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

ABSTRACT: A cell's shape and motion represent fundamental aspects of cell identity and can be highly predictive of function and pathology. However, automated analysis of the morphodynamic states remains challenging for most cell types, especially primary human cells where genetic labeling may not be feasible. To enable automated and quantitative analysis of morphodynamic states, we developed DynaMorph-a computational framework that combines quantitative live cell imaging with self-supervised learning. To demonstrate the robustness and utility of this approach, we used DynaMorph to annotate morphodynamic states observed with label-free measurements of optical density and anisotropy of live microglia isolated from human brain tissue. These cells show complex behavior and have varied responses to disea

SUBMITTER: Wu Z 

PROVIDER: S-EPMC9265147 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature