Abstract: SA-PO0710
Resilient and Degenerative States in Human Podocyte Subclusters Identify Divergent Disease Trajectories
Session Information
- Glomerular Diseases: Profiling Through Multiomics
November 08, 2025 | Location: Exhibit Hall, Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Glomerular Diseases
- 1401 Glomerular Diseases: Mechanisms, including Podocyte Biology
Authors
- Haydak, Jonathan C., Icahn School of Medicine at Mount Sinai, New York, New York, United States
- Menon, Rajasree, University of Michigan, Ann Arbor, Michigan, United States
- Lake, Blue, Altos Labs Inc, San Diego, California, United States
- Hansen, Jens, Icahn School of Medicine at Mount Sinai, New York, New York, United States
- Otto, Edgar A., University of Michigan, Ann Arbor, Michigan, United States
- Iyengar, Ravi, Icahn School of Medicine at Mount Sinai, New York, New York, United States
- Jain, Sanjay, Washington University in St Louis, St. Louis, Missouri, United States
- Eadon, Michael T., Indiana University School of Medicine, Indianapolis, Indiana, United States
- Hodgin, Jeffrey B., University of Michigan, Ann Arbor, Michigan, United States
- Kretzler, Matthias, University of Michigan, Ann Arbor, Michigan, United States
- Azeloglu, Evren U., Icahn School of Medicine at Mount Sinai, New York, New York, United States
Group or Team Name
- For the Kidney Precision Medicine Project (KPMP).
Background
Podocytes form the backbone of the glomerular filtration barrier, and their loss drives albuminuric kidney diseases. Yet, the full spectrum of their transcriptomic states in health and disease remains elusive.
Methods
We analyzed publicly available KPMP data for reference, CKD, and AKI patients using three technologies: scRNA-seq, snRNA-seq, and spatial transcriptomics. Cells were filtered based on standard mitochondrial gene percentage and gene counts thresholds. Using genes with a log2 fold-change of one or greater in podocytes in scRNA-seq or snRNA-seq relative to other kidney cells, we performed UMAP dimensionality reduction and applied hierarchical nearest-neighbor clustering methods, revealing five distinct podocyte clusters.
Results
We identified podocyte-enriched genes (log2FC ≥1) from the KPMP atlas and clustered podocytes into Healthy, Transitory, Recovering, Resilient, and Degenerative states. CKD and AKI cells were predominantly transitory. Pseudotime analysis revealed two trajectories from Healthy cells toward Degenerative or Resilient states, indicating divergent injury responses. Multiomics analyses highlighted Rho GTPase signaling, cytoskeletal, and adhesion pathways as key differentiators between recovery and degeneration.
Conclusion
Our study shows that even healthy podocytes are not monolithic and that injury triggers dramatic shifts in cytoskeletal profiles that can be used to predict recovery. These findings demonstrate the dynamic nature of podocyte biology, offering a clearer path for understanding and eventually intervening in kidney disease.
UMAP of podocytes from KPMP snRNA-seq data. (A) Five identified podocyte clusters. (B) Pseudotime trajectories originating from Healthy cells toward Recovering or Degenerative clusters.
Funding
- NIDDK Support