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Immune responses in checkpoint myocarditis across heart, blood and tumour

Nature volume 636, pages 215–223 (2024)Cite this article

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Abstract

Immune checkpoint inhibitors are widely used anticancer therapies1 that can cause morbid and potentially fatal immune-related adverse events such as immune-related myocarditis (irMyocarditis)2,3,4,5. The pathogenesis of irMyocarditis and its relationship to antitumour immunity remain poorly understood. Here we sought to define immune responses in heart, tumour and blood in patients with irMyocarditis by leveraging single-cell RNA sequencing coupled with T cell receptor (TCR) sequencing, microscopy and proteomics analyses of samples from 28 patients with irMyocarditis and 41 unaffected individuals. Analyses of 84,576 cardiac cells by single-cell RNA sequencing combined with multiplexed microscopy demonstrated increased frequencies and co-localization of cytotoxic T cells, conventional dendritic cells and inflammatory fibroblasts in irMyocarditis heart tissue. Analyses of 366,066 blood cells revealed decreased frequencies of plasmacytoid dendritic cells, conventional dendritic cells and B lineage cells but an increased frequency of other mononuclear phagocytes in irMyocarditis. Fifty-two heart-expanded TCR clones from eight patients did not recognize the putative cardiac autoantigens α-myosin, troponin I or troponin T. Additionally, TCRs enriched in heart tissue were largely nonoverlapping with those enriched in paired tumour tissue. The presence of heart-expanded TCRs in a cycling blood CD8 T cell population was associated with fatal irMyocarditis case status. Collectively, these findings highlight crucial biology driving irMyocarditis and identify putative biomarkers.

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Fig. 1: Enrichment of multiple intracardiac and blood cell populations in irMyocarditis.
Fig. 2: Cardiac T cell phenotypes in irMyocarditis.
Fig. 3: TCRs enriched in irMyocarditis are largely distinct from those in tumour, distinguish fatal cases and do not recognize putative cardiac autoantigens.
Fig. 4: cDCs are enriched in irMyocarditis heart tissue and are associated with disease severity.
Fig. 5: Inflammatory fibroblast enrichment in irMyocarditis.

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Data availability

scRNA-seq count matrices from this study and related data as well as TCR-seq data have been deposited into the Gene Expression Omnibus database (under accession GSE228597), and raw human sequencing data are available in the controlled access repository dbGaP (https://www.ncbi.nlm.nih.gov/gap; accession phs003413.v1.p1). Public Heart Atlas data18 utilized as heart control samples in our study are accessible from the Human Cell Atlas Data Coordination Platform with accession number ERP123138. Reads from scRNA-seq experiments were aligned to the human reference genome (GRCh38, v.3.0.0 from 10x Genomics).

Code availability

Source code for data analysis is available from GitHub (https://github.com/villani-lab/myocarditis) and has been archived to Zenodo (https://zenodo.org/doi/10.5281/zenodo.11519192)80. A full list of software packages and versions included in the analyses is included in Supplementary Table 21. A user-friendly portal81 is available to browse the single-cell data generated in this article at the Villani Laboratory website (https://villani.mgh.harvard.edu/myocarditis).

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Acknowledgements

We are grateful to all donors and their families; staff at the Mass General Cancer Center, Ellison 16 staff and staff at the cardiac catheterization laboratory and the Severe Immunotherapy Complications Service for their collaboration and support; staff at the Teichmann Laboratory for facilitating access to the Human Heart Cell Atlas data and their guidance in navigating this dataset18. S.M.B. was supported by a National Institutes of Health T32 Award (2T32CA071345-21A1) and a SITC-Mallinckrodt Pharmaceuticals Adverse Events in Cancer Immunotherapy Clinical Fellowship. D.A.Z. was supported by a National Institutes of Health T32 Award T32HL007208 and K24HL150238-02. L.Z. was supported by the Spanish Society of Medical Oncology (SEOM) grant for a 2-year translational project at the MGH Cancer Center. K.S. was supported by a NIAID grant T32AR007258. P.S. is supported by the National Institutes of Health K08 Award (NHLBI K08 HL157725) and American Heart Association Career Development Award. M.M.-K. was in part supported by a grant from the National Institutes of Health (R01CA240317). G.O. was supported by the Claudia Adams Barr Program for Innovative Cancer Research and by DF/HCC Kidney Cancer SPORE P50 CA101942. M.F.T. is supported by the National Institutes of Health K08 Award (1K08DK127246-01A1) and was supported by a National Institutes of Health T32 Award (T32DK007191). G.M.B. is supported by an Adelson Foundation award. T.G.N is supported by a gift from A. Curt Greer and P. Kohlberg and from C. and P. Kazilionis, the Michael and Kathryn Park Endowed Chair in Cardiology, a Hassenfeld Scholar Award, and has additional grant funding from the National Institutes of Health/National Heart, Lung, and Blood Institute (R01HL137562, K24HL150238 and R01HL130539). This work was made possible by support from the National Institute of Health Director’s New Innovator Award (DP2CA247831 to A.-C.V.), the Massachusetts General Hospital Transformative Scholar in Medicine Award (to A.-C.V.), the Damon Runyon-Rachleff Innovation Award (to A.-C.V.), The Melanoma Research Alliance Young Investigator Award (https://doi.org/10.48050/pc.gr.143739 to A.-C.V.), the MGH Howard M. Goodman Fellowship (to A.-C.V.), the Arthur, Sandra, and Sarah Irving Fund for Gastrointestinal Immuno-Oncology (to A.-C.V.), the Kraft Foundation Award (to. K.L.R. and A.-C.V.), and by the support of an anonymous donor (to. K.L.R. and A.-C.V.).

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Author notes

  1. Molly F. Thomas

    Present address: Division of Gastroenterology, Department of Medicine, Oregon Health and Sciences University, Portland, OR, USA

  2. Molly F. Thomas

    Present address: Department of Cell, Developmental, and Cancer Biology, Oregon Health and Sciences University, Portland, OR, USA

  3. These authors contributed equally: Steven M. Blum, Daniel A. Zlotoff, Neal P. Smith, Isabela J. Kernin, Swetha Ramesh

  4. These authors jointly supervised this work: Molly F. Thomas, Tomas G. Neilan, Kerry L. Reynolds, Alexandra-Chloé Villani

Authors and Affiliations

  1. Center for Immunology and Inflammatory Diseases, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA

    Steven M. Blum, Daniel A. Zlotoff, Neal P. Smith, Isabela J. Kernin, Swetha Ramesh, Leyre Zubiri, Nandini Samanta, Sidney Martin, Alice Tirard, Pritha Sen, Kamil Slowikowski, Jessica Tantivit, Kasidet Manakongtreecheep, Benjamin Y. Arnold, Mazen Nasrallah, Molly F. Thomas & Alexandra-Chloé Villani

  2. Mass General Cancer Center, Boston, MA, USA

    Steven M. Blum, Leyre Zubiri, Mike Wang, Christopher J. Pinto, Daniel McLoughlin, Monica Jackson, PuiYee Chan, Justin F. Gainor, Dejan Juric, Ryan J. Sullivan & Kerry L. Reynolds

  3. Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA

    Steven M. Blum, Daniel A. Zlotoff, Neal P. Smith, Isabela J. Kernin, Swetha Ramesh, Leyre Zubiri, Nandini Samanta, Sidney Martin, Alice Tirard, Pritha Sen, Kamil Slowikowski, Jessica Tantivit, Kasidet Manakongtreecheep, Benjamin Y. Arnold, Mazen Nasrallah, Catherine J. Wu, Dejan Juric, Giacomo Oliveira, Genevieve M. Boland, Molly F. Thomas & Alexandra-Chloé Villani

  4. Harvard Medical School, Boston, MA, USA

    Steven M. Blum, Daniel A. Zlotoff, Pritha Sen, Kamil Slowikowski, Mazen Nasrallah, Linda T. Nieman, Justin F. Gainor, Catherine J. Wu, Dejan Juric, Mari Mino-Kenudson, Giacomo Oliveira, Ryan J. Sullivan, Genevieve M. Boland, James R. Stone, Molly F. Thomas, Tomas G. Neilan, Kerry L. Reynolds & Alexandra-Chloé Villani

  5. Krantz Family Center for Cancer Research, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA

    Steven M. Blum, Daniel A. Zlotoff, Neal P. Smith, Isabela J. Kernin, Swetha Ramesh, Leyre Zubiri, Nandini Samanta, Sidney Martin, Alice Tirard, Yuhui Song, Katherine H. Xu, Pritha Sen, Kamil Slowikowski, Jessica Tantivit, Kasidet Manakongtreecheep, Benjamin Y. Arnold, Mazen Nasrallah, Aleigha Lawless, William A. Michaud, Tatyana Sharova, Linda T. Nieman, Genevieve M. Boland, Molly F. Thomas & Alexandra-Chloé Villani

  6. Cardio-Oncology Program, Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA

    Daniel A. Zlotoff, Joshua Caplin & Tomas G. Neilan

  7. Department of Pathology, Massachusetts General Hospital, Boston, MA, USA

    Jaimie Barth, Mari Mino-Kenudson & James R. Stone

  8. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Catherine J. Wu & Giacomo Oliveira

  9. Transplant, Oncology and Immunocompromised Host Program, Division of Infectious Diseases, Brigham and Women’s Hospital, Boston, MA, USA

    Pritha Sen

  10. Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    Pritha Sen & Catherine J. Wu

  11. Division of Rheumatology, North Shore Physicians Group, Department of Medicine, Mass General Brigham Healthcare Center, Lynn, MA, USA

    Pritha Sen & Mazen Nasrallah

  12. Clinical Research Center, Massachusetts General Hospital, Boston, MA, USA

    Christopher J. Pinto, Daniel McLoughlin, Monica Jackson & PuiYee Chan

  13. Department of Surgery, Massachusetts General Hospital, Boston, MA, USA

    Aleigha Lawless, William A. Michaud, Tatyana Sharova & Genevieve M. Boland

  14. Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA

    Molly F. Thomas

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Contributions

S.M.B., D.A.Z., M.F.T., K.L.R., T.G.N. and A.-C.V. conceived and led the study. S.M.B., D.A.Z., M.F.T. and A.-C.V. led the experimental design. S.M.B., D.A.Z., M.F.T. and P.S. carried out experiments (with assistance from N.S., S.M., A.T., K.M., J.T., B.Y.A. and J.B.). N.P.S., I.J.K., S.R., K.S. and M.N. designed and performed computational analyses, visualization and data portals. S.M.B., D.A.Z., Y.S., K.H.X. and L.T.N. designed and performed the microscopy experiments. S.M.B., D.A.Z., N.P.S., C.J.W. and G.O. designed and performed the antigen screening experiments. S.M.B., D.A.Z., L.Z., M.W., P.C., C.J.P., D.M., A.L., W.A.M., T.S., M.J., J.F.G., D.J., M.M.-K., R.J.S., G.M.B., J.R.S., T.G.N. and K.L.R. provided clinical expertise, coordinated and performed sample acquisition and/or established research protocols. M.M-K. and J.R.S. provided histopathology expertise. J.C. performed endomyocardial biopsies. A.-C.V. managed and supervised the study. A.-C.V., T.G.N. and K.L.R. provided funding for this work. S.M.B., D.A.Z., N.P.S., I.J.K., S.R. and A.-C.V. wrote the manuscript, with input from all authors.

Corresponding author

Correspondence to Alexandra-Chloé Villani.

Ethics declarations

Competing interests

S.M.B.: consultant to Two River Consulting and Third Rock Ventures; and equity in Candid Therapeutics, Kronos Bio, 76Bio and Allogene Therapeutics. D.A.Z.: consultant to Bristol Myers Squibb, Freeline Therapeutics and Intrinsic Imaging; and research funding from Abbott Laboratories. N.P.S.: consultant to Hera Biotech. L.Z.: consultant to Bristol Myers Squibb and Merck. J.F.G.: consultant to Amgen, Arcus Biosciences, AI Proteins, AstraZeneca, Beigene, Blueprint Medicines, Bristol Myers Squibb, Genentech/Roche, EMD Serono, InterVenn Biosciences, Gilead Sciences, iTeos Therapeutics, Jounce Therapeutics, Karyopharm Therapeutics, Lilly, Loxo, Merus, Mirati Therapeutics, Pfizer, Sanofi, Silverback Therapeutics, Merck, Moderna Therapeutics, Mariana Oncology and Takeda; honorarium from Merck, Pfizer, Novartis, Pfizer and Takeda; research funding from Adaptimmune, Alexo Therapeutics, Array BioPharma, AstraZeneca, Blueprint Medicines, Bristol Myers Squibb, Genentech, Jounce Therapeutics, Merck, Moderna Therapeutics, Novartis and Tesaro; has an immediate family member who is an employee with stock and other ownership interests in Ironwood Pharmaceuticals; and equity in AI Proteins. C.J.W.: equity in BionTech; research funding from Pharmacyclics; and is on the scientific advisory board of Repertoire, Adventris and Aethon Therapeutics. D.J.: grants and personal fees from Novartis, Genentech, Syros and Eisai; personal fees from Vibliome, PIC Therapeutics, Mapkure and Relay Therapeutics; and grants from Pfizer, Amgen, InventisBio, Arvinas, Takeda, Blueprint Medicines, AstraZeneca, Ribon Therapeutics and Infinity that are outside the submitted work. M.M.-K.: consultant to AstraZeneca, Pfizer, Repare, Boehringer Ingelheim, Sanofi, AbbVie and Daiichi-Sankyo; and royalties from Elsevier. G.O.: consultant to Bicycle Therapeutics. R.J.S.: consultant to Bristol Myers Squibb, Merck, Pfizer, Marengo Therapeutics, Novartis, Eisai, Iovance, OncoSec and AstraZeneca; and research funding from Merck. G.M.B.: sponsored research agreements through her institution with Olink Proteomics, Teiko Bio, InterVenn Biosciences and Palleon Pharmaceuticals; is on advisory boards for Iovance, Merck, Nektar Therapeutics, Novartis and Ankyra Therapeutics; is a consultant for Merck, InterVenn Biosciences, Iovance and Ankyra Therapeutics; and has equity in Ankyra Therapeutics. T.G.N.: consultant to Bristol Myers Squibb, Genentech, CRC Oncology, Roche, Sanofi and Parexel Imaging Pharmaceuticals; and grant funding from Astra Zeneca, Bristol Myers Squibb related to the cardiac effects of ICIs. K.L.R.: on the advisory board to SAGA Diagnostics; speaker’s fees from CMEOutfitters and Medscape; and research funding from Bristol Myers Squibb. A.-C.V.: consultant to Bristol Myers Squibb; and financial interest in 10x Genomics. 10x Genomics designs and manufactures gene sequencing technology for use in research, and such technology is being used in this research; these interests were reviewed by the Massachusetts General Hospital and Mass General Brigham in accordance with their institutional policies. All other authors (I.J.K., S.R., J.C., N.S., S.M., M.W., A.T., Y.S., K.H.X., J.B., P.S., K.S., J.T., K.M., B.Y.A., M.N., C.J.P., D.M., M.J., P.C., A.L., W.A.M., T.S., L.T.N., J.R.S. and M.F.T.) declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Cell lineages in heart and blood defined by scRNA-seq.

a, Dot plot showing top marker genes for each lineage in the heart. Dot size represents the percent of cells in the lineage with non-zero expression of a given gene. Colour indicates scaled expression across lineages. b, Stacked bar plots showing the composition of major cell lineages, coloured by data source (“MGH” refers to data generated in this study; “Sanger” refers to public heart atlas data)29. c, Stacked bar plot showing the lineage composition per patient, grouped by the histology of the primary malignancy. d, Differential abundance of cell subsets in scRNA-seq data from the heart, comparing irMyocarditis cases to controls. Each point represents a cell subset. The x-axis represents the odds ratio (OR) in all cases and controls, and the y-axis represents the OR in an analysis that excluded cases (SIC_171 and SIC_232) and a control (SIC_182) with evidence of cardiac metastases. e, Differentially expressed genes in scRNA-seq data from the heart, comparing irMyocarditis cases to controls. Each point represents a gene contrasted in each cluster. The x-axis represents the log2 fold change (log2FC) in all cases and controls, and the y-axis represents the log2FC in an analysis that excluded samples with evidence of cardiac metastases. f, A volcano plot depicting 71 cytokines and chemokines measured in serum from irMyocarditis and control patients, where each point represents a protein. The x-axis represents log2FC between irMyocarditis (n = 16) and control samples (n = 10), and the y-axis represents –log10(P value) of the comparison. Proteins are coloured based on enrichment in irMyocarditis cases (red, right side) and controls (blue, left side). Select proteins with an unadjusted p < 0.05 are labeled, and those with FDR < 0.1 are in bold (two-sided t-test). g, Box plots showing serum concentrations of selected cytokines and chemokines in irMyocarditis cases (n = 16, red) and controls (n = 10, blue) (two-sided t-test). Data points from a patient with evidence of regressed cardiac metastases (SIC_171) are highlighted in yellow. h, Circulating protein analysis, comparing irMyocarditis cases to controls where each point represents a circulating protein. The x-axis represents the t-statistic in all cases and controls, and the y-axis represents the t-statistic in an analysis that excluded the sample with evidence of cardiac metastases (SIC_171). i, Dot plot showing top marker genes for each lineage in the blood. Dot size represents the percent of cells in the lineage with non-zero expression of a given gene. Colour indicates scaled expression across lineages. j, Abundance analysis comparing PBMC samples from irMyocarditis cases prior to the initiation of corticosteroids (“pre-steroid,” n = 17, light green) to samples from irMyocarditis patients shortly after the initiation of high-dose corticosteroids (“post-steroid,” n = 19, dark green). Left: dots represent logistic regression odds-ratios. Error bars represent 95% confidence intervals. Unadjusted two-sided likelihood-ratio test P values for each subset are shown. Colour and bolded P value indicates FDR < 0.1. Right: individual-level data showing the frequency of the indicated lineages; each dot represents a patient. k, the number of DEGs for each cell lineage when comparing pre-steroid (right, light green) to post-steroid samples (left, dark green). l, GSEA of select gene sets by heart cell lineages, colour-coded by NES of differential gene expression of irMyocarditis pre-steroid samples compared to post-steroid samples. m, Left: timelines of blood collections for irMyocarditis patients included in the pre- and post-steroid analysis (n = 24). The length of the lines represents the relative time interval between the pre- and post-steroid samples and the time of steroid initiation. Right: peak troponin (ng/L) and percentage troponin reduction from peak at the time of post-steroid blood collection are shown. Fatal cases are shown in red, and cases are ordered by fatal status followed by interval between steroid initiation and post-steroid sample collection. Full patient metadata is available in Supplementary Table 1. n, The number of DEGs positively (red, right) and inversely (blue, left) correlated with time from steroid initiation in post-steroid samples are shown. o, GSEA of select gene sets when modeled by time from steroid initiation. For g and j, box plots display the median (line) and IQR, with whiskers no more than 1.5x IQR, and each dot represents one patient Related to Fig. 1. Abbreviations: Allo, allograft rejection; AP, antigen presentation; CAM, cell adhesion molecules; DNA, DNA synthesis; IFNG, interferon-γ signalling; KEGG, Kyoto Encyclopedia of Genes and Genomes NES, normalized enrichment score; Viral Myo, viral myocarditis.

Extended Data Fig. 2 PBMC cell subsets and transcriptional changes associated with irMyocarditis onset.

a, b, c, 135,712 circulating CD8T and NK cells are (a) displayed on a UMAP embedding with (b) a dot plot to show top marker genes for each subset and (c) case versus control abundance analysis. d, e, f, 45,022 circulating CD4T cells are (d) displayed on a UMAP embedding with (e) a dot plot of top marker genes and (f) case versus control abundance analysis. g, h, i, 149,101 circulating cDC, pDC, and MNP cells are (g) displayed on a UMAP embedding with (h) a dot plot of top marker genes and (i) case versus control abundance analysis. j, k, l, 21,171 circulating B and plasmablast cells (j) displayed on a UMAP embedding with (k) a dot plot showing selected marker genes for each subset and (l) case versus control abundance analysis. m, The number of DEGs (FDR < 0.1, Wald test) for each cell lineage when comparing pre-steroid irMyocarditis samples (n = 17, red, right) to ICI-treated controls (n = 28, blue, left). n, Heatmap showing selected differentially expressed genes (irMyocarditis versus control) and gene sets across each PBMC cell subset and lineage, grouped by biological themes. Colour scale indicates log2FC difference between irMyocarditis cases and controls for DEG analysis and NES for GSEA. Black dots indicate FDR < 0.1 (Wald test). In a, d, g, and j, UMAP pseudocolouring corresponds to the cell subsets labeled on the right. In b, e, h, and k, dot plots show top marker genes for each cell subset. Dot size represents the percent of cells in the subset with non-zero expression of a given gene. Colour indicates scaled expression across subsets. For c, f, i, and l, cell subset differential abundance analysis comparing pre-steroid samples from irMyocarditis cases at the pre-steroid timepoint (n = 17, red) to ICI-treated controls (n = 28, blue). Left panels: dots represent logistic regression odds-ratios. Error bars represent 95% confidence intervals. Unadjusted two-sided likelihood-ratio test P values for each subset are shown. Colour and bolded P value indicates FDR < 0.1. Right panels: box plots display the median (line) and IQR, with whiskers no more than 1.5x IQR. Each dot represents one patient. Abbreviations: Allo, allograft rejection; AP, antigen presentation; CAM, cell adhesion molecules; CC, co-inhibition or co-stimulation; CS; cytokine signalling; GG, glycolysis and gluconeogenesis; IFNA, interferon-α signalling; IFNG, interferon-γ signalling; ISG; interferon-stimulated genes; KEGG, Kyoto Encyclopedia of Genes and Genomes; NES, normalized enrichment score; TF, transcription factors; TGFB; TGF-β signalling; Viral Myo, viral myocarditis. Related to Fig. 1.

Extended Data Fig. 3 Blood CITE-Seq surface protein analysis.

a, b, Scatter plots showing transformed counts of the listed proteins in our CITE-Seq data. Boxes and arrows indicate the gating strategy used to define the populations labeled in red and analyzed in panels c and d. The same data are shown pseudocoloured by the lineage assigned to those cells by (a) scRNA-seq and (b) as density plots. c, Bar plots showing the percent of the cells in each indicated CITE-Seq-defined lineage that were assigned to each scRNA-seq-defined blood lineage (rows). d, Abundance analysis comparing the frequencies of CITE-seq defined populations from pre-steroid irMyocarditis cases (n = 17, red) to ICI-treated controls (n = 28, blue). Left: dots represent logistic regression odds-ratios. Error bars represent 95% confidence intervals. Unadjusted two-sided likelihood-ratio test P values for each population is shown. Colour and bolded P value indicates FDR < 0.1. Right: box plots display the median (line) and IQR, with whiskers no more than 1.5x IQR. Each dot represents one patient. Composition is reported as the percent of all PBMCs from a patient in each subset. The last row, “CD8/NK” is the sum of the “CD8T” and “NK” gates, included to mirror the scRNA-seq lineage level clustering solution. Related to Fig. 1.

Extended Data Fig. 4 Lymphoid cells in irMyocarditis tissue.

a, Selected marker genes for each T/NK cell subset. Dot size represents the percent of cells in the subset with non-zero expression of a given gene. Colour indicates scaled expression. b, Feature plots using colour to indicate gene expression (logCPM) levels of the indicated genes projected onto the T/NK UMAP embedding. Cell numbers and percentages represent gene expression across all heart T and NK cells. c, Feature plots using colour to indicate gene expression (logCPM) levels of the indicated genes projected onto the heart h-CD4T subset. Cell numbers and percentages represent gene expression in this subset only. d, UMAP embedding of cell density displaying the relative proportion of cells from irMyocarditis cases (n = 4,686) and controls (n = 4,448). e, Stacked bar plots showing the per-subset cellular composition per donor of each pre-steroid or unenriched control sample. Red patient labels denote samples from patients with fatal irMyocarditis. f, Plot showing the intracardiac frequency of h-CD8Tcycling cells (y-axis) versus serum troponin T level (x-axis) for irMyocarditis samples (n = 12). Linear regression p = 0.018, FDR < 0.1. g, Feature plots using colour to indicate gene expression (logCPM) levels of the indicated genes expressed by control (left) or irMyocarditis samples (right), projected onto the heart T/NK UMAP embedding. Cell numbers and percentages represent gene expression across T/NK cells in control or irMyocarditis samples. h, Top: heatmap showing selected DEGs across T/NK subsets from irMyocarditis versus control heart samples, grouped by biological categories. Colour scale indicates log2FC. Black dots indicate FDR < 0.1 (Wald test). Bottom: select GSEA results, colour-coded by NES of the gene set in irMyocarditis cases versus controls. “All T” row depicts pseudo-bulk DGE analysis of pooled T-cell subsets (subsets 2–6) that excluded NK cells. Bolded genes in the heatmap indicate leading edge genes in ≥1 GSEA gene set in ≥4 subsets. i, Bar graph showing the number of DEGs per lineage when gene expression was modeled against serum troponin T level, showing positively correlated (right, red) and negatively correlated (left, blue) genes. Only lineages with at least one troponin T-associated DEG are displayed. j, k, The 33 expanded TCR-β sequences (> 0.5% of TCR-β repertoire) from patient SIC_264 in T/NK UMAP space, colour coded by whether the cell was found prior to (“pre-steroid”, red) or after (“post-steroid”, blue) administration of corticosteroids and second-line immunosuppression. Data shown by (j) TCR-β clone and (k) in aggregate of all clones. Abbreviations: Allo, allograft rejection; AP, antigen presentation; CAM, cell adhesion molecules; CC, co-inhibition or co-stimulation; DNA, DNA synthesis; HM, Hallmark; IFNG, interferon-γ signalling; IS, immunoreceptor signalling; ISG, interferon-stimulated genes; K, Kyoto Encyclopedia of Genes and Genomes; MA, motility and adhesion; NES, normalized enrichment score; TCR Sig, TCR signalling; TF, transcription factors; Viral Myo, viral myocarditis. Related to Fig. 2.

Extended Data Fig. 5 Expanded T-cell receptor β (TCR-β) sequences in irMyocarditis tissue and tumour.

a, A box plot showing the relative proportion of cells that recovered a TCR-β CDR3 sequence from marked areas of irMyocarditis (n = 6, red) and control (n = 3, blue) tissue; p = 0.002 by two-sided t-test (via Adaptive Biotechnologies). Median and IRQ are shown, with whiskers no more than 1.5x IQR. Each dot represents one patient. b, Expanded TCR-β CDR3 (left) and total unique TCR-β CDR3 sequences (right) recovered on a per patient basis from both scRNA-seq and bulk TCR-β sequencing. c, Expanded TCR-β CDR3 from bulk sequencing (top) and scRNA-seq data (bottom) from irMyocarditis patients with matched blood samples. Donors with “healing” irMyocarditis were excluded. d, e, Smoothed Hill’s diversity index curves at diversity orders 0–4 for the TCR-β repertoires of irMyocarditis tissues (d) without diffuse metastases, coloured by histologic appearance at the time of autopsy, and (e) with an additional case with diffuse cardiac metastases (SIC_232) coloured by histologic appearance at the time of autopsy and by the presence of cardiac metastases (SIC_232 and SIC_136). f, Within each tissue type in each patient (“control”, “tumour”, or “irMyocarditis”), the frequency of each TCR-β clone is plotted on a per-patient basis and labeled by the pathological designation of the macroscopically dissected regions (SIC_17: Active; SIC_136: Borderline; SIC_3: Healing; SIC_175: Healing). Each point represents a TCR-β clone. For the left and middle columns, the y-axis represents the proportion of a given TCR-β clone in the patient’s control tissue repertoire, and the x-axis represents the proportion of the TCR-β clone in their (left column) tumour TCR-β repertoire or (middle column) irMyocarditis TCR-β repertoire. Right column: proportion of TCR-β clone in the tumour is shown on the y-axis and the proportion of that clone in irMyocarditis is shown on the x-axis. Points are pseudocoloured to represent a TCR-β clone that was expanded (> 0.5% of the heart or tumour repertoire) and enriched (Fisher’s exact test FDR < 5% compared to control) in heart (green), tumour (purple), both tissues (red), or neither tissue (grey). g, Each TCR-β clone enriched in heart or tumour relative to controls (FDR < 0.05, Fisher’s exact test) is plotted according to its proportion (among all TCR-β clones in the respective tissue) in heart (x-axis) and tumour tissue (y-axis), normalized by its proportion in control tissue. Each plot shows the enriched TCR-β clones within each donor projected onto the aggregate data across all donors (Fig. 3b, c). h, TCR-β CDR3 amino acid sequences for select GLIPH groups. Highlighted in red are the predicted enriched motifs. i, The same data as shown in g, with colour indicating if the corresponding TCR-β was (red; “true”) or was not (grey; “false”) found in a GLIPH group. j, The frequency of each expanded GLIPH group in heart and tumour tissue was calculated and then normalized by dividing by the frequency of that same GLIPH group in control tissue. Normalized GLIPH group frequencies for heart (x-axis) and tumour (y-axis) are plotted. Colour indicates the donor from which each GLIPH group was found, and the label shows the associated amino acid motif of the GLIPH group. Throughout the Figure, red patient labels denote cases of fatal irMyocarditis. Related to Fig. 3.

Extended Data Fig. 6 TCR clonotype sharing and antigen recognition screen.

a, Blood CD8T/NK UMAP highlighting circulating cells (in red) that express a TCR-β sequence found to be expanded in irMyocarditis hearts (combined scRNA-seq and bulk TCR-β sequencing data). b, A volcano plot showing the results of a logistic regression model investigating the likelihood of a cell in a given CD8T/NK cell subset from the blood containing a TCR-β CDR3 sequence that was expanded in heart tissue (n = 13). Points represent odds ratios for each cell subset. Red points denote cell subsets with statistically significant sharing (FDR < 0.05, two-sided likelihood-ratio test). Error bars represent 95% confidence intervals. c,d, Cells in blood for which the same TCR-β was expanded in paired irMyocarditis heart and blood sample from the same patient are shown in red projected on the (c) CD8T/NK blood UMAP embedding and (d) CD4T blood UMAP embedding. Each patient with paired heart and blood samples is shown. e, UMAP of heart T and NK cells highlighting cells that express expanded TCR-β CDR3 sequences that were found in CD8T/NK blood cells on a per-patient basis. f, Feature plots using colour to indicate gene expression (logCPM) levels of the indicated genes projected onto the heart T/NK UMAP embedding. Cell numbers and percentages represent gene expression across all heart T and NK cells. g, The flow cytometry gating strategy for the CD137 expression assay is shown. Lymphocytes are analyzed as either CD8+ or CD8- (to assess for TCRs isolated from CD8 or CD4T cells, respectively) and then gated on intensity of Far Red, CT-Violet, or CT-CFSE staining; each combination of stains represents a unique TCR. Each TCR is then gated on mTRBC+ to identify cells expressing the transduced TCR construct. Each row of histograms represents a condition of Epstein-Barr virus-immortalized lymphoblastoid cell lines (EBV-LCLs), which served as antigen presenting cells, pulsed with target peptides/peptide pools, and CD137 expression is shown. PMA/Iono serves as a positive control; “DMSO,” “Ova peptide,” and “CEF” represents negative controls; RINATLETK is an α-myosin peptide known to be recognized by the positive control TCR (“Myo-TCR”)16; α-myosin pool 4 contains a 20-aa peptide with the RINATLETK sequence; and all other pools are considered test peptide pools. Irrelevant TCRs are TCRs from an unrelated donor76 and serve as negative controls along with untransduced (UT) T cells. h, A heatmap showing the background subtracted CD137 expression of each TCR and peptide/peptide pool combination. Each column represents a unique TCR, colour coded by patient and then grouped by CD8 and CD4 TCRs. Each row represents peptides or peptide pools tested; these include the controls and pools covering the full length of the α-myosin, troponin I (TnI), and troponin T (TnT) proteins. α-myosin pool 11 could not be evaluated for TCR63 and is shown as empty space on the heatmap. In c, d, and e, red patient labels denote cases of fatal irMyocarditis. Abbreviations: CEF, Cytomegalovirus, Epstein-Barr virus, and influenza virus; EVB-LCLs, Epstein-Barr virus-immortalized lymphoblastoid cell lines; HLA, human leukocyte antigens; PMA/Iono, Phorbol 12-myristate 13-acetate/ionomycin; mTRBC, murine T-cell receptor β chain; TCR, T-cell receptor; TnI, troponin I; TnT, troponin T; UT, untransduced. Related to Fig. 3.

Extended Data Fig. 7 MNP populations in heart and blood.

a, Selected marker genes for each MNP subset. Dot size represents the percent of cells in the subset with non-zero expression of a given gene. Colour indicates scaled expression. b, Feature plots using colour to indicate gene expression (logCPM) levels of the indicated genes projected onto the heart MNP UMAP embedding. Cell numbers and percentages represent gene expression across all heart MNP cells. c, Embedding of cell density plot displaying the relative proportion of cells from irMyocarditis cases (n = 3,606) and controls (n = 6,218). d, Stacked bar chart depicting the relative contributions of cells in each MNP subset (coloured coded on the right) on a per donor basis from each pre-steroid irMyocarditis or unenriched control sample. e, Abundance analysis comparing intramyocardial frequencies of cell populations from pre-steroid irMyocarditis cases (n = 12, red) versus control (n = 8, blue). Left: dots represent logistic regression odds-ratios. Error bars represent 95% confidence intervals. Unadjusted two-sided likelihood-ratio test P values for each subset are shown. Statistics for h-pDCLILRA4, IRF8 are not included due to extremely low recovery from this cell subset. Right: box plots display the median (line) and IQR, with whiskers no more than 1.5x IQR. Each dot represents one patient. f, Representative image following immunohistochemical staining of irMyocarditis heart tissue section (n = 6) for CD1c (brown) and hematoxylin nuclear counterstaining (blue). g, h, Comparison of (g) cDCs (CLEC9A/CD1c+) and (h) CD8T cell density as measured by immunofluorescence staining in non-inflamed regions (left column) and inflamed regions (right column) of irMyocarditis heart sections (n = 6); one-sided t-test P values are shown. i, Bar graph showing average distance from each cDC to the nearest CD8 T cell (per section), with each row representing an individual case (red) or control (blue); the control slide labeled “NC” (no cells) had neither CD8A+ nor CLEC9A/CD1c+ cells detected. j, Heatmap showing select DEGs and GSEA pathways for irMyocarditis versus control heart tissue grouped by biological themes. The “All MNP” row depicts DEG results from pooling MNP subsets 1–5. Colour scale indicates log2FC between irMyocarditis cases and controls and NES for GSEA. Black dots indicate FDR < 0.1 (Wald test). Genes in bold were a part of the leading edges genes of the displayed GSEA pathways. k, UMAP Feature plots using colour to indicate gene expression (logCPM) levels of CXCL10 expressed by control (top) or irMyocarditis samples (bottom). Cell numbers and percentages represent expression across control or irMyocarditis MNP cells. Abbreviations: Allo, allograft rejection; AP, antigen presentation; CAM, cell adhesion molecules; CC, co-inhibition or co-stimulation; CS, cytokine signalling; DNA, DNA synthesis; IFNG, interferon-γ signalling; INF, inflammasome; ISG, interferon-stimulated genes; KEGG, Kyoto Encyclopedia of Genes and Genomes; MA, motility and adhesion; NES, normalized enrichment score; TF, transcription factors; Viral Myo, viral myocarditis. Related to Fig. 4.

Extended Data Fig. 8 Non-immune populations in irMyocarditis heart tissue.

a, UMAP embedding of 65,409 non-immune cells isolated from the heart, segregated by endothelial cells (top) and non-endothelial cells (bottom), and coloured by the 18 defined cell subsets labeled on the right. b, Dot plot showing top marker genes for each non-immune subset. Dot size represents the percent of cells in the subset with non-zero expression of a given gene. Colour indicates scaled expression across subsets. c, Abundance analysis comparing intramyocardial frequencies of cell populations from pre-steroid irMyocarditis heart samples (n = 12, red) versus controls (n = 8, blue). Left: dots represent logistic regression odds-ratios. Error bars represent 95% confidence intervals. Unadjusted two-sided likelihood-ratio test P values for each subset are shown. Colour and bolded P value indicates FDR < 0.1 Right: box plots display the median (line) and IQR, with whiskers no more than 1.5x IQR. Each dot represents one patient. d, Feature plots using colour to indicate marker gene expression (logCPM) levels of the indicated genes projected onto the heart non-immune UMAP embedding. Cell numbers and percentages represent gene expression across all heart non-immune cells. e, Feature plots showing (top panel) the number of genes expressed by each cell and (bottom panel) percent mitochondrial UMIs on the UMAP of non-immune cells derived from heart scRNA-seq data. f, Embedding of cell density plot displaying the relative proportion of cells from irMyocarditis cases (n = 20,439) and controls (n = 44,970). g, Stacked bar chart depicting the relative contributions of cells in each non-immune cell subset (colour coded to the right) from each pre-corticosteroid irMyocarditis or unenriched control sample. h, Top: the number of DEGs for each cell subset when comparing irMyocarditis cases (up, red) to control (down, blue). Bottom: heatmap showing select DEGs for irMyocarditis versus control heart tissue grouped by biological themes. Colour scale indicates log2FC difference between irMyocarditis cases and controls. Black dots indicate FDR < 0.1 (Wald test). Abbreviations: AP, antigen presentation; CC, co-inhibition or co-stimulation; CS, cytokine signalling; ISG, interferon-stimulated genes; MA, motility and adhesion; TF, transcription factors. Related to Fig. 5.

Extended Data Fig. 9 Cardiac fibroblasts in irMyocarditis and graphical abstract summarizing key findings.

a, Feature plots using colour to indicate marker gene expression (logCPM) levels of the indicated genes projected onto the heart fibroblast UMAP embedding. Cell numbers and percentages represent gene expression across all heart fibroblast and myofibroblast cells. b, Abundance analysis comparing intramyocardial frequencies of cell populations from pre-steroid irMyocarditis heart samples (n = 12, red) versus controls (n = 8, blue). Left: dots represent logistic regression odds-ratios. Error bars represent 95% confidence intervals. Unadjusted two-sided likelihood-ratio test P values for each subset are shown. Right: box plots display the median (line) and IQR, with whiskers no more than 1.5x IQR. Each dot represents one patient. c, Volcano plot of the genes within the fibroblast lineage modeled by serum troponin T, with select genes highlighted. Coloured points represent FDR < 0.1 (two-sided Wald test). d, Summary of key findings. Clusters whose abundances correlated with serum troponin T levels were considered correlates of disease severity. Abbreviations: FB, fibroblast. Related to Fig. 5.

Extended Data Fig. 10 Flow QC.

a, Pseudocolour plots showing the applied sequential gating strategy to sort live cells for downstream scRNA-seq profiling from a representative myocardial sample. Numbers indicate the percentage within the indicated gate. DAPI−CD235a− cells were collected for downstream analysis. Abbreviations: BSC-A, back scatter-area; FSC-A, forward scatter-area; FSC-H; forward scatter-height; DAPI, 4’,6-diamidino-2-phenylindole. Related to all Figures; Methods.

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Blum, S.M., Zlotoff, D.A., Smith, N.P. et al. Immune responses in checkpoint myocarditis across heart, blood and tumour. Nature 636, 215–223 (2024). https://doi.org/10.1038/s41586-024-08105-5

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