Exploring Shared Biomarkers of Myocardial Infarction and Alzheimer’s Disease via Single-Cell/Nucleus Sequencing and Bioinformatics Analysis

Figures

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  Fig. 1. Flow chart of this study.

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  Fig. 2. A) Heatmap of DEGs in AD. B) Heatmap of DEGs in MI. C) Venn diagram shows that 26 genes are identified from the intersection of DEGs in MI and AD. D) Circle plot of GO enrichment analysis of common DEGs.

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  Fig. 3. A) Biomarkers screening in the LASSO model. The number of genes (n = 31) corresponding to the lowest point of the curve was the most suitable for AD diagnosis. B) LASSO regression obtained 23 genes which were most suitable for MI diagnosis. C) Venn diagram shows the hub gene, RGS1, which was identified from the intersection of genes in AD and MI using LASSO model and PPI network. D) GSEA of RGS1 in AD dataset. E) GSEA of RGS1 in MI dataset. PPI, protein-protein interaction; AD, Alzheimer’s disease; MI, myocardial infarction.

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  Fig. 4. Quality control, cell clustering, and annotation of CA single-cell data. A) Violin plot shows cell characteristics after quality control. B) Elbow plot for the single cell data. The top 30 PCs were shown. C) Batch effect between AC and PA data eliminated by harmony. D) Cells in dataset were classified into 22 clusters. E) Cells were annotated by the “singleR” package reference Human Primary Cell Atlas Data. CA, carotid atherosclerosis; AC, atherosclerotic core; PA, proximal adjacent.

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  Fig. 5. A) Manual annotation of cell clusters in CA with reference to previous studies. B) The heatmap shows good specificity of the cellular marker genes. C) The bars depict the percentage of each cell type in each group and patient. D) Biomarkers screening in the LASSO model. The number of genes (n = 16) corresponding to the lowest point of the curve was the most suitable for MI diagnosis. E) The violin plot demonstrates the expression of DEGs between CA and MI in each cell cluster. CA, carotid atherosclerosis; MI, myocardial infarction.

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  Fig. 6. Quality control, cell clustering, and annotation of AD snRNA-seq data. A) Violin diagram shows cell characteristics after quality control. B) Elbow plot for the single cell data. The top 30 PCs were shown. C) Batch effect between AC and PA data eliminated by harmony. D) Cells in dataset was classified into 22 clusters. E) Manual annotation of cell clusters in AD with reference to previous studies. F) The heatmap shows good specificity of the cellular marker genes. AD, Alzheimer’s disease.

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  Fig. 7. A) Feature plot shows the expression of RGS1 in different cell clusters of CA. The ochre dots indicate the expression of RGS1 in this cell cluster. B) There was a difference in the expression of RGS1 in macrophages in the two groups, and the difference was statistically significant. ****p < 0.0001. C–E) Pseudotime analysis of macrophages in GSE159677. C) Timing differences in cell differentiation. Darker blue represents an earlier stage of differentiation, while lighter blue indicates a later stage of differentiation. D) Differentiation of AC macrophages from PA macrophages. E) Timing differences in the expression of RGS1 in different groups. It was upregulated in the AC group and downregulated over time in the PA group. CA, carotid atherosclerosis; AC, atherosclerotic core; PA, proximal adjacent.

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  Fig. 8. A) Feature plot shows the expression of RGS1 in different cell clusters of AD. The ochre dots demonstrate that RGS1 expressed almost exclusively in microglia. B) There was a difference in the expression of RGS1 in microglia in the two groups, and the difference was statistically significant. ****p < 0.0001. C–E) Analysis of pseudotime in GSE159677. C) Timing differences in cell differentiation. Darker blue represents an earlier stage of differentiation, while lighter blue indicates a later stage of differentiation. D) Differentiation of AD microglia from NC microglia. E) Timing differences in the expression of RGS1 in different groups. It was upregulated in the AD group and downregulated in the NC group. AD, Alzheimer’s disease; NC, normal control.

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  Fig. 9. A) Sankey diagram shows the flow among TFs, genes, and immune cells. The TF-gene interaction was obtained from Networkanalysis. B) The sequence features obtained from motif enrichment analysis. The high and low bases in the graph indicate the likelihood of the occurrence of that base in the motif sequence. C) Regulatory network of RelA and genes.