Control of cellular cortical tension and shape by RhoGTPase signalling

Shape changes are ubiquitous in biology, from cytokinesis at the single cell scale to tissue-scale morphogenesis involving coordinated changes in hundreds of cells. In all cases, morphogenesis is powered by gradients in mechanical tension that arise downstream of signalling. Many pathways converge on RhoGTPases that modulate the cytoskeleton and cell contractility to control cell mechanics and, subsequently, shape. Despite their physiological importance, we lack a quantitative understanding of how changes in signalling alter cortical mechanics to drive cell shape change. Here, we use optogenetics to quantitatively characterise the relationship between the amount of RhoGEF localised to the membrane, the downstream myosin recruitment, and the subsequent mechanical changes. We then show that cortical myosin amount and cortical tension increase linearly with the amount of membranous RhoGEF signalling. Based on these data, we develop a predictive model of the temporal evolution of RhoGEF membrane localisation, cortical myosin enrichment, and cortical tension in response to a pulse of light. Using this model together with an active surface model of the cell cortex, we show that the cellular shape changes induced by localised optogenetic recruitment of RhoGEF signalling can be predicted, directly linking gradients in signalling to shape change. Significance statementShape changes are ubiquitous in biology, during division in single cells and in tissue during embryogenesis. In all cases, shape change is powered by gradients in mechanical tension that arise downstream of changes in biochemical signals. Despite their importance, we lack a quantitative understanding of how changes in signals alter cell mechanics to drive cell shape change. Here, we control the location and amount of biochemical signal using light to quantitatively characterise the relationship between signals and their resulting biological and mechanical changes. We show that mechanical change scales linearly with the amount of biochemical signal. Based on this, we develop a mathematical model that predicts cell mechanical and shape changes from the location and amount of biochemical signals.