DynaMorph: learning morphodynamic states of human cells with live imaging and sc-RNAseq

Morphological states of human cells are widely imaged and analyzed to diagnose diseases and to discover biological mechanisms. Morphodynamics of cells capture their functions more fully than their morphology. Discovery of morphodynamic states of human cells is challenging, because genetic labeling or manual annotation may not be feasible. We propose a computational framework, DynaMorph, that combines quantitative label-free imaging and deep learning for automated discovery of morphodynamic states. As a case study, we apply DynaMorph to study the morphodynamic states of live primary human microglia, which are mobile immune cells of the brain that exhibit complex functional states. DynaMorph identifies two distinct morphodynamic states of microglia under perturbation by cytokines and glioblastoma supernatant. We find that microglia actively transition between the two states. Moreover, single-cell RNA-sequencing of the perturbed microglia shows that the morphodynamic states correspond to distinct transcriptomic clusters of the cells, revealing how perturbations alter gene expression and phenotype. DynaMorph can broadly enable automated discovery of functional states of cellular systems.