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. 2022 May 10:10:798316.
doi: 10.3389/fcell.2022.798316. eCollection 2022.

Single-Cell RNA Sequencing Profiles Identify Important Pathophysiologic Factors in the Progression of Diabetic Nephropathy

Xi Lu  1 , Li Li  2 , Luolan Suo  2 , Ping Huang  2 , Hongjie Wang  3 , Su Han  4 , Mingming Cao  2
Affiliations

Single-Cell RNA Sequencing Profiles Identify Important Pathophysiologic Factors in the Progression of Diabetic Nephropathy

Xi Lu et al. Front Cell Dev Biol. .

Abstract

Objective: Single-cell RNA sequencing (scRNA-seq) analyses have provided a novel insight into cell-specific gene expression changes in diseases. Here, this study was conducted to identify cell types and pathophysiologic factors in diabetic nephropathy. Methods: Single-cell RNA sequencing data of three human diabetic kidney specimens and three controls were retrieved from the GSE131882 dataset. Following preprocessing and normalization, cell clustering was presented and cell types were identified. Marker genes of each cell type were identified by comparing with other cell types. A ligand-receptor network analysis of immune cells was then conducted. Differentially expressed marker genes of immune cells were screened between diabetic nephropathy tissues and controls and their biological functions were analyzed. Diabetic nephropathy rat models were established and key marker genes were validated by RT-qPCR and Western blot. Results: Here, 10 cell types were clustered, including tubular cells, endothelium, parietal epithelial cells, podocytes, collecting duct, mesangial cells, immune cells, distal convoluted tubule, the thick ascending limb, and proximal tubule in the diabetic kidney specimens and controls. Among them, immune cells had the highest proportion in diabetic nephropathy. Immune cells had close interactions with other cells by receptor-ligand interactions. Differentially expressed marker genes of immune cells EIF4B, RICTOR, and PRKCB were significantly enriched in the mTOR pathway, which were confirmed to be up-regulated in diabetic nephropathy. Conclusion: Our findings identified immune cells and their marker genes (EIF4B, RICTOR, and PRKCB) as key pathophysiologic factors that might contribute to diabetic nephropathy progression.

Keywords: diabetic nephropathy; immune cells; mTOR pathway; marker genes; single-cell RNA sequencing.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Pre-processing, quality control, and normalization of scRNA-seq data of the control kidney specimens. (A–C) The total count against the negative log-probability in three healthy kidney specimens. Red indicated cell droplet and black indicated empty droplet. (D–F) Barcode rank plots in three healthy kidney specimens. The inflection points of the knee indicate the transition of the total count distribution. (G–L) The ratios of mitochondrial and ribosomal genes expressed in each cell of the three healthy kidney specimens.
FIGURE 2
FIGURE 2
Pre-processing, quality control, and normalization of scRNA-seq data of the diabetic kidney specimens. (A–C) The total count against the negative log-probability in the three diabetic kidney specimens. Red indicated cell droplet and black indicated empty droplet. (D–F) Barcode rank plots in the three diabetic kidney specimens. The inflection points of the knee indicate the transition of the total count distribution. (G–L) The ratios of mitochondrial and ribosomal genes expressed in each cell of the three diabetic kidney specimens.
FIGURE 3
FIGURE 3
Screening highly variable genes and cell clustering. (A) Screening the top 2,000 highly variable genes across cells based on scRNA-seq data. Red dots indicate the highly variable genes and black dots indicate no significant genes. (B) Contribution of genes in the first two PCs. (C) PCA of filtered cells from the diabetic kidney specimens (green dots) and controls (red dots). (D) Elbow plot for identifying the optimal PCs. (E) Heat map visualizing the expression of marker genes in each PC. Cell clustering based on the (F,G) t-SNE and (H,I) UMAP methods.
FIGURE 4
FIGURE 4
Identification of marker genes in each cell cluster. (A) Heat map showing the top ten marker genes in each cell cluster. Yellow represented high expression. (B) The expression distribution of the top one marker gene (SCNN1G, SLC12A3, SLC12A1, SLC5A12, FTCD, SLC26A7, KRT19, BX571818.1, EGFL7, CFH, PTPRQ, SLC4A9, CARMN, and PTPRC) in each cell cluster.
FIGURE 5
FIGURE 5
Identification of cell types and their marker genes. (A,B) Identification of cell types in the diabetic kidney specimens and controls. Each cell type was marked by a unique color. (C) The expression patterns of marker genes among cell types (D) Heat map for the top ten marker genes in each cell type. The expression distribution of marker genes in (E) podocytes, (F) endothelium, (G) tubular cells; (H) collecting duct; (I) immune cells; (J) distal convoluted tubule; (K) the thick ascending limb; (L) proximal tubule; (M) parietal epithelial cells; and (N) mesangial cells.
FIGURE 6
FIGURE 6
Comparisons of the difference in various cell types between diabetic kidney specimens and controls and the establishment of a ligand–receptor network. (A) Box plots showing the differences in the cell ratios of collecting duct, distal convoluted tubule, endothelium, immune cells, mesangial cells, parietal epithelial cells, podocytes, proximal tubule, the thick ascending limb, and tubular cells between the diabetic kidney specimens and controls. (B) Stacked graph for the cell ratios among the aforementioned cell types both in diabetic kidney specimens and controls. (C) The ligand–receptor network among the aforementioned cell types. The number represents the number of relationship pairs.
FIGURE 7
FIGURE 7
Identification of differentially expressed marker genes of immune cells in diabetic nephropathy and analysis of their biological implications. (A) Volcano diagram showing the expression differences in marker genes of immune cells between the diabetic kidney specimens and controls. Blue: down-regulation; red: up-regulation and grey: no significance. (B) Hierarchical clustering analysis visualizing the expression patterns of differentially expressed marker genes of immune cells between diabetic kidney specimens and controls. Blue indicates down-regulation and red indicates up-regulation. (C) The PPI network based on the differentially expressed marker genes of immune cells. The depth of the color of bubble was proportional to |log2fold-change|. Blue indicated down-regulation and red indicated up-regulation. (D) Biological processes, (E) cellular components, (F) molecular functions, and (G) KEGG pathways enriched by the differentially expressed marker genes of immune cells. The size of the bubble was proportional to the number of enriched genes.
FIGURE 8
FIGURE 8
Single-cell regulatory network analysis. (A) The top 50 transcription factors according to the t values by comparing AUC values between diabetic nephropathy and controls. (B) Heat map for the AUC values of the main transcription factors in diabetic kidney specimens compared to controls. (C) The expression levels (upper) and AUC values (bottom) of the main transcription factors in immune cells.
FIGURE 9
FIGURE 9
Validation of the expression of the mTOR pathway markers in diabetic nephropathy. (A,B) Box plots of the expression of mTOR pathway markers EIF4B, RICTOR, and PRKCB in control and diabetic nephropathy samples in the (A) GSE111154 and (B) GSE142025 datasets. DN: diabetic nephropathy. (C) H&E staining of the morphology of kidney tissues in the control group and the diabetic nephropathy rat model group. Scale bar, 100 μm; magnification, ×400. (D) The mRNA expression of EIF4B, RICTOR, and PRKCB was determined in kidney tissues from the control and diabetic nephropathy groups through RT-qPCR. (E,F) The expression of EIF4B, RICTOR, and PRKCB proteins was determined in kidney tissues from the control and diabetic nephropathy groups through Western blot. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001.
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