Abstract

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 much enabled using Deep Learning. Hence, here we have developed 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 has enabled 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 axis of division is biased according to lines of tissue tension and that wounding triggers a synchronised (but not oriented) wave of cell divisions back from the leading edge.
Original languageEnglish
DOIs
Publication statusPublished - 23 Mar 2023

Publication series

NamebioRxiv
PublisherCold Spring Harbor Laboratory

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