Deep learning for rapid analysis of cell divisions in vivo during epithelial morphogenesis and repair

Cell division is fundamental to all healthy tissue growth, as well as being rate-limiting in the tissue repair response to wounding and during cancer progression. However, the role that cell divisions play in tissue growth is a collective one, requiring the integration of many individual cell division events. It is particularly difficult to accurately detect and quantify multiple features of large numbers of cell divisions (including their spatio-temporal synchronicity and orientation) over extended periods of time. It would thus be advantageous to perform such analyses in an automated fashion, which can naturally be enabled using Deep Learning. Hence, we develop a pipeline of Deep Learning Models that accurately identify dividing cells in time-lapse movies of epithelial tissues in vivo. Our pipeline also determines their axis of division orientation, as well as their shape changes before and after division. This strategy enables us to analyse the dynamic profile of cell divisions within the Drosophila pupal wing epithelium, both as it undergoes developmental morphogenesis and as it repairs following laser wounding. We show that the division axis is biased according to lines of tissue tension and that wounding triggers a synchronised (but not oriented) burst of cell divisions back from the leading edge. Highlights{square} Accurate and efficient detection of epithelial cell divisions can be automated by deep learning of dynamic time-lapse imaging data {square}Optimal division detection is achieved using multiple timepoints and dual channels for visualisation of nuclei and cell boundaries {square}Epithelial cell divisions are orientated according to lines of global tissue tension after post-division shuffling {square}Spatio-temporal cell division analyses following wounding reveal spatial synchronicity that scales with wound size {square}Additional deep learning tools enable rapid analysis of cell division orientation