ForSys: non-invasive stress inference from time-lapse microscopy

Abstract

During tissue development and regeneration, cells interpret and exert mechanical forces that are challenging to measure in vivo. Therefore, stress inference algorithms have emerged as powerful tools to estimate tissue stresses. However, how to incorporate tissue dynamics effectively into the inference remains elusive. Here, we present ForSys, a Python-based software that estimates intercellular stresses and intracellular pressures using time-lapse microscopy. We validated ForSys in silico and in vivo using the well-characterized mucociliary epithelium of the Xenopus embryo. We applied ForSys to study the migrating zebrafish lateral line primordium. We found that stress increases during cell rounding just before cell division and predicted the onset of epithelial rosettogenesis with high accuracy. Finally, we analyzed the development of the zebrafish neuromast and inferred mechanical asymmetries in a cell type-specific adhesion pattern. The versatility and simplicity of ForSys enhance the toolkit for studying spatiotemporal patterns of mechanical forces during tissue morphogenesis in vivo.