLSM imaging setup. Laser 1: ytterbium mode-locked laser, with linear polarization, maximum power 1.1 W, wavelength λ 1 =1030 nm, repetition rate 50 MHz, pulse duration 200 fs. Laser 2: tunable Ti-Sapphire mode-locked laser used at fixed wavelength λ 2 =980 nm, with linear polarization, maximum power 1 W at λ 2 =980 nm, repetition rate 80 MHz, pulse duration < 100 fs. HWP: achromatic half-wave plate. PC: polarizing cube (HWP+PC is used as a hand power controller). BS: 50/50 beamsplitter (broadband, non-polarizing cube) to transform two separate beams with distinct wavelengths into two dual-wavelength beams with equal optical power. M: broadband dielectric mirror.

Table S1 :

 S1 Comparitive performance of BioEmergences and eight software tools on dataset Dr1. Time in hour post-fertilization (hpf). = 4.36 t = 6.22 t = 8.08 t = 9.95 t = 4.36 t = 6.22 t = 8.08 t = 9.95 t = 4.36 t = 6.22 t = 8.08 t = 9.95 t = 4.36 t = 6.22 t = 8.08 t = 9.95 displayed rates are averages of four ratios, one per time interval centered in 4.36 hpf, 6.22 hpf, 8.08 hpf and 9.95 hpf = 4.36 t = 6.22 t = 8.08 t = 9.95 t = 4.36 t = 6.22 t = 8.08 t = 9.95 t = 4.36 t = 6.22 t = 8.08 t = 9.95 t = 4.36 t = 6.22 t = 8.08 t = 9.95 Software Tool t = 4.36 t = 6.22 t = 8.08 t = 9.95 t = 4.36 t = 6.22 t = 8.08 t = 9.95 t = 4.36 t = 6.22 t = 8.08 t = 9.95 t = 4.36 t = 6.22 t = 8.08 t = 9.95

											Center Detection Counts
			#Gold Std = GS				#True Pos = TP					#False Pos = FP							#False Neg = FN
	Software Tool t BioEmergences Workflow			Lineage		Center Detection Rates 4292 4035 10600	7262		15		1	Linkage Rates 25	18			64		25	Mitosis Detection Rates 357 164
	Imaris Autoregressive Motion Expert			Score			1547	1682			2700		1479		only on TP centers with a GS past link 16 67 51 33 2809		2378	only on TP centers 8257 5947
	Imaris Autoregressive Motion Imaris Brownian Motion			%sensitivity of centers ×		2226 2322	1751 %false 1791		2797 2793	1528 %false 1525		3 4		11 3			7 %false 1		0 9		2130 %false 2034		2309 2269	8160 8164	5898 %false 5901	%false
	Imaris Connected Component	4356	4060	%sensitivity 10957	%sensitivity: 7426 2427	detection: 1448	2298	negative: 1263		%sensitivity: 1 0	detection: 0		0	negative: 1929	%sensitivy: 2612	8659	detection: 6163	negative:
	Software Tool Icy Spot Tracking			of links	TP/(TP+FN) a 2849	FP/(TP+FP) 2048 4717 FN/(TP+FN) 2264	TP/(TP+FN) 26 3	FP/(TP+FP) 15		FN/(TP+FN) 4 1507	TP/(TP+FN) 2012 6240 FP/(TP+FP) 5162	FN/(TP+FN)
	BioEmergences Workflow Volocity Shortest Path		96.04%	98.11% 1936	0.21% 946		2871	1.89% 2244		97.89% 1156	1096			1.03% 2192	1669	2.11% 2420		67.37% 3114	8086	37.03% 5182	32.63%
	Imaris Autoregressive Motion Expert Volocity Trajectory Variation	15.50%	30.38% 1930	2.22% 953		2869	69.62% 2252		51.03% 1184	1098		42.43% 2231	48.97% 1663 2426		0.00% 3107	8088	N/A 5174	100.00%
	Imaris Autoregressive Motion Amat et al., 2014		27.40%	35.08% 3748	0.25% 3926		9730	64.92% 5810		78.10% 43	22		20.71% 42		78	21.90% 608		0.00% 134	1227	N/A 1616	100.00%
	Imaris Brownian Motion		29.43%	36.22%	0.24%			64.14%		82.05%			17.02%			17.95%			0.00%	N/A	100.00%
	Imaris Connected Component		3.33%		32.34%	0.01%			67.66%		10.31%				8.60%			89.69%			0.00%	N/A	100.00%
	Icy Spot Tracking			41.42%	47.35%	0.39%			52.65%		87.49%				9.72%			12.51%			0.00%	N/A	100.00%
	Volocity Shortest Path			17.80%	31.04%	44.25%			68.96% Linkage Counts 57.33%			39.26%			42.67%			0.00%	N/A	100.00%
	Volocity Trajectory Variation		12.82%	31.07%	44.44%	68.93% only on TP centers with a GS past link 41.27%	43.02%			58.73%			0.00%	N/A	100.00%
	Amat et al., 2014		83.06% #Gold Std = GS	87.45%	0.86% #True Pos = TP	12.55%		94.99% #False Pos = FP = WL 4.06%				5.01%	12.93% #False Neg = FN = WL + ML 82.06%	87.07%
																#Wrong Links (WL) a							#Missing Links (ML) b
	BioEmergences Workflow						3079	3641			9548		6501		44		15			98			84			132		26	171	123
															Mitosis Detection Counts 44 15 98		84			88		11	73	39
	Imaris Autoregressive Motion Expert						356	715			858		518		145	only on TP centers 571 939		409		284		773	1067	409
				#Gold Std = GS				#True Pos = TP 145		571			939	#False Pos = FP 409 139		202	128	#False Neg = FN 0
	Software Tool Imaris Autoregressive Motion t BioEmergences Workflow				1075	1167	203	1426	22	805	12	100 100	15	288 288	6		606 606	7	264 264	17	119 19	25	341 53	53	621 15	4	264 0	12	12
	Imaris Autoregressive Motion Expert Imaris Brownian Motion				1170	1258	0		1559	0	847	0	101	0	239	0		478	0	221	0	112	0	283	37	492	7	221	8	4
	Imaris Autoregressive Motion							0			0		0	101	0	239	0		478	0	221	0	11	0	44	153	14	10	0	9	6
	Imaris Brownian Motion Imaris Connected Component	3329	3841	10283		7029	103	94	0		97	0	167	0	2	0	9	0		23	0		8		0	1273	0	1100	162	1502	11	676	10	7
	Imaris Connected Component		264	26	24	27	0			0		0	2	0	9	0		23	0		8		0	1271	0	1091	170	1479	5	668	8	4
	Icy Spot Tracking Icy Spot Tracking						1456	1566	0		3482	0	1632	0	136	0	170	0		481	0	150	0	343	0	182	197	481	14	150	13	4
	Volocity Shortest Path								0			0		0	136	0	170	0		481	0	150	0	207	0	12	52	0	3	0	4	4
	Volocity Trajectory Variation Volocity Shortest Path					503	446	0		1235	0	920	0	336	0	223	0		782	0	749	0	389	0	256	51	921	3	845	4	4
	Amat et al., 2014								83			2		1	336	0	223	49	782	23	749	111	53	116	33	135	139	20	96	21	23
	Volocity Trajectory Variation						399	299			890		647		272		205			672		594		486		409	1264	1128
															272		205			672		594		214		204	592	534
	Amat et al., 2014						2543	3510			8495		4754		84		54			344		394		141		66	378	452
															84		54			344		394		57		12	34	58

a

Table S2 :

 S2 Developmental table of the imaged embryos (3 species, 2 specimens for each species). TS: Time Step. ID: Imaging Duration (in hour). TDT: Total Development Time (in hour post-fertilization, hpf). DS: Developmental Stage as described previously (in

  for Danio rerio, Ciona intestinalis and Strongylocentrotus sp. respectively).

			Danio rerio			Phallusia mammillata			Paracentrotus lividus
	dataset	Dr1 (∆t = 67sec)	Dr2 (∆t = 153sec)	Pm1 (∆t = 180sec)	Pm2 (∆t = 180sec)	Pl1 (∆t = 207sec)	Pl2 (∆t = 180sec)
	TS	ID TDT	DS	ID TDT	DS	ID TDT	DS	ID TDT	DS	ID TDT	DS	ID TDT	DS
	0	0.00 4.17 late sphere 0.00 5.40 50% epiboly 0.00 5.00	initial gastrula 0.00 3.00	35-cell	0.00 4.33	32-cell	0.00 4.33	32-cell
	10	0.19 4.35		0.43 5.83		0.50 5.50		0.50 3.50		0.58 4.91		0.50 4.83
	20	0.37 4.54		0.85 6.25	shield	1.00 6.00		1.00 4.00		1.15 5.48		1.00 5.33
	30	0.56 4.73		1.28 6.68		1.50 6.50		1.50 4.50		1.73 6.06		1.50 5.83
	40	0.74 4.91		1.70 7.10		2.00 7.00		2.00 5.00		2.30 6.63		2.00 6.33
	50	0.93 5.10		2.13 7.53		2.50 7.50		2.50 5.50		2.88 7.21		2.50 6.83
	60	1.12 5.28		2.55 7.95		3.00 8.00		3.00 6.00		3.45 7.78		3.00 7.33
	70	1.30 5.47		2.98 8.38 75% epiboly 3.50 8.50 early tail bud II 3.50 6.50		4.03 8.36		3.50 7.83
	80	1.49 5.66		3.40 8.80				4.00 7.00		4.60 8.93		4.00 8.33
	90	1.68 5.84		3.83 9.23				4.50 7.50		5.18 9.51 h. blastula 4.50 8.83
	100	1.86 6.03		4.25 9.65				5.00 8.00				5.00 9.33
	110	2.05 6.21		4.68 10.08	tail bud			5.50 8.50				5.50 9.83
	120	2.23 6.40 late shield 5.10 10.50				6.00 9.00 early tail bud II			6.00 10.33 h. blastula
	150	2.79 6.96		6.38 11.78
	200	3.72 7.89		8.50 13.90	8-somite
	250	4.65 8.82
	300	5.58 9.75
	320	5.95 10.12	tail bud
	360	6.70 10.87 1-somite

Table S3 :

 S3 Description of parameters

		Parameters for the GMCF filtering method a
					For
	Name	Description	Useful range	Used values	noisy
					data
		parameter in the "diffusivity" function g; a larger K
	K	means that edges are better respected (make stronger obstacles) and thus better preserved by the nonlinear	0.1 -100	5.0	2.5
		diffusion process
	τ	time step in the discretization of the nonlinear diffu-sion model; a larger τ means that more smoothing is applied in one time step	1 -1.0 × 10 -4

  Parameters for the nucleus and membrane segmentation SubSurf method a,b . time step for the linear diffusion used to pre-smooth the segmented image gradient (edge detector); a larger σ means more smoothing of the gradient inside the edge detector, thus the final result is less sensitive to noise but it can be oversmoothed as well

		Name	Description				Useful range	Used values
			parameter in the edge detection function g; a larger K
		K	means that edges (but also noisy structures) are better	1 -10 4	10 3
			respected by the segmentation process
		V adv	speed of advection in the SubSurf model				1 -20	10
		V curv	strength of the mean curvature flow regularization in the SubSurf model	0.1 -5	0.2
			time step for the segmentation method; τ is restricted by
		τ	the Courant-Friedrichs-Lewy (CFL) stability condition in the advective part of the SubSurf model and should not	0.05 -1	0.1
			be larger than 1/V adv
		σ						1 -2.0 × 10 -3	10 -4
		ϵ	regularization parameter to prevent zero gradients in the denominators of the numerical scheme	1 × 10 -6	10 -6
		edge power	parameter inside the function g; the norm of gradient is raised to the power edge power	1 -6		1
			if equal to 1, the linear diffusion smoothing (convolution)
		convolution U	with parameter σ is applied to the segmented image; if	0 or 1		1
			equal to 0, the linear diffusion is not applied
	p 12	if equal to 1, the linear diffusion smoothing (convolution) 100 -200 140 150 160 too early after a previous division 0 -10 2 3 2 diffusion is not applied coefficient of the cost penalizing divisions occurring convolution PMC with parameter σ is applied to the pixelwise output of the edge detection function g; if equal to 0, such linear	150 2 0 or 1 150 2	150 2	0
	p 13 p 14	coefficient of the cost penalizing acceleration coefficient of the cost penalizing speeds above a cer-tain threshold (the threshold is hardcoded) iter number of time steps in the segmentation process 0 -7 2 2 0 -10 3 4	2 2	1 200 -500 for 2 3 3 membranes -2000 for nuclei, 1000	250 for nuclei, 1200 3 4
	p 15	coefficient of the cost penalizing a cell at time t + 1 not linked to its closest neighbor at time t	0 -50	20	20	15	20	20	20
		coefficient favoring sisterhood of simultaneously born
	p 16	cells; p 16 is used only when external information about	0 -50	10	15	15	15	15	15
		division is available

Dans le cadre de cette thèse, nous nous sommes intéressés à la variabilité du développement embryonnaire, et plus spécifiquement à la possibilité de déterminer l'espace des possibles et de prédire l'embryogénèse animale. Est-il possible de découvrir quand et dans quelle partie de l'embryon un comportement anormal émerge ? Est-il possible de corréler un comportement variable à des différences d'hérédité et d'histoire individuelle ? Nous tentons de répondre à ces questions avec l'étude des variations de lignage cellulaire chez l'oursin et le lapin. En plus de s'intéresser à la variabilité du développement embryonnaire et à ses composants -les variabilités acquise et transmise -cette thèse s'intéresse aux processus sous-jacents à la résilience et à la robustesse développementale. Nous avons choisi une approche exploratrice (phenomenology-driven), basée sur nos observations sans a priori des phénomènes afin d'en construire des hypothèses fortes. Cette approche nécessitant

In rabbits, hamsters, humans and rats. Cows, dogs and pigs have only three glycoproteins: ZP2, ZP3 and ZP4. The mouse is the only species having only ZP1, ZP2 and ZP3.

The mammalian secondary oocyte has no centriole.

-15 depending on τ 5 for Dr1 nuclei, 10 for all others

a A more detailed explanation of the method, the meaning and choice of the parameters can be found in[START_REF] Krivá | 3D early embryogenesis image filtering by nonlinear partial differential equations[END_REF]

Remerciements

List of Abbreviations

Appendix Table S1 and Appendix Figure S4). On average over four windows of 21 time-steps distributed in the first 360 time-steps of dataset Dr1 (1h30 out of 6h40 of imaging from prior gastrulation to the 1 somite stage), the average linkage sensitivity of BioEmergences was 97.89%, meaning that only 2.11% of the cells were missed or not correctly tracked between time-steps. False positive nuclei were a negligible source Chapter 4

Variability of normal and cloned rabbit preimplantatory development

In this chapter, we discuss normal development seen through the prism of mammalian embryology. We describe the development and the various observations we made in wild types and clones, which may unveil novel regulatory mechanisms.

Publication information

Chapter 4 was written for publication. It has not been submitted yet. It will be submitted under the title: Variation and natural selection in the cell lineage of normal and cloned rabbit embryos (Fabrèges, Daniel, Duranthon, and Peyriéras).

Somatic cell nuclear transfer (SNCT) has successfully been used to produce clones in different mammal species [START_REF] Wakayama | Mice cloned from embryonic stem cells[END_REF][START_REF] Hochedlinger | Monoclonal mice generated by nuclear transfer from mature B and T donor cells[END_REF][START_REF] Inoue | Generation of cloned mice by direct nuclear transfer from natural killer T cells[END_REF]. An unfertilized oocyte is enucleated and the somatic nucleus of a donor cell is introduced in place. Then the cloned embryo undergoes dramatic chromatin remodeling which eventually starts the embryonic development. Despite low efficiency [START_REF] Hochedlinger | Nuclear Transplantation, Embryonic Stem Cells, and the Potential for Cell Therapy[END_REF][START_REF] Yang | Nuclear reprogramming of cloned embryos and its implications for therapeutic cloning[END_REF], SNCT is still one of the most promising methods for cloning. However, the essential mechanisms involved in successful 115 APPENDIX . FIGURES Tables 161 Note S1: Computational speed and scalability. Typical image datasets processed here contain 512 × 512 × 120 voxels and 2 channels (e.g., Dr2 dataset). Approximate computational cost of the algorithms:

• The filtering step with the GMCF method and 10 iterations takes 5 minutes for each 3D volume using 8 processors (communicating by MPI). • The cell center detection step by FBLS runs for 20 iterations and takes 200 seconds for one 3D volume using 8 processors (communicating by MPI). • The cell center detection step by DoG (combining filtering and detection processes) takes on average 5.5 seconds per time-step, while time-steps can be processed in parallel on several processors. • For nucleus segmentation using SubSurf, it takes on average 0.75 seconds to process each nucleus. With 6,000 nuclei, a single 3D volume is segmented in 1.25 hours. • The computation time for the membrane segmentation is 6 seconds per cell (10 hours for a 3D volume containing 6,000 cells). • Whole embryo shape segmentation can also be performed in parallel.

• The cell-tracking algorithm SimAnn takes 4 hours on 8 processors to process a dataset with 360 time steps and an average of 6,000 cells per 3D volume. In sum, reconstructing the cell lineage tree (e.g., with DoG and SimAnn) from a typical zebrafish dataset (512 × 512 × 120 voxels with on average 6,000 cells over 360 time steps) with the standalone version of the BioEmergences workflow takes less than 5 hours on a local computer with 8 cores. Performing the complete reconstruction (e.g. lineage tree and shapes from a two-channel dataset) in the web service mode with computation on EGI (European Grid Infrastructure) can take 48 hours including data transfer and job queuing until execution. However, it should be noted that a number of datasets can be processed in parallel during this period.

Note S2:

In silico fate mapping can be performed in three different ways. For the ascidian embryos, the state-of-the-art fate map proposed at the 110-cell stage [START_REF] Lemaire | Unfolding a chordate developmental program, one cell at a time: Invariant cell lineages, short-range inductions and evolutionary plasticity in ascidians[END_REF] is implemented by defining distinct cell populations with the Mov-IT visualization software, and assigning them specific colors. The fate map is then propagated along the reconstructed cell lineage. It is this propagation across cohorts of specimens that can tell us whether the lineage is invariant, as is traditionally assumed. It should be noted that a similar strategy led our colleagues working with C. elegans to revise their ideas about lineage invariance in this species [START_REF] Bao | Automated cell lineage tracing in Caenorhabditis elegans[END_REF]. Alternatively, and without a priori, cell fate can be assessed as in classical embryology studies, i.e., by following cell clonal history long enough to be able to conclude about the contribution of progenitors to organs, and also about cell differentiation in specific cell types defined by their shape, position and neighborhood. The limitation here is the duration of the time lapse and the evolution of image quality, hence of tracking accuracy. We now know that beyond 15 hpf, it becomes very difficult to resolve individual nuclei in ubiquitously stained zebrafish embryos, even when zooming in on a specific compartment. In this case, mosaic or rainbow type staining is required to decrease image complexity and improve tracking accuracy. The methods presented here are expected to perform well at any stage of development with mosaic staining of nuclei. Finally, there is also the possibility to backtrack cells from their location at a late stage when compartments or presumptive organs are morphologically recognizable. This is achieved with Mov-It by using the cell selection function and backpropagating along the cell tracking.

Note S3: M2 medium. Merck Millipore and Sigma Aldrich commercialize good quality M2 medium with phenol red (MR-015-D and M7167 respectively). However, phenol red is highly auto-fluorescent and must be avoided with fluorescence acquisition. One can make one's own solution.

Note S4: Heating and temperature feedback stability. OKO-lab system requires about 30 minutes to stabilize both temperature and CO 2 concentration (Appendix Figure S7). To avoid temperature shock or pH shock, we strongly advise pre-heating the water bath to the final temperature at least one hour before imaging. CO 2 regulation can be performed only 20 minutes before imaging.

Note S5: Sea urchin egg collection and preparation. Adult sea urchins were sacrificed and gonades were directly collected from them. Male gonades were stored at 4°C in a dry plastic tube for up to 3 days. Female gonades were immersed in artificial sea water (ASW). The osmotic shock induces egg release into ASW. The solution containing the eggs was cleaned by filtration (100 µm) and used for experimentation during the same day. The sea urchin egg is protected by a thick jelly coat which limits experimental manipulation. The jelly coat was removed by decreasing the pH of ASW from 8.2 to 5.4 with HCl 0.1 M. pH was maintained at 5.4 for one minute and rapidly increased back to pH 8 with a Tris HCl buffer. The pH shock was sufficient to remove the jelly coat. Uncoated eggs were then washed with clean ASW, which has been proven to increase the fertilization rate. Fertilization was triggered by adding a small drop of diluted sperm to the solution containing uncoated eggs. Good fertilization is marked by the formation of a fertilization envelope around the eggs. To facilitate microinjection and twinning, ASW used for fertilization was supplemented with 3-amino-1,2,3-triazole (ATA, Sigma A8056) at 2 mM final and with p-aminobenzoic acid, free acid (PABA, Sigma A9878) at 2 mM final. ATA and PABA are known to inhibit peroxydases and prevent the hardening of fertilization envelopes.

Note S6: Sea urchin microinjection. Eggs without jelly coats were attached to the bottom of plastic Petri dishes covered with protamine sulfate 0.5% prior to fertilization. Attaching embryos is a necessary step to avoid embryo displacement during injection. Embryos showing a visible fertilization envelope were microinjected with solution of mRNA encoding fusion proteins for chromatin staining (H2B-mCherry) and membrane staining (EGFP-Ras). Microinjection must not exceed 20 minutes post-fertilization.

Note S7: Sea urchin twinning. 20 minutes after fertilization, embryos were washed twice in Ca ++ -free ASW (ASWCF) for 5 minutes, then embryos were washed in Ca ++free and Mg ++ -free ASW (ASWCMF) for 5 minutes. Finally, embryos were washed and maintained at 18°C in 75% ASWCF + 25% ASWCMF until the first cleavage. Because fertilization took place in ASW supplemented with ATA/PABA, fertilization envelopes were soft and thin and could be easily removed. Gentle aspiration was often sufficient to tear the fertilization envelopes and separate blastomeres. Twins were then identified and isolated into their own 35 mm Petri dish with 4 mL of ASW for further monitoring. Free blastomeres without fertilization envelopes are very fragile and must not come into contact with glass or plastic. Except for microinjection, every dish must be covered with agarose 1.5% in ASW. Dishes were stored at 18°C in humid chambers.

Software

Software S1: Standalone BioEmergences workflow versions for MacOSX and Linux respectively. We propose two versions of the standalone workflow package. Both of them contain an updater script to keep the different components of the standalone software updated. These packages include the interactive visualization software Mov-IT that we also propose independently from the image processing modules (Appendix Software S2). Users will access updated versions of the Standalone BioEmergences workflow online at: http://bioemergences.iscpif.fr/bioemergences/openworkflow-workflow.php. Software S2: Mov-IT interactive visualization tool. We propose three different versions of Mov-IT for Linux, MacOSX or Windows 32-bit respectively, that can be installed and used independently from the image processing workflow. Users will access updated versions of Mov-IT and tutorials online at: http://bioemergences.iscpif.fr/bioeme rgences/openworkflow-movit.php.

Movies

Movie S1: Danio rerio (Dr1), 3D raw data rendering. See full movie here: http://bioe mergences.iscpif.fr/users/dimitri.fabreges/thesis/movies/movie1.mp4.