德国亥姆霍兹慕尼黑中心计算生物学研究所Fabian J. Theis研究组近日取得一项新成果。经过不懈努力，他们的研究通过动态建模可概括RNA速度瞬时细胞状态。 相关论文于2020年8月3日发表在《自然—生物技术》杂志上。

研究小组开发了scVelo，这是一种基于似然的动力学模型，通过解决剪接动力学的完整转录动态来克服估计误差的限制。这个模型可推广RNA速度到瞬时细胞状态体系，该体系在发育和对扰动的反应中都很常见。研究人员将scVelo应用于神经发生和胰腺内分泌发生的亚群动力学研究。

研究人员从而推断特定基因的转录，剪接和降解速率，揭示每个细胞在基础分化过程中的位置，并检测推定的驱动基因。scVelo将助力血统决定和基因调控的研究。

据了解，RNA速度是一种在单细胞RNA测序数据中研究细胞分化的新方法。基于剪接和未剪接的信使核糖核酸（mRNA）比例，它描述了在给定时间点单个基因的基因表达变化的速度。然而，当核心假设即常见剪接速度和在稳态mRNA水平下观察到的全剪接动态相违背时，对于速度估计的误差就会产生。

附：英文原文

Title: Generalizing RNA velocity to transient cell states through dynamical modeling

Author: Volker Bergen, Marius Lange, Stefan Peidli, F. Alexander Wolf, Fabian J. Theis

Issue&Volume: 2020-08-03

Abstract: 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.

DOI: 10.1038/s41587-020-0591-3

Source: https://www.nature.com/articles/s41587-020-0591-3