Abstract
The cell’s shape and motion represent fundamental aspects of the cell identity, and can be highly predictive of the 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 fidelity and robustness of this approach, we used DynaMorph to annotate morphodynamic states observed with label-free measurements of density and anisotropy of live microglia isolated from human brain tissue. These cells show complex behavior and have varied responses to disease-relevant stimuli. DynaMorph generates quantitative morphodynamic representations that can be used to evaluate the effects of disease-relevant perturbations. Using DynaMorph, we identify distinct morphodynamic states of microglia polarization and detect rare transition events between states. The methodologies presented here can facilitate automated discovery of functional states of diverse cellular systems.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
* We introduce the terminology of shape space and trajectory feature vectors to clarify the information reported by the learned representation. * The text is re-written for clarity and brevity. * Fig. 2 and its supplements are updated to clarify improved robustness of learned shape space when complementary imaging modalities are used. * Supplements to Fig. 2 now report performance of various autoencoder models for the task of learning the shape space. * Fig. 5 is updated to report that dynamorph can be employed to analyze dynamic states of multiple cell types. * The data on the transcritomic states of microglia is now moved from Fig. 5 to supplements of Fig. 4.