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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Data is available on the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database (accession no. GSE199103).
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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.
The authors declare no competing interests.
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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.
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.
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.
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.
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.
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.
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).
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 Tables 1–3 and 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.
Statistical source data for Fig. 3f.
Statistical source data for Fig. 5a–d.
Statistical source data for Fig. 6b.
Statistical source data for Fig. 7a,b.
Statistical source data for Extended Data Fig. 6c–g.
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