RNA velocity—current challenges and future perspectives

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Abstract

RNA velocity has enabled the recovery of directed dynamic information from single‐cell transcriptomics by connecting measurements to the underlying kinetics of gene expression. This approach has opened up new ways of studying cellular dynamics. Here, we review the current state of RNA velocity modeling approaches, discuss various examples illustrating limitations and potential pitfalls, and provide guidance on how the ensuing challenges may be addressed. We then outline future directions on how to generalize the concept of RNA velocity to a wider variety of biological systems and modalities.

Abstract

This Review discusses the emerging challenges and potential pitfalls of current RNA velocity modeling approaches and provides guidance on how to address them.

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Most cited references 25

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Massively parallel digital transcriptional profiling of single cells

Grace Zheng, Jessica M. Terry, Phillip Belgrader … (2017)

Characterizing the transcriptome of individual cells is fundamental to understanding complex biological systems. We describe a droplet-based system that enables 3′ mRNA counting of tens of thousands of single cells per sample. Cell encapsulation, of up to 8 samples at a time, takes place in ∼6 min, with ∼50% cell capture efficiency. To demonstrate the system's technical performance, we collected transcriptome data from ∼250k single cells across 29 samples. We validated the sensitivity of the system and its ability to detect rare populations using cell lines and synthetic RNAs. We profiled 68k peripheral blood mononuclear cells to demonstrate the system's ability to characterize large immune populations. Finally, we used sequence variation in the transcriptome data to determine host and donor chimerism at single-cell resolution from bone marrow mononuclear cells isolated from transplant patients.

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RNA velocity of single cells

Gioele La Manno, Ruslan Soldatov, Amit Zeisel … (2018)

RNA abundance is a powerful indicator of the state of individual cells. Single-cell RNA sequencing can reveal RNA abundance with high quantitative accuracy, sensitivity and throughput1. However, this approach captures only a static snapshot at a point in time, posing a challenge for the analysis of time-resolved phenomena, such as embryogenesis or tissue regeneration. Here we show that RNA velocity—the time derivative of the gene expression state—can be directly estimated by distinguishing unspliced and spliced mRNAs in common single-cell RNA sequencing protocols. RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a timescale of hours. We validate its accuracy in the neural crest lineage, demonstrate its use on multiple published datasets and technical platforms, reveal the branching lineage tree of the developing mouse hippocampus, and examine the kinetics of transcription in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.

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Nature, nurture, or chance: stochastic gene expression and its consequences.

Arjun Raj, Alexander van Oudenaarden (2008)

Gene expression is a fundamentally stochastic process, with randomness in transcription and translation leading to cell-to-cell variations in mRNA and protein levels. This variation appears in organisms ranging from microbes to metazoans, and its characteristics depend both on the biophysical parameters governing gene expression and on gene network structure. Stochastic gene expression has important consequences for cellular function, being beneficial in some contexts and harmful in others. These situations include the stress response, metabolism, development, the cell cycle, circadian rhythms, and aging.

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Author and article information

Contributors

Fabian J Theis:

ORCID: https://orcid.org/0000-0002-2419-1943

fabian.theis@helmholtz-muenchen.de

Journal

Journal ID (nlm-ta): Mol Syst Biol

Journal ID (iso-abbrev): Mol Syst Biol

Journal ID (doi): 10.1002/(ISSN)1744-4292

Journal ID (publisher-id): MSB

Journal ID (hwp): msb

Title: Molecular Systems Biology

Publisher: John Wiley and Sons Inc. (Hoboken )

ISSN (Electronic): 1744-4292

Publication date (Electronic): 26 August 2021

Publication date Collection: August 2021

Volume: 17

Issue: 8 ( doiID: 10.1002/msb.v17.8 )

Electronic Location Identifier: e10282

Affiliations

[ ¹ ] Institute of Computational Biology Helmholtz Center Munich Munich Germany

[ ² ] Department of Mathematics Technical University of Munich Munich Germany

[ ³ ] Department of Biomedical Informatics Harvard Medical School Boston MA USA

Author notes

[*] [* ] Corresponding author. Tel: +49 89 3187 2211; E‐mail: fabian.theis@ 123456helmholtz-muenchen.de

Author information

Fabian J Theis https://orcid.org/0000-0002-2419-1943

Article

Publisher ID: MSB202110282

DOI: 10.15252/msb.202110282

PMC ID: 8388041

PubMed ID: 34435732

SO-VID: 97db6a00-bee9-4fe4-85f5-0187bcef12e3

License:

This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

History

Date revision received : 08 June 2021

Date received : 09 February 2021

Date accepted : 29 June 2021

Page count

Figures: 5, Tables: 0, Pages: 9, Words: 5699

Funding

Funded by: Bundesministerium für Bildung und Forschung (BMBF) , doi 10.13039/501100002347;

Award ID: 01IS18036A,01IS18053A

Award ID: 01IS18053A

Funded by: Deutsche Forschungsgemeinschaft (DFG) , doi 10.13039/501100001659;

Award ID: Collaborative Research Centre 1243

Award ID: Subproject A17

Funded by: Helmholtz Association (亥姆霍兹联合会致力) , doi 10.13039/501100009318;

Award ID: sparse2big,ZT‐I‐0007

Funded by: Chan Zuckerberg Initiative (CZI) , doi 10.13039/100014989;

Award ID: 182835

Funded by: National Science Foundation (NSF) , doi 10.13039/100000001;

Award ID: CAREER (NSF‐14‐532) award

Funded by: EC | H2020 | H2020 Priority Excellent Science | H2020 European Research Council (ERC) , doi 10.13039/100010663;

Award ID: Synergy (85629) award

Custom metadata

source-schema-version-number 2.0

cover-date August 2021

details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.6 mode:remove_FC converted:26.08.2021

ScienceOpen disciplines: Quantitative & Systems biology

Keywords: challenges,dynamics,limitations,perspectives,rna velocity,chromatin, epigenetics, genomics & functional genomics,computational biology

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ScienceOpen disciplines: Quantitative & Systems biology

Keywords: challenges, dynamics, limitations, perspectives, rna velocity, chromatin, epigenetics, genomics & functional genomics, computational biology

RNA velocity—current challenges and future perspectives

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Most cited references 25

Massively parallel digital transcriptional profiling of single cells

RNA velocity of single cells

Nature, nurture, or chance: stochastic gene expression and its consequences.

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