Reverse engineering cell competition from a single-cell perspective using time-lapse microscopy and deep learning image analyses

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Cell competition is a widely conserved, fundamental tissue quality control mechanism. The cell competition assay of MDCK wild-type vs. MDCK mutants (Scribble-knockdown) relies on a mostly mechanical mechanism of competition [Norman, 2012] which posits that the compressive stresses of the tissue drive the outcome of the competition. According to this mechanism, proliferative wild-type cells outcompete mutant Scribble cells resulting in these apoptotic 'loser' cells being apically extruded from the epithelia. Previous studies show that there is an increased division rate of wild-type cells [Bove 2017], but what still remains a mystery is whether this is a cause or consequence of increased apoptosis in the 'loser' cell population. In my research, I aim to answer this question by using multi-modal, time-lapse microscopy to image competition assays continuously for several days. The images are then segmented into wild-type or mutant objects using a convolutional neural network (CNN) that can differentiate between the cell types, after which they are tracked throughout the time-lapse using a Bayesian multi-object tracker. Utilising a conjugate analysis of fluorescent cell-cycle indicator probes, key timepoints of cellular fate commitment can be identified that highlight which cells are driving the competition and what local conditions those cells are responding to.