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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Published in:	Nature biotechnology Vol. 38; no. 12; pp. 1408 - 1414
Main Authors:	Bergen, Volker, Lange, Marius, Peidli, Stefan, Wolf, F Alexander, Theis, Fabian J
Format:	Journal Article
Language:	English
Published:	United States Nature Publishing Group 01.12.2020
Subjects:	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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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.
AbstractList	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 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 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 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.scVelo reconstructs transient cell states and differentiation pathways from single-cell RNA-sequencing data.
Author	Wolf, F Alexander Bergen, Volker Lange, Marius Theis, Fabian J Peidli, Stefan
Author_xml	– sequence: 1 givenname: Volker surname: Bergen fullname: Bergen, Volker organization: Department of Mathematics, Technical University of Munich, Munich, Germany – sequence: 2 givenname: Marius orcidid: 0000-0002-4846-1266 surname: Lange fullname: Lange, Marius organization: Department of Mathematics, Technical University of Munich, Munich, Germany – sequence: 3 givenname: Stefan orcidid: 0000-0002-4257-8690 surname: Peidli fullname: Peidli, Stefan organization: Department of Mathematics, Technical University of Munich, Munich, Germany – sequence: 4 givenname: F Alexander orcidid: 0000-0002-8760-7838 surname: Wolf fullname: Wolf, F Alexander email: alex.wolf@helmholtz-muenchen.de organization: Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany. alex.wolf@helmholtz-muenchen.de – sequence: 5 givenname: Fabian J orcidid: 0000-0002-2419-1943 surname: Theis fullname: Theis, Fabian J email: fabian.theis@helmholtz-muenchen.de, fabian.theis@helmholtz-muenchen.de organization: Department of Mathematics, Technical University of Munich, Munich, Germany. fabian.theis@helmholtz-muenchen.de
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/32747759$$D View this record in MEDLINE/PubMed
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Snippet	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...
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SubjectTerms	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
Title	Generalizing RNA velocity to transient cell states through dynamical modeling
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