Department of Mathematics, University of California, Irvine, Irvine, CA 92697, USA.
The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA 92697, USA.
Nucleic Acids Res. 2020 Sep 25;48(17):9505-9520. doi: 10.1093/nar/gkaa725.
Rapid growth of single-cell transcriptomic data provides unprecedented opportunities for close scrutinizing of dynamical cellular processes. Through investigating epithelial-to-mesenchymal transition (EMT), we develop an integrative tool that combines unsupervised learning of single-cell transcriptomic data and multiscale mathematical modeling to analyze transitions during cell fate decision. Our approach allows identification of individual cells making transition between all cell states, and inference of genes that drive transitions. Multiscale extractions of single-cell scale outputs naturally reveal intermediate cell states (ICS) and ICS-regulated transition trajectories, producing emergent population-scale models to be explored for design principles. Testing on the newly designed single-cell gene regulatory network model and applying to twelve published single-cell EMT datasets in cancer and embryogenesis, we uncover the roles of ICS on adaptation, noise attenuation, and transition efficiency in EMT, and reveal their trade-off relations. Overall, our unsupervised learning method is applicable to general single-cell transcriptomic datasets, and our integrative approach at single-cell resolution may be adopted for other cell fate transition systems beyond EMT.
单细胞转录组数据的快速增长为深入研究动态细胞过程提供了前所未有的机会。通过研究上皮-间充质转化 (EMT)，我们开发了一种整合工具，将单细胞转录组数据的无监督学习和多尺度数学建模相结合，分析细胞命运决定过程中的转变。我们的方法允许识别在所有细胞状态之间发生转变的单个细胞，并推断驱动转变的基因。单细胞尺度输出的多尺度提取自然揭示了中间细胞状态 (ICS) 和 ICS 调节的转变轨迹，产生新兴的群体尺度模型，以探索设计原则。在新设计的单细胞基因调控网络模型上进行测试，并将其应用于癌症和胚胎发生中 12 个已发表的单细胞 EMT 数据集，我们揭示了 ICS 在 EMT 中的适应、噪声衰减和转变效率中的作用，并揭示了它们的权衡关系。总的来说，我们的无监督学习方法适用于一般的单细胞转录组数据集，我们在单细胞分辨率下的综合方法可应用于 EMT 以外的其他细胞命运转变系统。
