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

Extracellular signals induce changes to molecular programs that modulate cellular phenotypes, but the connection between dynamically adapting phenotypic states and the molecular programs that define them is not well understood. Here, we develop data-driven models of single-cell phenotypic responses by linking gene transcription levels to "morphodynamics"-changes in cell morphology and motility observable in single-cell trajectories extracted from time-lapse image data. The single-cell trajectories enable a computational approach to map live-cell dynamics to snapshot gene transcript levels, which we term MMIST, molecular and morphodynamics-integrated single-cell trajectories. MMIST identifies a cell state landscape bound by epithelial and mesenchymal endpoints, with distinct sequences of intermediates. This analysis predicts expression of thousands of RNA transcripts through extracellular signal-induced epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) with near-continuous time resolution. The MMIST framework leverages true single-cell dynamical behavior to generate molecular-level omic inferences and is broadly applicable across biological domains, imaging approaches, and molecular snapshot data.