Single-Cell Sequencing Reveals the Expression of Immune-Related Genes in Macrophages of Diabetic Kidney Disease

被引:6
|
作者
Shao, Xian [1 ,2 ,3 ]
Shi, Yueyue [4 ]
Wang, Yao [5 ,6 ]
Zhang, Li [1 ,2 ,3 ]
Bai, Pufei [1 ,2 ,3 ]
Wang, Junmei [1 ,2 ,3 ]
Aniwan, Ashanjiang [1 ,2 ,3 ]
Lin, Yao [1 ,2 ,3 ]
Zhou, Saijun [1 ,2 ,3 ]
Yu, Pei [1 ,2 ,3 ]
机构
[1] Tianjin Med Univ, Chu Hsien I Mem Hosp, NHC Key Lab Hormones & Dev, Tianjin 300134, Peoples R China
[2] Tianjin Med Univ, Tianjin Inst Endocrinol, Tianjin 300134, Peoples R China
[3] Tianjin Med Univ, Tianjin Key Lab Metab Dis, Tianjin 300134, Peoples R China
[4] Tianjin Med Univ, Canc Inst & Hosp, Tianjin 300134, Peoples R China
[5] Chengdu Univ, Clin Med Coll, Chengdu 610081, Sichuan, Peoples R China
[6] Chengdu Univ, Affiliated Hosp, Chengdu 610081, Sichuan, Peoples R China
关键词
diabetic kidney disease; single-cell RNA sequencing; marker genes; inflammation; macrophages; RENAL FIBROSIS; INFLAMMATION; NEPHROPATHY; INJURY; SYK; INHIBITION;
D O I
10.1007/s10753-023-01906-2
中图分类号
Q2 [细胞生物学];
学科分类号
071009 ; 090102 ;
摘要
Diabetic kidney disease (DKD) is characterized by macrophage infiltration, which requires further investigation. This study aims to identify immune-related genes (IRGs) in macrophage and explore their potential as therapeutic targets. This study analyzed isolated glomerular cells from three diabetic mice and three control mice. A total of 59 glomeruli from normal kidney samples and 66 from DKD samples were acquired from four kidney transcriptomic profiling datasets. Bioinformatics analysis was conducted using both single-cell RNA (scRNA) and bulk RNA sequencing data to investigate inflammatory responses in DKD. Additionally, the "AUCell" function was used to investigate statistically different gene sets. The significance of each interaction pair was determined by assigning a probability using "CellChat." The study also analyzed the biological diagnostic importance of immune hub genes for DKD and validated the expression of these immune genes in mice models. The top 2000 highly variable genes (HVGs) were identified after data normalization. Subsequently, a total of eight clusters were identified. It is worth mentioning that macrophages showed the highest percentage increase among all cell types in the DKD group. Furthermore, the present study observed significant differences in gene sets related to inflammatory responses and complement pathways. The study also identified several receptor-ligand pairs and co-stimulatory interactions between endothelial cells and macrophages. Notably, SYK, ITGB2, FCER1G, and VAV1 were identified as immunological markers of DKD with promising predictive ability. This study identified distinct cell clusters and four marker genes. SYK, ITGB2, FCER1G, and VAV1 may be important roles. Consequently, the present study extends our understanding regarding IRGs in DKD and provides a foundation for future investigations into the underlying mechanisms.Graphical abstractIt shows the work flow of the study. The study comprised four sections. In the first section, the scRNA-seq data (GSE127235) from DKD were analyzed. The second section involved cell communication network analysis and AUCell scoring of cell clusters in DKD. The third section utilized bulk RNA-seq data (GSE 96804, GSE104948, GSE30122, and GSE30528) to validate and screen macrophage-specific IRGs and estimate immune cell infiltration. Finally, the fourth part involved in vivo experiments (RT-qPCR, western blot, and immunohistochemistry) to validate the expression of hub genes. Abbreviations: UMAP, uniform manifold approximation and projection; DKD, diabetic kidney disease; RNA-seq, RNA sequencing; DEGs, differentially expressed genes; ROC, receiver operating characteristic.
引用
收藏
页码:227 / 243
页数:17
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