Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Multi-omic single-cell velocity models epigenome–transcriptome interactions and improves cell fate prediction

Nature Biotechnology volume 41, pages 387–398 (2023)Cite this article

Subjects

Abstract

Multi-omic single-cell datasets, in which multiple molecular modalities are profiled within the same cell, offer an opportunity to understand the temporal relationship between epigenome and transcriptome. To realize this potential, we developed MultiVelo, a differential equation model of gene expression that extends the RNA velocity framework to incorporate epigenomic data. MultiVelo uses a probabilistic latent variable model to estimate the switch time and rate parameters of chromatin accessibility and gene expression and improves the accuracy of cell fate prediction compared to velocity estimates from RNA only. Application to multi-omic single-cell datasets from brain, skin and blood cells reveals two distinct classes of genes distinguished by whether chromatin closes before or after transcription ceases. We also find four types of cell states: two states in which epigenome and transcriptome are coupled and two distinct decoupled states. Finally, we identify time lags between transcription factor expression and binding site accessibility and between disease-associated SNP accessibility and expression of the linked genes. MultiVelo is available on PyPI, Bioconda and GitHub (https://github.com/welch-lab/MultiVelo).

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Schematic of MultiVelo approach.
Fig. 2: MultiVelo reveals two distinct mechanisms of gene regulation.
Fig. 3: MultiVelo captures epigenomic priming and decoupling in embryonic mouse brain.
Fig. 4: MultiVelo quantifies epigenomic priming in mouse skin.
Fig. 5: MultiVelo identifies priming in HSPCs.
Fig. 6: MultiVelo infers epigenome and transcriptome dynamics in fetal human brain.

Similar content being viewed by others

Data availability

10x embryonic mouse brain dataset can be accessed at the 10x website at https://www.10xgenomics.com/resources/datasets/fresh-embryonic-e-18-mouse-brain-5-k-1-standard-1-0-0.

SHARE-seq9 mouse skin dataset can be found at the GEO (GSE140203).

Human brain multi-ome dataset43 can be found at the GEO (GSE162170) and the authors’ GitHub page.

ChIP-seq peaks for bulk CD34+ HSPC69 were downloaded from the GEO (GSE70677).

The processed files of the newly sequenced 10x Multiome HSPC samples are available at the GEO (GSE209878). Raw sequences were uploaded to dbGaP phs002915.v1.p1 under restricted access due to patient privacy concerns. Source data are provided with this paper.

Code availability

MultiVelo is implemented in Python. The package is available on GitHub (https://github.com/welch-lab/MultiVelo), PyPI and Bioconda.

References

  1. Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genom. 19, 477 (2018).

    Article  Google Scholar 

  3. Ji, Z. & Ji, H. Tscan: pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic Acids Res. 44, 117 (2016).

    Article  Google Scholar 

  4. Setty, M. et al. Characterization of cell fate probabilities in single-cell data with Palantir. Nat. Biotechnol. 37, 451–460 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Welch, J. D., Hartemink, A. J. & Prins, J. F. SLICER: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data. Genome Biol. 17, 106 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  6. La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. 38, 1408–1414 (2020).

    Article  CAS  PubMed  Google Scholar 

  8. Gorin, G., Svensson, V. & Pachter, L. Protein velocity and acceleration from single-cell multiomics experiments. Genome Biol. 21, 39 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ma, S. et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell 183, 1103–1116 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Tedesco, M. et al. Chromatin velocity reveals epigenetic dynamics by single-cell profiling of heterochromatin and euchromatin.Nat. Biotechnol. 40, 235–244 (2021).

    Article  PubMed  Google Scholar 

  11. Chen, S., Lake, B. B. & Zhang, K. High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell. Nat. Biotechnol. 37, 1452–1457 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Weake, V. M. & Workman, J. L. Inducible gene expression: diverse regulatory mechanisms. Nat. Rev. Genet. 11, 426–437 (2010).

    Article  CAS  PubMed  Google Scholar 

  13. Rada-Iglesias, A. et al. A unique chromatin signature uncovers early developmental enhancers in humans. Nature 470, 279–283 (2011).

    Article  CAS  PubMed  Google Scholar 

  14. Ostuni, R. et al. Latent enhancers activated by stimulation in differentiated cells. Cell 152, 157–171 (2013).

    Article  CAS  PubMed  Google Scholar 

  15. Lara-Astiaso, D. et al. Chromatin state dynamics during blood formation. Science 345, 943–949 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Klemm, S. L., Shipony, Z. & Greenleaf, W. J. Chromatin accessibility and the regulatory epigenome. Nat. Rev. Genet. 20, 207–220 (2019).

    Article  CAS  PubMed  Google Scholar 

  17. Merkle, F. T., Tramontin, A. D., García-Verdugo, J. M. & Alvarez-Buylla, A. Radial glia give rise to adult neural stem cells in the subventricular zone. Proc. Natl. Acad. Sci. U.S.A. 101, 17528–17532 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Hansen, D. V., Lui, J. H., Parker, P. R. L. & Kriegstein, A. R. Neurogenic radial glia in the outer subventricular zone of human neocortex. Nature 464, 554–561 (2010).

    Article  CAS  PubMed  Google Scholar 

  19. Pollen, A. A. et al. Molecular identity of human outer radial glia during cortical development. Cell 163, 55–67 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Nadarajah, B. & Parnavelas, J. G. Modes of neuronal migration in the developing cerebral cortex. Nat. Rev. Neurosci. 3, 423–432 (2002).

    Article  CAS  PubMed  Google Scholar 

  21. Englund, C. et al. Pax6, Tbr2, and Tbr1 are expressed sequentially by radial glia, intermediate progenitor cells, and postmitotic neurons in developing neocortex. J. Neurosci. 25, 247–251 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Mayer, S. et al. Multimodal single-cell analysis reveals physiological maturation in the developing human neocortex. Neuron 102, 143–158 (2019).

    Article  Google Scholar 

  23. Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Arnold, S. J. et al. The T-box transcription factor Eomes/Tbr2 regulates neurogenesis in the cortical subventricular zone. Genes Dev. 22, 2479–2484 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Vasistha, N. A. et al. Cortical and clonal contribution of Tbr2 expressing progenitors in the developing mouse brain. Cereb. Cortex 25, 3290–3302 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  26. McEvilly, R. J., de Diaz, M. O., Schonemann, M. D., Hooshmand, F. & Rosenfeld, M. G. Transcriptional regulation of cortical neuron migration by POU domain factors. Science 295, 1528–1532 (2002).

    Article  CAS  PubMed  Google Scholar 

  27. Zahr, S. K. et al. A translational repression complex in developing mammalian neural stem cells that regulates neuronal specification. Neuron 97, 520–537 (2018).

    Article  Google Scholar 

  28. Cusanovich, D. A. et al. A single-cell atlas of in Vivo mammalian chromatin accessibility. Cell 174, 1309–1324 (2018).

    Article  Google Scholar 

  29. Lake, B. B. et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat. Biotechnol. 36, 70–80 (2018).

    Article  CAS  PubMed  Google Scholar 

  30. Ranzoni, A. M. et al. Integrative single-cell RNA-seq and ATAC-seq analysis of human developmental hematopoiesis. Cell Stem Cell 28, 472–487.e7 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Jia, G. et al. Single cell RNA-seq and ATAC-seq analysis of cardiac progenitor cell transition states and lineage settlement. Nat. Commun. 9, 4877 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Zhang, B. & Hsu, Y.-C. Emerging roles of transit-amplifying cells in tissue regeneration and cancer. Wiley. Interdiscip. Rev. Dev. Biol. 6, e282 (2017).

    Article  Google Scholar 

  33. Coifman, R. R. et al. Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. Proc. Natl. Acad. Sci. U.S.A. 102, 7426–7431 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Millar, S. E. et al. WNT signaling in the control of hair growth and structure. Dev. Biol. 207, 133–149 (1999).

    Article  CAS  PubMed  Google Scholar 

  35. Berndt, D. J. & Clifford, J. Using dynamic time warping to find patterns in time series. In Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, AAAIWS’94, 359–370 (AAAI Press, 1994).

  36. Buenrostro, J. D. et al. Integrated single-cell analysis maps the continuous regulatory landscape of human hematopoietic differentiation. Cell 173, 1535–1548 (2018).

    Article  Google Scholar 

  37. Orkin, S. H. & Zon, L. I. Hematopoiesis: an evolving paradigm for stem cell biology. Cell 132, 631–644 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Görgens, A. et al. Revision of the human hematopoietic tree: granulocyte subtypes derive from distinct hematopoietic lineages. Cell Rep. 3, 1539–1552 (2013).

    Article  PubMed  Google Scholar 

  39. Laurenti, E. & Göttgens, B. From haematopoietic stem cells to complex differentiation landscapes. Nature 553, 418–426 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Pellin, D. et al. A comprehensive single cell transcriptional landscape of human hematopoietic progenitors. Nat. Commun. 10, 2395 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Bergen, V., Soldatov, R. A., Peter V, K. & Theis, F. J. RNA velocity—current challenges and future perspectives. Mol. Syst. Biol. 17, e10282 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Trevino, A. E. et al. Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution. Cell 184, 5053–5069.e23 (2021).

    Article  PubMed  Google Scholar 

  44. Schep, A. N., Wu, B., Buenrostro, J. D. & Greenleaf, W. J. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat. Methods 14, 975–978 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Tong, A., Huang, J., Wolf, G., van Dijk, D. & Krishnaswamy, S. Trajectorynet: a dynamic optimal transport network for modeling cellular dynamics. In Proceedings of the 37th International Conference on Machine Learning, ICML’20 (JMLR.org, 2020).

  46. Schiebinger, G. et al. Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming. Cell 176, 928–943 (2019).

    Article  Google Scholar 

  47. Qiao, C. & Huang, Y. Representation learning of RNA velocity reveals robust cell transitions. Proc. Natl. Acad. Sci. U.S.A. 118, e2105859118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Nelder, J. A. & Mead, R. A simplex method for function minimization. Comput. J. 7, 308–313 (1965).

    Article  Google Scholar 

  49. Bentley, J. L. Multidimensional binary search trees used for associative searching. Commun. ACM 18, 509–517 (1975).

    Article  Google Scholar 

  50. Wilks, S. S. The large-sample distribution of the likelihood ratio for testing composite hypotheses. Ann. Math. Stat. 9, 60–62 (1938).

    Article  Google Scholar 

  51. Kim, J., Sheu, K. M., Cheng, Q. J., Hoffmann, A. & Enciso, G. Stochastic models of nucleosome dynamics reveal regulatory rules of stimulus-induced epigenome remodeling. Cell Rep. 40, 111076 (2022).

    Article  CAS  PubMed  Google Scholar 

  52. Larsson, A. J. M. et al. Genomic encoding of transcriptional burst kinetics. Nature 565, 251–254 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Giorgino, T. Computing and visualizing dynamic time warping alignments in R: the dtw package. J. Stat. Softw. 31, 1–24 (2009).

    Article  Google Scholar 

  54. Tormene, P., Giorgino, T., Quaglini, S. & Stefanelli, M. Matching incomplete time series with dynamic time warping: an algorithm and an application to post-stroke rehabilitation. Artif. Intell. Med. 45, 11–34 (2009).

    Article  PubMed  Google Scholar 

  55. Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, 5233 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Batiuk, M. Y. et al. Identification of region-specific astrocyte subtypes at single cell resolution. Nat. Commun. 11, 1220 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Polioudakis, D. et al. A single-cell transcriptomic atlas of human neocortical development during mid-gestation. Neuron 103, 785–801.e8 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Cahoy, J. D. et al. A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. J. Neurosci. Res. 28, 264–278 (2008).

    CAS  Google Scholar 

  59. Hochgerner, H., Zeisel, A., Lönnerberg, P. & Linnarsson, S. Conserved properties of dentate gyrus neurogenesis across postnatal development revealed by single-cell RNA sequencing. Nat. Neurosci. 21, 290–299 (2018).

    Article  CAS  PubMed  Google Scholar 

  60. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Stuart, T., Srivastava, A., Madad, S., Lareau, C. A. & Satija, R. Single-cell chromatin state analysis with Signac. Nat. Methods 18, 1333–1341 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Welch, J. D. et al. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177, 1873–1887 (2019).

    Article  Google Scholar 

  63. Karamitros, D. et al. Single-cell analysis reveals the continuum of human lympho-myeloid progenitor cells. Nat. Immunol. 19, 85–97 (2018).

    Article  CAS  PubMed  Google Scholar 

  64. Yáñez, A. et al. Granulocyte-monocyte progenitors and monocyte-dendritic cell progenitors independently produce functionally distinct monocytes. Immunity 47, 890–902.e4 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Villani, A.-C. et al. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science 356, eaah4573 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Grajkowska, L. T. et al. Isoform-specific expression and feedback regulation of E protein TCF4 control dendritic cell lineage specification. Immunity 46, 65–77 (2017).

    Article  CAS  PubMed  Google Scholar 

  67. Paul, F. et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163, 1663–1677 (2015).

    Article  CAS  PubMed  Google Scholar 

  68. Eden, E., Navon, R., Steinfeld, I., Lipson, D. & Yakhini, Z. GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinform. 10, 48 (2009).

    Article  Google Scholar 

  69. Romano, O. et al. Transcriptional, epigenetic and retroviral signatures identify regulatory regions involved in hematopoietic lineage commitment. Sci. Rep. 6, 24724 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Fornes, O. et al. JASPAR 2020: update of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 48, D87–D92 (2019).

    PubMed Central  Google Scholar 

  72. Koldamova, R. et al. Genome-wide approaches reveal EGR1-controlled regulatory networks associated with neurodegeneration. Neurobiol. Dis. 63, 107–114 (2014).

    Article  CAS  PubMed  Google Scholar 

  73. Tosic, J. et al. Eomes and Brachyury control pluripotency exit and germ-layer segregation by changing the chromatin state. Nat. Cell Biol. 21, 1518–1531 (2019).

    Article  CAS  PubMed  Google Scholar 

  74. Spiteri, E. et al. Identification of the transcriptional targets of FOXP2, a gene linked to speech and language, in developing human brain. Am. J. Hum. Genet. 81, 1144–1157 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Golonzhka, O. et al. Pbx regulates patterning of the cerebral cortex in progenitors and postmitotic neurons. Neuron 88, 1192–1207 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by National Institutes of Health grants R01AI149669 to K.L.C. and J.D.W., R01HG010883 to J.D.W., F31AI155047 to M.C.V., training grants T32GM070449 to C.L. and T32GM007315 to M.C.V. and additional funding provided by the Rackham Regents Fellowship to M.C.V. The HSPC sample was provided by Cooperative Center of Excellence in Hematology grant DK106829. We thank J. Li, S.C.J. Parker, Y. Gu and members of the Collins lab for helpful discussions.

Author information

Authors and Affiliations

  1. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA

    Chen Li, Maria C. Virgilio & Joshua D. Welch

  2. Cellular and Molecular Biology Program, University of Michigan, Ann Arbor, MI, USA

    Maria C. Virgilio & Kathleen L. Collins

  3. Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA

    Kathleen L. Collins

  4. Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA

    Kathleen L. Collins

  5. Department of Computer Science and Engineering, University of Michigan, Ann Arbor, MI, USA

    Joshua D. Welch

Authors
  1. Chen Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maria C. Virgilio
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kathleen L. Collins
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Joshua D. Welch
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.C.V. and K.L.C. generated the 10x Multiome HSPC data. J.D.W conceived the idea of multi-omic extension of RNA velocity. C.L. and J.D.W. developed and implemented the method, performed data analyses and wrote the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Joshua D. Welch.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Biotechnology thanks Yuanhua Huang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Additional figures for mouse brain dataset.

a, Canonical marker gene expression for embryonic mouse brain cell types. b, Comparison of Cdh13 fits from scVelo and MultiVelo. An elevating transcription rate due to opening of chromatin produces a more linear fit and better captures the observed phase portrait. c, Scatterplot of gene likelihood against log total spliced count. Gene likelihood is not significantly affected by model assignment or trajectory type. Likelihood does increase with spliced count, as this usually indicates higher quality or highly variable genes. d, Switch times can be used to rank genes by the length of priming and decoupling intervals. Each range is scaled to 1 with outliers (n=1) removed. Top two rows: Histogram of priming intervals. Pbx3 and Celsr1 possess short and long priming phases, respectively. Bottom two rows: Histogram of decoupled intervals. While Rspo3 has a short decoupling phase with few cells within, Tgfbr1’s decoupling phase extends from RNA induction to RNA repression, and up to the end of the trajectory.

Source data

Extended Data Fig. 2 Additional figures for HSPC dataset.

a, Canonical marker gene expression for HSPCs. b, Cell cycle (S phase and G2M phase) scores and total unspliced ratio (U/(U+S)) plotted on UMAP coordinates. These factors were regressed out of the total RNA expression (but not the unspliced and spliced counts) during the preprocessing step as they do not appear to be cell-type or lineage specific. c, Box plots of histone modification levels from bulk ChIP-seq of FACS-purified HSCs (center line, median; box, Q1 and Q3; whiskers, 1.5x IQR; points, outliers). Each point in the box plot represents the sum of histone modification signal at chromatin accessibility peaks linked to a Model 1 or Model 2 gene. P-values are from a one-sided Wilcoxon rank-sum test. d, Velocity stream plot from MultiVelo analysis of Day 0 and Day 7 HSPC samples (Top). The majority of arrows go from Day 0 stem cells toward more differentiated Day 7 cells. UMAP coordinates colored by cell-type labels (Middle). UMAP coordinates colored by expression of CD133 (PROM1), an HSPC marker (Bottom).

Source data

Extended Data Fig. 3 Additional figures for human brain dataset.

a, Validation of the direction of MEF2C. Left: UMAP with cell types. Top: scVelo’s MEF2C fit produces inconsistency between gene time and global latent time. Bottom: MultiVelo’s results show consistent progression from nIPC to deeper layer (ExDp). b, DTW and UMAP results for EOMES and FOXP2 transcription factors. c, Additional motif DTW alignment results showing time lags between TF gene expression and corresponding motif accessibility. d, The accessibility of TF motifs binned across latent time. The latent time scale was split into 20 equal-sized bins, and the average motif accessibility of cells in each bin was computed and plotted. The motif sequence logos (downloaded from jaspar2020.genereg.net) are shown next to the TF names. e, Time-lag analysis of transcription factors and the expression of their validated downstream target genes. Top: UMAP plots colored by TF and target gene expression. Bottom: Line plots of TF and target gene expression, with correspondences from DTW alignment shown as dotted lines. Magenta: TFs. Cyan: target genes.

Extended Data Fig. 4 Chromatin dynamics, Model 0, the necessity of chromatin preprocessing, and pre-fitting illustrations.

a, Chromatin dynamics illustration: chromatin opening and closing are modeled as asymptotically approaching fully opened (1) or fully closed (0) starting from any initial value. b, Chromatin accessibility change as a function latent time inferred by scVelo using only the RNA portion of the 10X multiome mouse brain dataset (colored by mouse brain cell types). Black lines connect the mean accessibilities within 20 equal-sized windows. The shapes of the ATAC trends are qualitatively very similar to the ODE model we propose. c, Simulation of Model 0 samples. The long delay between chromatin closing and transcription initiation is unlikely to happen in real biological systems. In the rare cases when high chromatin accessibility but low expression or high expression but low accessibility pattern is observed, it is likely due to technical issues such as dropout or background noise. d, The need for normalization as a preprocessing step for ATAC-seq. e, The need for smoothing as a preprocessing step for ATAC-seq. f, Chromatin accessibility results after peak-to-gene aggregation, TF-IDF normalization, and WNN smoothing. It is the same as Fig. S2E. g, Illustration of bi-modal expression pattern for complete genes. Cells at the lower quantile can be far apart in low-dimensional embedded expression space. h, Simplified illustration of model predetermination reasoning. The highest chromatin accessibility region appears in different RNA phases in M1 and M2 genes. i, Illustration of the internal unspliced modality rescaling factor initialization.

Extended Data Fig. 5 Simulation study to assess parameter estimation and model determination.

A total of 1000 genes were simulated with various parameters for both model 1 and model 2. a, C-U view of noiseless simulations of 2000 time-points in the 0-20 hr range. b, U-S view of noiseless simulations from A. c, 3D view of noiseless simulations from A. d, Noise added to simulated points to mimic real data. e, f, Model 1 and Model 2 fits for the same simulated gene (S17). The likelihood is higher under Model 1, consistent with the ground truth. e, Left: 3D view of the fit Model 1 trajectory colored by states, along with predicted switch time points. Middle: simulation with ground-truth switch times. Right: U-S view of fitted trajectory colored by log(c). f, Similar to e, but the fit shown is for Model 2 (the incorrect model). g, h, Model fits for simulated gene S41, similar to e and f, but this time, Model 2 is the ground truth model. MultiVelo correctly identifies the sample to be Model 2 with accurate switch time estimations. The model assignments of 985/1000 samples were correctly predicted based on likelihood.

Supplementary information

Supplementary Information

Supplementary Notes, Figs. 1–6 and Table 1.

Reporting Summary

Source data

Source Data Fig. 2

Statistical Source Data.

Source Data Fig. 3

Statistical Source Data.

Source Data Fig. 4

Statistical Source Data.

Source Data Fig. 5

Statistical Source Data.

Source Data Fig. 6

Statistical Source Data.

Source Data Extended Data Fig. 1

Statistical Source Data.

Source Data Extended Data Fig. 2

Statistical Source Data.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, C., Virgilio, M.C., Collins, K.L. et al. Multi-omic single-cell velocity models epigenome–transcriptome interactions and improves cell fate prediction. Nat Biotechnol 41, 387–398 (2023). https://doi.org/10.1038/s41587-022-01476-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41587-022-01476-y

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing