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Integrated multi-omic characterization of congenital heart disease

Abstract

The heart, the first organ to develop in the embryo, undergoes complex morphogenesis that when defective results in congenital heart disease (CHD). With current therapies, more than 90% of patients with CHD survive into adulthood, but many suffer premature death from heart failure and non-cardiac causes1. Here, to gain insight into this disease progression, we performed single-nucleus RNA sequencing on 157,273 nuclei from control hearts and hearts from patients with CHD, including those with hypoplastic left heart syndrome (HLHS) and tetralogy of Fallot, two common forms of cyanotic CHD lesions, as well as dilated and hypertrophic cardiomyopathies. We observed CHD-specific cell states in cardiomyocytes, which showed evidence of insulin resistance and increased expression of genes associated with FOXO signalling and CRIM1. Cardiac fibroblasts in HLHS were enriched in a low-Hippo and high-YAP cell state characteristic of activated cardiac fibroblasts. Imaging mass cytometry uncovered a spatially resolved perivascular microenvironment consistent with an immunodeficient state in CHD. Peripheral immune cell profiling suggested deficient monocytic immunity in CHD, in agreement with the predilection in CHD to infection and cancer2. Our comprehensive phenotyping of CHD provides a roadmap towards future personalized treatments for CHD.

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Fig. 1: Profiling of tissues from paediatric controls and patients with CHD or heart failure.
Fig. 2: snRNA-seq showing the unique transcriptional signature of cardiomyocytes in paediatric patients with CHD.
Fig. 3: Profiling of cardiac fibroblasts in paediatric patients.
Fig. 4: Tissue histology and validation of tissue snRNA-seq results across paediatric cardiac diseases.
Fig. 5: High-dimensional histopathology of paediatric cardiac diseases.
Fig. 6: Intercellular communication in paediatric cardiovascular disease.

Data availability

Raw and processed next-generation sequencing data have been deposited at the NCBI Gene Expression Omnibus under accession number GSE203275. The snRNA-seq data are available online at the Broad Single Cell Portal under study number SCP1852.

Code availability

The pseduobulk RNA-seq analysis script we used is available at https://hbctraining.github.io/scRNA-seq/lessons/pseudobulk_DESeq2_scrnaseq.html.

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Acknowledgements

This work was supported by the Department of Defense (CDMRP) W81XWH-17-1-0418 (J.F.M.); National Institutes of Health (1F31HL156681-01 (H.L.), F30HL145908 (Z.A.K.), 5T32HL007208-42 (M.C.H.), R56 HL142704 and R01HL142704 (J.W.), and R01HL 127717, R01HL 130804 and R01HL 118761 (J.F.M.); the Vivian L. Smith Foundation (J.F.M.); Baylor Research Advocates for Student Scientists and Baylor College of Medicine Medical Scientist Training Program (Z.A.K.); the LeDucq Foundation’s Transatlantic Networks of Excellence in Cardiovascular Research (14CVD01: Defining the Genomic Topology of Atrial Fibrillation), the MacDonald Research Fund Award (16RDM001), and a grant from the Saving Tiny Hearts Society (J.F.M.); and NIH HL149164, HL148785 and University of Kentucky Myocardial Recovery Alliance (E.J.B. and K.S.C.). The TCBR is supported by: Children’s Discovery Institute of Washington University and St Louis Children’s Hospital (PM-LI-2019-829) (J.N. and K.L.), the Baylor College of Medicine Pathology and Histology Core and the BCM Breast Cancer Core. This project was supported by the Optical Imaging and Vital Microscopy (OiVM) core at BCM. This research was performed in the Flow Cytometry and Cellular Imaging Core Facility, which is supported in part by the National Institutes of Health through M. D. Anderson's Cancer Center Support Grant CA016672, the NCI’s Research Specialist 1 R50 CA243707-01A1, and a Shared Instrumentation Award from the Cancer Prevention Research Institution of Texas (CPRIT), RP121010. This project was supported by the Cytometry and Cell Sorting Core at Baylor College of Medicine with funding from the CPRIT Core Facility Support Award (CPRIT-RP180672), the NIH (CA125123 and RR024574) and the assistance of J. M. Sederstrom. This project was supported in part by the Genomic and RNA Profiling Core at Baylor College of Medicine with funding from the NIH S10 grant (1S10OD023469). We acknowledge the Gill Cardiovascular Biorepository at the University of Kentucky for providing paediatric control myocardium samples. N. Stancel provided editorial support. Artwork for some figures was generated with BioRender.com.

Author information

Authors and Affiliations

Authors

Contributions

Conceptualization: I.A., M.C.H. and J.F.M. Methodology: M.C.H., Z.A.K., H.L. and Y.M. Writing, original draft: M.C.H. and J.F.M. Writing, review and editing: J.F.M., M.C.H., I.A., D.T., J.W., Z.A.K., H.L. and Y.M. Funding acquisition: I.A., J.F.M. and J.W. Resources: J.F.M., J.W., I.A., E.J.B., K.S.C., J.N., K.L. and L.W. Supervision: J.F.M. and I.A. Data curation: M.C.H. and T.J.M.

Corresponding author

Correspondence to James F. Martin.

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Nature thanks Eva van Rooij and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Cardiac tissue snRNA-seq profiling.

a, The number of nuclei detected per sample. Calculations for cell (nuclei) per library performed be dividing total number of nuclei by the number of technical replicates (libraries). b, UMAP showing sample identity. Colored according to Extended Data Fig. 1a. c, Cluster composition across cell types. (Top) Number of samples detected per cluster. (Bottom) Stacked bar graph indicating the percentage of each samples contribution to the indicated cluster. Colored according to Extended Data Fig. 1a. d, Pseduobulk RNA-seq from all cell types collapsed. All technical and biological and technical replicates (libraries) are shown. e, Feature plot showing expression of marker genes across global UMAP from Fig. 1b.

Extended Data Fig. 2 Characterization of pediatric cardiomyocytes.

a, Bar plot indicating the number of cardiomyocyte nuclei detected from each pediatric human sample. b, Stacked bar plot showing the composition of each patient sample across the indicated CM clusters. c, Beeswarm plot showing the log-fold distribution of changes across disease and donors in neighborhoods from different cardiomyocyte clusters. Differentially abundant neighborhoods are shown in color. d, A scree plot displaying the proportion of explained variance for all principal components derived from the pseudo-bulk RNA-seq analysis of CMs. e, Bar plot showing the significance of relation with PC1 (red) and PC2 (blue). f, Venn diagram for the intersection of all individual Diagnoses from pseudobulk RNA-seq analysis. g, Venn diagram for the intersection of all age-related and diagnosis-related genes from pseudo-bulk analysis of CM nuclei. h, Gene signature scores projected across UMAP embedding of CM nuclei. i, Violin plot of CM maturation gene module scores for all CMs separated by patient. The patients are ordered by age, from youngest (left) to oldest (right). j, Violin plot of CM maturation gene module scores for all CMs derived from Sim et. al.

Extended Data Fig. 3 Cardiomyocyte transcriptomic Maturation.

a, Heatmap of log2 foldchange values from pseudo-bulk RNA-seq analysis of CMs. Adjusted p-value < 0.01. b, Gene ontology (GO) analysis of mature and young gene signatures identified in Extended Data Fig. 2j.

Extended Data Fig. 4 Epigenomic characterization of cardiomyocytes in CHD.

a, Feature plots showing gene expression in cardiomyocytes. b–d, RNAscope for CRIM1 and CORIN. e, Genome browser tracks displaying cardiomyocyte-specific ATAC-seq data. f, Venn diagram showing the overlap of genes annotated from ATAC-seq peaks and snRNA-seq clusters.

Extended Data Fig. 5 Transcriptional profiling of pediatric cardiac fibroblasts and endothelial cells.

a, Bar plot indicating the number of fibroblast nuclei detected from each sample. b, Stacked bar plot showing the composition of each sample across the indicated CF clusters. c, Stacked bar graph displaying the total number of detected CF nuclei from each patient group. The composition of each CF cluster is highlighted. d, Enrichment map for gene pathway over-representation analysis colored by CF cluster. e, UMAP manifold of cardiac ECs colored by cluster. (f) Dot plot of snRNA-seq expression for EC marker genes. g, Embedding of the Milo K-NN differential abundance testing results for ECs. All nodes represent neighborhoods, colored by their log fold changes for disease versus donor. Neighborhoods with insignificant log fold changes (FDR 10%) are white. Layout of nodes determined by UMAP embedding, shown in Extended Data Fig. 5e. h, Beeswarm plot showing the log-fold distribution of changes across disease and donors in neighborhoods from different EC clusters. Differentially abundant neighborhoods are shown in color. i, Top, cluster composition bar plot colored by patient diagnosis. Bottom, heatmap displaying average expression for all differentially expressed genes for EC clusters. j, Enrichment map for gene pathway over-representation analysis colored by EC cluster.

Extended Data Fig. 6 Additional tissue histology.

a–e, H&E and trichrome staining of additional myocardial samples from donor (a), TOF (b), Neo-HLHS (c), HF-HLHS (d), and DCM (e) patients. Left image is a 2-mm core, and the dashed box outlines the highlighted perivascular region at high magnification in the right image.

Extended Data Fig. 7 Transcriptional profiling of pediatric cardiac immune cell populations.

a, Representative images of cell-segmentation and phenotyping analysis performed on imaging mass cytometry (IMC) images. b, Representative images of IMC (top) and immunofluorescence (bottom)of LYZ marker expression. Solid boxes indicate same regions with LYZ expression. c, Embedding of the Milo K-NN differential abundance testing results for cardiac immune cell populations. All nodes represent neighborhoods, colored by their log fold changes for disease versus Donor. Neighborhoods with insignificant log fold changes (FDR 10%) are white. Layout of nodes determined by UMAP embedding, shown in Fig. 6f. d, Beeswarm plot showing the log-fold distribution of changes across disease and donors in neighborhoods from different immune cell clusters. Differentially abundant neighborhoods are shown in color. Compiled from Extended Data Fig. 7a. (e) Top, cluster composition bar plot colored by patient diagnosis. Bottom, heatmap displaying average expression for all differentially expressed genes for myeloid and lymphoid cell clusters. (f) Enrichment map for gene pathway over-representation analysis colored by cardiac immune cell cluster. (g) Left, protein expression from IMC data across UMAP embedding. Right, feature plot displaying gene expression from snRNA-seq. (h) Feature plot displaying gene expression from snRNA-seq. Related to Fig. 1b.

Extended Data Fig. 8 Single-cell transcriptomic analysis of PBMCs from pediatric patients with CHD.

a, Diagram depicting the overall study design for hematological profiling in CHD patients. b, UMAP embedding of PBMCs colored by cell type. c, Feature plots showing the expression of marker genes. d, UMAP embedding of PBMC scRNA-seq data colored by patient sample Identification number. e, Cluster composition analysis of PBMC scRNA-seq data. f, Cluster composition stacked-bar plot highlighting the percent of cells from each diagnosis group within each single cell cluster. g, Density plot for each indicated diagnosis over the UMAP embedding from Extended Data Fig. 8b. h, UMAP embedding of peripheral monocyte cells. i, Dot plot displaying marker gene expression from monocyte cell clusters. j, Cluster composition analysis of monocyte clusters colored by patient diagnosis. k, UMAP embedding of peripheral NK cells. l, Dot plot displaying marker gene expression from NK cell clusters. m, Cluster composition analysis of NK cell clusters colored by patient diagnosis.

Extended Data Fig. 9 Transcriptomic and epigenomic characterization of peripheral immune cell populations in CHD.

a, Top, PCA plot of NK and CD14+ cell ATAC-seq data. Bottom, Genome Browser tracks for NK cell and CD14+ cell ATAC-seq data. Each track is patient matched by diagnosis. b, Heatmap displaying differential chromatin accessibility analysis of NK cells versus CD14+ monocytes. c, Genome browser tracks displaying genes identified from differential expression analysis in scRNA-seq. d, Left, heatmap displaying the average mRNA expression per cluster of each cytokine (rows) detected in the plasma of CHD patients. Right, heatmap showing the protein expression of each cytokine (row) detected in CHD patient-derived plasma (columns). e, Heatmap depicting the expression of each cognate receptor from the putative ligands identified in Extended Data Fig. 9d from the cardiac snRNA-seq data. f, receptor-ligand map showing the highly expressed receptors identified in Extended Data Fig. 9e with their respective ligands identified in patient plasma. Connections colored by snRNA-seq cluster.

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Hill, M.C., Kadow, Z.A., Long, H. et al. Integrated multi-omic characterization of congenital heart disease. Nature 608, 181–191 (2022). https://doi.org/10.1038/s41586-022-04989-3

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