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MIRA: joint regulatory modeling of multimodal expression and chromatin accessibility in single cells

Abstract

Rigorously comparing gene expression and chromatin accessibility in the same single cells could illuminate the logic of how coupling or decoupling of these mechanisms regulates fate commitment. Here we present MIRA, probabilistic multimodal models for integrated regulatory analysis, a comprehensive methodology that systematically contrasts transcription and accessibility to infer the regulatory circuitry driving cells along cell state trajectories. MIRA leverages topic modeling of cell states and regulatory potential modeling of individual gene loci. MIRA thereby represents cell states in an efficient and interpretable latent space, infers high-fidelity cell state trees, determines key regulators of fate decisions at branch points and exposes the variable influence of local accessibility on transcription at distinct loci. Applied to epidermal differentiation and embryonic brain development from two different multimodal platforms, MIRA revealed that early developmental genes were tightly regulated by local chromatin landscape whereas terminal fate genes were titrated without requiring extensive chromatin remodeling.

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Fig. 1: Schematic of MIRA’s cell-level topic and gene-level RP models for integrated analysis of single-cell multimodal transcription and accessibility data.
Fig. 2: MIRA topic modeling determined regulatory factors driving key fate decisions in hair follicle differentiation.
Fig. 3: MIRA RP modeling identified genes for which changes in expression were insufficiently explained by local chromatin accessibility.
Fig. 4: Gene-level and cell-level analysis of NITE gene regulation in the hair follicle explained regulatory mechanisms of fate commitment.
Fig. 5: MIRA joint representation reconstructed complex multi-axis differentiation in the IFE.
Fig. 6: MIRA explained regulatory factors driving fate decisions in key developmental trajectories in the developing brain.

Data availability

The authors of the SHARE-seq skin study3 provide the RNA-seq count matrix at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4156608 and the ATAC-seq peak count matrix at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4156597. 10X Genomics provides the brain dataset14 RNA-seq count matrix and ATAC-seq peak count matrix at https://www.10xgenomics.com/resources/datasets/fresh-embryonic-e-18-mouse-brain-5-k-1-standard-2-0-0. RNA-seq and ATAC-seq count matrices used for the benchmarking study may be found at https://www.10xgenomics.com/resources/datasets/pbmc-from-a-healthy-donor-granulocytes-removed-through-cell-sorting-10-k-1-standard-2-0-0.

Code availability

MIRA is available as a Python package at https://github.com/cistrome/MIRA. Frankencell, a Python program we developed to generate synthetic differentiation trajectories for benchmarking, is available at https://github.com/AllenWLynch/frankencell-dynverse.

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Acknowledgements

We thank the X.S. Liu laboratory members, M. Oser and K. Wucherpfennig for helpful scientific discussions. This work was supported by the National Institutes of Health (NIH) grant no. U24 CA237617 to C.A.M. C.V.T. was supported by the Helen Hay Whitney Foundation Postdoctoral Fellowship and grant no. NIH T32GM007748.

Author information

Authors and Affiliations

Authors

Contributions

A.W.L. developed MIRA, designed analyses and analyzed the SHARE-seq dataset. C.V.T. codeveloped MIRA, designed analyses and analyzed the 10X Genomics dataset. H.W.L. and M.B. contributed to analysis design. X.S.L. and C.A.M. designed analyses and supervised the work. A.W.L., C.V.T., X.S.L. and C.A.M. wrote the manuscript. A.W.L. and C.A.M. originated the work. All authors edited and approved the manuscript.

Corresponding authors

Correspondence to X. Shirley Liu or Clifford A. Meyer.

Ethics declarations

Competing interests

M.B. is a consultant to and receives sponsored research support from Novartis. M.B. serves on the SAB of H3 Biomedicine, Kronos Bio and GV20 Oncotherapy. X.S.L. conducted the work while being on the faculty at the Dana Farber Cancer Institute and is currently a board member and CEO of GV20 Therapeutics. The remaining authors declare no competing interests.

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Nature Methods thanks Eran Mukamel, Fangming Xie and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Lin Tang, in collaboration with the Nature Methods team.

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Extended data

Extended Data Fig. 1 Overview of MIRA topic model architecture.

a, The MIRA topic model uses a variational autoencoder (VAE) approach to learn stochastic mappings between observations in X-space, gene-counts or peak-counts in a cell, which are high-dimensional and noisy, and a simpler latent Z-space or topic space, which exists on the simplex basis with a Dirichlet prior. (bottom right) The generative model relates the observations X to the estimated composition 𝞺 over features (genes or peaks), sampling a negative binomial distribution for RNA counts and a multinomial distribution for ATAC peaks. (top right) The composition over features is given by the topic matrix 𝜷 encoding topic-feature associations and the latent topics Z of a cell, which are sampled from the distribution qφ(Z|X), the variational approximation of p𝜗(Z|X). (top left) The distribution of Z is parameterized by 𝞵 and 𝞼², outputs from the encoder neural network given the X-space observations as inputs. (bottom left) The encoder neural network for RNA data performs deviance residual featurization of counts which are passed through feed-forward layers. The ATAC data encoder passes binarized peak accessibility features through a deep averaging network. (Illustration adapted from Kingma and Welling, Foundations and Trends in Machine Learning, 2019). b, Ratio of probability of medulla fate commitment versus cortex commitment of each cell in the hair follicle, arranged by pseudotime. MIRA defines branch points between cell states where probabilities of differentiating into one terminal state diverges from another. c, MIRA joint representation UMAP colored by ratio of probability of medulla fate commitment within the ORS, matrix, medulla, and cortex populations. Differentiation in the hair follicle proceeds from ORS to progenitor matrix cells, which then specify into the medulla or cortex fate. (IRS cells indicated in black are not included in this trajectory).

Extended Data Fig. 2 MIRA outperforms standard methodology for resolving cell state trajectories using expression data alone.

Benchmarking results comparing MIRA to standard methodology of Seurat PCA + Slingshot in the indicated metrics of cell state trajectory inference using expression data alone. Top row shows ground truth scaffolds, which are computationally synthesized by mixing reads from distinct populations of single cells from a 10X Genomics dataset63 of peripheral blood mononuclear cells (PBMCs). Scaffold difficulty increases from left to right, where more difficult scaffolds contain cell states where mixture components are more similar (increased entropy), making them more difficult to distinguish by the tested lineage inference methodologies. Line plots indicate MIRA (red) versus Seurat PCA + Slingshot (blue) performance in each of the four scaffold difficulties with trials for three different mean read depths (lower read depth further increases the difficulty of solving the topology). For each trial, 5 replicates were tested for each modeling approach. Edge accuracy measures the accuracy of the inferred edges compared to ground truth (dynverse’s edge flip score64). Branch F1 score64 measures the precision and recall of the inferred branches compared to ground truth. Pseudotime correlation64 measures the correlation between inferred versus ground truth pseudotime for each cell. The bottom rows show example UMAPs for MIRA or Seurat PCA + Slingshot for each scaffold difficulty with black edges showing cell state parsing from each algorithm. Cells colored by ground truth branch assignment where blue cells are the origin state. In the line plots above, black outlines indicate the points for the models shown in the example UMAPs.

Extended Data Fig. 3 MIRA outperforms standard methodology for resolving cell state trajectories using accessibility data alone.

Benchmarking results comparing MIRA to standard methodology of Seurat LSI + Slingshot in the indicated metrics of cell state trajectory inference using accessibility data alone. Top row shows ground truth scaffolds with scaffold difficulty increasing from left to right. No models solved the topology of the most difficult scaffold using accessibility alone so metric comparisons are shown for the other three scaffolds. See Extended Data Fig. 3 for description of metrics.

Extended Data Fig. 4 MIRA outperforms standard methodology for resolving cell state trajectories using both expression and accessibility data jointly.

Benchmarking results comparing MIRA joint representation to standard methodology of joint representation combining Seurat PCA of expression data and Seurat LSI of accessibility data followed by Slingshot. See Extended Data Fig. 3 for description of metrics. For expression data, mean read depth n = 4000; for accessibility data, mean read depth n = 14000.

Extended Data Fig. 5 MIRA topics describing hair follicle cells were sparse and nonredundant.

a, UMAP based on standard methodology versus MIRA topic modeling for expression or accessibility. Standard PCA-based representation of expression shows matrix population as shifted away from its predecessor ORS and descendant IRS, medulla, and cortex cells. However, MIRA topic modeling of expression appropriately represents matrix cells as an intermediate population between the aforementioned lineages. Standard LSI-based representation of accessibility shows ORS cells interjected between matrix and its descendant IRS and shows medulla situated between two separate cortex populations. Conversely, MIRA topic modeling of accessibility appropriately represents matrix cells as continuous with its descendant IRS and better separates medulla and cortex into two distinct branches. b, MIRA joint topic representation of expression and accessibility. In (a-b), colors demonstrate expression of marker genes of indicated lineages. c, MIRA expression topics e1-6 and d, MIRA accessibility topics a1-7 on joint representation UMAP. In (c-d), colored boxes correspond to topic colors as on stream graphs in Fig. 2c and Extended Data Fig. 7a.

Extended Data Fig. 6 MIRA topics described gene modules activated in each lineage.

a, Stream graph of window-averaged cell-topic compositions starting from ORS cell state, progressing rightward through pseudotime (to facilitate visualization of all lineages concurrently, pseudotime scale is not log-transformed, unlike other presented stream graphs). b, MIRA joint topic representation colored by expression of genes highly activated in each of the indicated topics, which described the activated gene modules in each lineage. c, MIRA joint topic representation colored by indicated motif scores.

Extended Data Fig. 7 Terminal medulla and cortex cells showed significantly higher NITE regulation compared to cells earlier in hair follicle differentiation.

a, MIRA joint topic representation colored by expression of Hoxc genes, indicating that Hoxc motifs activated in both the medulla and cortex accessibility topics (a5 and a6, respectively) were most attributable to Hoxc13 based on its expression in these lineages. b, Correlation matrix between expression and accessibility topics. While some topics had a clear one-to-one correlation between modalities (for example expression topic e1 with accessibility topic a1), others did not strongly correlate with a single topic from the opposing modality (for example branch accessibility topic a4). c, Comparison of motif enrichment in top peaks of preceding matrix versus subsequent branch accessibility topics (a2 and a4, respectively). While most motifs were shared between these topics, accessibility of Wnt signaling-related motifs uniquely arose at the branch. d, Distribution of NITE scores among genes expressed in the hair follicle. Scores of example LITE gene Braf and NITE gene Krt23 are indicated by arrows. e, LITE gene Braf as shown in Fig. 3c but extended to include further downstream region. As described in Fig. 3c, plot shows chromatin accessibility fragments across pseudotime (moving downwards) in trajectories from ORS to matrix to cortex or medulla. Colored bars on the right indicate the identity of cells (colored by clusters in Fig. 2a) within each bin reflected by each row of accessibility fragments. Line plots across pseudotime depict the indicated gene’s observed expression (red) and LITE model prediction of expression (black), which is informed by the local accessibility reflected in the fragment plot. f, Medulla and cortex cells showed significantly more NITE regulation than other cells in the hair follicle (data are presented as mean values +/− standard deviation; rest n = 4565, cortex/medulla n = 1607; *p < 0.05 (1.4e-13), two-sided Wilcoxon rank-sum). g, Genes ultimately expressed in medulla or cortex that were primed at the branch were defined as those with a NITE regulation score above the indicated thresholds that had positive chromatin differential at the branch, indicating that expression was overestimated based on local chromatin accessibility. Branch-primed genes must also be upregulated in the downstream lineage relative to matrix cells. h, Driver transcription factor analysis of non-primed medulla versus cortex genes.

Extended Data Fig. 8 MIRA expression topics describing IFE cells captured shared and lineage-specific states.

a, Expression of marker genes of indicated lineages on MIRA expression, accessibility, and joint topic UMAPs. b, MIRA expression topics e1-13 on joint representation UMAP.

Extended Data Fig. 9 MIRA accessibility topics describing IFE cells captured shared and lineage-specific states.

a, MIRA accessibility topics a1-15 on joint representation UMAP. Colored boxes correspond to topics indicated in Fig. 5h, which are shared or lineage-specific within the basal-spinous-granular or intermediate basal-spinous-granular differentiation trajectories as annotated in Fig. 5a,b. b, Thbs1 and c, Egr2 expression distinguished basal cells distant from the hair follicle from those within the intermediate basal-spinous-granular trajectory near the hair follicle (*p < 0.05, two-sided Wilcoxon rank-sum, Benjamini-Hochberg corrected).

Extended Data Fig. 10 Terminal granular cells were enriched for NITE regulation.

a, Stream graph of expression topic compositions of basal-spinous-granular (top) and intermediate basal-spinous-granular (bottom) lineages. b, Terminal IFE granular cells showed significantly more NITE regulation than cells earlier in the differentiation trajectory (basal and spinous cells) (data are presented as mean values +/− standard deviation; basal and spinous n = 10850, granular n = 1596; *p < 0.05 (1.5e-15), two-sided Wilcoxon rank-sum). c, Genes upregulated in granular cells that were differentially-expressed between granular populations had significantly higher NITE scores than other genes (data are presented as mean values +/− standard deviation; rest n = 4641, terminal and differentially-expressed granular genes n = 241; *p < 0.05 (0.041), two-sided Wilcoxon rank-sum). d, Examples of terminally upregulated, differentially-expressed granular genes’ local chromatin accessibility (LITE model prediction) and expression. Despite accessibility increasing in both lineages, expression only increased in one lineage. e, Mef2c was more highly expressed in excitatory neurons, indicating that Mef2 motifs enriched in the terminal excitatory neuron topic were likely attributable to Mef2c. f, Stream graphs of expression topics across cells state trajectory colored by NITE versus LITE regulation of the top genes in each topic. Topics describing earlier states tended towards LITE regulation with the notable exception of topic e3, which is composed of cell cycle genes that have been previously described to be regulated with minimal influence of local chromatin accessibility state3. Topics describing terminal states tended more towards NITE regulation, including the major terminal excitatory and inhibitory neuron topics that are composed of neurotransmitter genes. Overall, expression topics describing the excitatory and inhibitory progenitor states (labeled mixed progenitor) were significantly enriched for LITE regulation, whereas after commitment to either the excitatory or inhibitory fate, topics were significantly enriched for NITE regulation (*p < 0.05, two-sided Wilcoxon rank-sum, Benjamini-Hochberg corrected). g, Genes predicted by MIRA pISD modeling to be regulated by pioneer transcription factor Ascl1 showed significantly more LITE regulation compared to genes predicted to be regulated by non-pioneer-like Egr1 (data are presented as mean values +/− standard deviation; n = 200; *p < 0.05 (0.0464), two-sided Wilcoxon rank-sum).

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Supplementary Tables

Supplementary Table 1 (T1) Gene set enrichments of each MIRA expression topic in the hair follicle dataset. Indicated P values by one-sided Fisher’s exact test; adjusted P values are Enrichr z-scores corrected for multiple comparisons. Supplementary Table 2 (T2) Motif enrichments of each MIRA accessibility topic in the hair follicle dataset. Supplementary Table 3 (T3) Gene set enrichments of each MIRA expression topic in the IFE dataset. Indicated P values by one-sided Fisher’s exact test; adjusted P values are Enrichr z-scores corrected for multiple comparisons. Supplementary Table 4 (T4) Motif enrichments of each MIRA accessibility topic in the IFE dataset. Supplementary Table 5 (T5) Gene set enrichments of each MIRA expression topic in the embryonic brain dataset. Indicated P values by one-sided Fisher’s exact test; adjusted P values are Enrichr z-scores corrected for multiple comparisons. Supplementary Table 6 (T6) Motif enrichments of each MIRA accessibility topic in the embryonic brain dataset.

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Lynch, A.W., Theodoris, C.V., Long, H.W. et al. MIRA: joint regulatory modeling of multimodal expression and chromatin accessibility in single cells. Nat Methods 19, 1097–1108 (2022). https://doi.org/10.1038/s41592-022-01595-z

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  • DOI: https://doi.org/10.1038/s41592-022-01595-z

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