Competitive interactions in heterogeneous cell populations: a multimodal imaging, machine learning and computational modelling

Cell competition is a quality control mechanism through which tissues eliminate unfit cells. Cell competition can result from short-range biochemical inductions or long-range mechanical cues. However, little is known about how cell-scale interactions give rise to tissue-scale population shifts, due to the lack of experimental and computational tools to efficiently characterise interactions at the single-cell level.  Here, we address these challenges by developing long-term automated microscopy with deep learning image analysis to decipher how single-cell behaviour determines tissue make-up during competition. 

Using our high-throughput analysis pipeline, we show that competitive interactions between MDCK wild-type cells and cells depleted of the polarity protein scribble are governed by differential sensitivity to local density and the composition of each cell’s neighbourhood.  Our data suggest that tissue-scale population shifts are strongly affected by cellular-scale tissue organisation. We present a quantitative mathematical model that demonstrates the effect of neighbour cell-type dependence of apoptosis and division in determining the fitness of competing cell lines.