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Defining cardiac functional recovery in end-stage heart failure at single-cell resolution

Nature Cardiovascular Research volume 2, pages 399–416 (2023)Cite this article

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Abstract

Recovery of cardiac function is the holy grail of heart failure therapy yet is infrequently observed and remains poorly understood. In this study, we performed single-nucleus RNA sequencing from patients with heart failure who recovered left ventricular systolic function after left ventricular assist device implantation, patients who did not recover and non-diseased donors. We identified cell-specific transcriptional signatures of recovery, most prominently in macrophages and fibroblasts. Within these cell types, inflammatory signatures were negative predictors of recovery, and downregulation of RUNX1 was associated with recovery. In silico perturbation of RUNX1 in macrophages and fibroblasts recapitulated the transcriptional state of recovery. Cardiac recovery mediated by BET inhibition in mice led to decreased macrophage and fibroblast Runx1 expression and diminished chromatin accessibility within a Runx1 intronic peak and acquisition of human recovery signatures. These findings suggest that cardiac recovery is a unique biological state and identify RUNX1 as a possible therapeutic target to facilitate cardiac recovery.

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Fig. 1: Study design, global clustering and DE analysis of cardiac remodeling after LVAD implantation.
Fig. 2: Cell-type specific cardiac recovery.
Fig. 3: Cardiomyocytes do not revert to a healthy state in cardiac recovery.
Fig. 4: Pro-inflammatory macrophages and RUNX1 are diminished in reverse remodeling, whereas tissue resident macrophages show signs of recovery.
Fig. 5: RUNX1 is downregulated in fibroblasts in cardiac recovery.
Fig. 6: RUNX1 perturbation in silico and in vivo facilitates cardiac recovery.

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Data availability

Raw sequencing files and processed normalized data can be found on the Gene Expression Omnibus (GSE226314). Donors were used from published data (GSE183852). All other data supporting the findings in this study are included in the main article and associated files. Source data are provided with this paper.

Code availability

All scripts used for analysis in this manuscript can be found on GitHub (https://github.com/jamrute/2023_Amrute_et_al_NatureCVR_CardiacRecovery).

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Acknowledgements

K.L. is supported by the Washington University in St. Louis Rheumatic Diseases Research Resource-Based Center Grant (National Institutes of Health (NIH) P30AR073752, NIH R01 HL138466, R01 HL139714, R01 HL151078, R01 HL161185 and R35 HL161185); the Leducq Foundation Network (20CVD02); the Burroughs Welcome Fund (1014782); the Children’s Discovery Institute of Washington University and St. Louis Children’s Hospital (CH-II-2015-462, CH-II-2017-628 and PM-LI-2019-829); the Foundation of Barnes-Jewish Hospital (8038-88); and generous gifts from Washington University School of Medicine. S.D. is supported by the American Heart Association Heart Failure Strategically Focused Research Network (grant 16SFRN29020000); National Heart, Lung, and Blood Institute (NHLBI) RO1 grant HL135121, NHLBI RO1 grant HL132067, NHLBI R01 grant HL156667 and NHLBI R01 grant HL151924; Merit Review Award I01 CX002291, US Department of Veterans Affairs; and Nora Eccles Treadwell Foundation grants. J.M.A. is supported by an American Heart Association Predoctoral Fellowship (826325) and the Washington University in St. Louis School of Medicine Medical Scientist Training Program. P.M. is supported by an American Heart Association Postdoctoral Fellowship (916955). T.S. is supported by an American Heart Association Postdoctoral Fellowship (23POST1019351). Figures 1a, 2d and 6c,j were created with BioRender. Histology was performed in the Digestive Diseases Research Core Centers Advanced Imaging and Tissue Analysis Core, supported by grant P30 DK52574. Imaging was performed in the Washington University Center for Cellular Imaging, which is funded, in part, by the Children’s Discovery Institute of Washington University and St. Louis Children’s Hospital (CDI-CORE-2015-505 and CDI-CORE-2019-813) and the Foundation for Barnes-Jewish Hospital (3770). We thank the Genome Technology Access Center at the McDonnell Genome Institute at Washington University School of Medicine for help with genomic analysis. The center is partially supported by National Cancer Institute Cancer Center Support Grant P30 CA91842 to the Siteman Cancer Center. This publication is solely the responsibility of the authors and does not necessarily represent the official views of the National Center for Research Resources or the NIH. The authors are grateful to the donor families for their generosity, and DonorConnect (https://www.donorconnect.life/), Salt Lake City, Utah, for facilitating the work of our research team members acquiring myocardial tissue in the operating rooms of several hospitals in Utah and several other states. The authors are grateful to the University of Utah cardiothoracic surgery team for the invaluable help acquiring the myocardial tissue from chronic heart failure patients.

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Author notes

  1. These authors contributed equally: Junedh M. Amrute, Lulu Lai.

Authors and Affiliations

  1. Cardiovascular Division, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA

    Junedh M. Amrute, Pan Ma, Andrew L. Koenig, Andrea Bredemeyer, Farid F. Kadyrov, Dylan Stoutenburg, Benjamin J. Kopecky, Douglas L. Mann & Kory J. Lavine

  2. Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA

    Lulu Lai & Kory J. Lavine

  3. Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA

    Kenji Kamimoto, Samantha A. Morris & Kory J. Lavine

  4. Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA

    Kenji Kamimoto & Samantha A. Morris

  5. Center for Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA

    Kenji Kamimoto, Samantha A. Morris & Kory J. Lavine

  6. Division of Cardiovascular Medicine & Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah Health & School of Medicine, Salt Lake City, UT, USA

    Thirupura S. Shankar, Sutip Navankasattusas, Joseph Visker & Stavros G. Drakos

  7. Institute of Experimental Medicine and Systems Biology and Division of Nephrology, RWTH Aachen University, Aachen, Germany

    Christoph Kuppe & Rafael Kramann

  8. Division of Cardiothoracic Surgery, Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA

    Linda J. Schulte

  9. Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands

    Rafael Kramann

  10. Department of Cardiology, University Hospital Heidelberg, Heidelberg, Germany

    Florian Leuschner

  11. German Centre for Cardiovascular Research (DZHK), Partner Site Heidelberg, Heidelberg, Germany

    Florian Leuschner

Authors
  1. Junedh M. Amrute
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  2. Lulu Lai
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Contributions

S.D. contributed to LVAD sample acquisition and clinical phenotyping. P.M., L.L., A.B. and A.K. isolated nuclei for snRNA-seq. J.A. performed all computational analysis. J.M.A. and K.K. performed GRN analysis and in silico transcription factor perturbation analysis. J.A., L.L., P.M., L.S., D.S., F.K., T.S.S. and B.K. performed RNA in situ hybridization and immunohistochemistry experiments and analyzed and processed images. J.A., F.L., R.K., S.M., D.M., S.D. and K.L. assisted in the interpretation of the data. J.A. made all figures, and J.A. and K.L. drafted the manuscript. K.L. is responsible for all aspects of this manuscript, including experimental design, data analysis and manuscript production. All authors approved the final version of the manuscript.

Corresponding author

Correspondence to Kory J. Lavine.

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The authors declare no competing interests.

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Nature Cardiovascular Research thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Quality control metrics.

nCount_RNA, nFeature_RNA, percent.mt, and scrublet doublet score split by (A) condition and (B) cell type.

Extended Data Fig. 2 Global clustering.

(A) Heatmap of top10 marker gens for each cell type identified via DE analysis. (B) DotPlot for cell type gene set scores from (A) where the x-axis is cell type gene signature and y-axis is the cluster. (C) Gene set z-scores for top gene markers for each cell type plotted in the UMAP embedding. (D) Cell type composition for each of the patient samples.

Extended Data Fig. 3 Pseudobulk DE analysis to unravel cardiac recovery.

(A) Pseudobulk DE analysis in each cell type in 3 comparison groups: pre-LVAD HF vs donor, RR-post vs donor, and RR-post vs pre-LVAD HF. Red dots indicate statistically significant genes (adjusted p-value < 0.05). (B) Total number of statistically significant (adjusted p-value < 0.05 and log2FC > 0.58) per cell type in comparisons from (A). (C) Number of overlapping genes in five major cell populations which are up and down in the comparisons from (A). Red number is the number of cardiac recovery genes. P-values calculated using Wald test adjusted for multiple corrections.

Extended Data Fig. 4 Cardiac recovery overlap amongst cell types.

UpSet plot showing overlap in cardiac recovery genes from (Fig. 2) in five major cell populations which are (A) up and (B) down in cardiac recovery.

Extended Data Fig. 5 Cell-specific pseudobulk analysis.

Pseudobulk PCA analysis in each cell type colored by five conditions (donor, U-pre, U-post, RR-pre, and RR-post).

Extended Data Fig. 6 ABRA expression enriched in unloaded group.

(A) DotPlot of cardiomyocyte specific recovery up- and down signature grouped by CM cell states. (B) Density plot of ABRA expression in UMAP embedding. (C) DotPlot of ABRA expression in cardiomyocytes grouped by condition. (D) RNAscope images of ABRA in 5 conditions and scale bar is 100 um. (E) RNAscope images quantified across an array of patients. N = 37 biologically independent samples and p-values calculated using Wald test adjusted for multiple corrections; donor vs U-pre (*P = 0.023), U-pre vs RR-pre (***P < 0.0001), U-pre vs RR-post (***P = 0.0003), U-post vs RR-pre (***P = 0.0007), U-post vs RR-post (*P = 0.0188), and RR-pre vs RR-post (***P = 0.0007).

Source data

Extended Data Fig. 7 Macrophage diversity in recovery.

(A) Gene set z-scores for top gene markers for each cell state plotted in the UMAP embedding. (B) Enrich GO using compareclusters from cluster Prolifer across macrophage cell states. P-value calculated using Fisher exact test. (C) WikiPathways enriched in cardiac recovery. P-value calculated using Fisher exact test. (D) Paired comparison of Mac 2 cluster composition at patient level split by U and RR group from biologically independent samples. (E) DoRothEA TF enrichment analysis in U-post and RR-post zoomed in on some key differentially enriched TFs. (F) Overlap between Runx1 target genes and DE genes between U-pre and RR-pre with heatmap of respective genes split by condition.

Source data

Extended Data Fig. 8 Fibroblast diversity in recovery.

(A) Gene set z-scores for top gene markers for each cell state plotted in the UMAP embedding. (B) DotPlot for cell type gene set scores from (A) where the x-axis is cell type gene signature and y-axis is the cluster. (C) Enrich GO using compareclusters from cluster profiler across fibroblast cell states. P-value calculated using Fisher exact test.

Extended Data Fig. 9 CellOracle simulation in myeloid cells in TAC.

(A) Myeloid cell states, (B) Marker genes for cell states, (C) Cell state composition and cell density plots in TAC and TAC + JQ1, and (D) Cell oracle Runx1 KO perturbation score with vector field.

Extended Data Fig. 10 CellOracle simulation in fibroblasts in TAC.

(A) Fibroblast cell states, (B) Marker genes for cell states, (C) Cell state composition and cell density plots in TAC and TAC + JQ1, and (D) Cell oracle Runx1 KO perturbation score with vector field.

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–22.

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Source Data Extended Data Fig. 6

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Amrute, J.M., Lai, L., Ma, P. et al. Defining cardiac functional recovery in end-stage heart failure at single-cell resolution. Nat Cardiovasc Res 2, 399–416 (2023). https://doi.org/10.1038/s44161-023-00260-8

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