Generalizing RNA velocity to transient cell states through dynamical modeling

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...

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Vydané v:Nature biotechnology Ročník 38; číslo 12; s. 1408 - 1414
Hlavní autori: Bergen, Volker, Lange, Marius, Peidli, Stefan, Wolf, F Alexander, Theis, Fabian J
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Nature Publishing Group 01.12.2020
Predmet:
Animals
Cell Cycle
Cell differentiation
Cell Lineage
Dentate Gyrus - metabolism
Differentiation (biology)
Dynamic models
Endocrine System - metabolism
Gene expression
Gene regulation
Gene sequencing
Humans
Kinetics
Mice
Models, Genetic
Neurogenesis
Neurogenesis - genetics
Pancreas
Ribonucleic acid
RNA
RNA - genetics
RNA Splicing - genetics
Single-Cell Analysis
Splicing
Stem Cells - metabolism
Stochastic Processes
Transcription
Transcription, Genetic
Velocity
ISSN:1087-0156, 1546-1696, 1546-1696
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Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1087-0156
1546-1696
1546-1696
DOI:10.1038/s41587-020-0591-3