Introduction: Diabetic kidney disease (DKD) is one common microvascular complication of diabetes. When the kidney is damaged, it may communicate abnormally with cells in the blood system. Renipuncture is invasive and traumatic for patients, and a noninvasive panel that predicting DKD occurrence or progression needs to be identified.
Method: The single-cell RNA sequencing (scRNA-seq) dataset (GSE183279) comprise kidney samples from normal individuals (n=18) and patients with DKD (n=12). The bulk RNA sequencing (RNA-seq) dataset (GSE96804) consist of renal glomerular tissue samples from normal individuals (n=20) and DKD patients (n=41). The proteomics data (PXD047872) include serum samples from normal, diabetic mellitus, early-stage and late-stage diabetic nephropathy. In brief, we utilize "Seurat" package in R (version 4.3.1) to analyze the scRNA-seq data, Tensor-cell2cell to identify intercellular communication, and ClusterGVis package to extract protein expression matrices.
Results: By integrating bulk-seq and scRNA-seq, we have identified 5 critical receptor-ligand pairs and 17 hub genes, emphasizing the vital role of integrin-mediated cell interactions in DKD's pathogenesis. By Integrating the serum protein data and scRNA-seq, we have identified 7 significantly upregulated proteins and 3 significantly downregulated proteins in DKD group.
Conclusion: These findings set a foundation for future clinical applications. We will verify these markers in clinical samples and confirm their functions for the kidney in the future.
J. Wu: None.
This research was funded by National Natural Science Foundation project (82370823, 82370356); Beijing Tongzhou District Science and Technology Plan Project (JCQN2023001, KJ2023SS011).
