Section: New Results

Dynamical characterization of morphogenesis at cellular scale

Participants : Guillaume Cerutti, Emmanuel Faure [External Collaborator] , Christophe Godin, Bruno Leggio, Jonathan Legrand, Patrick Lemaire [External Collaborator] , Grégoire Malandain [External Collaborator] , Jan Traas [External Collaborator] .

• Research Axes: RA1 (Representation of biological organisms and their forms in silico) & RA3 (Plasticity & robustness of forms)

• Key Modeling Challenges: KMC3 (Realistic integrated digital models)

The modeling of morphogenesis requires to explore the interconnection of different spatial and temporal scales of developing organisms. Non-trivial questions such as whether the observed robustness of morphogenesis is rooted in some highly conserved properties at the cellular level or whether it emerges as a macroscopic phenomenon, necessitate precise, quantitative analyses of complex 3D dynamic structures. The study of dynamical properties at the cellular scale poses at the same time key technical challenges and fundamental theoretical questions. An example of the former category is how to characterize and follow the change of shape of cells within tissues and of tissues within organs, and how to couple this change with, for instance, gene expression dynamics; an illustration of the latter is how to define cell-scale variability of morphogenesis within and between species. Our team has produced this year several results in this context:

Cells spatio-temporal properties and patterns characterization. Over the past few years, we have achieved quantitative characterization of some of the cells physical properties, such as volumes or curvatures, in a developing tissue. Together with cell lineaging, it also enabled the quantification of temporal properties at cellular scale such as volumetric growth rate or strain patterns. To ease-up the analysis and to structure the previously described data, we have implemented a dedicated spatio-temporal graph structure, formalizing the cell network and its change in time.

To further characterize the tissue development, we developed clustering methods to identify cellular patterns based on a selection of quantified cell properties, including topology. Since such data are highly structured, both in time and space, we developed two complementary approaches:

1. spatial oriented: this approach use the cell neighborhood and a selection of cell descriptors to create pairwise distance maps latter clustered by a distance-based method, such as Ward's hierarchical clustering.

2. temporal oriented: this approach uses the lineage forest and a selection of cell descriptors to infer cell identities using Hidden Markov Tree (HMT) models.

Both approaches allow later characterization of the detected cluster or groups of cells based on their properties and should be published during the first half of 2019.

Atlases. One fundamental requirement to understand morphogenesis is the creation of atlases of different properties and different species. This year we have started creating two morphogenetic atlases: the atlas of gene expression patterns in the Arabidopsis thaliana flower development and the atlas of early embryonic development of the ascidian Phallusia mammillata.

Phallusia mammillata embryos develop with an invariant cellular lineage and with a relatively low number of cells ($\sim 700$) up until the end of neurulation. This allows the creation of atlases with cellular resolution. Developing embryos from in-vitro fertilised dechorionated eggs have been injected with mRNA to fluorescently label their cell membranes and imaged by light-sheet microscopy for several hours of development. Automated image reconstruction through the segmentation pipeline ASTEC allowed to collect a large number of wild-type and mutated development with single-cell resolution and with a temporal resolution of two minutes. Based on this amount of data and on the invariant early ascidian lineage, we started curating an atlas of wild-type cellular, tissue and embryonic properties. Each cell, classified by its unique name, is identified in each wild-type embryo and analyzed through the dedicated computational pipelines. The result of this work provides a comprehensive view on the variability (in time, within and between embryos) of properties such as cell volume, cell surface, cell and tissue shape, cell topology, length of cell cycle, cell position within its tissue and globally within the embryo, orientation of cell's cleavage plane. This cellular networks have been coupled via cell names with genetic data coming from the the ascidian genetic database (ANISEED) and a specific tool, Morphonet, has been developed to explore these morphodynamic atlases seamlessly within a web-browser (paper in revision).

On the other hand, developing digital atlases of organism or organs development is a complex challenge for organisms presenting a strong variability in the cellular layout. Indeed contrary to C. Elegans or P. mammillata, for instance, that posses a very strict cell lineage, the development of most organisms or organs is under the influence of robust genetic patterns but without a unique cellular layout. In that respect, proposing a cell-based atlas of flower development for instance is not straightforward and specific methods have been developed to choose a representative examples of the developing A. thaliana flower. Using this representative flower we have generated an atlas in which we have introduced manually the expression patterns of 27 genes. The knowledge generated by the creation of this atlas makes it possible to have a first quantitative (correlative) view on the relation between gene activity and growth.

Both these works should result in publications in 2019.

Robustness of ascidian embryonic development. The image segmentation pipeline ASTEC developed by the team allows the 3D dynamic reconstruction of early ascidian embryogenesis at cellular resolution. Based on the high-quality wild-type data of our ascidian morphogenetic atlas and on ANISEED, we investigated the robustness of ascidian embryonic development and established a model to explain its origin. Thanks to the image-analysis pipelines we developed, we could extract relevant information from data and to perform cell-to-cell comparisons between different embryos of the same species (Phallusia mammillata). Since embryos developing from dechorionated eggs are left-right symmetric, we assessed the degree of cell-level variability between two embryos with different genomes (genetic variability) by comparing it to the intrinsic left-right variability in cellular properties within each embryo (stochastic variability). We showed that the same degree of variability is observed within and between embryos, demonstrating how ascidian embryonic development is highly canalised, and that the high reproducibility of shapes observed during embryogenesis is rooted in the robustness of cellular geometry and topology. Based on these observations, we studied the dynamics of embryonic patterning by developing a quantitative mathematical model for cellular fate-restriction events based on kinetic equations describing biochemical signalling. This model suggests that the robustness of cell topology and geometry is necessary for cell-cell biochemical interactions to give rise to the correct fate restriction events, a phenomenon which might represent a strong evolutive constraint to cell-scale variability in ascidians.

These results gave rise to a work which is currently under review and published as a preprint [16].

Digital reconstruction of developing Arabidopsis ovule. The ovule is a relatively simple organ, with limited developmental variability, which makes it an excellent case study for the computational modeling of organ development. In order to test various hypotheses of cellular growth, we reconstructed a first 4D digital tissue structure of a developing ovule as a triangulated cellular complex. It can be used as an input for FEM-based simulations, and will allow to compare quantitatively the results of growth models with actual ovule development.

This work was part of the Imago project.