Cell Trajectory Clustering: Towards the Automated Identification of Morphogenetic Fields in Animal Embryogenesis

The recent availability of complete cell lineages from live imaging data opens the way to novel methodologies for the automated analysis of cell dynamics in animal embryogenesis. We propose a method for the calcula- tion of measure-based dissimilarities between cells. These dissimilarity measures allow the use of clustering algorithms for the inference of time-persistent patterns. The method is applied to the digital cell lineages reconstructed from live zebrafish embryos imaged from 6 to 13 hours post fertilization. We show that the position and velocity of cells are sufficient to identify relevant morphological features including bilateral sym- metry and coherent cell domains. The method is flexible enough to readily integrate larger sets of measures opening the way to the automated identification of morphogenetic fields.