Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany.
Department of Mathematics, Technical University of Munich, Munich, Germany.
Nat Biotechnol. 2020 Dec;38(12):1408-1414. doi: 10.1038/s41587-020-0591-3. Epub 2020 Aug 3.
RNA velocity has opened up new ways of studying cellular differentiation in single-cell RNA-sequencing data. It describes the rate of gene expression change for an individual gene at a given time point based on the ratio of its spliced and unspliced messenger RNA (mRNA). However, errors in velocity estimates arise if the central assumptions of a common splicing rate and the observation of the full splicing dynamics with steady-state mRNA levels are violated. Here we present scVelo, a method that overcomes these limitations by solving the full transcriptional dynamics of splicing kinetics using a likelihood-based dynamical model. This generalizes RNA velocity to systems with transient cell states, which are common in development and in response to perturbations. We apply scVelo to disentangling subpopulation kinetics in neurogenesis and pancreatic endocrinogenesis. We infer gene-specific rates of transcription, splicing and degradation, recover each cell's position in the underlying differentiation processes and detect putative driver genes. scVelo will facilitate the study of lineage decisions and gene regulation.
RNA 速度为单细胞 RNA 测序数据中的细胞分化研究开辟了新途径。它基于特定时间点单个基因的拼接和未拼接信使 RNA（mRNA）的比率来描述基因表达变化的速度。然而，如果违反了常见拼接率的中心假设和具有稳定 mRNA 水平的完整拼接动力学的观察，则速度估计会出现错误。在这里，我们提出了 scVelo，一种通过使用基于似然的动态模型来解决完整转录动力学的拼接动力学来克服这些限制的方法。这将 RNA 速度推广到了在发育和对干扰的反应中常见的瞬态细胞状态的系统中。我们将 scVelo 应用于神经发生和胰腺内分泌发生中的亚群动力学的分离。我们推断出转录、拼接和降解的基因特异性速率，恢复每个细胞在潜在分化过程中的位置，并检测潜在的驱动基因。scVelo 将有助于谱系决策和基因调控的研究。
