Single-cell morphodynamical trajectories enable prediction of gene expression accompanying cell state change.

Epithelial cells change behavior and state in response to signals, which is necessary for the function of healthy tissue, while aberrant responses can drive diseases like cancer. To decode and potentially steer these responses, there is a need to link live-cell behavior to molecular programs, but high-throughput molecular measurement is generally destructive or requires fixation. Here we present a novel method which connects single-cell morphology and motility over time to bulk molecular readouts. Our model predicts gene expression from the observation of label-free live-cell imaging, as a step toward understanding and ultimately controlling cell state change.