PhyloVelo enhances transcriptomic velocity field mapping using monotonically expressed genes
Abstract
Single-cell RNA sequencing (scRNA-seq) is a powerful approach for studying cellular differentiation, but accurately tracking cell fate transitions can be challenging, especially in disease conditions. Here we introduce PhyloVelo, a computational framework that estimates the velocity of transcriptomic dynamics by using monotonically expressed genes (MEGs) or genes with expression patterns that either increase or decrease, but do not cycle, through phylogenetic time. Through integration of scRNA-seq data with lineage information, PhyloVelo identifies MEGs and reconstructs a transcriptomic velocity field. We validate PhyloVelo using simulated data and Caenorhabditis elegans ground truth data, successfully recovering linear, bifurcated and convergent differentiations. Applying PhyloVelo to seven lineage-traced scRNA-seq datasets, generated using CRISPR-Cas9 editing, lentiviral barcoding or immune repertoire profiling, demonstrates its high accuracy and robustness in inferring complex lineage trajectories while outperforming RNA velocity. Additionally, we discovered that MEGs across tissues and organisms share similar functions in translation and ribosome biogenesis.
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p21+ and p16+ populations are distinct and do not transition into one another.
“This suggests that these populations are distinct and do not transition into one another (Wang et al, 2024b) (Fig. 1E).”
From Distinct senotypes in p16- and p21-positive cells across human and mouse aging tissues (2025) · cites this paper as Wang et al, 2024b
The ‘PhyloVelo identifies lung tumor dedifferentiation’ section discusses transitions among lung tumor cell types, not p21+ and p16+ populations. Searches for p21, p16, CDKN1A, CDKN2A, and senescence found no passage addressing whether those populations are distinct or transition into one another. (LLM, conf=0.74)
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Cites
Generalizing RNA velocity to transient cell states through dynamical modeling
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Deep generative modeling for single-cell transcriptomics
Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018) · Nature Methods · doi:10.1038/s41592-018-0229-2 · Cited by 2,530 · see citing works
Single-cell transcriptional diversity is a hallmark of developmental potential
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Lineage tracing on transcriptional landscapes links state to fate during differentiation
Weinreb, C., Rodriguez-Fraticelli, A., Camargo, F. D., & Klein, A. M. (2020) · Science · doi:10.1126/science.aaw3381 · Cited by 730 · cited 6× · see citing works
“Previous effort, such as the CoSpar algorithm 36 , has been made to use the paired scRNA-seq and lineage information to infer transition maps and predict fate bias of progenitor cells, which is a better fit for static barcoding information (for example, the LARRY system 37 ).”
InfluentialThe emergent landscape of the mouse gut endoderm at single-cell resolution
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Single-Cell Transcriptomics Meets Lineage Tracing
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Cited by
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Distinct senotypes in p16- and p21-positive cells across human and mouse aging tissues
Saul, D., Jurk, D., Doolittle, M. L., Kosinsky, R. L., Han, Y., Zhang, X., Franco, A. C., Kim, S. Y., Wyles, S. P., Prakash, Y. S., et al. (2025) · The EMBO Journal · doi:10.1038/s44318-025-00601-2 · Cited by 45 · see citing works
- Authors
- Wang, K., Hou, L., Wang, X., Zhai, X., Lu, Z., Zi, Z., Zhai, W., He, X., Curtis, C., Zhou, D., & Hu, Z.
- Affiliations
- Xiamen University (CN)
- Chinese Academy of Sciences (CN)
- Sun Yat-sen University (CN)
- Stanford Medicine (US)
- Corresponding author
- Zhou, Da
- Hu, Zheng
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2023-07-31
- Volume
- 42
- Issue
- 5
- Pages
- 778-789
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W4385415871
- Cited by
- 66
- References
- 8
- Citations by year
- 2026: 22 · 2025: 29 · 2024: 12 · 2023: 3
- FWCI
- 5.87
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (11971405, 32270693)
- China Postdoctoral Science Foundation (2021M693303)
- 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 › Genomics and Phylogenetic Studies
- Keywords
- single-cell RNA sequencing, cell fate transitions, lineage tracing, RNA velocity, cell differentiation, ribosome biogenesis, translation (biology)
- MeSH terms
- Animals, Cell Differentiation, Humans, Phylogeny, Caenorhabditis elegans, Sequence Analysis, RNA, Cell Lineage, Computational Biology, Gene Expression Profiling, Single-Cell Analysis, Transcriptome
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