Cell competition is a quality control mechanism through which tissues eliminate unfit cells. Cell competition can result from short-range biochemical signals or long-range mechanical cues. However, little is known about how cell-scale interactions give rise to population shifts in tissues, due to the lack of experimental and computational tools to efficiently characterise interactions at the single-cell level. In the work presented in this thesis, I address these challenges by combining long-term automated microscopy with deep learning image analysis to decipher how single-cell behaviour determines tissue make-up during competition. Using a novel high-throughput analysis pipeline, I 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 cell-type of each cell’s neighbours. I find that local density has a dramatic effect on the rate of division and apoptosis under competitive conditions. Strikingly, such analysis reveals that proliferation of the winner cells is up-regulated in neighbourhoods mostly populated by loser cells. These data suggest that tissue-scale population shifts are strongly affected by cellular-scale tissue organisation. I 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
