RNA velocity—current challenges and future perspectives
Open accessAbstract
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.
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In steady-state populations, RNA velocity infers erroneous directions between independent terminal cell populations.
“Additionally, steady-state populations pose further challenges where RNA velocity infers erroneous directions between independent, terminal cell populations70,71.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [70]
RNA velocity is inferred incorrectly in the presence of transcriptional bursts.
“Cases in which RNA velocity is inferred incorrectly include the presence of transcriptional bursts70,71.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [70]
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Cites
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353 · see citing works
La Manno, G., Soldatov, R., Zeisel, A., Braun, E., Hochgerner, H., Petukhov, V., Lidschreiber, K., Kastriti, M. E., Lönnerberg, P., Furlan, A., et al. (2018) · Nature · doi:10.1038/s41586-018-0414-6 · Cited by 4,118 · see citing works
Self-citationGeneralizing RNA velocity to transient cell states through dynamical modeling
Bergen, V., Lange, M., Peidli, S., Wolf, F. A., & Theis, F. J. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0591-3 · Cited by 2,951 · see citing works
Self-citationNature, Nurture, or Chance: Stochastic Gene Expression and Its Consequences
Raj, A., & van Oudenaarden, A. (2008) · Cell · doi:10.1016/j.cell.2008.09.050 · Cited by 2,593 · see citing works
Diffusion pseudotime robustly reconstructs lineage branching
Haghverdi, L., Büttner, M., Wolf, F. A., Buettner, F., & Theis, F. J. (2016) · Nature Methods · doi:10.1038/nmeth.3971 · Cited by 1,385 · see citing works
Self-citationChromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin
Ma, S., Zhang, B., LaFave, L. M., Earl, A. S., Chiang, Z., Hu, Y., Ding, J., Brack, A., Kartha, V. K., Tay, T., et al. (2020) · Cell · doi:10.1016/j.cell.2020.09.056 · Cited by 1,146 · see citing works
A single-cell molecular map of mouse gastrulation and early organogenesis
Pijuan-Sala, B., Griffiths, J. A., Guibentif, C., Hiscock, T. W., Jawaid, W., Calero-Nieto, F. J., Mulas, C., Ibarra-Soria, X., Tyser, R. C. V., Ho, D. L. L., et al. (2019) · Nature · doi:10.1038/s41586-019-0933-9 · Cited by 1,033 · see citing works
Characterization of cell fate probabilities in single-cell data with Palantir
Setty, M., Kiseliovas, V., Levine, J., Gayoso, A., Mazutis, L., & Pe’er, D. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0068-4 · Cited by 833 · cited 1× · see citing works
“We made the same observation in human bone marrow CD34 + hematopoietic cells (Setty et al , 2019).”
Schiebinger, G., Shu, J., Tabaka, M., Cleary, B., Subramanian, V., Solomon, A., Gould, J., Liu, S., Lin, S., Berube, P., et al. (2019) · Cell · doi:10.1016/j.cell.2019.01.006 · Cited by 829 · see citing works
Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
Pratapa, A., Jalihal, A. P., Law, J. N., Bharadwaj, A., & Murali, T. M. (2020) · Nature Methods · doi:10.1038/s41592-019-0690-6 · Cited by 792 · see citing works
Xia, C., Fan, J., Emanuel, G., Hao, J., & Zhuang, X. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912459116 · Cited by 755 · see citing works
A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors
Slyper, M., Porter, C. B. M., Ashenberg, O., Waldman, J., Drokhlyansky, E., Wakiro, I., Smillie, C. S., Smith-Rosario, G., Wu, J., Dionne, D. A., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0844-1 · Cited by 739 · see citing works
CellRank for directed single-cell fate mapping
Lange, M., Bergen, V., Klein, M., Setty, M. N., Reuter, B., Bakhti, M., Lickert, H., Ansari, M., Gote-Schniering, J., Schiller, H. B., et al. (2022) · Nature Methods · doi:10.1038/s41592-021-01346-6 · Cited by 735 · see citing works
Self-citationSpatiotemporal structure of cell fate decisions in murine neural crest
Soldatov, R., Kaucka, M., Kastriti, M. E., Petersen, J., Chontorotzea, T., Englmaier, L., Akkuratova, N., Yang, Y., Häring, M., Dyachuk, V., et al. (2019) · Science · doi:10.1126/science.aas9536 · Cited by 546 · see citing works
Self-citationControl of Transcript Variability in Single Mammalian Cells
Battich, N., Stoeger, T., & Pelkmans, L. (2015) · Cell · doi:10.1016/j.cell.2015.11.018 · Cited by 324 · see citing works
scSLAM-seq reveals core features of transcription dynamics in single cells
Erhard, F., Baptista, M. A. P., Krammer, T., Hennig, T., Lange, M., Arampatzi, P., Jürges, C. S., Theis, F. J., Saliba, A.-E., & Dölken, L. (2019) · Nature · doi:10.1038/s41586-019-1369-y · Cited by 233 · see citing works
Self-citationListening to the noise: random fluctuations reveal gene network parameters
Munsky, B. E., Trinh, B., & Khammash, M. (2009) · Molecular Systems Biology · doi:10.1038/msb.2009.75 · Cited by 205 · see citing works
Single nuclei RNA-seq of mouse placental labyrinth development
Marsh, B., & Blelloch, R. H. (2020) · eLife · doi:10.7554/elife.60266 · Cited by 191 · see citing works
Inferring Causal Gene Regulatory Networks from Coupled Single-Cell Expression Dynamics Using Scribe
Qiu, X., Rahimzamani, A., Wang, L., Ren, B., Mao, Q., Durham, T., McFaline-Figueroa, J. L., Saunders, L., Trapnell, C., & Kannan, S. (2020) · Cell Systems · doi:10.1016/j.cels.2020.02.003 · Cited by 185 · see citing works
Sequencing metabolically labeled transcripts in single cells reveals mRNA turnover strategies
Battich, N., Beumer, J., de Barbanson, B., Krenning, L., Baron, C. S., Tanenbaum, M. E., Clevers, H. C., & van Oudenaarden, A. (2020) · Science · doi:10.1126/science.aax3072 · Cited by 166 · see citing works
Sci-fate characterizes the dynamics of gene expression in single cells
Cao, J., Zhou, W. Y., Steemers, F. J., Trapnell, C., & Shendure, J. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0480-9 · Cited by 162 · see citing works
Schiebinger, G., Shu, J., Tabaka, M., Cleary, B., Subramanian, V., Solomon, A., Gould, J., Liu, S., Lin, S., Berube, P., et al. (2019) · Cell · doi:10.1016/j.cell.2019.02.026 · Cited by 139 · see citing works
Coordinated changes in gene expression kinetics underlie both mouse and human erythroid maturation
Barile, M., Imaz-Rosshandler, I., Inzani, I., Ghazanfar, S., Nichols, J., Marioni, J. C., Guibentif, C., & Göttgens, B. (2021) · Genome Biology · doi:10.1186/s13059-021-02414-y · Cited by 77 · see citing works
Hahl, S. K., & Kremling, A. (2016) · Frontiers in Genetics · doi:10.3389/fgene.2016.00157 · Cited by 68 · see citing works
The emergence and promise of single-cell temporal-omics approaches
Lederer, A. R., & Manno, G. L. (2020) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2019.12.005 · Cited by 41 · see citing works
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Best practices for single-cell analysis across modalities
Heumos, L., Schaar, A. C., Lance, C., Litinetskaya, A., Drost, F., Zappia, L., Lücken, M. D., Strobl, D. C., Henao, J., Curion, F., et al. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00586-w · Cited by 1,051 · see citing works
“Moreover, tools such as Nichenet 130 or Cytotalk 131 , which provide complementary estimates of intracellular activities, such as induced gene expression changes or spatial information, can be used to increase the confidence in predicted interactions.”
BackgroundSelf-citationGorin, G., Fang, M., Chari, T., & Pachter, L. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010492 · Cited by 186 · see citing works
Li, C., Virgilio, M. C., Collins, K. L., & Welch, J. D. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01476-y · Cited by 130 · see citing works
Biologically informed deep learning to query gene programs in single-cell atlases
Lotfollahi, M., Rybakov, S., Hrovatin, K., Hediyeh-zadeh, S., Talavera-López, C., Misharin, A. V., & Theis, F. J. (2023) · Nature Cell Biology · doi:10.1038/s41556-022-01072-x · Cited by 100 · see citing works
Self-citationFolguera-Blasco, N., Boshier, F. A. T., Uatay, A., Pichardo-Almarza, C., Lai, M., Biasetti, J., Dearden, R., Gibbs, M., & Kimko, H. (2024) · Frontiers in Systems Biology · doi:10.3389/fsysb.2024.1380685 · Cited by 15 · see citing works
Computational challenges of cell cycle analysis using single cell transcriptomics
Chervov, A., & Zinovyev, A. (2022) · arXiv · doi:10.48550/arxiv.2208.05229 · Cited by 4 · see citing works
“Computational methods play a pivotal role in these studies [58], [59], [60], [61].”
BackgroundMethodInfluential
- Authors
- Bergen, V., Soldatov, R. A., Kharchenko, P. V., & Theis, F. J.
- Affiliations
- Helmholtz Munich (DE)
- Harvard University (US)
- Corresponding author
- Theis, Fabian J
- Venue
- Molecular Systems Biology
- Published
- 2021-08-01
- Volume
- 17
- Issue
- 8
- Pages
- e10282-e10282
- ISSN
- 1744-4292 (ISSN-L)
- PMC
- PMC8388041
- OpenAlex
- W3194520274
- Cited by
- 271
- References
- 25
- Citations by year
- 2026: 51 · 2025: 55 · 2024: 66 · 2023: 48 · 2022: 46 · 2021: 5
- FWCI
- 13.88
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Molecular Systems Biology (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Site cant be reached (submittedVersion, cc-by)
- Funders
- Bundesministerium für Bildung und Forschung (01IS18036A,01IS18053A, 01IS18053A)
- Deutsche Forschungsgemeinschaft (Collaborative Research Centre 1243, Subproject A17)
- Helmholtz Association (sparse2big,ZT‐I‐0007)
- Chan Zuckerberg Initiative (182835)
- National Science Foundation (CAREER (NSF‐14‐532) award)
- H2020 European Research Council (Synergy (85629) award)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- RNA velocity, single-cell transcriptomics, gene expression kinetics, cell dynamics, velocity model building, model limitations
- MeSH terms
- Kinetics, RNA, Transcriptome
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