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Single-nucleus profiling of human dilated and hypertrophic cardiomyopathy

Nature volume 608, pages 174–180 (2022)Cite this article

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Abstract

Heart failure encompasses a heterogeneous set of clinical features that converge on impaired cardiac contractile function1,2 and presents a growing public health concern. Previous work has highlighted changes in both transcription and protein expression in failing hearts3,4, but may overlook molecular changes in less prevalent cell types. Here we identify extensive molecular alterations in failing hearts at single-cell resolution by performing single-nucleus RNA sequencing of nearly 600,000 nuclei in left ventricle samples from 11 hearts with dilated cardiomyopathy and 15 hearts with hypertrophic cardiomyopathy as well as 16 non-failing hearts. The transcriptional profiles of dilated or hypertrophic cardiomyopathy hearts broadly converged at the tissue and cell-type level. Further, a subset of hearts from patients with cardiomyopathy harbour a unique population of activated fibroblasts that is almost entirely absent from non-failing samples. We performed a CRISPR-knockout screen in primary human cardiac fibroblasts to evaluate this fibrotic cell state transition; knockout of genes associated with fibroblast transition resulted in a reduction of myofibroblast cell-state transition upon TGFβ1 stimulation for a subset of genes. Our results provide insights into the transcriptional diversity of the human heart in health and disease as well as new potential therapeutic targets and biomarkers for heart failure.

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Fig. 1: Cellular composition in the LV from healthy donors and those with cardiomyopathy.
Fig. 2: Transcriptional differences between NF and cardiomyopathy LVs.
Fig. 3: DCM- and HCM-specific activated fibroblast populations.
Fig. 4: Cellular assay of myofibroblast transition in cardiac fibroblasts.

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Data availability

Processed single-nucleus transcriptomic data are available through the Broad Institute’s Single Cell Portal (https://singlecell.broadinstitute.org/single_cell) under project ID SCP1303 (https://singlecell.broadinstitute.org/single_cell/study/SCP1303/). Raw sequence data are available to authorized users through dbGaP (the database of Genotypes and Phenotypes) accession number phs001539. Use of the raw sequencing data is limited to health, medical and/or biomedical purposes, including methods development research. Source data are provided with this paper.

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Acknowledgements

The Precision Cardiology Laboratory is a joint effort between the Broad Institute and Bayer AG. This work was supported by the Fondation Leducq (14CVD01), and by grants from the National Institutes of Health to P.T.E. (1RO1HL092577, R01HL128914 and K24HL105780), N.R.T. (5K01HL140187), K.B.M. (1R01HL105993) and M.C.H. (5T32HL007208-42). This work was also supported by a grant from the American Heart Association Strategically Focused Research Networks to P.T.E. (18SFRN34110082).

Author information

Authors and Affiliations

  1. Precision Cardiology Laboratory and the Cardiovascular Disease Initiative, The Broad Institute, Cambridge, MA, USA

    Mark Chaffin, Bridget Simonson, Matthew C. Hill, Alessandro Arduini, Stephen J. Fleming, Ondine Atwa, Jiangchuan Ye & Patrick T. Ellinor

  2. Precision Cardiology Laboratory, Bayer US LLC, Cambridge, MA, USA

    Irinna Papangeli, Amer-Denis Akkad, Sikander Hayat & Christian M. Stegmann

  3. Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA

    Matthew C. Hill, Matthias Nahrendorf & Patrick T. Ellinor

  4. Data Sciences Platform, The Broad Institute, Cambridge, MA, USA

    Stephen J. Fleming

  5. Center for the Development of Therapeutics, The Broad Institute, Cambridge, MA, USA

    Michelle Melanson, Maria Kost-Alimova & Virendar K. Kaushik

  6. Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Kenneth C. Bedi Jr & Kenneth B. Margulies

  7. Center for Systems Biology, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA

    Matthias Nahrendorf

  8. Masonic Medical Research Institute, Utica, NY, USA

    Nathan R. Tucker

  9. Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA

    Patrick T. Ellinor

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Contributions

M.C., N.R.T., C.M.S. and P.T.E. conceived and designed the study. I.P., B.S., A.-D.A. and A.A. generated snRNA-seq data. M.C. and S.J.F. analysed the snRNA-seq data. I.P., M.C.H., S.H., J.Y., M.N. and P.T.E. interpreted snRNA-seq analyses. B.S. performed RNA in situ hybridization and immunofluorescence staining. M.M., M.K.-A., M.C.H., O.A. and V.K.K. developed, executed, validated and interpreted cardiac fibrosis cellular assay. K.C.B. and K.B.M. acquired LV samples for this study. M.C., I.P., B.S. and P.T.E. drafted the manuscript, which was revised by all authors.

Corresponding author

Correspondence to Patrick T. Ellinor.

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Competing interests

A.-D.A. is an employee of Bayer US LLC (a subsidiary of Bayer AG) and may own stock in Bayer AG. I.P., S.H. and C.M.S. were full-time employees of Bayer when this work was performed. C.M.S. is now a full-time employee of Vifor Pharma Management. V.K.K. was a co-founder of Dogma Therapeutics and Apolo1 Bio, and is now an employee of Anji Pharmaceuticals. P.T.E. has received sponsored research support from Bayer AG and IBM Health, and he has served on advisory boards or consulted for Bayer AG, MyoKardia and Novartis. K.B.M. has research grant funding from Sanofi-Aventis, USA and has also served on advisory boards for MyoKardia and Pfizer. M.N. has received funds or material research support from Lilly, Alnylam, Biotronik, CSL Behring, GlycoMimetics, GSK, Medtronic, Novartis and Pfizer, as well as consulting fees from Biogen, Gimv, IFM Therapeutics, Molecular Imaging, Sigilon and Verseau Therapeutics. The Broad Institute, Massachusetts General Hospital and Bayer AG have a pending patent application (US application no. 17/669,904) on the identification of activated fibroblasts for the diagnosis and treatment of cardiomyopathy based on the results from this work with M.C., I.P., A.-D.A., C.M.S. and P.T.E. listed as co-inventors. All remaining authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Sample quality control assessment.

a, Distribution of key quality control metrics from CellRanger count as boxplots stratified by non-failing (NF, n = 32), dilated cardiomyopathy (DCM, n = 24), and hypertrophic cardiomyopathy (HCM, n = 32). Cutoffs for low quality are shown by the dashed line, and samples that fail on a given metric are labeled. Center line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range; points, outliers. b, Exemplar high quality UMI (unique molecular identifier) decay curves (green) and low quality UMI decay curves (red) are shown. Cell barcodes are ranked by the total UMI. c, Total counts from Y chromosome transcripts in phenotypically classified females (red) and males (blue) across all samples (n = 88).

Source data

Extended Data Fig. 2 Nuclei quality control assessment.

a, Uniform manifold and projection (UMAP) representation of all CellBender non-empty droplets (n = 885,944), colored by Leiden clustering. b, Distribution of the median of four quality control metrics across clusters (n = 47) in a as boxplots, including percent of unique molecular identifiers mapping to mitochondrial genes (%MT), fraction of reads mapping exclusively to exons (Exonic fraction), entropy, and Scrublet estimated doublet score. Outlier clusters removed based on the criteria are highlighted in red. Center line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range; points, outliers. c, Distribution of number of unique molecular identifiers (nUMI), number of unique genes (nGene), %MT, and entropy across nuclei of each unique cell type (n = 13). Center line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range. d, UMAP representation of non-empty droplets after removal of low-quality clusters identified in a and b (n = 745,342). Additional low-quality nuclei as detected per cluster and per-sample are colored in red. e, Proportion of each sample (top, n = 80) and cluster (bottom, n = 47) removed during the quality control procedure. f, UMAP representation of non-empty droplets after removal of low-quality clusters and per-cluster quality control (n = 605,314). Red nuclei were deemed as misclassified or low-quality nuclei based on sub-cluster analysis within each cluster. g, The average score for each sub-cluster (x-axis) based on marker genes for each major cell type (y-axis), see Methods. When a sub-cluster scores highly for an unrelated cell type (black border), it was removed. CM, Cardiomyocyte; FB, Fibroblast; EC, Endothelial; PC, Pericyte; MP, Macrophage; VSMC, Vascular smooth muscle cell; LC, Lymphocyte; EndoC, Endocardial; AD, Adipocyte; NRN, Neuronal; LEC, Lymphatic endothelial; ActFB, Activated fibroblast; MC, Mast Cell; ProfMP, Proliferating macrophage; EpiC, Epicardial; MT, Mitochondrial.

Source data

Extended Data Fig. 3 Marker genes and cell type clustering.

a, Dot plots showing the expression profile of the top 4 marker genes for each cell type in single nuclei RNA-sequencing data (n = 592,689). The size of the dot reflects the percent of nuclei expressing the gene at non-zero levels and the shade reflects the mean log-normalized expression. b, Hierarchical clustering of cell types and expression profiles of the top 2,000 most highly variable genes. Avg Expr, Average log-normalized expression; Pct Nuclei Expr > 0, Percent of nuclei expressing the gene at non-zero levels; VSMC, Vascular smooth muscle cell.

Source data

Extended Data Fig. 4 Compositional changes of global cell types.

Proportion of each cell type across patients stratified by disease status. Statistically credible changes in cell types comparing dilated cardiomyopathy (n = 11) or hypertrophic cardiomyopathy (n = 15) to non-failing patients (n = 16), as tested with scCODA, are denoted with a *, see Methods. Less prevalent cell types are shown in inset to improve readability. Center line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range; VSMC, vascular smooth muscle cell.

Source data

Extended Data Fig. 5 Sample-level principal component analysis by cell type.

Principal component analysis (PCA) using the top 500 most highly variables genes after summing expression counts for each sample (nNF = 16, nDCM = 11, nHCMrEF = 7, nHCMpEF = 8), for pseudo-bulk (a), cardiomyocyte (b), fibroblast (c), endothelial cell I (d), pericyte (e), macrophage (f), vascular smooth muscle cell (VSMC) (g), and lymphocyte (h). The analysis was performed across all samples (left) and restricted to cardiomyopathy samples (right). The percent of total variation for each principal component (PC) is shown in parentheses. DCM, Dilated cardiomyopathy; HCMrEF, Hypertrophic cardiomyopathy with reduced ejection fraction; HCMpEF, Hypertrophic cardiomyopathy with preserved ejection fraction; LVEF, left ventricular ejection fraction.

Source data

Extended Data Fig. 6 Whole genome sequencing analysis of cardiomyopathy patients.

a, Number of deleterious variant carriers of known clinical cardiomyopathy testing panel genes by disease state (nDCM = 10, nHCM = 15, nNF = 15). Grey indicates patients with no loss-of-function (LOF) or pathogenic variant in known cardiomyopathy genes. b, c, d Volcano plots displaying the log fold-change (log(FC)) and two-sided p-value from a limma-voom differential expression analysis between dilated cardiomyopathy (DCM) TTN LOF carriers and DCM non-carriers (n = 4 vs 6) (b), hypertrophic cardiomyopathy (HCM) MYBPC3 LOF carriers and HCM non-carriers (n = 3 vs 12) (c), and HCM MYH7 pathogenic variant carriers and HCM non-carriers (n = 5 vs 10) (d) based on CellBender remove-background counts. Dots are colored by cell type with outlined dots representing genes with FDR < 0.01 as calculated with the Benjamini-Hochberg procedure. Only genes deemed to have a low probability of background contamination are displayed. VSMC, vascular smooth muscle cell.

Source data

Extended Data Fig. 7 Sub-clustering of abundant cell types.

a-f, Sub-clustering results for cardiomyocytes (n = 158,469) (a), fibroblasts (n = 147,219) (b), endothelial cells (n = 112,977) (c), pericytes (n = 69,304) (d), vascular smooth muscle cells (VSMC, n = 18,137) (e), and lymphocytes (n = 16,246) (f). Uniform manifold approximation and projection (UMAP) visualization colored by Leiden clusters is shown on the left. The distribution of sub-populations across patients by disease status represented as box plots with statistically credible changes indicated with a * (middle, see Methods). Sub-population labels are colored as in the UMAP visualization. Center line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range; points, outliers. Dot plots of the most selective markers for each sub-population compared to all other sub-populations (right). The size of the dot reflects the percent of nuclei expressing the gene at non-zero levels and the shade reflects the mean log-normalized expression. Avg norm expr, Average log-normalized expression scaled to the max value for each gene; Pct Nuclei Expr > 0, Percent of nuclei expressing the gene at non-zero levels; Act. FB, Activated fibroblast; L-EC, Lymphatic endothelial.

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Extended Data Fig. 8 Macrophage sub-populations.

a, Uniform manifold approximation and projection (UMAP) representation of 53,730 nuclei classified as macrophage or proliferating macrophage in the global analysis colored by sub-population. b, UMAP plot with the S phase cell cycle score overlaid (see Methods). c, UMAP plot with the G2M phase cell cycle score overlaid (see Methods). d, Expression of markers for each sub-population. The size of each dot reflects the percent of nuclei expressing the gene at non-zero levels and the shade reflects the mean log-normalized expression. e, Distribution of macrophage sub-populations across patients (nDCM = 11, nHCM = 15, nNF = 16) by disease status, with statistically credible differences denoted with a *, see Methods. Center line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range; points, outliers. f, Representative immunofluorescence staining for macrophage marker CD163, cycling marker MKI67, and nuclei with DAPI from single tissue sections of four patients. The percent of macrophages with MKI67 expression across full tissue sections are shown on each image. g, Dendrogram demonstrating similarity of sub-population centroids based on the top 2000 most highly variable genes using Euclidean distance and the Ward method. h-i, Expression of marker genes for CCR2 positive (CCR2+) (h) and CCR2 negative (CCR2-) (i) cardiac macrophages obtained from Bajpai et al., 201815. The size of each square represents the percent of nuclei expressing the gene at non-zero levels while the shade represents a log fold-change (log(FC)) estimate comparing the expression in the given sub-population to all other sub-populations. Genes significantly up- or down-regulated in a sub-population (FDR < 0.01, as calculated with the Benjamini-Hochberg procedure) are shown with a black border. Mφ, Macrophage; NF, Non-failing; DCM, Dilated cardiomyopathy; HCM, Hypertrophic cardiomyopathy; Pct nuclei expr > 0; Avg norm expr, Average normalized expression scaled to the max expression for each gene across all sub-populations.

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Extended Data Fig. 9 Imaging validation.

a, Additional immunofluorescence staining of four patients for macrophage marker CD163 and cycling marker MKI67 from single tissue sections of each patient shown in Extended Data Fig. 8f. Images are displayed both with, and without, DAPI to allow better visualizing of cells co-expressing CD163 and MKI67. b,in situ hybridization with RNAscope showing localization of canonical fibroblast marker, DCN (green/blue), and activated fibroblast marker, COL22A1 (red), across several images of a single section from each of four patients: dilated cardiomyopathy patient P1304, hypertrophic cardiomyopathy patient P1425, and non-failing patients P1515 and P1516. Nuclear localization is shown with hematoxylin (blue). Negative control sections are shown in the second row for each sample. NF, non-failing; DCM, dilated cardiomyopathy; HCM, hypertrophic cardiomyopathy.

Extended Data Fig. 10 Validation of computational deconvolution analysis for activated fibroblasts.

a, in situ hybridization with RNAscope showing localization of canonical fibroblast marker DCN (green/blue), and activated fibroblast marker COL22A1 (red), across single sections derived from 6 patients. Nuclear localization is shown with hematoxylin (blue). The computationally predicted percent of activated fibroblasts from CIBERSORTx (% Act. FB) for each patient is shown above their respective image. b, c, Uniform manifold and projection representation of all nuclei from both the primary analysis (npatient = 42) and validation single-nuclei RNA-sequencing (npatient = 3) (b), and separately for validation samples alone (c). The total number of patients (n_patients) and total nuclei (n_nuclei) are shown above each respective figure. d, Relative contribution of fibroblasts and activated fibroblasts to each primary analysis patient (left) and validation patients only (right). DCM, Dilated cardiomyopathy; HCM, Hypertrophic cardiomyopathy; NF, Non-failing; VSMC, Vascular smooth muscle cell.

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Extended Data Fig. 11 Replication of cardiac fibroblast activation assay across up to five screens.

The percent change in the fraction of myofibroblasts across up to five screens for each sgRNA (independent sgRNAs denoted as “G”). Within each screen, well values were normalized to the median effect of TGFBR1 sgRNAs and non-targeting control sgRNAs. The specific screen replicate (Rep.) is shown above each respective bar where “P” indicates the primary screen, and r1-r4 indicate replicate screens 1–4. The number of wells included in each screen (Nwell) are displayed below each bar with individual dots displayed for each well value. Error bars depict standard error to the mean.

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Extended Data Fig. 12 Global expression profiles of genes included in the cardiac fibroblast activation assay.

Dot plot representation of expression profiles (npatient = 42; nnuclei = 592,689) of all genes included in cardiac fibroblast activation assay. The size of the dot reflects the percent of nuclei expressing the gene at non-zero levels and the shade reflects the mean log-normalized expression. Avg expr, Average log-normalized expression; Pct nuclei expr > 0, Percent of nuclei expressing the gene at non-zero levels; VSMC, Vascular smooth muscle cell.

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Chaffin, M., Papangeli, I., Simonson, B. et al. Single-nucleus profiling of human dilated and hypertrophic cardiomyopathy. Nature 608, 174–180 (2022). https://doi.org/10.1038/s41586-022-04817-8

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