Abstract: FR-PO0307
Trajectory-Based Single-Cell Transcriptomic Analysis Reveals Early Pathway Dysregulation in the Transition from Type 2 Diabetes (T2D) to Diabetic Kidney Disease (DKD)
Session Information
- Diabetic Kidney Disease: Basic and Translational Science Advances - 1
November 07, 2025 | Location: Exhibit Hall, Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Diabetic Kidney Disease
- 701 Diabetic Kidney Disease: Basic
Authors
- Alakwaa, Fadhl, University of Michigan, Ann Arbor, Michigan, United States
- Mariani, Laura H., University of Michigan, Ann Arbor, Michigan, United States
- Eddy, Sean, University of Michigan, Ann Arbor, Michigan, United States
- Menon, Rajasree, University of Michigan, Ann Arbor, Michigan, United States
- Otto, Edgar A., University of Michigan, Ann Arbor, Michigan, United States
- Pyle, Laura, University of Washington School of Medicine, Seattle, Washington, United States
- Choi, Ye Ji, University of Washington School of Medicine, Seattle, Washington, United States
- Bjornstad, Petter, University of Washington School of Medicine, Seattle, Washington, United States
- Kretzler, Matthias, University of Michigan, Ann Arbor, Michigan, United States
Group or Team Name
- For the Kidney Precision Medicine Project (KPMP).
Background
DKD is the leading cause of kidney failure, but early molecular events driving its onset are not well understood. We used trajectory-based single-cell transcriptomics to identify early gene and pathway changes from type 2 diabetes (T2D) to DKD.
Methods
Kidney biopsies from Living Donors (LD, n=26), T2D (early DKDn=16), and DKD (CKD 2–3, n=12) patients (Schaub et al., 2023 and Lake et al., 2023) were processed using the KPMP scRNA-seq protocol. We analyzed 24,886 proximal tubule (PT) cells using Slingshot pseudotime. ssGSEA with 1,381 Reactome pathways produced patient-level scores, and unsupervised clustering identified five major pathway modules.
Results
Trajectory analysis showed that PT cells were primarily ordered by disease progression (LD → T2D → DKD), not by age or sex. Pathway heatmaps revealed five modules with progressive changes. Cluster 1 (e.g., complement, autophagy) was upregulated in DKD. Cluster 2 (gluconeogenesis) was suppressed. Cluster 3 included glycolysis, pyruvate metabolism, and citric acid (TCA) cycle pathways, all of which were downregulated in DKD, reflecting disrupted energy metabolism. Cluster 4 involved purine catabolism pathways that were elevated in DKD. Finally, Cluster 5 encompassed oxidative stress response pathways, which were markedly activated in T2D.
Conclusion
Our trajectory-based analysis of kidney single-cell transcriptomics reveals that key immune, fibrotic, and metabolic pathways are progressively activated from T2D to DKD. These findings provide insight into early disease triggers and support the use of pseudotime as a molecular ruler to stage patients and identify intervention points to halt DKD progression.