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Quantifying cell-state densities in single-cell phenotypic landscapes using Mellon

Abstract

Cell-state density characterizes the distribution of cells along phenotypic landscapes and is crucial for unraveling the mechanisms that drive diverse biological processes. Here, we present Mellon, an algorithm for estimation of cell-state densities from high-dimensional representations of single-cell data. We demonstrate Mellon’s efficacy by dissecting the density landscape of differentiating systems, revealing a consistent pattern of high-density regions corresponding to major cell types intertwined with low-density, rare transitory states. We present evidence implicating enhancer priming and the activation of master regulators in emergence of these transitory states. Mellon offers the flexibility to perform temporal interpolation of time-series data, providing a detailed view of cell-state dynamics during developmental processes. Mellon facilitates density estimation across various single-cell data modalities, scaling linearly with the number of cells. Our work underscores the importance of cell-state density in understanding the differentiation processes, and the potential of Mellon to provide insights into mechanisms guiding biological trajectories.

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Fig. 1: Mellon design principles for cell-state density.
Fig. 2: Mellon maps human hematopoietic differentiation densities.
Fig. 3: Dynamics of chromatin accessibility and gene expression during B-cell-fate specification.
Fig. 4: Depiction of time-continuous cell-state density estimation during mouse gastrulation using Mellon.
Fig. 5: Application of Mellon density estimation to single-cell chromatin data modalities.
Fig. 6: Scalability and linear complexity of Mellon.

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Data availability

All datasets used in the paper have been previously published and the accession numbers are as follows: sc-Multiome dataset of human T-cell-depleted bone marrow (GSE200046)19, sc-Multiome dataset of human CD34+ bone marrow (GSE200046)19, Human cell atlas bone marrow atlas26, scRNA-seq dataset of murine pancreatic development (GSE132188)28, scRNA-seq dataset of in vitro human pancreatic development (GSE180967)29, bulk RNA-seq of laser capture microdissected murine villus epithelium (GSE109413)30, scRNA-seq dataset of murine lung regeneration (GSE141259)27, scRNA-seq dataset of murine models of lung adenocarcinoma metastasis (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA803321)9, scRNA-seq dataset of mouse gastrulation (https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-6967)43, scRNA-seq dataset of iPS reprogramming to fibroblasts (GSE122662)8, scATAC-seq dataset of murine models of lung adenocarcinoma metastasis (GSE134812)47, sortChIC dataset of murine hematopoietic chromatin landscape (GSE164779)46, sc-Multiome dataset of skin differentiation (GSE140203)32 and CITE-seq dataset of COVID-19 atlas (https://covid19cellatlas.org/)55. See Supplementary Table 1 for additional information. Mellon results and cell-type metadata information for the T-cell-depleted bone marrow and the mouse gastrulation data are available via Zenodo at https://doi.org/10.5281/zenodo.8118723 (ref. 56).

Code availability

Mellon is available as a Python module at https://github.com/settylab/Mellon (https://doi.org/10.5281/zenodo.10724828)57. Jupyter notebooks detailing the usage of Mellon including cell-state density estimation, gene-change computation, time-continuous cell-state density estimation and enhancer classification are available at https://mellon.readthedocs.io/en/latest/. Pipelines for running SEACells, computing gene–peak correlations, primed and lineage-specific accessibility scores are available at https://github.com/settylab/atac_metacell_utilities.

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Acknowledgements

We thank members of the Setty laboratory for discussions and comments on the manuscript. This study was supported by National Institute of General Medical Studies grant no. R35 GM147125 and Brotman Baty Institute Pilot Award to M.S., and the National Institutes of Health grant no. ORIP S10OD028685 to support high-performance computing at the Fred Hutchinson Cancer Research Center.

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Authors and Affiliations

Authors

Contributions

D.J.O. and M.S. conceived and designed the study, developed Mellon, developed additional analysis methods and statistical tests. D.J.O. and B.D. developed the heuristics, performed robustness analyses and implemented the framework. C.J. and M.S. performed analysis of enhancer dynamics. C.D. supported enhancer dynamics analysis. D.J.O., C.J. and M.S. wrote the manuscript.

Corresponding author

Correspondence to Manu Setty.

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Nature Methods thanks Adam MacLean and Wei Vivian 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 Comparison of cell-state density estimation approaches.

A. UMAP of scRNA-seq dataset of T-cell depleted bone marrow dataset, colored by cell-type (i) and leiden clusters (ii). B. UMAPs colored by Mellon density (left), density computed as inverse of distance to kth nearest neighbor (middle) and density computed using UMAP coordinates (right). K = 15. Mellon density and DistToKNN computed using high-dimensional cell-state representation of diffusion maps. UMAP density computed using 2D UMAP coordinates. C. Violin plots to compare cell-state densities among different hematopoietic cell types. Left: Mellon, Middle: Inverse of distance to kth nearest neighbor, Right: UMAP densities. Mellon densities are most consistent with the expected landscape of human hematopoiesis. Plots display median, 25th(Q1) and 75th (Q3) percentiles; whiskers extend to furthest data point in range 1.5*(Q3-Q1). Cell numbers and minimum and maximum values are available in Supplementary Table 2. D. Plots comparing Palantir pseudo time to log-density for different hematopoietic lineages. Top row: Mellon, Middle row: Inverse of distance to kth nearest neighbor. Bottom row: UMAP densities. Mellon provides the most robust and interpretable density estimates with clear separation of high- and low-density regions. Log densities are shown for all comparisons.

Extended Data Fig. 2 Validation of Mellon density estimation using simulated datasets with known ground truth.

A. UMAP of simulated data colored according to differentiation tree nodes. B. Correlation plots between known ground truth log-density (x-axis) and Mellon-inferred density (y-axis) across all simulated cells. Each point represents a simulated cell. C. UMAPs colored by Mellon-inferred density (left) and ground truth log-density (right). Density values falling below the 20th percentile are projected to the 20th percentile for visualization. D-F. Same as A-C for a second simulated dataset. Cells in (D) are colored by differentiation tree nodes. G-I. Same as A-C for a third simulated dataset. Cells in (G) are colored by clusters. J-L. Same as A-C for a larger simulated dataset. Cells in (D) are colored by differentiation tree nodes.M-O. Same as A-C for a second larger simulated dataset. Cells in (D) are colored by differentiation tree nodes.

Extended Data Fig. 3 Genes driving low-density state transitions.

A. UMAPs highlighting the cells spanning hematopoietic stem-cells to fate committed cells along each lineage. B. Heatmaps showing the gene expression dynamics along pseudo time for genes in the 95th percentile of change scores associated with the respective lineage. High and low-density regions were assigned manually by comparing Mellon density with Palantir pseudo time (Fig. 2e). C. Gene ontology results of up and downregulated genes along each lineage. Genes with higher expression in the first high-density region in (A) were nominated as downregulated and the rest as upregulated. Bonferroni corrected p-values were determined using hypergeometric tests using the c8 gene set from MSigDB.

Extended Data Fig. 4 Gene expression dynamics along B-cell differentiation.

A. Histogram showing the distribution of gene change scores for the low-density cell-states encompassing hematopoietic stem cells through pro B-cells. EBF1 has the highest change score. B. UMAPs colored by MAGIC-imputed gene expression of key B-cell regulators. C. Plots comparing Palantir pseudo-time with Mellon log density along B-cell trajectory with cells colored by PAX5 expression, PAX5 expression change, IL7R expression and IL7R expression change. Cells involved in B-cell fate specification are highlighted. MAGIC imputed expression is used. D. UMAPs colored by expression of B-cell checkpoint markers. MAGIC imputed expression is used for visualization.

Extended Data Fig. 5 Cell-state density is a property of the homeostatic system.

A. UMAP of scRNA-seq dataset of lung regeneration with cells colored by time point of measurement. D0 is prior to injury and the subsequent timepoints show recovery from injury induced by bleomycin. B. Same as (A), colored by cell-types. C. Plots comparing log-density of D0 with the specified timepoints. Subset of cells from D0 and the corresponding timepoint were used for this comparison. D. UMAPs colored by the difference of densities between D0 and the specified timepoint. All densities were computed using high-dimensional cell-state space (Diffusion maps).

Extended Data Fig. 6 B-cell primed and lineage specific peaks.

A. Schematic illustrating the role of primed and lineage-specific peaks in achieving rapid transcriptional changes. Rows represent gene loci in different cell-states. Top: Primed peaks are pre-established in stem cells without turning on gene expression. Expression of the gene is upregulated with the emergence of lineage specific peaks (Middle, bottom). B. Cell-type annotations for all cells in the T cell depleted bone marrow dataset (8627 cells) plotted on the RNA UMAP embedding. C. Identical cell type annotations to A for all cells in the dataset, plotted on the ATAC UMAP embedding. D. Schematic illustrating the approach to identify primed and lineage specific peaks. (i) Metacells are used to identify peaks with accessibility that significantly correlate with gene expression. (ii) Correlated peaks with greater accessibility in the target lineage compared to other cell-types are nominated as primed if they are accessible in stem cells (Orange) and lineage-specific other-wise (Blue). (iii) Accessibility of peaks associated with each gene are summarized to derive primed and lineage-specific scores. Dynamics in other lineages are shown as dotted lines. E. Violin plots determined using averaged imputed accessibility for B-cell primed peaks, scaled between 0 and 1 for each cell type. All cell populations are derived across two biologically independent replicates. Plots display median, 25th(Q1) and 75th (Q3) percentiles; whiskers extend to furthest data point in range 1.5*(Q3-Q1). Cell numbers and minimum and maximum values are available in Supplementary Table 2. F. Same as (C), for B-cell lineage specific peaks. All cell populations are derived across two biologically independent replicates. Plots display median, 25th(Q1) and 75th (Q3) percentiles; whiskers extend to furthest data point in range 1.5*(Q3-Q1). Cell numbers and minimum and maximum values are available in Supplementary Table 2. G. Coverage plots highlighting the EBF1 correlated peaks. Primed peaks are in orange and lineage specific peaks are in blue.

Extended Data Fig. 7 Gene expression and chromatin dynamics during B-cell specification.

A. Left: Heatmap showing the trend of local expression variability during the trajectory of B-cell specification (Fig. 3b). Upregulated genes in the top 5th percentile of B-cell specification gene change scores are shown. Genes are ordered by change scores. EBF1 has the highest change score. Middle: Plots depicting the number of B-cell lineage specific peaks associated with each gene. Right: Plots depicting the number of B-cell primed peaks associated with each gene. B. UMAPs colored by EBF1 MAGIC imputed expression, local variability, primed accessibility score and lineage-specific accessibility scores (Related to Fig. 3c). C. Heatmap of the difference in normalized B-cell primed and lineage-specific accessibility trends for genes associated with density change in B-cell specification (Related to Fig. 3e,f). Stronger red signal at the start of the trajectory indicates primed enhancers are associated with the gene. D. Heatmap of gene expression dynamics along B-cell specification. Genes are colored by expression along trajectory. E. Heatmap of ArchR gene scores derived from the ATAC modality. Genes are in the same order as (D).

Extended Data Fig. 8 Cell-state density correlation across timepoints in the mouse gastrulation dataset.

A. UMAP of all cells (116k) in the mouse gastrulation dataset colored by Mellon-derived density for cells at E7.5. B. Correlation plot comparing densities at stages E7.5 (x-axis) and E7.75 (y-axis). C. Pearson correlation matrix illustrating density correlation between all combinations of time points. D. Same as (A), showing density for cells at E7.75. E. Plot of Pearson correlation for all time point pairs (y-axis) against their temporal difference (x-axis), represented as black dots. The covariance of the optimized Matern52 length scale over time distances is displayed as a blue line. F. The covariance matrix produced by the Matern52 kernel (from E) based on the temporal differences between sample pairs. G. A representative slice at time point E7.75 from Mellon’s time-continuous density, inferred excluding E7.75 cells. L-O-O: Leave-one-out. H. Correlation plot comparing the density at E7.75 (from (B)) and the time-continuous density slice from the leave-one-out method (from (G)). I. Bar plot indicating both Pearson and Spearman correlations for all leave-one-out tests; each test omits one timepoint and compares the resulting time-slice at this omitted timepoint to the density of cells specifically from this timepoint.

Extended Data Fig. 9 Depiction of the selected Erythroid developmental trajectory within the mouse gastrulation dataset.

A-B. UMAPs colored by fate probabilities for terminal cell states (A) and pseudo time (B) as determined by Palantir. C. Cells chosen to represent the Erythroid branch, extending from early epiblasts to erythroid cells. D. UMAP colored by time-agnostic density across the UMAP. Erythroid trajectory is represented by a black line.

Extended Data Fig. 10 Mellon generalizes to different single-cell data and cell-state representations.

A. UMAPs colored by cell-type (left) and density (right) for scATAC-seq modality of T-cell depleted bone marrow dataset. B Force directed layout of the mouse lung adenocarcinoma scATAC-seq dataset colored by cell-type (left) and Mellon density (right). C. Same as (A), for the scRNA-seq data of mouse models of lung adenocarcinoma. UMAPs and diffusion maps for density estimation were computed using scVI latent space. D. Same as (A), for SHARE-seq data of mouse skin differentiation. UMAPs and diffusion maps for density estimation were computed using MIRA multimodal representation.

Supplementary information

Supplementary Information

Supplementary Notes 1–11, Figs. 1–16 and Tables 1 and 2.

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Supplementary Video 1

Supplementary Video 1: Animation demonstrating the progression of time-continuous density across the entire range of time covered by the dataset. The video displays a UMAP plot where the right panel presents the density for a specific timepoint evaluated across all cells, and the left panel depicts the time derivative of this density. Red signifies an enrichment of cells over time, while blue indicates depletion. A red vertical line on the timeline at the bottom marks the current time for the displayed frame, while black vertical lines denote the measured timepoints.

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Otto, D.J., Jordan, C., Dury, B. et al. Quantifying cell-state densities in single-cell phenotypic landscapes using Mellon. Nat Methods 21, 1185–1195 (2024). https://doi.org/10.1038/s41592-024-02302-w

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