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Single-nucleus chromatin accessibility profiling highlights regulatory mechanisms of coronary artery disease risk

Nature Genetics volume 54, pages 804–816 (2022)Cite this article

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An Author Correction to this article was published on 29 June 2022

This article has been updated

Abstract

Coronary artery disease (CAD) is a complex inflammatory disease involving genetic influences across cell types. Genome-wide association studies have identified over 200 loci associated with CAD, where the majority of risk variants reside in noncoding DNA sequences impacting cis-regulatory elements. Here, we applied single-nucleus assay for transposase-accessible chromatin with sequencing to profile 28,316 nuclei across coronary artery segments from 41 patients with varying stages of CAD, which revealed 14 distinct cellular clusters. We mapped ~320,000 accessible sites across all cells, identified cell-type-specific elements and transcription factors, and prioritized functional CAD risk variants. We identified elements in smooth muscle cell transition states (for example, fibromyocytes) and functional variants predicted to alter smooth muscle cell- and macrophage-specific regulation of MRAS (3q22) and LIPA (10q23), respectively. We further nominated key driver transcription factors such as PRDM16 and TBX2. Together, this single-nucleus atlas provides a critical step towards interpreting regulatory mechanisms across the continuum of CAD risk.

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Fig. 1: snATAC-seq profiling of 28,316 nuclei from human coronary arteries reveals cell-type chromatin accessibility patterns across 41 individuals.
Fig. 2: Human coronary artery cell types display distinct gene regulatory processes.
Fig. 3: Subcluster analysis of SMC accessible chromatin identifies fibromyocyte regulatory programs.
Fig. 4: Single-nucleus chromatin accessibility further resolves mechanisms for functional CAD GWAS loci.
Fig. 5: Identification of genetic variants that regulate chromatin accessibility within coronary artery cell types.
Fig. 6: PRDM16 is a CAD-associated key driver transcriptional regulator in SMCs.

Data availability

All raw and processed single-nucleus chromatin accessibility sequencing datasets are made available on the Gene Expression Omnibus (GEO) database (accessions codes GSE175621 and GSE188422). The processed and analyzed snATAC-seq data will also be made available on the PlaqView single-cell data portal (https://www.plaqview.com). All caQTL data are available in the Supplementary Data. Low-pass whole-genome sequencing-based genotyping data are available on dbGaP (accession code phs002855.v1.p1). The human coronary artery scRNA-seq dataset we used in this study from Wirka et al.7 is available through GEO (accession code GSE131778). The mouse atherosclerosis scRNA-seq dataset from Pan et al.9 is available through GEO (accession code GSE155513). The reprocessed and analyzed human and mouse datasets are also available on PlaqView. Gene expression levels, expression quantitative trait locus (eQTL) data and eQTL boxplots were obtained from the Genotype-Tissue Expression (GTEx) v.8 portal website (https://www.gtexportal.org), GEO and HeartBioPortal (www.heartbioportal.com). Gene regulatory network analysis data from the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) are available at http://starnet.mssm.edu.

Code availability

Our results make use of published software tools with detailed parameters included in the Methods. All custom scripts used to generate these results are available on GitHub (https://github.com/MillerLab-CPHG/Coronary_snATAC and https://github.com/MillerLab-CPHG/coronary_histology).

Change history

  • 29 June 2022

    In the version of this article initially published, the Reporting Summary linked to this article was incorrect and has now been replaced.

  • 29 June 2022

    A Correction to this paper has been published: https://doi.org/10.1038/s41588-022-01142-8

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Acknowledgements

This work was supported by grants from: the National Institutes of Health (grant nos. R01HL148239 and R00HL125912 to C.L.M.; grant no. R35GM133712 to C.Z.; grant no. R01HL141425 to A.V.F.; grant no. R01HL125863 to J.L.M.B.; grant nos. R01HL130423, R01HL135093 and R01HL148167-01A1 to J.C.K.; grant nos. R35HL144475, R01HL125224, R01HL134817 and R01HL139478 to T.Q.; and grant no. R01HL123370 to N.J.L.), the American Heart Association (grant no. 20POST35120545 to A.W.T.; grant no. A14SFRN20840000 to J.L.M.B.; grant no. 19EIA34770065 to N.J.L.), the Swedish Research Council and Heart Lung Foundation (grant nos. 2018-02529 and 20170265 to J.L.M.B.) and the Fondation Leducq (grant no. ‘PlaqOmics’ 18CVD02 to N.J.L., J.L.M.B., A.V.F. and C.L.M.). We thank P. Chiu, P. Chang and M. Wong at Stanford University for surgical assistance and research donor heart procurement. We thank T. Koyano at Stanford University for assistance in extracting clinical information. We thank all of the transplant recipients and heart donors, family members, study coordinators and the transplant tissue procurement team at Stanford. We thank B. Liu and N. Kumasaka for helpful discussions on QTL scripts. Finally, we thank P. Pramoonjago and S. VanHoose at the University of Virginia for histological support and all staff of the core facilities for tissue processing, library construction and sequencing assistance.

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Author notes

  1. These authors contributed equally: Adam W. Turner, Shengen Shawn Hu.

Authors and Affiliations

  1. Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA

    Adam W. Turner, Shengen Shawn Hu, Jose Verdezoto Mosquera, Wei Feng Ma, Chani J. Hodonsky, Doris Wong, Gaëlle Auguste, Yipei Song, Emily Farber, Suna Onengut-Gumuscu, Chongzhi Zang & Clint L. Miller

  2. Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA

    Jose Verdezoto Mosquera, Doris Wong, Chongzhi Zang & Clint L. Miller

  3. Medical Scientist Training Program, University of Virginia, Charlottesville, VA, USA

    Wei Feng Ma

  4. Department of Pathology, University of Virginia, Charlottesville, VA, USA

    Wei Feng Ma & Katia Sol-Church

  5. Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA, USA

    Chani J. Hodonsky, Doris Wong & Clint L. Miller

  6. Genome Analysis & Technology Core, University of Virginia, Charlottesville, VA, USA

    Katia Sol-Church

  7. Genome Sciences Laboratory, University of Virginia, Charlottesville, VA, USA

    Emily Farber & Suna Onengut-Gumuscu

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

    Soumya Kundu, Anshul Kundaje & Euan A. Ashley

  9. Department of Computer Science, Stanford University, Stanford, CA, USA

    Anshul Kundaje

  10. Division of Vascular Surgery, Department of Surgery, Stanford University, Stanford, CA, USA

    Nicolas G. Lopez & Nicholas J. Leeper

  11. Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Lijiang Ma & Johan L. M. Björkegren

  12. CVPath Institute, Gaithersburg, MD, USA

    Saikat Kumar B. Ghosh & Aloke V. Finn

  13. Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA

    Suna Onengut-Gumuscu, Chongzhi Zang & Clint L. Miller

  14. Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA

    Euan A. Ashley & Thomas Quertermous

  15. Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Jason C. Kovacic

  16. Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales, Australia

    Jason C. Kovacic

  17. St. Vincent’s Clinical School, University of New South Wales, Sydney, New South Wales, Australia

    Jason C. Kovacic

  18. Integrated Cardio Metabolic Centre, Department of Medicine, Karolinska Institutet, Huddinge, Sweden

    Johan L. M. Björkegren

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Contributions

C.L.M. and C.Z. jointly supervised research primarily related to the study. J.L.M.B., J.C.K., N.J.L., A.V.F. and T.Q. jointly supervised research secondarily related to the study. A.W.T., S.S.H., C.Z. and C.L.M. conceived and designed the experiments. A.W.T., K.S.-C., E.F. and S.K.B.G. performed the experiments. A.W.T., S.S.H., J.V.M. and G.A. performed the statistical analyses. A.W.T., S.S.H., J.V.M. W.F.M., C.J.H., D.W., G.A., Y.S. and C.L.M. analyzed the data. K.S.-C., E.F., S.K., A.K., N.G.L., L.M., S.K.B.G., S.O.-G., E.A.A., T.Q., A.V.F., N.J.L., J.C.K. and J.L.M.B. contributed reagents/materials/analysis tools. A.W.T., S.S.H., J.V.M., W.F.M., C.J.H., D.W., G.A., C.Z. and C.L.M. wrote the paper.

Corresponding authors

Correspondence to Chongzhi Zang or Clint L. Miller.

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Competing interests

J.L.M.B. is a shareholder in Clinical Gene Network AB who have a vested interest in STARNET. A.V.F. at CVPath also acknowledges receiving financial support from the following entities: 4C Medical, 4Tech, Abbott Vascular, Ablative Solutions, Absorption Systems, Advanced NanoTherapies, Aerwave Medical, Alivas, Amgen, Asahi Medical, Aurios Medical, Avantec Vascular, BD, Biosensors, Biotronik, Biotyx Medical, Bolt Medical, Boston Scientific, Canon, Cardiac Implants, Cardiawave, CardioMech, Cardionomic, Celonova, Cerus, EndoVascular, Chansu Vascular Technologies, Children’s National, Concept Medical, Cook Medical, Cooper Health, Cormaze, CRL, Croivalve, CSI, Dexcom, Edwards Lifesciences, Elucid Bioimaging, eLum Technologies, Emboline, Endotronix, Envision, Filterlex, Imperative Care, Innovalve, Innovative, Cardiovascular Solutions, Intact Vascular, Interface Biolgics, Intershunt Technologies, Invatin, Lahav, Limflow, L&J Bio, Lutonix, Lyra Therapeutics, Mayo Clinic, Maywell, MDS, MedAlliance, Medanex, Medtronic, Mercator, Microport, Microvention, Neovasc, Nephronyx, Nova Vascular, Nyra Medical, Occultech, Olympus, Ohio Health, OrbusNeich, Ossio, Phenox, Pi-Cardia, Polares Medical, Polyvascular, Profusa, ProKidney, LLC, Protembis, Pulse Biosciences, Qool Therapeutics, Recombinetics, Recor Medical, Regencor, Renata Medical, Restore Medical, Ripple Therapeutics, Rush University, Sanofi, Shockwave, SMT, SoundPipe, Spartan Micro, Spectrawave, Surmodics, Terumo Corporation, The Jacobs Institute, Transmural Systems, Transverse Medical, TruLeaf, UCSF, UPMC, Vascudyne, Vesper, Vetex Medical, Whiteswell, WL Gore, Xeltis. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Histological characterization of human coronary artery sections.

(a) Representative histology staining of adjacent frozen human coronary artery sections at different disease categories used for snATAC-seq profiling. Category 1 reflects normal to Stary atherosclerosis stage I/II lesions with adaptive intimal thickening and early lipid (Oil Red O (ORO)) and collagen (Sirius Red) accumulation in the subintimal layer. Category 2 reflects Stary stage III/IV early/intermediate atheroma lesions with increased lipid and collagen accumulation and proliferation (Hematoxylin & Eosin (H&E)). Category 3 reflects Stary stage V/VI advanced fibroatheroma or complex lesions with more severe lipid and collagen deposition as well as lipid core and thin media layer. (b) Whole slide quantitative results of ORO area (mm2) normalized to overall tissue area and (c) Sirius Red based quantitation of intima-media thickness (IMT) with maximum intima and average media width captured from >6 automatically defined measurements (Methods). (a-c) Similar results were observed from n = 3, n = 5, and n = 10 independent donor samples per lesion stage, respectively. ANOVA p-values shown for comparisons across lesion stages. Boxplots represent the median and interquartile range (IQR). Scale bar = 1 mm.

Extended Data Fig. 2 Coronary artery cell type marker genes from snATAC-seq gene scores.

(a) Representative UMAP plots of snATAC-seq imputed gene activity scores and integrated RNA scores for SMC and fibromyocyte marker genes. (b) UMAP plots of imputed gene scores for additional cell type marker genes and CAD GWAS genes. (c) Top candidate genes at CAD GWAS loci with cell type enriched chromatin accessibility. Negative Log10 FDR enrichment values shown for CAD GWAS marker genes.

Extended Data Fig. 3 Integration of human coronary artery snATAC-seq data with human coronary artery scRNA-seq (from Wirka et al.7).

(a) UMAP showing projection of scRNA-seq cluster labels onto cells in the snATAC-seq dataset. Colors represent the assigned cellular identities from scRNA-seq label transfer. Detailed parameters of the snATAC-seq/scRNA-seq integration are provided in the Methods section. (b) Heatmap of marker gene scores after ArchR scRNA-seq/snATAC-seq integration highlights 4,649 marker features. (c) Correlation of cell type specific scRNA-seq and snATAC-seq promoter accessibility (pseudo bulk reads from ATAC signal centered on TSS (+/− 3 kb) for each gene). Log2 transformed data is represented as scatter plots and Pearson correlation coefficients are shown for each cell type. White lines represent missing gene counts from scRNA-seq dataset, which is most apparent in the low abundant Mast cells.

Extended Data Fig. 4 Coronary artery snATAC-seq peak cell type and functional annotation.

(a) Pie chart showing genomic annotations of the consensus set of coronary peaks across all cell types (n = 323,767). Peaks were annotated using the ChIPseeker R/Bioconductor package (Yu et al. Bioinformatics 2015). (b) Pie chart of cell type annotation for peaks in the consensus peak set (n = 323,767) according to ArchR (Granja et al.41). Peaks were annotated with a cell type according to the group from which each peak originated according to ArchR’s iterative overlap procedure. (c) Functional enrichment analysis of cell type marker peaks using GREAT.

Extended Data Fig. 5 snATAC-seq co-accessibility and integration with scRNA-seq link putative regulatory elements to target promoters.

(a) Genome browser tracks highlighting CAD-associated SNPs located within peaks linked to the VEGFA promoter peak through co-accessibility. (b) Genome browser tracks highlighting the intronic CAD SNP rs7500448 located in a smooth muscle cell peak in the CDH13 gene linked to the CDH13 promoter peak through co-accessibility. (c) Heatmap summary of ArchR Peak2Gene links (n = 148,617) at 10 kb resolution where chromatin accessibility is highly correlated with target gene expression. Shown on the left are Z-scores for snATAC-seq peak accessibility and on the right are Z-scores for RNA expression.

Extended Data Fig. 6 Additional CAD-associated variants that are coronary artery chromatin accessibility QTLs (caQTLs).

(a-b) Smooth muscle cell caQTL boxplots for variants at the BMP1 (rs73551705) and SMAD3 (rs17293632) CAD loci (n = 40 unique individuals). (c) Macrophage caQTL boxplot for the rs72844419 variant at the GGCX CAD locus (n = 39). Chromatin accessibility reads were normalized using variance stabilizing transformation (vst) in DESeq2. Boxplots represent the median and interquartile range (IQR), while the whisker represent up to 1.5 X IQR. (d-e) Comparison of effect size directions between smooth muscle cell caQTLs (5% FDR) and bulk coronary artery caQTLs (5% FDR), as visualized in scatter plot (d) and donut plot (e). For this analysis, 503 caQTL peaks are shared between both datasets (peaks with a corresponding significant caQTL variant). The rsID reported in the SMC caQTL results (n = 40 individuals) was compared with the rsID reported in the bulk caQTL results (n = 35 individuals). Two variants were considered to be in linkage disequilibrium (LD) if the r2 value between them was between 0.2 and 1 (in EUR population). If variants had an r2 value < 0.2 (in EUR population), the variants were considered to be in low LD (blue). For the caQTL effect size direction, we considered the RASQUAL Pi statistic. The RASQUAL Pi statistic can range from 0–1, where Pi < 0.5 reflects lower peak accessibility for the alternative allele and Pi > 0.5 reflects higher accessibility for the alternative allele. The effect sizes for linked variants go in the same direction (green) if the Pi values in SMCs and bulk coronary artery are both < 0.5 or both > 0.5. Linear regression line and Pearson correlation coefficient shown in (d).

Extended Data Fig. 7 Examples of candidate CAD functional variants within macrophage accessible chromatin.

(a) CAD GWAS locus MAP1S/FCHO1 on chromosome 19 depicting multiple genome-wide significant variants (above dashed line). Highlighted variant rs10418535 is located within a macrophage/immune cell ATAC peak as shown in the genome browser tracks. gkm-SVM importance scores show the predicted effects of the T allele to form a functional binding site, while the C allele (non-effect) is predicted to disrupt TF binding. (b) Genome browser view showing 95% credible CAD SNPs (blue), highlighting rs7296737 located within a strong macrophage marker peak in the first intron of SCARB1 on chr12. (c) Genome browser view highlighting top credible CAD SNP rs17680741 residing in macrophage marker peak in the second intron of TSPAN14 on chr10.

Extended Data Fig. 8 Co-accessibility and gene regulatory analyses prioritize transcriptional regulators TBX2 and PRDM16.

(a) Genome browser track highlighting the association between CAD associated SNPs and SMC marker genes through co-accessibility (peak2gene) detected by snATAC-seq data (Methods). The red loops represent the association between TBX2 promoter and CAD associated SNPs. (b) Network visualization of TBX2 key driver target genes in STARNET atherosclerotic aortic root (AOR) tissue. (c) Clinical trait enrichment for PRDM16 module 28 in STARNET liver tissue. (d) Network visualization of PRDM16 key driver target genes in STARNET mammary artery (MAM) and liver tissues.

Extended Data Fig. 9 Immunostaining of PRDM16 protein in coronary atherosclerosis sections.

(a) Representative negative control (no primary antibody) immunofluoresence (IF) staining in human coronary artery - left anterior descending (LAD). Positive staining of rabbit anti-PRDM16 in vessels in control kidney tissues. Similar results were observed from n = 4 independent donor samples per tissue. Scale bar = 100 um. (b) Representative IF staining of PRDM16 and LMOD1 in atherosclerotic human coronary artery (LAD) segments from normal-Stage II, Stage III-IV, and Stage V-VI lesions based on Stary classification stages. Red = PRDM16 or LMOD1, Green = alpha smooth muscle actin (a-SMA) and blue = DAPI (nuclei). Scale bar = 1 mm (whole slide) or 100 um (highlighted regions of interest). (c) Representative hematoxylin & eosin (H&E) and MOVAT histology staining of distinct human coronary artery segments with similar lesion stages as (b). Scale bar = 1 mm. (b-c) Similar results were observed from n = 4 (Normal-stage II), n = 6 (Stage III-IV), and n = 6 (Stage V-VI) independent donor samples per group.

Supplementary information

Supplementary Information

Supplementary Figs. 1–7 and methods.

Reporting Summary

Peer Review File

Supplementary Tables

Supplementary Tables 1–9.

Supplementary Data 1

Top coronary artery cell type snATAC marker peaks and genes.

Supplementary Data 2

Consensus set of human coronary artery snATAC-seq peaks across all cell types.

Supplementary Data 3

Transcription factor motif enrichment within cell type peaks.

Supplementary Data 4

Differentially accessible regulatory elements and functional annotation between traditional smooth muscle cells and fibromyocytes.

Supplementary Data 5

CAD GWAS variants overlapping coronary artery snATAC-seq accessible chromatin sites.

Supplementary Data 6

Chromatin accessibility QTLs within individual coronary artery cell types, calculated using RASQUAL.

Supplementary Data 7

Chromatin accessibility QTLs from bulk coronary artery ATAC-seq data, calculated using RASQUAL.

Supplementary Data 8

Machine learning prediction and annotation results of functional CAD variants for individual coronary artery cell types.

Supplementary Data 9

Sample size estimations for top fibromyocyte genes comparing traditional smooth muscle cells and fibromyocytes.

Supplementary Data 10

List of PRDM16 and TBX2 eQTLs in atherosclerosis-relevant human gene expression datasets.

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Turner, A.W., Hu, S.S., Mosquera, J.V. et al. Single-nucleus chromatin accessibility profiling highlights regulatory mechanisms of coronary artery disease risk. Nat Genet 54, 804–816 (2022). https://doi.org/10.1038/s41588-022-01069-0

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