Targeting pro-inflammatory T cells as a novel therapeutic approach to potentially resolve atherosclerosis in humans

Abstract

Atherosclerosis (AS), a leading cause of cardio-cerebrovascular disease worldwide, is driven by the accumulation of lipid contents and chronic inflammation. Traditional strategies primarily focus on lipid reduction to control AS progression, leaving residual inflammatory risks for major adverse cardiovascular events (MACEs). While anti-inflammatory therapies targeting innate immunity have reduced MACEs, many patients continue to face significant risks. Another key component in AS progression is adaptive immunity, but its potential role in preventing AS remains unclear. To investigate this, we conducted a retrospective cohort study on tumor patients with AS plaques. We found that anti-programmed cell death protein 1 (PD-1) monoclonal antibody (mAb) significantly reduces AS plaque size. With multi-omics single-cell analyses, we comprehensively characterized AS plaque-specific PD-1⁺ T cells, which are activated and pro-inflammatory. We demonstrated that anti-PD-1 mAb, when captured by myeloid-expressed Fc gamma receptors (FcγRs), interacts with PD-1 expressed on T cells. This interaction turns the anti-PD-1 mAb into a substitute PD-1 ligand, suppressing T-cell functions in the PD-1 ligands-deficient context of AS plaques. Further, we conducted a prospective cohort study on tumor patients treated with anti-PD-1 mAb with or without Fc-binding capability. Our analysis shows that anti-PD-1 mAb with Fc-binding capability effectively reduces AS plaque size, while anti-PD-1 mAb without Fc-binding capability does not. Our work suggests that T cell-targeting immunotherapy can be an effective strategy to resolve AS in humans.

Fig. 1. Retrospective cohort analyses reveal that anti-PD-1 therapy impedes and reverses AS plaque progression in vivo.

a Flowchart showing the identification of eligible patients in the retrospective cohort study. b, c Representative ultrasound images of carotid plaques (b) and comparisons of AS plaque areas (c) in patients treated with or without anti-PD-1 mAb at two scanning time points (Scan 1 and Scan 2). Scar bars, 10 mm. d Comparison of the compositions of AS plaque progression (decrease: ΔA < –1 mm²; no decrease: ΔA ≥ –1 mm²) in groups treated without (n = 82) or with (n = 86) anti-PD-1 treatment. e Comparison of the changes of AS plaque areas (ΔA) between patients with or without anti-PD-1 treatment. f Univariate and multivariate (modified Poisson) regression analysis of the relative ratio (RR) of anti-PD-1 treatment to AS plaque progression in tumor patients (n = 168). Multivariate analyses were adjusted using age, gender, ΔBMI, ΔHDL, ΔLDL, statin usage, tumor types, tumor stage, and tumor progression. Data are represented as median with interquartile range (IQR) in c and e. Paired Mann–Whitney test was used in c and unpaired Mann–Whitney test was in e, and the χ2 test was in d.

Fig. 2. scRNA-seq profiling reveals the T-cell atlas of human AS plaques.

a Experimental design for paired scRNA-seq and αβTCR-seq analyses. b DEGs of T cell clusters, the phenotypical definition of each cluster was labeled on the top, and the clusters are colored by both clusters (left and top) and tissue sources of individual cells (top). Typical genes of each cluster are labeled on the right. c Uniform Manifold Approximation and Projection (UMAP) plots of 40,985 T cells from scRNA-seq data, colored by clusters (left) and tissue sources (right). d Composition of CD4⁺ (left) and CD8⁺ (right) T cell clusters, colored by sample sources, and clusters were ranked by mean frequencies in AS plaques. Data are represented as means ± SEM. e, f Scatter plots showing log₂(fold change) of overlapped DEGs (left) and enriched pathways (right) between CD4-C4 and CD4-C5 clusters (e) and between CD8-C3 and CD8-C4 clusters (f). g UMAP plots of T cells in plaque-specific T cell clusters (mapping based on cell–regulon expression matrix), colored by T cell clusters (top) and AUCell clusters (bottom). h Heatmap showing pairwise TF–regulon correlations, the left bar is colored with the most expressed AUCell cluster, and the right bar is labeled with the dominant AUCell cluster and typical TF–regulons. i Violin plots showing AUCell scores of regulons on identified plaque-specific T cell clusters. Data are represented as means ± SEM in d. A two-sided Student’s t-test with Benjamini–Hochberg adjustment was used in d, and the one-way ANOVA test was used in i.

Fig. 3. T-cell atlas of human AS plaques and AS PB revealed by single-cell CyTOF analysis.

a Heatmap displaying the median expression of 35 T cell clusters (T-cell panel), labeled with major or functional subsets (left) and cluster frequency (right). b t-SNE plots of T cells, colored by clusters or sample groups. c Compositions of major (left) and functional (right) T cell subsets in AS PB and AS plaques. d Volcano plots showing different frequencies of CD4⁺ (left) and CD8⁺ (right) T cell clusters in AS plaques compared to those in AS PB, colored by dominating tissue types, and arrows indicate PD-1⁺ T cell clusters. e Multicolor IFC staining confirming PD-1⁺CD4⁺ and PD-1⁺CD8⁺ T cells in a representative human AS plaque. Scale bars, 20 μm. f Histograms showing selected functional marker expressions on PD-1⁺CD4⁺ (top) and PD-1⁺CD8⁺ (bottom) T cell clusters. g Histograms showing ICOS, HLA-DR, CD27, and CD28 expressions on plaque-derived PD-1⁺ and PD-1^– T cells. h Dot plots showing expressions of exhaustion-related co-inhibitory and regulatory genes in T cell clusters, colored by scaled mean expression and sized by fraction of cells expressing specified genes. T cell clusters were ranked by their AUCell scores of PDCD1 gene signature as displayed in Supplementary information, Fig. S4l. i Venn plot of shared DEGs between PDCD1⁺ T cell clusters (CD4-C5 and CD8-C4), with the numbers of intersected or exclusive genes labeled. j Dot plots showing the calculated regulon specificity scores of PDCD1⁺ T cell clusters (CD4-C5 and CD8-C4), with the top-10 regulons labeled. Data are presented as median with IQR in c. A two-sided Student’s t-test with Benjamini–Hochberg adjustment was used for statistical analyses in c and d. The Kolmogorov–Smirnov test was used in g and the hypergeometric test in i.

Fig. 4. Ex vivo validations of the activated phenotype of AS plaque-specific PD-1⁺ T cells.

a Coverage plots of the chromatin accessibilities of identified gene loci, colored by T cell clusters. b Bar plots showing the inferred gene activity scores for specified genes, colored by T cell clusters. c Heatmap plot showing the scaled chromVAR scores of TF motifs in T cell clusters, and motifs were clustered by hierarchy clustering. d Boxplots of chromVAR scores of selected TF motifs across T cell clusters. A one-way ANOVA test was used to compare chromVAR scores between AS plaque-specific T cells and tumor-specific Tex cells. e Histograms of selected T-cell functional marker expressions on PD-1⁺CD4⁺ (PT04, PT09, and PT14) and PD-1⁺CD8⁺ (PT23) T cell clusters. f Schematic diagram showing ex vivo T-cell stimulation assay for activating PD-1⁺ T cells derived from lung tumors (n = 11) or AS plaques (n = 5) to secret cytokines, in comparison to PD-1^– T cells derived from lung tumors (n = 5). g, h Concentrations of IL-2 and IFN-γ in the supernatant were displayed (g) and their relative activation levels (h) were compared. Data are presented as means ± SEM in b and as the median with IQR in g and h. A two-sided Student’s t-test with Benjamini–Hochberg adjustment was used in b and h.

Fig. 5. Paired scRNA-seq and αβTCR-seq reveal T-cell dynamics and differentiation trajectory of T cells in AS plaque.

a, b Scatter plots (left) showing the counts and correlation tests of shared TCR clonal size in T cell cluster pairs, and gray dashed lines indicate counts equal to 10 (horizontal line) and P value equal to 0.05 (vertical line); graph plots (right) showing T cell cluster connections, with color and line width corresponding to counts of shared TCR clonotypes, and solid lines indicate T cell cluster pairs with significant (P < 0.05) correlation of the shared TCR clonal size in CD4⁺ (a) and CD8⁺ (b) T cell clusters. c, d Scatter plots showing the clone size of TCR clonotypes across the selected CD4⁺ (c) and CD8⁺ (d) T cell cluster-pairs, colored by shared (red) or non-shared (blue and green) TCR clonotypes. The diagonal line indicates equal TCR clone sizes, and other dashed lines separate non-shared clonotypes, n represents the number of shared clonotypes, and the correlation coefficient (r) and P value were labeled. e, f UMAP plots of T cells with identified TCR clonotypes in CD4-C5 (e) and CD8-C4 (f) clusters (left), and the related cell counts in individual T cell clusters (right). g, i UMAP plots showing RNA velocities for CD4-C4, CD4-C5, and CD4-C6 (g) and CD8-C3, CD8-C4, and CD8-C5 (i), colored by clusters (top) and cellular pseudotime (bottom). Arrows indicate the directions of T-cell differentiation. h, j Heatmaps showing the top 50 DEGs in analyzed CD4⁺ (h) or CD8⁺ (j) T cells arranged by pseudotime, and rows indicate genes and columns denote cells. Pseudotime and clusters of analyzed T cells were labeled on the bottom, and genes were labeled on the left. Pearson’s correlation test was used in a–d.

Fig. 6. FcγRI-captured anti-PD-1 mAb serves as a proxy PD-1 ligand to suppress AS-derived PD-1⁺ T cells in vitro and reduces AS plaque areas in humans.

a Frequency comparison of PD-L1⁺ and PD-L2⁺ cells in CD45^– and CD45⁺ cells in lung tumors (n = 4) and AS plaques (n = 5). b Diagram showing that CD64-captured anti-PD-1 mAb served as the proxy PD-1 ligand. c Mean fluorescence intensity (MFI) of PE-Nivolumab on PBMC-derived CD64⁺ cells treated with Nivolumab (5 μg/mL) or not. d Diagram showing micro-pipette adhesion assay for examining the cell–cell adhesion frequency (Pa%) between CD64⁺ PBMCs and primary PD-1⁺ T cells bound with Nivolumab or not. e Quantification of the cell–cell adhesion frequency (P_a%) between CD64⁺ PBMCs and PD-1⁺ T cells bound with Nivolumab or not. f–h Schematic diagram (f) showing ex vivo stimulation assays of primary CD64⁺ myeloid cells sorted from PBMC (n = 7) and AS plaque-derived PD-1⁺ T cells (n = 7) co-cultured with anti-PD-1 mAb (Condition 1; g), or CD45⁺ cells sorted from either lung tumors (n = 5) or AS plaques (n = 5) (Condition 2; h) co-cultured with Nivolumab. Concentrations of IL-2, IFN-γ, and TNF-α in the supernatant of Condition 1 (g) or Condition 2 (h) were measured, and the relative inhibition levels (%) of cytokines in different CD45⁺ cells treated with Nivolumab were compared. i, j Comparing the changes of AS plaque areas (ΔA) (i) and the compositions of AS plaque progression (j) among three groups treated without (n = 88) or with anti-PD-1 mAb of FcγR-binding (n = 48) or non-FcγR-binding capability (n = 35) (decrease: ΔA < –1 mm²; no decrease: ΔA ≥ –1 mm²). Data are presented as means ± SEM in e and as median with IQR in a, c, g, h, i. Unpaired Student’s t-test was used in a and c, the paired t-tests in e, g, and h, the Mann–Whitney test in i, and the χ2 test in j.