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Single cell transcriptomics and TCR reconstruction reveal CD4 T cell response to MHC-II-restricted APOB epitope in human cardiovascular disease

Nature Cardiovascular Research volume 1, pages 462–475 (2022)Cite this article

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Abstract

Atherosclerosis is accompanied by a CD4 T cell response to apolipoprotein B (APOB). Major histocompatibility complex class II (MHC-II) tetramers can be used to isolate antigen-specific CD4 T cells by flow sorting. Here, we produce, validate and use an MHC-II tetramer, DRB1*07:01 APOB-p18, to sort APOB-p18-specific CD4 T cells from peripheral blood mononuclear cell samples from eight DRB1*07:01+ women with and without subclinical cardiovascular disease (sCVD). Single-cell RNA sequencing showed that transcriptomes of tetramer-positive cells were between regulatory and memory T cells in healthy women and moved closer to memory T cells in women with sCVD. T cell receptor sequencing of tetramer-positive cells showed clonal expansion and V and J segment usage similar to those found in regulatory T cells. These findings suggest that APOB-specific regulatory T cells may switch to a more memory-like phenotype in women with atherosclerosis. Mouse studies showed that such switched cells promote atherosclerosis.

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Fig. 1: Restimulation assay and gating strategy.
Fig. 2: TCR clonotypes of the combination of TCRα and TCRβ sequences and VDJ usage.
Fig. 3: TCRβ sequences and VDJ usage.
Fig. 4: UMAP with Louvain clustering.
Fig. 5: Comparison of Tet+ transcriptome to other cell types in HIV−.
Fig. 6: Antigen-specific response in mice immunized with ApoB-p6.
Fig. 7: Analysis of exTreg cells from ApoB-p6-immunized mice in intracellular staining and adoptive transfer study.
Fig. 8: Differentially expressed genes between Tet+ cells and the other cell types sharing the same TCRβ clonotypes as shown in Fig. 3a.

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Data availability

Data is available on the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database (accession no. GSE199103).

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Acknowledgements

Data in this manuscript were collected by WIHS, now MWCCS. The contents of this publication are solely the responsibility of the authors and do not represent the official views of the National Institutes of Health (NIH). MWCCS (principal investigators): Atlanta CRS (I. Ofotokun, A. Sheth and G. Wingood), U01-HL146241; Baltimore CRS (T. Brown and J. Margolick), U01-HL146201; Bronx CRS (K. Anastos, D. B. Hanna and A. Sharma), U01-HL146204; Brooklyn CRS (D. Gustafson and T. Wilson), U01-HL146202; Data Analysis and Coordination Center (G. D’Souza, S. Gange and E. Golub), U01-HL146193; Chicago-Cook County CRS (M. Cohen and A. French), U01-HL146245; Chicago-Northwestern CRS (S. Wolinsky), U01-HL146240; Northern California CRS (B. Aouizerat, J. Price and P. Tien), U01-HL146242; Los Angeles CRS (R. Detels and M. Mimiaga), U01-HL146333; Metropolitan Washington CRS (S. Kassaye and D. Merenstein), U01-HL146205; Miami CRS (M. Alcaide, M. Fischl and D. Jones), U01-HL146203; Pittsburgh CRS (J. Martinson and C. Rinaldo), U01-HL146208; UAB-MS CRS (M.-C. Kempf, J. Dionne-Odom and D. Konkle-Parker), U01-HL146192; and UNC CRS (A. Adimora), U01-HL146194. The MWCCS is funded primarily by the National Heart, Lung, and Blood Institute (NHLBI), with additional co-funding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institute on Aging (NIA), National Institute of Dental and Craniofacial Research (NIDCR), National Institute of Allergy and Infectious Diseases (NIAID), National Institute of Neurological Disorders and Stroke (NINDS), National Institute of Mental Health (NIMH), National Institute on Drug Abuse (NIDA), National Institute of Nursing Research (NINR), National Cancer Institute (NCI), National Institute on Alcohol Abuse and Alcoholism (NIAAA), National Institute on Deafness and Other Communication Disorders (NIDCD), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) and National Institute on Minority Health and Health Disparities (NIMHD), and in coordination and alignment with the research priorities of the NIH Office of AIDS Research (OAR). MWCCS data collection is also supported by UL1-TR000004 (UCSF CTSA), UL1-TR003098 (JHU ICTR), UL1-TR001881 (UCLA CTSI), P30-AI-050409 (Atlanta CFAR), P30-AI-073961 (Miami CFAR), P30-AI-050410 (UNC CFAR), P30-AI-027767 (UAB CFAR) and P30-MH-116867 (Miami CHARM). This study was supported by the Japan Society for the Promotion of Science overseas research fellowship and the Uehara Memorial Foundation research fellowship to R.S.; NIH HL 136275, 145241 and 148094 to K.L.; K01HL137557 to D.B.H.; NIH HL148094, HL1327941 and HL140976 to R.C.K. and American Heart Association Postdoctoral Fellowship (19POST34450228) and Career Development Award (942098) to L.W. The Zeiss LSM 880 Airyscan microscope was funded by the NIH S10OD021831 grant.

Author information

Authors and Affiliations

  1. La Jolla Institute for Immunology, La Jolla, CA, USA

    Ryosuke Saigusa, Payel Roy, Antoine Freuchet, Rishab Gulati, Yanal Ghosheh, Sujit Silas Armstrong Suthahar, Christopher P. Durant, William B. Kiosses, Marco Orecchioni, Lai Wen, Runpei Wu, Jenifer Vallejo, Catherine C. Hedrick, Alessandro Sette & Klaus Ley

  2. Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA

    David B. Hanna, Kathryn Anastos & Robert C. Kaplan

  3. Department of Epidemiology and Biostatistics, University at Albany, Rensselaer, NY, USA

    Mark H. Kuniholm

  4. Department of Internal Medicine, Rush University Medical Center, Chicago, IL, USA

    Alan L. Landay

  5. Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, USA

    Kathryn Anastos

  6. Department of Medicine, University of California San Francisco, San Francisco, CA, USA

    Phyllis C. Tien

  7. San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA

    Phyllis C. Tien

  8. Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA

    Stephen J. Gange

  9. Georgetown University Medical Center, Georgetown University, Washington, DC, USA

    Seble Kassaye

  10. Tetramer Core Laboratory, Benaroya Research Institute at Virginia Mason, Seattle, WA, USA

    William W. Kwok

  11. Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Howard N. Hodis

  12. Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Howard N. Hodis

  13. Atherosclerosis Research Unit, University of Southern California, Los Angeles, CA, USA

    Howard N. Hodis

  14. Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Robert C. Kaplan

  15. University of California San Diego, San Diego, CA, USA

    Klaus Ley

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  1. Ryosuke Saigusa
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Contributions

R.S., C.P.D. and K.L. designed the study. A.L.L., K.A., P.C.T., S.J.G., S.K. collected samples and data. H.N.H. designed and collected data for the B-mode ultrasound substudy. D.B.H., M.H.K. and R.C.K. analyzed clinical data. W.W.K provided tetramers. R.S. and C.P.D. ran the scRNA-seq experiments. P.R. conducted the restimulation assays. R.S., Y.G., R.G., P.R., J.V., C.C.H. and K.L. analyzed the data. Y.G., R.G. and S.S.A.S. conducted the bioinformatics analysis. R.S., P.R., A.F., M.O. and R.W. did mouse experiments. A.S. determined the MHC-II restrictions of the APOB peptides. W.B.K. and L.W. processed confocal imaging. R.S. and K.L. wrote the manuscript.

Corresponding author

Correspondence to Klaus Ley.

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Nature Cardiovascular Research thanks Andreas Habenicht and the other, anonymous, reviewer for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 MHC-II tetramer DRB1*07:01 APOB-p18 validation.

Peripheral blood mononuclear cells from healthy donors with DRB1:701 and with other DRB1 rather than DRB1:0701 were gated on CD3+CD4+TCRαβ+ CD4+. a, From the gated cells, we detected tetramer-PE and tet-APC double positive cells in the donor with DRB1:0701+ (right bottom), while no Tet+ cells were detected in no tetramer staining (left column) and mismatched controls (without DRB1*0701, right top). b, Backgating showed that tet-PE and tet-APC positive cells were in CD3+CD4+Dump-. To estimate the false positive rate of tetramer binding, we calculated the combinational specificity of tetramer binding. Let the fraction of APC-single positive cells in CD4 T cells be p(APC), the fraction of PE-single positive cells be p(PE), and the fraction of double positive cells be p(DP), then specificity can be calculated. Non-specific binding would randomly produce APC+PE- or APC-PE+ (single positive) cells. If all tetramer binding were non-specific, the fraction of DP would be expected to be equal to the product of p(APC) times p(PE). The fraction of true specific binding of tetramer APC and PE double positive cells in CD4 T cells can be calculated by p(DP)-p(APC)*p(PE). [p(DP)-p(APC)*p(PE)]/p(DP) was above 99.99% in all experiments (99.99782452%, 99.9998338%, 99.99946356% and 99.99800718%). Thus, there is negligible false positive staining. c, Confocal microscopy of human CD4+ T cells from donors with DRB1:701 after incubation with PE- and APC-labeled apoB: MHC-II tetramer DRB1*07:01 APOB-p18 and anti-TCR-β-FITC. The result was repeated once.

Extended Data Fig. 2 Schematic summary of sorting and hashtag oligo (HTO) staining.

Tet+ cells, Th1 cells, Treg, and CXCR3-memory T cells (Tmem) were sorted into 4 different tubes, and were stained with antibodies with hash tag oligo (HTO1-4). Treg, Th1, and Tmem were pre-gated for CD45RA-. After staining of HTO antibody, tet+ cells weren’t washed, not to lose any cells, because one wash would lose almost half of the cells. Other cells (Th1, Treg and Tmem) were washed three times following to the manufacturer’s instruction. The volume of each HTO antibody had previously been titrated. The cell number of these three cell types (Th1, Treg and Tmem) was appropriately adjusted, and they are merged into the tet+ cell tube. At the same time, bulk CD4 T cells from a healthy donor were merged into the tube for the following batch correction. After that, the sample proceeded to barcording, cDNA amplication, library preparation, and sequencing.

Extended Data Fig. 3 The analysis of the usage of TCRα and β, separately.

Separate TCR clonotypes of the TCRα and β sequences and VDJ usage. a-e, Pie chart for all the TCRβ clonotypes from all the cells (a) and each 4 cell type (b, Tet+; c, Treg; d, Th1; e, Tmem), with clonality index. f-j, Pie chart for all the TCRα clonotypes from all the cells (f) and each 4 cell type (g, Tet+; h, Treg; i, Th1; j, Tmem), with clonality index. Clonotypes with more than 1 clone exploded in the graph. Top 5 clonotypes with more than 1 clone are as shown. ct, clonotype. Clonotype3326, which was expanded in Tet+ cells, are shared with other cell types, and highlighted in red.

Extended Data Fig. 4 UMAPs of Th1 and Tmem, and signature genes and molecule expressions.

a, UMAP with Louvain clustering of all 16,644 cells. b, c, UMAP of Th1 (b) and Tmem (c) highlighted in red. Other cells light grey. d, Expression levels of Th1 signature genes on 4 cell types. TBX21 and IFNG expressions are shown. Dot plot: fraction of cells in cluster expressing each gene shown by size of circle and level of expression shown from white (=0) to dark blue (=max, log2 scale). e We checked FoxP3 expression in CD3+CD4+CD127-CD25+ cells and the percentage was 91.3±4.06% (Mean±SD), The representative image of plots and the histogram of FoxP3 expression was shown.

Extended Data Fig. 5 UMAPs of tet+ cells from sCVD- and sCVD+ participants without HIV.

a, b, APOB-p18 DRB1*07:01 tetramer positive cells (tet+ cells, solid circles) are plotted in UMAP of cell from HIV-sCVD- participants (a), from HIV-sCVD+ participants (b). Treg, Th1 and Tmem distribution are shown as contour plots of density.

Extended Data Fig. 6 The analysis of the similarity of tet+ cells to other cell types in HIV+.

APOB-p18 DRB1*07:01 tetramer positive cells (Tet+ cells, solid circles) are plotted in UMAP of cell from HIV+sCVD- participants (a), from HIV+sCVD+ participants (b). Treg, Th1 and Tmem distribution are shown as contour plots of density. c, Cumulative histogram of the distances of each Tet+ cells against Tmem, Treg and Th1 in HIV-. d-f, Cumulative histograms of the distances of each of the Tet+ cells against Treg (d), Th1 (e), and Tmem (f) cells, separately for sCVD+ (yellow) and sCVD- (purple) and HIV+ in the first 6 PCA components. Significance by Kolmogorov-Smirnov test. g, A Ternary plot of relative median positions of Tet+ cells relative to pseudobulk mean of Tregs, Th1 and Tmem in the first 6 PCA components in HIV+. h, i, Volcano plots comparing gene expression in single cells of tet+ cells compared to Treg, Th1, and Tmem in HIV+sCVD- (h), and HIV+sCVD+ (i). Differential expression analysis was performed using Seurat’s non-parametric Wilcoxon rank-sum te t to extract marker genes. Significant markers were selected based on Bonferroni-adjusted P-Values <0.05. Colored dots (upregulated genes in red, and downregulated genes in blue) indicate significantly differentiated expressed genes (adjusted p-value <0.05). Dashed line indicates adjusted p-value of 0.05. Full data set shown in Supplemental Excel File 5.

Source data

Extended Data Fig. 7 exTreg gating strategy and frequency of exTreg among CD4T cells in blood.

a exTreg gating strategy of exTreg adoptive transfer experiment. Cells from lymph nodes and spleens from pooled p6- or MOG-immunized lineage tracker mice were extracted and enriched for CD4 T cells. exTreg were sorted gating on lymphocyte morphology, single cells and live cells (DAPI-)CD4+TCRb+GFP-RFP+. b, c, Gating strategy of CD4T cell (b) and exTreg among them (c) in restimulation assay. d, Engraftment of exTreg gating strategy. Single, DAPI-CD4+TCRb+GFP-RFP+ were checked. e, Frequency of exTreg among CD4T cells in blood. 5 weeks after adoptive transfer. p6, the recipient mice of exTreg from p6-immunized mice (n=3); PBS, PBS injected mice (n=5). Kruskal-Wallis and Dunns’s multiple comparisons test was performed (p=0.0073, two-sided). **, p<0.01. Bars represent mean values with standard error of mean (SEM).

Source data

Extended Data Fig. 8 Hashtag oligo (HTO) expressions for cell type identification.

a-e, HTO expressions (HTO1-5) on UMAP from HTO expressions (a, HTO1; b, HTO2; c, HTO3; d, HTO4; e, HTO5). d, Cell calling based on HTO expressions. Doublets were removed.

Supplementary information

Supplementary Information

Supplementary Tables 1–3 and Supplementary Data.

Reporting Summary

Supplementary Data

Supplementary Data 1. Clonotypes of the combination of TCR? and TCR?. A, Clonotype list, B-F, clonotypes of all the cells (B), Tet+ (C), Treg (D), Th1 (E), and Tmem (F). Supplementary Data 2. Usage of TCRV? and TCRJ? chains in each patient type. V? in HIV-sCVD- (A), HIV-sCVD+ (B), HIV+sCVD- (C), and HIV+cSVD+ (D), and VJ in HIV-sCVD- (E), HIV-sCVD+ (F), HIV+sCVD- (G), and HIV+cSVD+ (H) Supplementary Data 3. The combination of TCRV? and J? in each cell type. (A) Tet+ cells, (B) Th1, (C) Treg, and (D) Tmem. Supplementary Data 4. The list of differentially expressed genes with p-value less than 0.05 on tet+ cells compared to other 3 cell types in CVD-HIV-, and CVD+HIV-, respectively. p_val, p-value; avg_logFC, average log2 fold-change; p_val_adj, adjusted p-value; Type, which cell type was tet+ cells compared. Supplementary Data 5. The list of differentially expressed genes with p-value less than 0.05 on tet+ cells compared to other 3 cell types in CVD-HIV+, and CVD+HIV+, respectively. p_val, p-value; avg_logFC, average log2 fold-change; p_val_adj, adjusted p-value; Type, which cell type was tet+ cells compared. Supplementary Data 6. The list of differentially expressed genes with p-value less than 0.05 on tet+ cells compared to other 3 cell types sharing the same TCR? clonotypes, separately. p_val, p-value; avg_logFC, average log2 fold-change; p_val_adj, adjusted p-value; Type, which cell type was tet+ cells compared.

Source data

Source Data Fig. 3

Statistical source data for Fig. 3f.

Source Data Fig. 5

Statistical source data for Fig. 5a–d.

Source Data Fig. 6

Statistical source data for Fig. 6b.

Source Data Fig. 7

Statistical source data for Fig. 7a,b.

Source Data Extended Data Fig. 6

Statistical source data for Extended Data Fig. 6c–g.

Source Data Extended Data Fig. 7

Statistical source data for Extended Data Fig. 7e.

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Saigusa, R., Roy, P., Freuchet, A. et al. Single cell transcriptomics and TCR reconstruction reveal CD4 T cell response to MHC-II-restricted APOB epitope in human cardiovascular disease. Nat Cardiovasc Res 1, 462–475 (2022). https://doi.org/10.1038/s44161-022-00063-3

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