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Integrated single-cell chromatin and transcriptomic analyses of human scalp identify gene-regulatory programs and critical cell types for hair and skin diseases

Nature Genetics volume 55, pages 1288–1300 (2023)Cite this article

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Abstract

Genome-wide association studies have identified many loci associated with hair and skin disease, but identification of causal variants requires deciphering of gene-regulatory networks in relevant cell types. We generated matched single-cell chromatin profiles and transcriptomes from scalp tissue from healthy controls and patients with alopecia areata, identifying diverse cell types of the hair follicle niche. By interrogating these datasets at multiple levels of cellular resolution, we infer 50–100% more enhancer–gene links than previous approaches and show that aggregate enhancer accessibility for highly regulated genes predicts expression. We use these gene-regulatory maps to prioritize cell types, genes and causal variants implicated in the pathobiology of androgenetic alopecia (AGA), eczema and other complex traits. AGA genome-wide association studies signals are enriched in dermal papilla regulatory regions, supporting the role of these cells as drivers of AGA pathogenesis. Finally, we train machine learning models to nominate single-nucleotide polymorphisms that affect gene expression through disruption of transcription factor binding, predicting candidate functional single-nucleotide polymorphism for AGA and eczema.

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Fig. 1: Multiomic single-cell atlas of primary human scalp.
Fig. 2: Gene-regulatory dynamics and modularity in human scalp.
Fig. 3: Scalp keratinocyte diversity and regulatory control of interfollicular keratinocyte differentiation.
Fig. 4: Regulatory dynamics of human hair follicle cycling.
Fig. 5: Identification of cell types and genes associated with hair, skin and autoimmune diseases.
Fig. 6: Candidate causal variants in skin and hair disease.

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Data availability

Sequencing data generated in this study have been deposited in the Gene Expression Omnibus (GEO) with accession code GSE212450. The full scalp dataset can be explored interactively at http://shiny.scscalpchromatin.su.domains/shiny_scalp/ (ref. 119). Reference genome files for alignment of single-cell data can be downloaded from https://support.10xgenomics.com/single-cell-gene-expression/software/release-notes/build. Predicted superenhancer-associated genes from 86 human cell types and tissues were downloaded from Supplementary Table 2 of https://doi.org/10.1016/j.cell.2013.09.053 (ref. 35). Predicted superenhancer-associated genes from mouse hair follicle cell populations were downloaded from Supplementary Table 1 of https://doi.org/10.1038/nature14289 (ref. 36). The ABC dataset generated from 131 human tissues and cell types was downloaded from https://www.engreitzlab.org/resources/ (ref. 31). Differentially expressed genes identified between control human keratinocytes and keratinocytes containing a mutant, binding-incompetent form of TP63 were obtained from Supplementary Table 1d of https://doi.org/10.1016/j.celrep.2018.11.039 (ref. 51). The counts matrix from short hairpin RNA knockdown of KLF4 in human adult keratinocytes is available on GEO with accession no. GSE111786 (ref. 54). Formatted summary statistics for partitioning heritability using LDSC can be downloaded from https://console.cloud.google.com/storage/browser/broad-alkesgroup-public-requester-pays/sumstats_formatted. Fine-mapped SNPs for 94 UK Biobank traits can be downloaded from www.finucanelab.org/data (ref. 79). Precomputed PICS fine-mapped SNPs for a variety of traits from the GWAS catalog are available at https://pics2.ucsf.edu/Downloads/ (refs. 78,80). Source data are provided with this paper.

Code availability

Custom code for data processing, peak-to-gene analyses and GWAS analyses is available on Github (https://github.com/GreenleafLab/scScalpChromatin and https://doi.org/10.5281/zenodo.7915926). Our analyses also make use of published software tools, with description of their use and parameter settings available in Methods and in the custom code above where applicable.

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Acknowledgements

We thank the Stanford FACS facility and Stanford Functional Genomics Facility for technical support. Figure schematics in Figure 1a,b were created with BioRender.com. This work was supported in part by grants from the NIH (nos. 2R37-ARO54780 to A.E.O. and RM1-HG007735, UM1-HG009442, UM1-HG009436, R01- HG009909, U19-AI057266, U54HG012723, UM1HG011972, R01NS128028 and U01-HG011762 to W.J.G). W.J.G. acknowledges support as a Chan-Zuckerberg Investigator (grant nos. 2017-174468 and 2018-182817). B.O.-R. was supported in part by Stanford MSTP training grant nos. T32-GM007365 and T32-GM145402. C.W. was supported in part by a Dermatology Foundation Physician Scientist Career Development Award.

Author information

Authors and Affiliations

  1. Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA

    Benjamin Ober-Reynolds & William J. Greenleaf

  2. Department of Dermatology, School of Medicine, Stanford University, Stanford, CA, USA

    Chen Wang, Justin M. Ko, Eon J. Rios, Sumaira Z. Aasi & Anthony E. Oro

  3. Division of Dermatology, Department of Medicine, Santa Clara Valley Medical Center, San Jose, CA, USA

    Chen Wang & Eon J. Rios

  4. Institute of Immunity, Transplantation and Infection, School of Medicine, Stanford University, Stanford, CA, USA

    Chen Wang & Mark M. Davis

  5. Department of Microbiology and Immunology, School of Medicine, Stanford University, Stanford, CA, USA

    Mark M. Davis

  6. Howard Hughes Medical Institute, School of Medicine, Stanford University, Stanford, CA, USA

    Mark M. Davis

  7. Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA, USA

    Anthony E. Oro

  8. Department of Applied Physics, Stanford University, Stanford, CA, USA

    William J. Greenleaf

  9. Chan Zuckerberg Biohub, San Francisco, CA, USA

    William J. Greenleaf

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  1. Benjamin Ober-Reynolds
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Contributions

Conceptualization was the responsibility of B.O.-R., C.W., A.E.O. and W.J.G. Investigation of single-cell experiments was carried out by B.O.-R. and C.W. B.O.-R. performed formal analysis. Resources and sample collection were done by C.W., A.E.O., J.M.K., E.J.R. and S.Z.A. B.O.-R. and W.J.G. wrote the original draft. All authors participated in paper review and editing. A.E.O., M.M.D. and W.J.G. supervised the project. Funding acquisition was carried out by A.E.O., M.M.D. and W.J.G.

Corresponding author

Correspondence to William J. Greenleaf.

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Competing interests

W.J.G. is a consultant for 10X Genomics, Guardant Health, Quantapore, Erudio Bio. and Lamar Health and cofounder of Protillion Biosciences and is named on patents describing ATAC-seq. The other authors declare no competing interests.

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Nature Genetics thank Matthias Huebenthal, Kerstin Meyer 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 Quality control of single cell RNA and ATAC datasets.

(a) Scatter plots of the number of unique fragments by the transcription start site (TSS) enrichment for each of the scATAC-seq samples. Gray dots indicate cells that did not pass quality control filters (Methods). Colorbar indicates the density of points. (b) Violin plots of the number of unique reads (UMIs, top) and the percent of reads from mitochondrial genes (bottom) for each of the scRNA-seq samples. The inset box plot represent the median, 25th percentile and 75th percentile of the data, and whiskers represent the highest and lowest values within 1.5 times the interquartile range of the boxplot. (c) Violin plots of the TSS enrichment (top) and number of unique fragments (bottom) for each of the scATAC-seq samples. Box plot as in (B). (d) UMAP projection of full scRNA-seq dataset, colored by patient sample. (e) UMAP projection of full scATAC-seq dataset, colored by patient sample. (f) Differential scATAC-seq peaks between samples processed immediately after collection or after cryopreservation for each of the major cell groupings. Differential peaks (FDR < 0.1) are indicated by colored dots. (g) Differential scRNA-seq genes between samples processed immediately after collection or after cryopreservation for each of the major cell groupings. Differential genes (FDR < 0.1) are indicated by colored dots.

Extended Data Fig. 2 Annotation of single cell RNA and ATAC datasets.

(a) UMAP projections of full scRNA-seq dataset colored by relative expression levels of representative cell compartment marker genes. (b) UMAP projections of full scATAC-seq dataset colored by relative gene activity scores of the same marker genes shown in (A). (c) Marker peaks (Wilcoxon FDR ≤ 0.1 and Log2 fold change ≥ 0.5) for each scATAC cluster. (d) The fraction of each scRNA-seq cluster comprising each sample. The total proportions for each cluster are shown in the rightmost column. (e) The fraction of each scATAC-seq cluster comprising each sample. The total proportions for each cluster are shown in the rightmost column.

Extended Data Fig. 3 Sub-clustering of major cell groups and integration of scRNA and scATAC datasets.

(a) UMAP representations of sub-clustered major cell groups using scATAC data. Cell compartments are labeled on the left, and cells are colored according to their high-resolution cluster labels. (b) UMAP representations of sub-clustered major cell groups using scRNA data. Cell compartments are labeled on the right, and cells are colored according to their high-resolution cluster labels. (c) scRNA gene expression for selected marker genes for each high-resolution scRNA-seq cluster from each sub-clustered cell group. The color indicates the relative expression across all high-resolution clusters and the size of the dot indicates the percentage of cells in that cluster that express the gene. (d) Correspondence between scRNA and scATAC-seq cluster labels for high-resolution clusters in each of the sub-clustered datasets. Heatmaps are colored according to the Jaccard index of cluster label overlap between the scRNA and scATAC-seq datasets. (e) Correspondence between scRNA and scATAC-seq cluster labels in the full scalp dataset.

Extended Data Fig. 4 Clustering and CCA-based integration robustness to subsampling.

(A through C) Repeated dimensionality reduction and clustering of the scRNA and scATAC-seq datasets with three samples (AA4, C_SD3, and C_PB3) removed from the full dataset. (a) UMAP representations of the full subsampled dataset and sub-clustered major cell groups using scRNA data. Cell compartments are labeled on the left, and cells are colored according to their high-resolution cluster labels as shown in the x-axis in (C). (b) UMAP representations of the full dataset and sub-clustered major cell groups using scATAC data. Cell compartments are labeled on the left, and cells are colored according to their high-resolution cluster labels as shown in the y-axis in (C). (c) Correspondence between scRNA and scATAC-seq cluster labels for the low- and high-resolution clusters in each of the subsampled datasets. (D through F) Repeated dimensionality reduction and clustering of the scRNA and scATAC-seq datasets with 25% of the cells randomly removed from the full dataset. (d) Same as in (A), but for the cell-subsampled dataset. (e) Same as in (B), but for the cell-subsampled dataset. (f) Same as in (C), but for the cell-subsampled dataset.

Extended Data Fig. 5 Identification and characterization of peak-to-gene linkages.

(a) Upset plot indicating the number of peak-to-gene linkages identified in the full dataset and in each of the sub-clustered datasets. (b) The distribution of the number of linked peaks per gene (median = 4). (c) The PhastCons 100-way vertebrate conservation scores for peaks with a linked gene in each dataset compared to unlinked peaks. Two-sided Wilcoxon rank-sum test comparing each dataset to unlinked peaks, p < 2.2 ×10–16. Boxplots represent the median, 25th percentile and 75th percentile of the data, and whiskers represent the highest and lowest values within 1.5 times the interquartile range of the boxplot. (d) Bar plot showing the proportion of peak-to-gene linkages where both peak and gene were validated by a multi-tissue dataset of activity-by-contact (ABC) model enhancer-gene predictions. Categories compared included the space of all possible peak-to-gene links, the mean of 100 permutations drawn from all possible peak-to-gene links where for each permutation 146,088 peaks were selected to match the anchor distance distribution of true peak-to-gene links, and the set of true peak-to-gene links identified on each sub-clustered dataset. One-sided Fisher’s exact test enrichment comparing each subgroup of true peak-to-gene links to a distance-matched background set, p < 2.2 ×10–16. (e) Venn-diagram indicating the overlap of peak-to-gene linkages and peak-to-nearest-gene associations. (f) Comparison of the linked peak score (sum of accessibility at linked peaks) compared to the gene activity score for predicting gene expression for the 1739 HRGs. Plotted is the Pearson R2 from 246 pseudo-bulked samples per gene. Boxplots represent the median, 25th percentile and 75th percentile of the data, and whiskers represent the highest and lowest values within 1.5 times the interquartile range of the boxplot.

Extended Data Fig. 6 Marker genes and cell type–specific TF regulator activity for sub-clustered interfollicular and hair-follicle associated keratinocytes.

(a) UMAP projections of sub-clustered keratinocyte scRNA-seq dataset colored by expression levels of representative marker genes. (b) UMAP projections of sub-clustered keratinocyte scATAC-seq dataset colored by gene activity scores of the same marker genes shown in (A). (c) Heatmap showing the chromatin accessibility (left) and gene expression (right) for 28,991 keratinocyte-specific peak-to-gene linkages. Peak-to-gene linkages were clustered using k-means clustering (k = 12). Rows indicate peak accessibility and gene expression on the left and right heatmaps respectively. Each column is a pseudo-bulk sample, with the colorbar on top of each heatmap indicating the cluster identity of each pseudo-bulk sample. (d) Hypergeometric enrichment p-values of TF motifs in peaks from each of the k-means clusters from (C). (e) Plot of TF motif activity correlation to corresponding TF gene expression across sub-clustered dataset against the maximum difference in chromVAR deviation z-score between clusters. TF’s with a maximum chromVAR difference in the top quartile and a pearson correlation greater than 0.5 are colored in red. (f) Prioritization of gene targets for LHX2. The x-axis shows the Pearson correlation between the TF motif activity and integrated gene expression for all expressed genes across all keratinocytes. The y-axis shows the TF Linkage Score (for all linked peaks, sum of motif score scaled by linkage correlation). Color of points indicates the hypergeometric enrichment of the TF motif in all linked peaks for each gene. Top gene targets are indicated in the shaded area (motif correlation to gene expression >0.25, linkage score >80th percentile). GO term enrichments for the top gene targets are shown in the inset bar plot. (g) Same as in (F), but for androgen receptor (AR). (h) Same as in (F), but for POU2F3.

Source data

Extended Data Fig. 7 Supplemental analyses of sub-clustered inferior segment hair follicle keratinocytes.

(a) UMAP projection of sub-clustered keratinocytes showing cells originating from alopecia areata. Cells originating from control samples are colored gray and sorted to the back of the plot. (b) UMAP projection of sub-clustered scRNA inferior segment hair follicle keratinocytes. (c) UMAP projection of sub-clustered scATAC inferior segment hair follicle keratinocytes colored by matched nearest scRNA cluster. (d) Correspondence between scRNA and scATAC-seq cluster labels for integrated inferior segment hair follicle keratinocytes. (e) Paired heatmaps of positive TF regulators whose TF motif activity (left) and matched gene expression (right) are positively correlated across the hair follicle keratinocyte differentiation pseudotime trajectory. (f) GO term enrichments of the most variable 10% of genes across the hair follicle keratinocyte differentiation pseudotime trajectory.

Extended Data Fig. 8 Supplemental analyses of GWAS signal enrichment in cell type–specific open chromatin regions and cell type–specific genes.

(a) Cluster-specificity of peaks used for LD score regression and for Fisher enrichment tests in Fig. 5. More than 50% of peaks are specific to <1/8 of high-resolution scATAC clusters, and 85% of peaks are specific to ≤ 1/4 of clusters. (b) Distribution of the number of clusters in which each peak is accessible. Peaks accessible in ≤ 1/4 of clusters (9 high-resolution clusters) were used for cluster-specific enrichment analyses. (c) Cluster-specificity of marker genes used for LD score regression and for Fisher enrichment tests in (E) and (G) respectively. (d) Distribution of the number of clusters identified as expressing a given marker gene. Marker genes expressed in ≤ 1/4 of clusters (10 high-resolution clusters) were used for cluster-specific enrichment analyses. (e) LD score regression identifies enrichment of GWAS SNPs for various skin and non-skin related conditions in gene regions specific to sub-clustered cell types (from the scRNA dataset) in human scalp. FDR-corrected P-values from LDSC enrichment tests are overlaid on the heatmap (*FDR < 0.05, **FDR < 0.005, ***FDR < 0.0005). (f) Same as in (E), but using only open-chromatin regions (from the scATAC dataset) that are implicated in peak-to-gene linkages (N = 98,188). (g) Fraction of fine-mapped SNPs for selected traits overlapping scalp CREs binned by fine-mapping posterior probability. (h) Fisher’s exact test enrichment of the nearest gene for fine-mapped trait-related SNPs in cell type–specific genes for sub-clustered cell types in human scalp. The FDR-corrected -log10 p-value is indicated by the color of the dots, and the dot size indicates the enrichment odds ratio.

Extended Data Fig. 9 Supplemental analyses of fmGWAS-linked genes.

(a) GO term enrichment for the top genes linked to fine-mapped SNPs by summed fine-mapping posterior probability in associated peak-to-gene linkages. (b) The top genes linked to peaks containing fine-mapped SNPs for alopecia areata. The heatmap shows relative gene expression for each high-resolution scRNA cluster. The number of linked fmSNPs per gene is indicated in the red bar plot to the right, and the total sum of fine-mapped posterior probability for linked SNPs is indicated in the blue bar plot. The grey bar plot shows the total number of identified peak-to-gene linkages for that gene in the entire scalp dataset. Gene names colored red indicate fine-mapped SNP to gene linkages supported by GTEx eQTLs. (c) Same as in (B), but for hair color.

Source data

Extended Data Fig. 10 Assessment of gkmSVM model performance and additional high-effect candidate fine-mapped SNPs.

(a) The area under the receiver operator (AUROC), or (b) precision recall (AUPRC) curves for the gkm-SVM machine learning classifiers for each of the cluster models. Each dot indicates a cross-validation fold (n = 10). Boxplots represent the median, 25th percentile and 75th percentile of the data, and whiskers represent the highest and lowest values within 1.5 times the interquartile range of the boxplot. (c) The overlap of training data (peak sequences) between models. (d) The performance of each cluster model on predicting test sequences from a non-target cluster. (e) Enrichment of high-effect fine-mapped SNPs from eczema relative to random fine-mapped SNPs in cis-regulatory regions. (f) Same as in (e), but for AGA. (g) Normalized chromatin accessibility landscape for cell type–specific pseudo bulk tracks around the BNC2 locus. Integrated BNC2 expression levels are shown in the violin plot for each cell type to the right. The position of ATAC-seq peaks, the GWAS lead SNP, the fine-mapped SNP candidates in LD with the lead SNP, and the candidate functional SNP are shown below the ATAC-seq tracks. Significant peak-to-gene linkages are indicated by loops connecting the BNC2 promoter to indicated peaks. (h) GkmExplain importance scores for the 50 bp region surrounding rs12350739, a hair color associated SNP that creates a JUN motif in a CRE linked to BNC2 expression. (i) Same as in (G), but for the ALX4 locus. (j) GkmExplain importance scores for the 50 bp region surrounding rs10769041, an AGA associated SNP that disrupts an ETS motif in a CRE linked to ALX4 expression.

Supplementary information

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Supplementary Notes and Methods

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Supplementary Tables 1–12, with titles and descriptions included in a table of contents on page 1 of the spreadsheet.

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Source Data

Statistical source data and additional details for Figs. 2c,d, 3j–l, 4,b,e, 5a,c–e and 6b, and Extended Data Figs. 6f–h and 9b,c. Source data are split and labeled by tab. Descriptions are included in a table of contents on page 1 of the spreadsheet.

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Ober-Reynolds, B., Wang, C., Ko, J.M. et al. Integrated single-cell chromatin and transcriptomic analyses of human scalp identify gene-regulatory programs and critical cell types for hair and skin diseases. Nat Genet 55, 1288–1300 (2023). https://doi.org/10.1038/s41588-023-01445-4

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