Abstract: TH-PO535
Single Nucleus Transcriptomic Landscape of Human Glomerular Diseases
Session Information
- Glomerular Diseases: Omics, Biomarkers, and Tools
October 24, 2024 | Location: Exhibit Hall, Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Glomerular Diseases
- 1401 Glomerular Diseases: Mechanisms, including Podocyte Biology
Authors
- Joo, Jeong Ho, Korea Advanced Institute of Science and Technology, Daejeon, Korea (the Republic of)
- Park, Sehoon, Seoul National University College of Medicine, Jongno-gu, Seoul, Korea (the Republic of)
- Lee, Jeong Seok, Korea Advanced Institute of Science and Technology, Daejeon, Korea (the Republic of)
- Kim, Dong Ki, Seoul National University College of Medicine, Jongno-gu, Seoul, Korea (the Republic of)
Background
Glomerular diseases, often progress to end-stage kidney disease, with each type following a distinct clinical course despite common features. Single-nucleus RNA-seq (SnRNA seq) enabled researchers to capture the gene expression of rare glomerular cells. In this study, we examined the transcriptomic differences among glomerular diseases in glomerular cells.
Methods
We collected snap-frozen kidney biopsy tissues from 6 cases of IgA nephropathy (IgAN), 6 cases of minimal change disease (MCD), 6 cases of PLA2R-Ab positive membranous nephropathy (MN), 3 cases of diabetic kidney disease (DKD), and 7 cases of adjacent normal tissue in renal cell carcinoma. We conducted snRNA seq using kidney tissue, comparing proportions and differentially expressed genes (DEGs) among various diseases. We also subdivided detailed clusters within each cell type and compared specific cell types with the patients’ clinical data to assess correlations.
Results
A total of 262,984 nuclei were obtained from 28 subjects with MCD, MN, IgAN, DKD, as well as the control, and UMAP was drawn. Thirteen clusters were identified including glomerular cells. When k-means clustering was performed on DEGs between each condition within each cell types, upregulated DEGs display patterns common to cell types and specific to diseases, whereas downregulated DEGs exhibit patterns specific to cell types and common across various cell types. After subclutering, we identified podocyte with endocytosis feature associated with proteinuria. The module score of marker genes of this cell type significantly increased with higher levels of proteinuria in patients. The proportion of myofibroblasts were increased in glomerular diseases, compared to control group significantly. Additionally, we found that gene expression of myofibroblast was directly associated with kidney function of glomerular diseases.
Conclusion
To our knowledge, our snRNA-Seq data is the largest datasets of biopsy-confirmed human glomerular disease to date. Our results provided novel insights regarding the kidney indwelling cell-specific or disease- specific transcriptomic alterations. The data serve as valuable asset for future investigations into the development of biomarkers or therapeutic targets for glomerular diseases.
Funding
- Government Support – Non-U.S.