Generalizing RNA velocity to transient cell states through dynamical modeling
Generalizing RNA velocity to transient cell states through dynamical modeling
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DOI：
10.1038/s41587-020-0591-3
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发表时间：
2020-08-03
影响因子：
46.9
通讯作者：
Theis, Fabian J.
中科院分区：
文献类型：
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作者：
Bergen, Volker;Lange, Marius;Theis, Fabian J.
scVelo从单细胞RNA测序数据中重建瞬时细胞状态和分化途径。RNA velocity开辟了在单细胞RNA测序数据中研究细胞分化的新方法。它描述了在给定时间点基于其剪接和未剪接信使RNA（mRNA）的比率的单个基因的基因表达变化速率。然而，错误的速度估计出现，如果一个共同的剪接速率和观察的完整剪接动态与稳态mRNA水平的中心假设被违反。在这里，我们提出了scVelo，一种方法，克服了这些限制，通过解决完整的转录动态剪接动力学使用基于似然的动态模型。这将RNA速度推广到具有瞬时细胞状态的系统，这在发育和对扰动的反应中很常见。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。我们推断基因特异性的转录，剪接和降解率，恢复每个细胞在潜在的分化过程中的位置，并检测推定的驱动基因。scVelo将促进谱系决定和基因调控的研究。
scVelo reconstructs transient cell states and differentiation pathways from single-cell RNA-sequencing data.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.