Unveiling macrophage dynamics and efferocytosis-related targets in diabetic kidney disease: insights from single-cell and bulk RNA-sequencing - PubMed (original) (raw)
Unveiling macrophage dynamics and efferocytosis-related targets in diabetic kidney disease: insights from single-cell and bulk RNA-sequencing
Binshan Zhang et al. Front Immunol. 2025.
Abstract
Background: Chronic inflammation and immune imbalance mediated by macrophages are considered pivotal in diabetic kidney disease (DKD). The study aims to clarify the macrophage heterogeneity and phenotype dynamics, and pinpoint critical targets within efferocytosis in DKD.
Methods: Utilizing early human DKD sequencing data, we computed the potential communication between leukocytes and renal intrinsic cells. Subsequently, we scrutinized the single-cell RNA sequencing (scRNA-seq) data from CD45-enriched immune cells, concentrating on the macrophage subsets in DKD. Pseudotime trajectory analysis was conducted to explore cell development. Differential expression genes (DEGs) from macrophage subgroups and bulk RNA-sequencing were used to identify shared hub genes. The NephroseqV5 platform was employed to evaluate the clinical significance, and the expression of key molecules was validated in DKD tissues.
Results: Macrophage infiltration rose in DKD, causing inflammation through the release of chemokines. As time progressed, the number of resident macrophages substantially dropped, with diminishing M1-like and increasing M2-like phenotypes relative to early stages. Further analysis pointed to the most enrichment of macrophage function is the phagosome. We overlapped the DEGs with efferocytosis-related genes and identified key genes, including CD36, ITGAM, and CX3CR1, which exhibited significant correlations with macrophages and T cells. The Nephroseq database revealed that they are associated with proteinuria and renal function. Consistent with the validation set, in vivo experiments verified elevated expression levels of key molecules.
Conclusions: In essence, our research elucidated the dynamics in macrophage subtype transitions. It emphasized three pivotal genes as critical modulators of macrophage efferocytosis in DKD, indicating their potential as innovative biomarkers and therapeutic targets.
Keywords: diabetic kidney disease; efferocytosis; inflammation; macrophages; single-cell RNA sequencing.
Copyright © 2025 Zhang, Wu, Wang, Gao, Liu, Lin and Yu.
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
Establishing the cell type in human diabetic kidney disease via snRNA-seq. (A) UMAP reduces the dimensionality of data, organizing it into 33 clusters. (B) The annotation of all cells yielding 11 cell types, comprising PCT, DCT, LOH, CT, ICA, ENDO, PEC, ICB, PODO, MES, LEUK. (C) The violin plot using width to show the average expression level of genes and shape to illustrate the expression variability, thereby visualizing the distribution patterns of marker genes. (D) Each cell cluster’s genes are shown in the heatmap, where the shading of colors is based on the logFC values. (E) The volcano plots depicting the alterations in gene expression across diverse cell types. (F) The distribution of each cell type in the control and diabetes group. UMAP, Uniform Manifold Approximation and Projection; PCT, proximal convoluted tubule; DCT, distal convoluted tubule; LOH, loop of Henle, CT, connecting tubule; ICA, type A intercalated cells; ENDO, endothelial cells; PEC, parietal epithelial cells; ICB, type B intercalated cells; PODO, podocytes; MES, mesangial cells; LEUK, leukocytes.
Figure 2
Ligand-receptor interactions surrounding immune cells. (A) Circle plot depicting the number and strength of interactions among different cell types, where red indicating an enhancement, and blue denotes a downturn in the DN group relative to the control. (B) The frequency of interaction leukocytes interaction with other cells, separately as a ligand and receptor. (C) CD80, (D) CD86, (E) CCL, (F) CXCL, (G) FN1 and (H) TGFb signaling pathway representing through the Hierarchy plot. (I) The Dotplot displaying the predicted likelihood of signaling pathways.
Figure 3
Analysis of immune cell subpopulations in diabetic kidney using single-cell RNA sequencing. (A–C) respectively display the UMAP plots (A) and dotplots of marker genes (B) for CD45+ cells in 3-month-old WT and OVE26 mice, as well as the trend of changes in the proportions of cells between the two groups (C). (D–F) sequentially present the UMAP (D), dotplot (E), and proportional change histograms (F) of 7-month-old mice. UMAP, Uniform Manifold Approximation and Projection. Mac, macrophage; Trem2hi, triggering receptor expressed on myeloid cells 2 high expressing; Mrc1hi, Mannose receptor C-type 1 high expressing; IFNhi, Interferon gene signature high expressing. DC, dendritic cell; cDC, conventional DC; pDC, plasmacytoid DC; NK cell, natural killer cell; ILCs, innate lymphoid cells.
Figure 4
The Macspectrum signatures of renal macrophages in WT and OVE26 mice. (A) Macrophages with sustained activation are segmented into four distinct phenotype based on the Macspectrum plot. (B) Scatter and contour plots visualizing the distribution patterns of the MPI and AMDI for total macrophages. (C–H) Contour plots for multiple macrophage subpopulations showing the proportion of various phenotypes. MPI, macrophage polarization index; AMDI, activation-induced macrophage differentiation index; Mac, macrophage; Trem2hi, triggering receptor expressed on myeloid cells 2 high expressing; Mrc1hi, Mannose receptor C-type 1 high expressing; IFNhi, Interferon gene signature high expressing.
Figure 5
Pseudo-time and trajectory analyses of renal macrophages. (A) The trajectory map of macrophage clusters in 7-month-old kidneys features a color scheme based on pseudotime, transitioning from deep to light blue to depict the cellular evolutionary process. The cell differentiation trajectories of the (B) WT and (C) OVE26 groups. (D) The heatmap visualizing how genes that are differentially expressed alter over the trajectory of cell differentiation.
Figure 6
Screening hub genes related to efferocytosis in diabetic kidney macrophages. The GO enrichment analysis of macrophage subpopulations in 3-month-old (A) and 7-month-old mice (B). (C) The chord diagram about the interaction among renal immune cells. (D) The Venn diagram illustrating the intersection between GSE195799 and the gene list of efferocytosis. (E) The PPI diagram, where the color varies from light to dark, signifies an ascending order of degree. (F–H) Violin diagrams of the distribution of CD36, ITGAM, and CX3CR1 in each cluster. GO, gene ontology; MIF, macrophage migration inhibitory factor; PPI, protein-protein interaction network.
Figure 7
A comprehensive evaluation of the hub genes using bulk RNA sequencing. (A) The volcano plot displaying DEG in renal tubulointerstitial tissue. (B) The heatmap showing the top 50 genes with notably distinct expression levels between the two groups. (C) The GSEA mountain plot showcasing the enriched pathways. (D) The shared genes are shown by the Venn diagram. (E) The LASSO regression fitting model. (F) The boxplot depicting the expression differences of hub genes between the DKD control group. (G) The ROC curve in training dataset. (H) Stacked histogram identifying each infiltrating immune cell. (I) The distribution of 22 different immune cell populations. Data were examined through Wilcoxon tests. (J) The interconnectivity of diverse immune cell types. (K) The correlation results between hub genes and immune cells. Statistical approaches draw on Spearman’s correlation analysis. DEGs, differentially expressed genes; GSEA, gene set enrichment analysis; LASSO, least absolute shrinkage and selection operator; ROC, receiver operating characteristic. *P < 0.05, **P < 0.01, ***P<0.001.
Figure 8
Validating the clinical relevance of hub molecules. (A–C) illustrating the distribution characteristics of the data via violin plots. Scatter plots describe the correlation between CD36 (D), ITGAM (E), and CX3CR1 (F) expression with clinical indicators. ***P<0.001.
Figure 9
Evaluation of target gene expression in DKD model. (A) Quantitative PCR analysis of ITGAM, CD36, and CX3CR1 mRNA expression. (B) Western blot showing the protein expression levels of ITGAM, CD36, and CX3CR1. (C) Quantitative analysis of protein band grayscale values for ITGAM, CD36, and CX3CR1. *P < 0.05, **P < 0.01, ***P<0.001.
Figure 10
Macrophage dynamics and efferocytosis decline in DKD. (A) TRME2, MRC1, iNOS, IL1Β, CCL3, and FN1 mRNA levels detected by qPCR. (B) Quantification of MERTK, AXL, and MFGE8 mRNA levels measured through quantitative PCR. (C) Under fluorescence microscopy, macrophages (red) are seen to have ingested apoptotic cells (green), with some macrophages remaining unbound. Scale bar represents 50μm. The efferocytosis index, calculated as the proportion of macrophages that have phagocytosed apoptotic cells relative to the total macrophage population. LG, low glucose; HG, high glucose. HK-2, human proximal tubular epithelial cells. **P < 0.01, ***P<0.001.
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