LMAM and School of Mathematical Sciences, Peking University, Beijing, China.
Department of Mathematics, University of California, Irvine, Irvine, CA, USA.
Nat Commun. 2021 Sep 23;12(1):5609. doi: 10.1038/s41467-021-25548-w.
Advances in single-cell technologies allow scrutinizing of heterogeneous cell states, however, detecting cell-state transitions from snap-shot single-cell transcriptome data remains challenging. To investigate cells with transient properties or mixed identities, we present MuTrans, a method based on multiscale reduction technique to identify the underlying stochastic dynamics that prescribes cell-fate transitions. By iteratively unifying transition dynamics across multiple scales, MuTrans constructs the cell-fate dynamical manifold that depicts progression of cell-state transitions, and distinguishes stable and transition cells. In addition, MuTrans quantifies the likelihood of all possible transition trajectories between cell states using coarse-grained transition path theory. Downstream analysis identifies distinct genes that mark the transient states or drive the transitions. The method is consistent with the well-established Langevin equation and transition rate theory. Applying MuTrans to datasets collected from five different single-cell experimental platforms, we show its capability and scalability to robustly unravel complex cell fate dynamics induced by transition cells in systems such as tumor EMT, iPSC differentiation and blood cell differentiation. Overall, our method bridges data-driven and model-based approaches on cell-fate transitions at single-cell resolution.
单细胞技术的进步使得人们能够深入研究异质细胞状态，然而，从快照式单细胞转录组数据中检测细胞状态转变仍然具有挑战性。为了研究具有瞬时特性或混合身份的细胞，我们提出了 MuTrans 方法，该方法基于多尺度约减技术来识别规定细胞命运转变的潜在随机动力学。通过在多个尺度上迭代统一转变动力学，MuTrans 构建了细胞命运动态流形，描绘了细胞状态转变的进程，并区分了稳定细胞和转变细胞。此外，MuTrans 使用粗粒化转变路径理论来量化细胞状态之间所有可能转变轨迹的可能性。下游分析确定了标记瞬时状态或驱动转变的独特基因。该方法与成熟的朗之万方程和转变率理论一致。将 MuTrans 应用于从五个不同单细胞实验平台收集的数据集，我们展示了其在肿瘤 EMT、iPSC 分化和血细胞分化等系统中识别由转变细胞诱导的复杂细胞命运动力学的能力和可扩展性。总的来说，我们的方法在单细胞分辨率上连接了基于数据驱动和基于模型的细胞命运转变方法。
