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.

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.