Single-Cell RNA Sequencing of Peripheral Blood Mononuclear Cells From Acute Myocardial Infarction

Figures

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  Figure 1. Circulating immune cell landscape in patients with and without PR. (A) t-SNE plot presenting clustering of total PBMCs reveals 27 clusters and 6 cell types. (B) t-SNE plot of marker genes of each cell type. (C) Dot plot showing the top marker genes revealed by single-cell RNA sequencing of cell types defined in (A). (D) Percentage of cell types and the comparison of cell number in each cell type between PR and NPR. (E) Cell proportion of each patient. (F) UMI number of each cluster. (G) t-SNE plot of patients with PR and NPR.

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  Figure 2. Characterizations of circulating monocytes in patients with and without plaque rupture. (A) t-SNE plot showing re-clustering of monocyte cells into 12 clusters. (B) Expression t-SNE plot of monocytes markers CD14 and FCGR3A (CD16). (C) t-SNE plot of monocytes divided into three cell types: classical, intermediate, and non-classical monocytes. (D) t-SNE plot showing contribution of monocytes for patients with PR and NPR. (E) Percentage of monocytes subtype in patients with PR and NPR. (F) Heatmap showing expression levels of discriminative genes for each cluster. (G) GSEA heatmap of total cluster in monocytes. (H, I) Prototypical pro-inflammatory hub genes such as CCL5, TLR7, and CX3CR1 identified by STRING and Cytoscape were significantly upregulated in classical and non-classical monocytes derived from patients with PR. (J) Top GO terms of classical monocytes in patients with PR. (K) Top GO terms of non-classical monocytes in patients with PR.

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  Figure 3. Impact of plaque rupture and without rupture on gene regulation and development trajectory of circulating monocytes. (A) Expression of prototypical leucocyte-endothelial cell adhesion genes such as ICAM1, ITGAL, and RUNX3 were significant upregulated in classical monocytes of patients with PR. (B) Expression of prototypical pro-inflammatory proteins such as TLR4, TNF, and TICAM1 were significantly expression in non-classical monocytes of patients with PR. (C) GO analysis of upregulated genes in intermediate monocytes derived from patients with NPR. (D) The PPI network of hub upregulated genes in intermediate monocytes in patients with NPR. (E) Significantly upregulated hub genes associated with neutrophil activation and degranulation in intermediate monocytes derived from patients with NPR. (F) The developmental trajectories of different monocyte subtypes with pseudo-time analysis. (G) Heatmap of significantly upregulated genes in pseudo-time developmental trajectories. (H) GO analysis of upregulated genes in different clusters in pseudo-time analysis. (I) Heatmap of upstream transcription factors in monocytes.

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  Figure 4. Characterizations of T cells in patients with and without plaque rupture. (A) t-SNE plot showing re-clustering of T cells into 17 clusters and t-SNE plot of patients with PR and NPR. (B) Expression t-SNE plot of T cell marker genes as CD4, CD8A, CCR7, CXCR3, FOXP3 and KLRG1. (C) Percentage of T cell subtype in patients with PR and NPR. (D) t-SNE plot showing the contribution of different T cell subsets. (E) Heatmap showing expression levels of discriminative genes for each cluster. (F) GSEA heatmap of T cell clusters. (G) GO analysis of upregulated genes in CD4+ effector T cells from patients with PR. (H) Expression of pro-inflammatory hub genes such as CCL5, CXCR3, NKG7, S100A8, S100A9, and LYZ were significantly upregulated in CD4+ effector T cells of patients with PR.

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  Figure 5. Impact of plaque rupture and without rupture on gene regulation and development trajectory of circulating T cells. (A, B) Expression of effector molecules associated with apoptosis and desquamation of endothelial cells as GZMB, GNLY, and PRF1 in CD8+ effector T cells of patients with NPR. (C, D) Expression of angiogenesis and wound healing genes as TIFAIP3, CXCR4, and B4GALT1 in CD8+ effector T cells of patients with NPR. (E) The developmental trajectories of different T cell subtypes with pseudo-time analysis. (F) Heatmap of significantly upregulated genes in pseudo-time developmental trajectories. (G) GO analysis of upregulated genes in different clusters in a pseudo-time analysis. (H) Heatmap of upstream transcription factors in T cell subtypes. (I) GO analysis of transcription factors in CD8+ effector T cells.

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  Figure 6. Characterization of NK cells in patients with and without plaque rupture. (A) t-SNE plot showing the re-clustering of NK cells into 9 clusters and t-SNE plot of patients with PR and NPR. (B) Expression t-SNE plot of NK cell marker genes as FCGR3A, CD8A, and NCAM1. (C) t-SNE plot showing the contribution of different NK cell subsets. (D) Percentage of NK cell subtypes in patients with PE and PR. (E) Heatmap showing expression levels of discriminative genes for each cluster. (F) GO analysis of upregulated genes in CD16bright NK, CD16dim NK, and NKT cells. (G) Expression of pro-inflammatory hub genes such as S100A9, LYZ, and TNFAIP3 were significantly upregulated in CD16bright NK cells of patients with PR.

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  Figure 7. B cells play an important role in producing inflammatory cytokines. (A) t-SNE plot showing re-clustering of B cells into 4 clusters and t-SNE plot of patients with PR and NPR. (B) Expression t-SNE plots of B cell marker genes as TCL1A, IGHD, YBX3, ITGB1, CRIP1, and CLEC1. (C) Percent of B cell subtypes in patients with PR and NPR. (D) t-SNE plot showing the contribution of different B cell subsets. (E) Heatmap showing expression levels of discriminative genes for each cluster. (F) GSEA heatmap of B cell clusters. (G) GO analysis of mature B cells and IL4R, IL7, and PPBP were significant upregulated in patients with PR.

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  Figure 8. Complex intercellular communication network in the circulating immune cells in patients with AMI. (A) Network visualizing potential specific interactions in patients with PR and NPR, in which nodes are clusters and edges represent the number of significant ligand–receptor pairs. (B) Incoming and outcoming signaling pattern in patients with PR. (C) Hierarchical plot showing the inferred intercellular communication network for MIF signaling pathway. Left and right portions highlight the autocrine and paracrine signaling to intermediate monocytes, Tregs, and to other monocytes, T cell types, NK, and B cell types, respectively. (D) Heatmap showing the relative importance of each cell group for the four network centrality measures-based MIF signaling network. (E) Violin plot showing the expression distribution of signaling genes involved in the inferred MIF signaling network. (F) Relative contribution of each ligand–receptor pair to the overall communication network of MIF signaling pathway. (G) The inferred GALECTIN signaling pathway network. (H) The computed network centrality measures of GALECTIN signaling. (I) The expression distribution of signaling genes involved in the inferred…