During embryo development, cells undergo the coordinated process of cell fate specification, proliferation, differentiation, migration and programmed death, forming the three main germ layers of mesoderm, ectoderm and endoderm that give rise to the body plan for early organogenesis. At system level, tissue morphogenesis is directed by the morphogenetic domain-domain level signaling interactions through the activator-repressor feedback controls or expansion-induction mechanism. C. elegans is a powerful model that allows researchers to study the dynamic process of molecular signaling through direct in vivo imaging. In this study, we established the virtual C. elegans embryo model coupled with the spatiotemporal transcriptome at single cell resolution. We developed a computational method that can be utilized to study the relationship of inter-morphogenetic domain signaling, gene expression regulation and tissue pattern formation. We trained a Machine Learning model with Random Forest Regression Trees, which predicted accurately the cell locations with the gene expression profiles in the developing embryo. We performed correlative image analysis on the patterns of embryo gene expression and anatomy. In doing so, we identified the inter-morphogenetic domain signaling networks that regulate complex tissue boundary pattern formation.

Presenter(s)

Thao Bach

Ben Niu

Xingyu Chen