Copperman Jeremy, Mclean Ian C, Gross Sean M, Singh Jalim, Chang Young Hwan, Zuckerman Daniel M, Heiser Laura M
Cancer Early Detection Advanced Research Center, Oregon Health and Science University, Portland OR 97239, U.S.A.
Department of Biomedical Engineering, Oregon Health and Science University, Portland OR 97239, U.S.A.
bioRxiv. 2024 Jun 25:2024.01.18.576248. doi: 10.1101/2024.01.18.576248.
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 alter 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 provides a prediction of thousands of RNA transcripts through extracellular signal-induced EMT and MET with near-continuous time resolution. The MMIST framework leverages true single-cell dynamical behavior to generate molecular-level omics inferences and is broadly applicable to other biological domains, time-lapse imaging approaches and molecular snapshot data.
细胞外信号会引发分子程序的变化，这些变化会调节多种细胞表型，包括增殖、运动和分化状态。动态适应的表型状态与定义它们的分子程序之间的联系尚未得到很好的理解。在这里，我们通过将基因转录水平与“形态动力学”（在延时图像数据中可观察到的细胞形态和运动变化）联系起来，开发了单细胞对细胞外刺激的表型反应的数据驱动模型。我们通过将单细胞轨迹分组为具有共享形态动力学反应的状态，采用了一种以动力学为先的细胞状态观点。单细胞轨迹使得能够开发一种首创的计算方法，将活细胞动力学映射到快照基因转录水平，我们将其称为MMIST，即分子与形态动力学整合单细胞轨迹。MMIST的关键概念进展在于，可以基于动态定义的状态对细胞行为进行量化，并且细胞外信号通过改变状态之间的转换速率来改变细胞状态的总体分布。我们发现了一个由上皮和间充质终点界定的细胞状态景观，其中上皮-间充质转化（EMT）和间充质-上皮转化（MET）中间体具有不同的序列。该分析得出了与精心策划的EMT基因集一致的基因表达变化预测，并通过细胞外信号诱导的EMT和MET以近乎连续的时间分辨率提供了数千种RNA转录本的预测。MMIST框架利用真实的单细胞动力学行为来生成分子水平的组学推断，并且广泛适用于其他生物学领域、延时成像方法和分子快照数据。
