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Single-cell immune landscape of human atherosclerotic plaques


Atherosclerosis is driven by multifaceted contributions of the immune system within the circulation and at vascular focal sites. However, specific characteristics of dysregulated immune cells within atherosclerotic lesions that lead to clinical events such as ischemic stroke or myocardial infarction are poorly understood. Here, using single-cell proteomic and transcriptomic analyses, we uncovered distinct features of both T cells and macrophages in carotid artery plaques of patients with clinically symptomatic disease (recent stroke or transient ischemic attack) compared to asymptomatic disease (no recent stroke). Plaques from symptomatic patients were characterized by a distinct subset of CD4+ T cells and by T cells that were activated and differentiated. Moreover, some T cell subsets in these plaques presented markers of T cell exhaustion. Additionally, macrophages from these plaques contained alternatively activated phenotypes, including subsets associated with plaque vulnerability. In plaques from asymptomatic patients, T cells and macrophages were activated and displayed evidence of interleukin-1β signaling. The identification of specific features of innate and adaptive immune cells in plaques that are associated with cerebrovascular events may enable the design of more precisely tailored cardiovascular immunotherapies.

Fig. 1: T cells and macrophages dominate the atherosclerotic plaque immune landscape.
Fig. 2: Diversity of the T cell compartment in human atherosclerosis.
Fig. 3: Combined epitope and transcriptomic analysis of paired atherosclerotic plaque and blood using CITE-seq.
Fig. 4: Dysregulation of CD4+ T cells associated with cerebrovascular events.
Fig. 5: Transcriptional dysregulation of CD8+ T cells and macrophages associated with cerebrovascular events.
Fig. 6: Cell–cell interactions associated with cerebrovascular events.

Data availability

The data discussed in this publication are deposited in Figshare (; doi:10.6084/m9.figshare.9206387) and GitHub with links to interactive Jupiter notebooks (,

Code availability

Codes and FASTQ files that also contain unpublished data from cell types not included in this publications are available upon request from the corresponding author.


  1. 1.

    Tabas, I. & Lichtman, A. H. Monocyte-macrophages and T cells in atherosclerosis. Immunity 47, 621–634 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Libby, P. Superficial erosion and the precision management of acute coronary syndromes: not one-size-fits-all. Eur. Heart J. 38, 801–803 (2017).

    PubMed  Google Scholar 

  3. 3.

    Moreno, P. R. et al. Macrophage infiltration in acute coronary syndromes. Implications for plaque rupture. Circulation 90, 775–778 (1994).

    CAS  PubMed  Google Scholar 

  4. 4.

    Jawien, J. The role of an experimental model of atherosclerosis: apoE-knockout mice in developing new drugs against atherogenesis. Curr. Pharm. Biotechnol. 13, 2435–2439 (2012).

    CAS  PubMed  Google Scholar 

  5. 5.

    van der Heiden, K., Hoogendoorn, A., Daemen, M. J. & Gijsen, F. J. Animal models for plaque rupture: a biomechanical assessment. Thromb. Haemost. 115, 501–508 (2016).

    PubMed  Google Scholar 

  6. 6.

    Finn, A. V., Nakano, M., Narula, J., Kolodgie, F. D. & Virmani, R. Concept of vulnerable/unstable plaque. Arterioscler. Thromb. Vasc. Biol. 30, 1282–1292 (2010).

    CAS  PubMed  Google Scholar 

  7. 7.

    Cochain, C. & Zernecke, A. Protective and pathogenic roles of CD8+ T cells in atherosclerosis. Basic Res. Cardiol. 111, 71 (2016).

    PubMed  Google Scholar 

  8. 8.

    Dumitriu, I. E. et al. High levels of costimulatory receptors OX40 and 4-1BB characterize CD4+CD28null T cells in patients with acute coronary syndrome. Circ. Res. 110, 857–869 (2012).

    CAS  PubMed  Google Scholar 

  9. 9.

    Methe, H. et al. Enhanced T-helper-1 lymphocyte activation patterns in acute coronary syndromes. J. Am. Coll. Cardiol. 45, 1939–1945 (2005).

    CAS  PubMed  Google Scholar 

  10. 10.

    Ghattas, A., Griffiths, H. R., Devitt, A., Lip, G. Y. & Shantsila, E. Monocytes in coronary artery disease and atherosclerosis: where are we now? J. Am. Coll. Cardiol. 62, 1541–1551 (2013).

    CAS  PubMed  Google Scholar 

  11. 11.

    Ridker, P. M. et al. Antiinflammatory therapy with canakinumab for atherosclerotic disease. New Engl. J. Med. 377, 1119–1131 (2017).

    CAS  PubMed  Google Scholar 

  12. 12.

    Ridker, P. M. et al. Low-dose methotrexate for the prevention of atherosclerotic events. New Engl. J. Med. 380, 752–762 (2019).

    CAS  PubMed  Google Scholar 

  13. 13.

    Cholesterol Treatment Trialists Collaborators et al. The effects of lowering LDL cholesterol with statin therapy in people at low risk of vascular disease: meta-analysis of individual data from 27 randomised trials. Lancet 380, 581–590 (2012).

    Google Scholar 

  14. 14.

    Winkels, H. et al. Atlas of the immune cell repertoire in mouse atherosclerosis defined by single-cell RNA-sequencing and mass cytometry. Circ. Res. 122, 1675–1688 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Cole, J. E. et al. Immune cell census in murine atherosclerosis: cytometry by time of flight illuminates vascular myeloid cell diversity. Cardiovasc. Res. 114, 1360–1371 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Cochain, C. et al. Single-cell RNA-seq reveals the transcriptional landscape and heterogeneity of aortic macrophages in murine atherosclerosis. Circ. Res. 122, 1661–1674 (2018).

    CAS  PubMed  Google Scholar 

  17. 17.

    Lin, J. D et al. Single-cell analysis of fate-mapped macrophages reveals heterogeneity, including stem-like properties, during atherosclerosis progression and regression. JCI Insight 4, 124574 (2019).

    PubMed  Google Scholar 

  18. 18.

    Moore, K. J., Sheedy, F. J. & Fisher, E. A. Macrophages in atherosclerosis: a dynamic balance. Nat. Rev. Immunol. 13, 709–721 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Stary, H. C. et al. A definition of advanced types of atherosclerotic lesions and a histological classification of atherosclerosis. A report from the Committee on Vascular Lesions of the Council on Arteriosclerosis, American Heart Association. Circulation 92, 1355–1374 (1995).

    CAS  PubMed  Google Scholar 

  20. 20.

    Kumar, B. V. et al. Human tissue-resident memory T cells are defined by core transcriptional and functional signatures in lymphoid and mucosal sites. Cell Rep. 20, 2921–2934 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Strioga, M., Pasukoniene, V. & Characiejus, D. CD8+CD28 and CD8+CD57+ T cells and their role in health and disease. Immunology 134, 17–32 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Kamphorst, A. O. et al. Rescue of exhausted CD8 T cells by PD-1-targeted therapies is CD28-dependent. Science 355, 1423–1427 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Wherry, E. J. & Kurachi, M. Molecular and cellular insights into T cell exhaustion. Nat. Rev. Immunol. 15, 486–499 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Yi, J. S., Cox, M. A. & Zajac, A. J. T-cell exhaustion: characteristics, causes and conversion. Immunology 129, 474–481 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Moore, K. J. & Tabas, I. Macrophages in the pathogenesis of atherosclerosis. Cell 145, 341–355 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Krammer, P. H., Arnold, R. & Lavrik, I. N. Life and death in peripheral T cells. Nat. Rev. Immunol. 7, 532–542 (2007).

    CAS  PubMed  Google Scholar 

  27. 27.

    Baker, C. M. et al. Opposing roles for RhoH GTPase during T-cell migration and activation. Proc. Natl Acad. Sci. USA 109, 10474–10479 (2012).

    CAS  PubMed  Google Scholar 

  28. 28.

    Hogan, P. G. Calcium-NFAT transcriptional signalling in T cell activation and T cell exhaustion. Cell Calcium 63, 66–69 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Cremer, S. et al. Hematopoietic deficiency of the long noncoding RNA MALAT1 promotes atherosclerosis and plaque inflammation. Circulation 139, 1320–1334 (2019).

    CAS  PubMed  Google Scholar 

  30. 30.

    Huang, C. et al. Exosomal MALAT1 derived from oxidized low-density lipoprotein-treated endothelial cells promotes M2 macrophage polarization. Mol. Med. Rep. 18, 509–515 (2018).

    CAS  PubMed  Google Scholar 

  31. 31.

    Huangfu, N. et al. LncRNA MALAT1 regulates oxLDL-induced CD36 expression via activating beta-catenin. Biochem. Biophys. Res. Commun. 495, 2111–2117 (2018).

    PubMed  Google Scholar 

  32. 32.

    Jain, A., Song, R., Wakeland, E. K. & Pasare, C. T cell-intrinsic IL-1R signaling licenses effector cytokine production by memory CD4 T cells. Nat. Commun. 9, 3185 (2018).

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Ayroldi, E. et al. Interleukin-6 (IL-6) prevents activation-induced cell death: IL-2-independent inhibition of Fas/fasL expression and cell death. Blood 92, 4212–4219 (1998).

    CAS  PubMed  Google Scholar 

  34. 34.

    Daynes, R. A., Dowell, T. & Araneo, B. A. Platelet-derived growth factor is a potent biologic response modifier of T cells. J. Exp. Med. 174, 1323–1333 (1991).

    CAS  PubMed  Google Scholar 

  35. 35.

    Ma, J., Wang, R., Fang, X. & Sun, Z. β-catenin/TCF-1 pathway in T cell development and differentiation. J. Neuroimmune Pharm. 7, 750–762 (2012).

    Google Scholar 

  36. 36.

    Rollings, C. M., Sinclair, L. V., Brady, H. J. M., Cantrell, D. A. & Ross, S. H. Interleukin-2 shapes the cytotoxic T cell proteome and immune environment sensing programs. Sci. Signal 11, eaap8112 (2018).

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Sutterwala, F. S., Haasken, S. & Cassel, S. L. Mechanism of NLRP3 inflammasome activation. Ann. N. Y. Acad. Sci. 1319, 82–95 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Herder, C. et al. RANTES/CCL5 and risk for coronary events: results from the MONICA/KORA augsburg case-cohort, athero-express and CARDIoGRAM studies. PloS One 6, e25734 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Lee, D. J., Cox, D., Li, J. & Greenberg, S. Rac1 and Cdc42 are required for phagocytosis, but not NF-κB-dependent gene expression, in macrophages challenged with Pseudomonas aeruginosa. J. Biol. Chem. 275, 141–146 (2000).

    CAS  PubMed  Google Scholar 

  40. 40.

    Vallieres, F. & Girard, D. IL-21 enhances phagocytosis in mononuclear phagocyte cells: identification of spleen tyrosine kinase as a novel molecular target of IL-21. J. Immunol. 190, 2904–2912 (2013).

    CAS  PubMed  Google Scholar 

  41. 41.

    Jia, D et al. Interleukin-35 promotes macrophage survival and improves wound healing after myocardial infarction in mice. Circ. Res. 124, 1323–1336 (2019).

    CAS  PubMed  Google Scholar 

  42. 42.

    Tziakas, D. N. et al. Interleukin-8 is increased in the membrane of circulating erythrocytes in patients with acute coronary syndrome. Eur. Heart J. 29, 2713–2722 (2008).

    CAS  PubMed  Google Scholar 

  43. 43.

    Xiao, H. et al. Anti-fibrotic effects of pirfenidone by interference with the hedgehog signalling pathway in patients with systemic sclerosis-associated interstitial lung disease. Int. J. Rheum. Dis. 21, 477–486 (2018).

    CAS  PubMed  Google Scholar 

  44. 44.

    Rigamonti, E., Chinetti-Gbaguidi, G. & Staels, B. Regulation of macrophage functions by PPAR-α, PPAR-γ, and LXRs in mice and men. Arterioscler. Thromb. Vasc. Biol. 28, 1050–1059 (2008).

    CAS  PubMed  Google Scholar 

  45. 45.

    McInnes, I. B. & Schett, G. Cytokines in the pathogenesis of rheumatoid arthritis. Nat. Rev. Immunol. 7, 429–442 (2007).

    CAS  PubMed  Google Scholar 

  46. 46.

    Pazianas, M., Rhim, A. D., Weinberg, A. M., Su, C. & Lichtenstein, G. R. The effect of anti-TNF-α therapy on spinal bone mineral density in patients with Crohn’s disease. Ann. N. Y. Acad. Sci. 1068, 543–556 (2006).

    CAS  PubMed  Google Scholar 

  47. 47.

    Mackey, R. H., Kuller, L. H. & Moreland, L. W. Update on cardiovascular disease risk in patients with rheumatic diseases. Rheum. Dis. Clin. North Am. 44, 475–487 (2018).

    PubMed  Google Scholar 

  48. 48.

    Singh, S., Kullo, I. J., Pardi, D. S. & Loftus, E. V. Jr. Epidemiology, risk factors and management of cardiovascular diseases in IBD. Nat. Rev. Gastroenterol. Hepatol. 12, 26–35 (2015).

    PubMed  Google Scholar 

  49. 49.

    Guo, L. et al. CD163+ macrophages promote angiogenesis and vascular permeability accompanied by inflammation in atherosclerosis. J. Clin. Invest. 128, 1106–1124 (2018).

    PubMed  PubMed Central  Google Scholar 

  50. 50.

    Kumar, M. P. et al. Analysis of single-cell RNA-seq identifies cell–cell communication associated with tumor characteristics. Cell Rep. 25, 1458–1468 (2018).

    CAS  PubMed  Google Scholar 

  51. 51.

    Eagar, T. N. et al. Notch 1 signaling regulates peripheral T cell activation. Immunity 20, 407–415 (2004).

    CAS  PubMed  Google Scholar 

  52. 52.

    Mathieu, M., Cotta-Grand, N., Daudelin, J. F., Thebault, P. & Labrecque, N. Notch signaling regulates PD-1 expression during CD8+ T-cell activation. Immunol. Cell Biol. 91, 82–88 (2013).

    CAS  PubMed  Google Scholar 

  53. 53.

    Marra, P. et al. IL15RA drives antagonistic mechanisms of cancer development and immune control in lymphocyte-enriched triple-negative breast cancers. Cancer Res. 74, 4908–4921 (2014).

    CAS  PubMed  Google Scholar 

  54. 54.

    Read, K. A., Powell, M. D., McDonald, P. W. & Oestreich, K. J. IL-2, IL-7 and IL-15: multistage regulators of CD4+ T helper cell differentiation. Exp. Hematol. 44, 799–808 (2016).

    CAS  PubMed  Google Scholar 

  55. 55.

    Waickman, A. T. et al. CD4 effector T cell differentiation is controlled by IL-15 that is expressed and presented in trans. Cytokine 99, 266–274 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Li, W. et al. GDF11 antagonizes TNF-α-induced inflammation and protects against the development of inflammatory arthritis in mice. FASEB J. 33, 3317–3329 (2019).

    CAS  PubMed  Google Scholar 

  57. 57.

    Tanay, A. & Regev, A. Scaling single-cell genomics from phenomenology to mechanism. Nature 541, 331–338 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Bu, D. X. et al. Impairment of the programmed cell death-1 pathway increases atherosclerotic lesion development and inflammation. Arterioscler. Thromb. Vasc. Biol. 31, 1100–1107 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Darvin, P., Toor, S. M., Sasidharan Nair, V. & Elkord, E. Immune checkpoint inhibitors: recent progress and potential biomarkers. Exp. Mol. Med. 50, 165 (2018).

    PubMed  PubMed Central  Google Scholar 

  60. 60.

    Edwards, J. D., Kapral, M. K., Fang, J. & Swartz, R. H. Long-term morbidity and mortality in patients without early complications after stroke or transient ischemic attack. CMAJ 189, E954–E961 (2017).

    PubMed  PubMed Central  Google Scholar 

  61. 61.

    Herrmann, J. et al. Expression of lipoprotein-associated phospholipase A(2) in carotid artery plaques predicts long-term cardiac outcome. Eur. Heart J. 30, 2930–2938 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Versari, D. et al. Dysregulation of the ubiquitin-proteasome system in human carotid atherosclerosis. Arterioscler. Thromb. Vasc. Biol. 26, 2132–2139 (2006).

    CAS  PubMed  Google Scholar 

  63. 63.

    Lai, L., Ong, R., Li, J. & Albani, S. A CD45-based barcoding approach to multiplex mass-cytometry (CyTOF). Cytom. Part A J. Int. Soc. Anal. Cytol. 87, 369–374 (2015).

    CAS  Google Scholar 

  64. 64.

    Lavin, Y. et al. Innate immune landscape in early lung adenocarcinoma by paired single-cell analyses. Cell 169, 750–765 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Amir, E. D. et al. Development of a comprehensive antibody staining database using a standardized analytics pipeline. Front Immunol. 10, 1315 (2019).

    PubMed  PubMed Central  Google Scholar 

  66. 66.

    Fienberg, H. G., Simonds, E. F., Fantl, W. J., Nolan, G. P. & Bodenmiller, B. A platinum-based covalent viability reagent for single-cell mass cytometry. Cytom. Part A J. Int. Soc. Anal. Cytol. 81, 467–475 (2012).

    Google Scholar 

  67. 67.

    Levine, J. H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Amir el, A. D. et al. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat. Biotechnol. 31, 545–552 (2013).

    Google Scholar 

  69. 69.

    Blondel, V. D., Guillaume, J. L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. 2008, P10008 (2008).

    Google Scholar 

  70. 70.

    Carlson, C. S. et al. Using synthetic templates to design an unbiased multiplex PCR assay. Nat. Commun. 4, 2680 (2013).

    PubMed  Google Scholar 

  71. 71.

    Kirsch, I., Vignali, M. & Robins, H. T-cell receptor profiling in cancer. Mol. Oncol. 9, 2063–2070 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Azizi, E. et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174, 1293–1308 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Lambrechts, D. et al. Phenotype molding of stromal cells in the lung tumor microenvironment. Nat. Med. 24, 1277–1289 (2018).

    CAS  PubMed  Google Scholar 

  75. 75.

    Fernandez, N. F. et al. Clustergrammer, a web-based heatmap visualization and analysis tool for high-dimensional biological data. Sci. Data 4, 170151 (2017).

    PubMed  PubMed Central  Google Scholar 

  76. 76.

    Haghverdi, L., Lun, A. T. L., Morgan, M. D. & Marioni, J. C. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36, 421–427 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. 77.

    Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–W97 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14, 128 (2013).

    PubMed  PubMed Central  Google Scholar 

  79. 79.

    Ramilowski, J. A. et al. A draft network of ligand–receptor-mediated multicellular signalling in human. Nat. Commun. 6, 7866 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

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We thank the Human Immune Monitoring Center (HIMC), in particular O. Mayovska, V. Guo, X.I. Qin, H.E. Xie, M. Patel, M. Davila, B. Lee, S. Bradford, L. Walker and K. Tuballes. We thank S. Sajja and P.S. Chong for their coordination efforts. We are grateful to A. Kamphorst for her critical review of this manuscript. We thank the Biorepository and Pathology Core of the Icahn School of Medicine at Mount Sinai. This work utilized mass cytometry instrumentation supported by NIH grant no. S10OD023547-01. This work was funded by NIH grants nos. K23HL111339 and R03HL135289. C.G. was also funded by NIH grants nos. R21TR001739 and UH2TR002067, and partially supported by the American Heart Association (14SFRN20490315). D.F. is supported by NIH grant no. T32HL007824. J.L.M.B. is supported by NIH grants nos. R01HL125863 and R21TR001739. A.M. and Z.W. are supported by NIH grants nos. U54HL127624 (LINCS-DCIC) and U24CA224260 (IDG-KMC). M.M. is supported by NIH grants nos. U24 AI118644 and U19 AI128949.

Author information




Conceptualization was provided by C.G., M.M. and A.H.R., methodology by C.G., A.H.R., E.D.A., N.F., A.M., S.G., D.M.F. and J.L.M.B., software by E.D.A., N.F., Z.W. and A.M., formal analysis by A.H.R., D.M.F., E.D.A., N.F., Z.W. and A.M., investigations by D.M.F., A.H.R., A.C., L.A., N.S.K., R.R., S.K.-S., C.K.W., R.S. and C.H., resources by J.R.L., C.P., N.M., C.F., A.J.A., J.M., P.F. and A.M., data curation by A.H.R., D.M.F., N.F., E.D.A., S.K.-S. and J.L.M.B., writing by C.G., revision and editing by C.G., A.H.R., D.M.F., M.M., S.K.-S., C.K.W., C.H., R.S. and N.F., data visualization by C.G., D.M.F., N.F., A.H.R., A.C. and A.M., supervision by C.G., A.H.R., S.K.-S. and M.M., project administration by C.G. and funding acquisition by C.G.

Corresponding author

Correspondence to Chiara Giannarelli.

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Competing interests

J.L.M.B. is the founder and former CEO of Clinical Gene Networks (CGN) and received financial compensation as a consultant for CGN.

Additional information

Peer review information Michael Basson was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Study overview.

(a) Schematic illustration of the collection and processing of paired atherosclerotic plaque and blood from each patient. (b) Schematic illustration of the CyTOF and CITE-seq studies of paired atherosclerotic tissue and blood. Immune cells from blood and plaque were used in CyTOF mass cytometry studies where data were defined using MetaLouvain clustering and Manual Gating. CITE-seq on blood and plaque cells related the surface marker expression of individual cells to the cell’s transcriptome. (c) Schematic illustration of single-cell analysis of plaques from patients with symptomatic disease (stroke or TIA) as compared to patients without symptomatic disease (no recent stroke): a three tier approach. The analysis included single-cell proteomic data from CyTOF studies, gene expression data from scRNA-seq studies, and cell-cell interaction studies based on patterns of ligand and receptor expression. ADT: Antibody-tag data from CITE-seq; GEX: gene expression data from CITE-seq.

Extended Data Fig. 2 Definition of MetaClusters from Cohort 1.

(a) Representative ViSNE plots of Louvain clustered immune cells (n = 9,490 cells) overlaid by protein expression in spectral colors. (b) Scatter bar plot of MC frequencies in blood and plaque from 15 patients. Statistical significance was determined using a two-sided unpaired t-test with multiple comparisons corrected by two-stage linear step procedure of Benjamini, Krieger and Yekuteli with FDR = 0.5%. Annotated MC communities are shown on the right. (c) Scatter bar plots of blood-enriched MCs from 15 patients, showing the MC frequency stratified by tissue type. MCs are annotated by their cell type. Data were analyzed with the two-sided Wilcoxon test. Values are mean ± SD.

Extended Data Fig. 3 Tissue-specific MetaClustering and canonical T cell definition by manual gating.

(a) MetaLouvain Clustering of CD45+ cells from atherosclerotic plaque tissue from n = 15 patients. (b) Average population frequency of plaque-derived MCs. (c, d) Representative Louvain clustered viSNE plot of n = 4,386 cells overlaid by MC distribution (c), and selected myeloid surface marker expression in spectral colors (d). (e) Volcano plot of the difference of MC frequencies in ASYM (n = 8 patients, left) and SYM (n = 7 patients, right) atherosclerotic plaques. Statistical significance was determined using two-sided unpaired t-test with multiple comparisons corrected using the two-stage linear step up procedure of Benjamini, Krieger, and Yekutieli with FDR = 0.5% (fh) Manual gating analysis of the T-cell compartment from 24 patients. (f) viSNE plots of n = 19,882 T cells overlaid by tissue origin (top left), CD4 and CD8 population (top right), effector status (bottom left), or classical definition (bottom right). (g, h) Scatter bar plots of manually gated cell populations from n = 24 patients. Values are mean ± SD, p values were determined by two-sided Wilcoxon test.

Extended Data Fig. 4 Definition of the T cell compartment from Cohort 2.

(a) viSNE plots of Louvain clustered T cells (n = 10,000 cells) overlaid by the protein marker expression in spectral colors. (b,) Scatter bar plots of T-cell MC frequencies in blood and plaque of n = 23 patients. (c) Bar chart of MC frequencies per patient of the 23 patients. (d) Scatter bar plots of normalized surface marker expression of plaque-enriched MCs in blood and plaque. Statistical significance for (b) and (d) were determined using multiple unpaired two-sided Student’s t-tests corrected by two-stage linear step procedure of Benjamini, Krieger and Yekuteli with FDR = 0.5%. For all scatter bar plots, values are mean ± SD. (e) Similarity matrix of T cell MetaClusters based on the cosine distance method.

Extended Data Fig. 5 Unique T-cell signature in atherosclerotic plaques.

Scatter bar plots of normalized protein marker expression in plaque-enriched T cell MetaClusters (MCs) across 23 patients, and stratified by tissue type (ad), and comparison of MCs 10, 11, and 20 in plaque tissue (e). Statistical analysis used an unpaired two sided Student’s t-test (a-d), and One-Way ANOVA test with Bonferroni’s post-hoc correction (e). Values are mean ± SD.

Extended Data Fig. 6 Tissue-specific T cell metaclustering identifies a replicative senescent CD8+ T cell population.

(a) Heatmap of MetaCluster communities of CD3+ T cells from atherosclerotic plaque by surface marker expression tissue (n = 23). (b) Correlations between TCR clonality in plaque and the frequency of MetaCluster 25 in 8 patients by two-sided Pearson correlation test. (c) Representative viSNE plots of n = 10,000 cells showing the MC distribution and expression of selected markers. (d, e) Analysis of manually gated CD8+ T cells from cohorts 1 and 2 combined (n = 37 patients). (d) Representative viSNE plot overlaid by CD8+CD127+ and CD8+CD127 populations. (e) Scatter bar plot of CD8+CD127+/- frequencies from 37 patients, assessed by two-sided paired Student’s t test. Values are mean ± SD. (f) viSNE plots of n = 8,086 cells overlaid with expression of T cell functional markers. (g) Scatter bar plots of the median values of T cell functional markers available between cohorts 1 and 2. CCR7, CD26, CD38, PD1, CD69 were available from 24 patients; CD27 from 25 patients, CD25, HLADR from 37 patients. p values were determined using Wilcoxon test, and values are mean ± SD. (h) Correlations between TCR clonality and CD8+CD127- cell frequency from 9 patients. Two-sided Spearman correlation test was used. (i) ViSNE plots of representative CD8+ T cell population in plaque of n = 9,025 cells overlaid with CD127 expression (top), or by subpopulation: CD127+ (blue), CD127- (yellow), and Ki67+ (red) (bottom). (j) Percent of Ki67+ cells in CD127 subpopulations from 3 independent experiments (n = 3). Statistic determined using a two-sided paired Student’s t test. Values are mean ± SD. (k) Representative contour plots of CD8+CD127- plaque T cells gated for CD57 expression, and the CD8+CD127-CD57+ subpopulation gated for Ki67 expression.

Extended Data Fig. 7 Analysis of the CITE-seq ADT expression.

viSNE plots of T cells (n = 2,315 cells) clustered using the ADT data, and overlaid by (a) tissue type, or expression of canonical (c) or functional (d) T cell markers. (b) Frequency of T cell subtypes per tissue. (e) Functional surface marker expression in individual cells per tissue type. For CD8+ T cells, n = 379 plaque cells and n = 159 blood cells. For CD4+ T cells, n = 287 plaque cells and n = 1,387 blood cells. The approximate P values were determined using a two-sided Mann-Whitney test, lines indicate the mean. (f, g) viSNE plots of plaque cells (n = 1,708 cells) overlaid to indicate macrophage population (f), or expression of macrophage markers (g). (h) viSNE plot of clustered macrophages (n = 300 cells) overlaid with CD206 expression. (i) Macrophage population of the CITE-seq experiment gated for expression of CD206 and CD64.

Extended Data Fig. 8 Single-cell gene expression analysis of CD8+ and CD4+ T cells in paired blood and plaque.

(a, b) Volcano plot of the top 5000 Differentially Expressed Genes (DEGs) (determined by two-sided Welch’s T- Test and Benjamini-Hochberg correction) in (a) CD4+ T Cells and (b) CD8+ T Cells T cells of plaque or blood. (c, d) Pathway analysis of (c) CD4+ and (d) CD8+ T cell DEGs with q < 0.05 (CD4+, n = 930 genes; CD8+, n = 310 genes) upregulated in blood (left) and plaque (right). The combined score metric corresponds to the P value (two-sided Fisher’s exact test) multiplied by the Z-score of the deviation from the expected rank, and q values determined by Benjamini-Hochberg correction. (e, f) Heatmap of hierarchically clustered top 50 variable genes across (e) CD4+ (n = 1,830 total cells, n = 347 plaque cells, n = 1,483 blood cells) and (f) CD8+ (n = 579 total cells, n = 420 plaque cells, n = 159 blood cells) T cells in plaque and blood. Rows: z-scored gene expression values; columns: individual cells. Above the heatmap, categories of identified cell clusters (top) cell’s origin from plaque or blood (bottom). Below the heatmap, cluster enrichment in tissue type is displayed with p values (two-sided binomial proportions test). Boxes (right) list key genes found in clusters. (g, h) Canonical signaling pathway analysis of the top 5000 DEGs in the indicated cell clusters from (g) CD4+ and (h) CD8+ T cells.

Extended Data Fig. 9 Dysregulation of T cells between SYM and ASYM patients.

Scatter bar plots of MetaCluster (MC) frequencies and protein expression in plaque-enriched MCs from 23 patients. (a) T cell MC frequencies and MC marker expression (b, c) in plaque enriched CD4+ (b) or CD8+ (c) T cell MCs. Blood (right) and plaque (left). Asymptomatic patients (A-SYM, blue) and symptomatic patients (SYM, red). Statistics were determined by two-sided multiple t-test and FDR (1%) correction using the two-stage step up procedure of Benjamini, Krieger, and Yekuteli. Values are mean ± SD. (d) AHA plaque type classification in SYM and ASYM plaques of cohort 2 (n = 23). (e, f) Volcano plot of T cell MCs (n = 23 patients) according to clinical phenotype in type VI plaques from SYM and ASYM patients (e), or according to plaque type (type VI vs. type IV) in all 23 patients (f). q values determined by two-sided multiple t-test with FDR = 0.5% corrected using Benjamini, Krieger and Yekuteli method.

Extended Data Fig. 10 Protein–ligand interactions.

(a) Circos plots of the significant ligand-receptor interactions between cell types, mediated by CD4+ T cells (top row), CD8+ T Cells (middle row), or macrophages (bottom row). (b) Venn diagrams of ligand-receptor pairs from the top 5000 genes ( > 0.5 Log2 fold change) show unique and overlapping paired between cell-types. Gene Ontology terms were identified for each group using Enrichr.

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Fernandez, D.M., Rahman, A.H., Fernandez, N.F. et al. Single-cell immune landscape of human atherosclerotic plaques. Nat Med 25, 1576–1588 (2019).

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