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Regionalization is a critical, highly conserved step in the development of the vertebrate brain. Discrepancies exist in how regionalization of the anterior vertebrate forebrain is conceived since the “preoptic area” is proposed to be a part of the telencephalon in tetrapods but not in teleost fish. To gain insight into this complex morphogenesis, formation of the anterior forebrain was analyzed in 3D over time in zebrafish embryos, combining visualization of proliferation and differentiation markers, with that of developmental genes. We found that the region containing the preoptic area behaves as a coherent morphogenetic entity, organized around the optic recess and located between telencephalon and hypothalamus. This optic recess region (ORR) makes clear borders with its neighbor areas and expresses a specific set of genes (dlx2a, sim1a and otpb). We thus propose that the anterior forebrain (secondary prosencephalon) in teleosts contains three morphogenetic entities (telencephalon, ORR and hypothalamus), instead of two (telencephalon and hypothalamus). The ORR in teleosts could correspond to “telencephalic stalk area” and “alar hypothalamus” in tetrapods, resolving current inconsistencies in the comparison of basal forebrain among vertebrates.

International audience; Embryogenesis is a dynamic process with an intrinsic variability whose understanding requires the integration of molecular, genetic, and cellular dynamics. Biological circuits function over time at the level of single cells and require a precise analysis of the topology, temporality, and probability of events. Integrative developmental biology is currently looking for the appropriate strategies to capture the intrinsic properties of biological systems. The "-omic" approaches require disruption of the function of the biological circuit; they provide static information, with low temporal resolution and usually with population averaging that masks fast or variable features at the cellular scale and in a single individual. This data should be correlated with cell behavior as cells are the integrators of biological activity. Cellular dynamics are captured by the in vivo microscopy observation of live organisms. This can be used to reconstruct the 3D + time cell lineage tree to serve as the basis for modeling the organism's multiscale dynamics. We discuss here the progress that has been made in this direction, starting with the reconstruction over time of three-dimensional digital embryos from in toto time-lapse imaging. Digital specimens provide the means for a quantitative description of the development of model organisms that can be stored, shared, and compared. They open the way to in silico experimentation and to a more theoretical approach to biological processes. We show, with some unpublished results, how the proposed methodology can be applied to sea urchin species that have been model organisms in the field of classical embryology and modern developmental biology for over a century.

International audience; We present nonlinear diffusion equations, numerical schemes to solve them and their application for filtering 3D images obtained from laser scanning microscopy (LSM) of living zebrafish embryos, with a goal to identify the optimal filtering method and its parameters. In the large scale applications dealing with analysis of 3D+time embryogenesis images, an important objective is a correct detection of the number and position of cell nuclei yielding the spatio-temporal cell lineage tree of embryogenesis. The filtering is the first and necessary step of the image analysis chain and must lead to correct results, removing the noise, sharpening the nuclei edges and correcting the acquisition errors related to spuriously connected subregions. In this paper we study such properties for the regularized Perona-Malik model and for the generalized mean curvature flow equations in the level-set formulation. A comparison with other nonlinear diffusion filters, like tensor anisotropic diffusion and Beltrami flow, is also included. All numerical schemes are based on the same discretization principles, i.e. finite volume method in space and semi-implicit scheme in time, for solving nonlinear partial differential equations. These numerical schemes are unconditionally stable, fast and naturally parallelizable. The filtering results are evaluated and compared first using the Mean Hausdorff distance between a gold standard and different isosurfaces of original and filtered data. Then, the number of isosurface connected components in a region of interest (ROI) detected in original and after the filtering is compared with the corresponding correct number of nuclei in the gold standard. Such analysis proves the robustness and reliability of the edge preserving nonlinear diffusion filtering for this type of data and lead to finding the optimal filtering parameters for the studied models and numerical schemes. Further comparisons consist in ability of splitting the very close objects which are artificially connected due to acquisition error intrinsically linked to physics of LSM. In all studied aspects it turned out that the nonlinear diffusion filter which is called geodesic mean curvature flow (GMCF) has the best performance.

In this paper, we use partial-differential-equation-based segmentation to accurately extract the shapes of membranes and nuclei from time lapse confocal microscopy images, taken throughout early Zebrafish embryogenesis. This strategy is a prerequisite for an accurate quantitative analysis of cell shape and morphodynamics during organogenesis and is the basis for an integrated understanding of biological processes. This data will also serve for the measurement of the variability between individuals in a population. The segmentation of cellular structures is achieved by first using an edge-preserving image filtering method for noise reduction and then applying an algorithm for cell shape reconstruction based on the Subjective Surfaces technique.