#161 Single-Nucleus RNA-Seq of Human Kidney Biopsy Tissue Reveals Microstructure-Specific Similarities and Differences in Various Glomerulonephritis
Authors: Park Sehoon , Cho Jeong Min , Joo Jeongho , Joo Kwon Wook , Kim Dong Ki 
Published: May 2024

Background and Aims

Transcriptomic alterations in kidney diseases may lead to the discovery of novel biomarkers. Single-nucleus RNA-seq (SnSeq) is a valuable method for profiling the transcriptome of kidney-residing cells. Additional SnRNA-seq data is necessary, including cases of human biopsy-confirmed glomerulonephritis.

Method

We collected snap-frozen kidney biopsy tissues from 6 cases of IgA nephropathy (IgAN), 6 cases of minimal change disease, 6 cases of PLA2R-Ab positive membranous nephropathy, 3 cases of diabetic kidney disease, and 7 nephrectomy control cases. SnSeq was performed on the kidney tissues, and kidney cells were identified in the dataset through UMAP clustering. The proportions of each cell subcluster were compared between the study groups. Differentially expressed genes (DEGs) were identified. K-means clustering and Weighted Gene Co-expression Network Analysis (WGCNA) were performed.

Results

We successfully collected transcriptomic profiles of 55 615 cells from IgAN, 53 370 from minimal change disease, 53 495 from membranous nephropathy, 34 343 from diabetic kidney disease, and 66 161 from control cases. The SnSeq data successfully identified major kidney cell clusters, including a considerable number of glomerular cells such as podocytes, mesangial cells, endothelial cells, and others. DEGs were largely identified in podocytes and proximal tubule cells, as well as in other glomerular cells. K-means clustering identified diverse similarities and differences between the study groups for each cell type. We identified diverse cell type-specific but disease-common DEGs, particularly in the downregulated genes, suggesting that the studied diseases share similar destruction in kidney microstructure. In comparison, cell type-common but disease-specific DEGs were also identified, considering a common pathophysiologic mechanism may occur in total kidney cortex. Diseases that can feature nephrotic syndrome showed high expression of AVP receptors, suggesting the association with edema, or highly expressed renin-angiotensin-aldosterone pathway was noted in IgAN or diabetic kidney disease, which was not evident in minimal change disease or membranous nephropathy patients. The studied disease shared certain types of inflammatory pathways (e.g. interferon-alpha or TGF-beta) but some pathways were differently enriched according to the disease types. WGCNA analysis revealed gene modules that were highly correlated with age, proteinuria, and estimated GFR, regardless of the diagnosis subtype.

Conclusion

The current SnSeq data is, to date, the largest one, including biopsy-confirmed human kidney glomerulonephritis cases. The results identified disease-specific changes in specific cell types. Additionally, common transcriptomic alterations occurring following changes in estimated GFR, age, and proteinuria were investigated. The data may be a valuable resource for future research on glomerulonephritis biomarker development.