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Yu-Chen Cheng, Yun Zhang, Shubham Tripathi, B V Harshavardhan, Mohit Kumar Jolly, Geoffrey Schiebinger, Herbert Levine, Thomas O McDonald, Franziska Michor
Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215.
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215.
Proc Natl Acad Sci U S A. 2024 Aug 6;121(32):e2406842121. doi: 10.1073/pnas.2406842121. Epub 2024 Aug 2.
Exploring the complexity of the epithelial-to-mesenchymal transition (EMT) unveils a diversity of potential cell fates; however, the exact timing and mechanisms by which early cell states diverge into distinct EMT trajectories remain unclear. Studying these EMT trajectories through single-cell RNA sequencing is challenging due to the necessity of sacrificing cells for each measurement. In this study, we employed optimal-transport analysis to reconstruct the past trajectories of different cell fates during TGF-beta-induced EMT in the MCF10A cell line. Our analysis revealed three distinct trajectories leading to low EMT, partial EMT, and high EMT states. Cells along the partial EMT trajectory showed substantial variations in the EMT signature and exhibited pronounced stemness. Throughout this EMT trajectory, we observed a consistent downregulation of the and genes. This finding was validated by recent inhibitor screens of EMT regulators and CRISPR screen studies. Moreover, we applied our analysis of early-phase differential gene expression to gene sets associated with stemness and proliferation, pinpointing , , and as genes differentially expressed in the initial stages of the partial versus high EMT trajectories. We also found that , , and showed significant upregulation in the high EMT trajectory. While the first group of genes aligns with findings from previous studies, our work uniquely pinpoints the precise timing of these upregulations. Finally, the identification of the latter group of genes sheds light on potential cell cycle targets for modulating EMT trajectories.
探索上皮-间质转化 (EMT) 的复杂性揭示了多种潜在的细胞命运；然而，早期细胞状态分化为不同 EMT 轨迹的确切时间和机制仍不清楚。由于需要为每个测量牺牲细胞，因此通过单细胞 RNA 测序研究这些 EMT 轨迹具有挑战性。在这项研究中，我们采用最优传输分析来重建 MCF10A 细胞系中 TGF-β诱导 EMT 过程中不同细胞命运的过去轨迹。我们的分析揭示了导致低 EMT、部分 EMT 和高 EMT 状态的三个不同轨迹。沿着部分 EMT 轨迹的细胞在 EMT 特征上表现出显著的变异性，并表现出明显的干性。在整个 EMT 轨迹中，我们观察到基因和基因的持续下调。这一发现通过最近的 EMT 调节剂抑制剂筛选和 CRISPR 筛选研究得到了验证。此外，我们将早期差异基因表达分析应用于与干性和增殖相关的基因集，确定、和作为部分 EMT 与高 EMT 轨迹中差异表达的基因。我们还发现基因在高 EMT 轨迹中显著上调。虽然第一组基因与先前研究的结果一致，但我们的工作独特地确定了这些上调的精确时间。最后，后一组基因的鉴定揭示了潜在的细胞周期靶点，可用于调节 EMT 轨迹。
