DynaMorph: self-supervised learning of morphodynamic states of live cells

Cellular morphology and dynamic behavior are highly predictive of their function and pathology. However, automated analysis of the morphodynamic states remains challenging for human cells where genetic labeling may not be feasible. We developed DynaMorph – a computational framework that combines quantitative live cell imaging with self-supervised learning and applied it to microglia derived from developing human brain tissue. Our model generates interpretable and generalizable morphological representations for microglia, and we found that microglia adopt distinct morphodynamic states upon exposure to disease-relevant perturbations.