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

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).

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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.

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.

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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.

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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.

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

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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.

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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 (2022). https://doi.org/10.1038/s41587-022-01476-y

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