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

作者：Jeremy Copperman, Ian C. McLean, Sean M. Gross, Jalim Singh, Vaibhav Murthy, Young Hwan Chang, Alexander E. Davies, Daniel M. Zuckerman, Laura M. Heiser · 发表于：bioRxiv (Cold Spring Harbor Laboratory) · 年份：2024 · DOI：10.1101/2024.01.18.576248 · 被引用次数：4 · 研究领域：Single-cell and spatial transcriptomics、Cell Image Analysis Techniques、Gene Regulatory Network Analysis

Abstract Extracellular signals induce changes to molecular programs that modulate multiple cellular phenotypes, including proliferation, motility, and differentiation status. 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 to extracellular stimuli by linking gene transcription levels to “morphodynamics” – changes in cell morphology and motility observable in time-lapse image data. We adopt a dynamics-first view of cell state by grouping single-cell trajectories into states with shared morphodynamic responses. The single-cell trajectories enable development of a first-of-its-kind computational approach to map live-cell dynamics to snapshot gene transcript levels, which we term MMIST, Molecular and Morphodynamics-Integrated Single-cell Trajectories. The key conceptual advance of MMIST is that cell behavior can be quantified based on dynamically defined states and that extracellular signals change the overall distribution of cell states by altering rates of switching between states. We find a cell state landscape that is bound by epithelial and mesenchymal endpoints, with distinct sequences of epithelial to mesenchymal transition (EMT) and mesenchymal to epithelial transition (MET) intermediates. The analysis yields predictions for gene expression changes consistent with curated EMT gene sets and predicts expression of thousands...