Towards a digital model of zebrafish embryogenesis. Integration of Cell Tracking and Gene Expression Quantification

We elaborate on a general framework composed of a set of computational tools to accurately quantificate cellular position and gene expression levels throughout early zebrafish embryogenesis captured over a time-lapse series of in vivo 3D images. Our modeling strategy involves nuclei detection, cell geometries extraction, automatic gene levels quantification and cell tracking to reconstruct cell trajectories and lineage tree which describe the animal development. Each cell in the embryo is then precisely described at each given time t by a vector composed of the cell 3D spatial coordinates (x; y; z) along with its gene expression level g. This comprehensive description of the embryo development is used to assess the general connection between genetic expression and cell movement. We also investigate genetic expression propagation between a cell and its progeny in the lineage tree. More to the point, this paper focuses on the evolution of the expression pattern of transcriptional factor goosecoid (gsc) through the gastrulation process between 6 and 9 hours post fertilization (hpf

Quantification of cell behaviors and computational modelling show that cell directional behaviors drive zebrafish pectoral fin morphogenesis

Motivation: Understanding the mechanisms by which the zebrafish pectoral fin develops is expected to produce insights on how vertebrate limbs grow from a 2D cell layer to a 3D structure. Two mechanisms have been proposed to drive limb morphogenesis in tetrapods: a growth-based morphogenesis with a higher proliferation rate at the distal tip of the limb bud than at the proximal side, and directed cell behaviors that include elongation, division and migration in a nonrandom manner. Based on quantitative experimental biological data at the level of individual cells in the whole developing organ, we test the conditions for the dynamics of pectoral fin early morphogenesis. Results: We found that during the development of the zebrafish pectoral fin, cells have a preferential elongation axis that gradually aligns along the proximodistal axis (PD) of the organ. Based on these quantitative observations, we build a center-based cell model enhanced with a polarity term and cell proliferation to simulate fin growth. Our simulations resulted in 3D fins similar in shape to the observed ones, suggesting that the existence of a preferential axis of cell polarization is essential to drive fin morphogenesis in zebrafish, as observed in the development of limbs in the mouse, but distal tip-based expansion is not. Availability: Upon publication, biological data will be available at http://bioemergences.eu/modelingFin, and source code at https://github.com/guijoe/MaSoFin. Contact: [email protected], [email protected] or [email protected] Supplementary information: Supplementary data are included in this manuscript

Image Processing Challenges in the Creation of Spatiotemporal Gene Expression Atlases of Developing Embryos

To properly understand and model animal embryogenesis it is crucial to obtain detailed measurements, both in time and space, about their gene expression domains and cell dynamics. Such challenge has been confronted in recent years by a surge of atlases which integrate a statistically relevant number of different individuals to get robust, complete information about their spatiotemporal locations of gene patterns. This paper will discuss the fundamental image analysis strategies required to build such models and the most common problems found along the way. We also discuss the main challenges and future goals in the field

3D + t Morphological Processing: Applications to Embryogenesis Image Analysis

We propose to directly process 3D + t image sequences with mathematical morphology operators, using a new classi?cation of the 3D+t structuring elements. Several methods (?ltering, tracking, segmentation) dedicated to the analysis of 3D + t datasets of zebra?sh embryogenesis are introduced and validated through a synthetic dataset. Then, we illustrate the application of these methods to the analysis of datasets of zebra?sh early development acquired with various microscopy techniques. This processing paradigm produces spatio-temporal coherent results as it bene?ts from the intrinsic redundancy of the temporal dimension, and minimizes the needs for human intervention in semi-automatic algorithms

Big Data et Santé publique : plus que jamais, les enjeux de la connaissance

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Estimation of a controlled direct effect when an effect of the exposure confounds the mediator-outcome relationship: a comparison of different methods.

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Estimating the Causal Effect of an Exposure on Change from Baseline Using Directed Acyclic Graphs and Path Analysis

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Mixed Hidden Markov Model for Heterogeneous Longitudinal Data with Missingness and Errors in the Outcome Variable

International audienceAnalysing longitudinal declarative data raises many difficulties, such as the processing of errors and missingness in the outcome variable. Moreover, long-term monitored cohorts (commonly encountered in life-course epidemiology) may reveal a problem of time heterogeneity, especially regarding the way subjects respond to the investigator. We propose a Mixed Hidden Markov Model which considers several causes of randomness in response and also enables the effect of a past health outcome to act on present responses through a memory state. Hence, we take into account both errors and missing responses, time heterogeneity, and retrospective questions. We thus propose a Stochastic Expectation Maximization algorithm (SEM), which is less time-consuming than usual EM algorithms to perform the estimation of the parameters of our MHMM. We carry out a simulation study to assess the performances of this algorithm in the context of cancer epidemiology with the British NCDS 1958 cohort. Simulations show that the effect of covariates on the transitions probabilities is estimated with moderate bias. At last, we investigate a brief real data application on the effect of early social class on cancer through a smoking behaviour. It appears that in the female sample we used, the early social class does not mainly act on smoking behaviours. Moreover, more information is needed to compensate for data missingness and declarative errors in the view to improve our statistical analysis. Résumé : L'analyse de données déclaratives longitudinales fait apparaître de nombreuses difficultés, comme le traitement des erreurs et des données manquantes de la variable de sortie. En outre, les cohortes suivies sur le long terme, telles que celles utilisées en épidémiologie "life-course" peuvent soulever un problème d'hétérogénéité du temps, surtout en ce qui concerne la façon de répondre aux questions de l'enquêteur. Nous proposons dans cet article l'introduction d'un modèle de Markov caché mixte qui comprend les possibilités d'erreur et de non-réponse, et permet également de considérer que l'effet d'un résultat de santé passé peut agir sur les réponses actuelles à travers une mémoire d' état. En ce qui concerne les estimations, nous avons proposé d'utiliser un algorithme EM Stochastique (SEM), qui est moins gourmand en temps de calcul que l'algorithme EM usuel utilisant une intégration sur les effets aléatoires. Nous avons effectué une étude par simulation afin d'évaluer les performances de cet algorithme dans le contexte de l'épidémiologie du cancer avec les données de la cohorte britanniques "NCDS 1958". Les simulations ont montré que l'effet des covariables sur les probabilités de transitions a été estimée avec un biais modéré. Enfin, nous avons réalisé une application à des données réelles en étudiant l'effet de la classe sociale précoce sur le cancer à travers un comportement tabagique. Il est apparu que, dans l'échantillon de femmes utilisé pour cette enquête, la classe sociale précoce n'agit pas principalement sur l'usage du tabac. Cependant, plus d'information est nécessaire pour compenser les données manquantes et les erreurs de déclaration et obtenir de meilleurs résultats statistiques

Mixed Hidden Markov Model for Heterogeneous Longitudinal Data with Missingness and Errors in the Outcome Variable

International audienceAnalysing longitudinal declarative data raises many difficulties, such as the processing of errors and missingness in the outcome variable. Moreover, long-term monitored cohorts (commonly encountered in life-course epidemiology) may reveal a problem of time heterogeneity, especially regarding the way subjects respond to the investigator. We propose a Mixed Hidden Markov Model which considers several causes of randomness in response and also enables the effect of a past health outcome to act on present responses through a memory state. Hence, we take into account both errors and missing responses, time heterogeneity, and retrospective questions. We thus propose a Stochastic Expectation Maximization algorithm (SEM), which is less time-consuming than usual EM algorithms to perform the estimation of the parameters of our MHMM. We carry out a simulation study to assess the performances of this algorithm in the context of cancer epidemiology with the British NCDS 1958 cohort. Simulations show that the effect of covariates on the transitions probabilities is estimated with moderate bias. At last, we investigate a brief real data application on the effect of early social class on cancer through a smoking behaviour. It appears that in the female sample we used, the early social class does not mainly act on smoking behaviours. Moreover, more information is needed to compensate for data missingness and declarative errors in the view to improve our statistical analysis. Résumé : L'analyse de données déclaratives longitudinales fait apparaître de nombreuses difficultés, comme le traitement des erreurs et des données manquantes de la variable de sortie. En outre, les cohortes suivies sur le long terme, telles que celles utilisées en épidémiologie "life-course" peuvent soulever un problème d'hétérogénéité du temps, surtout en ce qui concerne la façon de répondre aux questions de l'enquêteur. Nous proposons dans cet article l'introduction d'un modèle de Markov caché mixte qui comprend les possibilités d'erreur et de non-réponse, et permet également de considérer que l'effet d'un résultat de santé passé peut agir sur les réponses actuelles à travers une mémoire d' état. En ce qui concerne les estimations, nous avons proposé d'utiliser un algorithme EM Stochastique (SEM), qui est moins gourmand en temps de calcul que l'algorithme EM usuel utilisant une intégration sur les effets aléatoires. Nous avons effectué une étude par simulation afin d'évaluer les performances de cet algorithme dans le contexte de l'épidémiologie du cancer avec les données de la cohorte britanniques "NCDS 1958". Les simulations ont montré que l'effet des covariables sur les probabilités de transitions a été estimée avec un biais modéré. Enfin, nous avons réalisé une application à des données réelles en étudiant l'effet de la classe sociale précoce sur le cancer à travers un comportement tabagique. Il est apparu que, dans l'échantillon de femmes utilisé pour cette enquête, la classe sociale précoce n'agit pas principalement sur l'usage du tabac. Cependant, plus d'information est nécessaire pour compenser les données manquantes et les erreurs de déclaration et obtenir de meilleurs résultats statistiques