Prediction of cellular targets in diabetic kidney diseases with single-cell transcriptomic analysis of db/db mouse kidneys

Abstract

Diabetic kidney disease is the leading cause of impaired kidney function, albuminuria, and renal replacement therapy (dialysis or transplantation), thus placing a large burden on health‐care systems. This urgent event requires us to reveal the molecular mechanism of this disease to develop more efficacious treatment. Herein, we reported single‐cell RNA sequencing analyses in kidneys of db/db mouse, an animal model for type 2 diabetes and diabetic kidney disease. We first analyzed the hub genes expressed differentially in the single cell resolution transcriptome map of the kidneys. Then we figured out the communication among the renal and immune cells in the kidneys. Data from this report may provide novel information for better understanding the cell‐specific targets involved in the aetiologia of type 2 diabetic kidney disease and for cell communication and signaling between renal cells and immune cells of this complex disease.

Interaction between Plasma Metabolomics and Intestinal Microbiome in db/db Mouse, an Animal Model for Study of Type 2 Diabetes and Diabetic Kidney Disease

“…In terms of lipid metabolites, all 40 lipids with significant differences, except 13-Hotre(R), were upregulated in the DKD group. Among them, the increase in L-Carnitine, 7-Kcho, Lysopa and their derivatives also indicates a more active inflammatory state, which may cause the activation of various immune cells, including Mac and T Cells [25,26]. However, many lipid derivatives (e.g., Carnitine, 7-KCHO, Isoproterenol) were elevated in DKD (Figure 5E), and it has been shown that some lipid derivatives have pro-inflammatory [26], pro-oxidative stress and pro-apoptotic effects [27].…”

GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership

“…Numbers in parentheses next to each tissue give the number of cells in that tissue. In D, marker genes for S1 (topic 4) and S3 (topic 5) proximal epithelial tubular cells are highlighted in red (marker genes are listed in Table 1 of [97]). “Mean l.e.…”

“…Most interestingly, spatial organization of the proximal tubule is captured by two topics; topic 4 is enriched for Slc5a2 (also known as Sglt2 ) and Slc2a2 (also known as Glut2 ), associated with the S1 segment of the proximal tube [97, 102, 103]) and topic 5 is enriched for Slc5a8 ( Smct1 ) and Atp11a , related to the S3 segment [97, 104]). This result illustrates the ability of the topic model to capture continuous variation in membership of two (somewhat complementary) processes, which traditional clustering methods are not designed to do.…”

The Role of Immune Cells in DKD: Mechanisms and Targeted Therapies

“…They found that, as DKD progressed, resident and infiltrating macrophage subpopulations in the kidneys increased, accompanied by subpopulation-specific increases in pro- or anti-inflammatory gene expression and gene expression tending towards an undifferentiated phenotype, while M1-like inflammatory phenotypes increased over time. Furthermore, based on scRNA-seq data from db/db mice kidneys, it has been predicted that M1 macrophages can target renal stromal cells via SPP1 and activate T cells via the MHC-II pathway, which then recognize other renal cells via the MHC-I pathway; 112 however, this study by Fu et al raises several unanswered questions. 113 Renal inflammation is a result of intricate cell–cell communication among various cell types.…”