Single-Nucleus RNA-Sequencing Identifies a Differential Profibrotic Response in Parietal Epithelial Cells in Primary Versus Maladaptive Focal Segmental Glomerulosclerosis

Single-Nucleus RNA-Sequencing Identifies a Differential Profibrotic Response in Parietal Epithelial Cells in Primary Versus Maladaptive Focal Segmental Glomerulosclerosis

UMAP FSGS

 

In this study, we used single-nucleus RNA-sequencing to identify differentially expressed transcriptional signatures in kidney biopsy cores of patients with well-phenotyped primary FSGS, maladaptive FSGS and controls. We identified 120,751 nuclei, including 2,471 podocytes and 1,574 parietal epithelial cells (PECs). In primary FSGS, podocytes showed a more pronounced but not specific injury pattern with upregulation of immune pathways, such as antigen presentation. Glomerular cell-cell interaction analysis showed increased profibrotic transforming growth factor (TGF)-beta and platelet-derived growth factor receptor (PDGFR)-beta signaling in primary FSGS PECs, which also upregulated genes that compose the normal PEC-derived extracellular matrix. In maladaptive FSGS, podocytes showed few differentially expressed genes and PEC-PEC interactions predominated. Here, a (myo-)fibroblast-like PEC subpopulation upregulated non-type IV fibril- and network-forming collagens, which may further contribute to the development of glomerulosclerosis. Taken together, our study provided a single-cell transcriptional landscape of well-phenotyped FSGS patients and provided evidence for a differential profibrotic PEC response in primary vs. maladaptive FSGS.

Single-Nucleus RNA-Sequencing Identifies a Differential Profibrotic Response in Parietal Epithelial Cells in Primary Versus Maladaptive Focal Segmental Glomerulosclerosis. Deleersnijder et al. - 2025 - Kidney International Reports

 

Data availability

A download of the filtered read count data for the kidney biopsy samples is readily available from this website. In addition to the read counts, the following metadata have been added to the files: i) patient number, ii) type of sample (Primary FSGS, Maladaptive FSGS, proteinuric controls and healthy controls) and iii) cell type. Please note that patient and sample identifiers have been anonymized and randomized in these datasets, and may not correspond directly to identifiers used in the paper by Deleersnijder et al. 
 
Note: The read counts and metadata have been updated on 20/03/2026.
 

Data access