Abstract: FR-PO146
Using Imaging Mass Cytometry to Define Cell Identities in AKI and CKD in Humans
Session Information
- AKI: Mechanisms
October 25, 2024 | Location: Exhibit Hall, Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Acute Kidney Injury
- 103 AKI: Mechanisms
Authors
- Budiman, Tifanny, Yale University School of Medicine, New Haven, Connecticut, United States
- Shelar, Ashish, Yale University, New Haven, Connecticut, United States
- Baker, Megan Leila, Yale University School of Medicine, New Haven, Connecticut, United States
- Weiss, Marlene, Yale University School of Medicine, New Haven, Connecticut, United States
- Cantley, Lloyd G., Yale University School of Medicine, New Haven, Connecticut, United States
- Kakade, Vijayakumar R., Yale University School of Medicine, New Haven, Connecticut, United States
Group or Team Name
- Kidney Precision Medicine Project (KPMP).
Background
Acute kidney injury (AKI) is a complex clinical syndrome that arises in patients in response to many etiologies, with up to 50% of critically ill patients developing AKI. An episode of AKI is associated with an increased risk of developing chronic kidney disease (CKD), leading to risks of both short- and long-term mortality. Though we have made significant progress in our understanding of many kidney diseases, less attention has been focused on the pathogenesis and treatment of human AKI. Imaging mass cytometry (IMC), allows staining of up to 40 proteins on a formalin fixed paraffin embedded (FFPE) section and provides semiquantitative expression data of each protein with a one square micron resolution. In this study, we applied IMC to define cell types, their activation states, and cell-cell interactions with spatial context in human reference, AKI, and CKD samples from KPMP participants.
Methods
IMC with a validated panel of 34 antibodies was performed on healthy reference tissue (HRT n=6), AKI (n=8), CKD (n=9) and QC tissues. Cell segmentation was performed using the deep learning Mesmer segmentation algorithm and the data was analyzed by imcRtools.
Results
391,738 cells were identified across 23 KPMP biopsies, with 42 distinct cell clusters generated using Rphenograph clustering. Analysis of tubule cell type marker expression in proximal tubule and thick ascending limb clusters showed that megalin expression per proximal tubule cell is significantly reduced in both AKI (1.641 ± 1.078, p<0.0001) and CKD (2.633 ± 2.283, p<0.0001) as compared to HRT (6.793 ± 8.442, a.u.), and is significantly lower in AKI as compared to CKD (p<0.0001). Similarly, the expression of uromodulin in thick ascending limb cells is significantly decreased in both AKI (2.503 ± 1.552, p<0.0001) and CKD (6.300 ± 5.171, p<0.0001) as compared to HRT (12.48 ± 11.56, a.u.), and significantly lower in AKI compared to CKD (p<0.0001).
Conclusion
We applied quantitative high-dimensional imaging analysis to human reference, AKI and CKD biopsies. This analysis shows a significant reduction in the expression of the tubular cell proteins megalin and UMOD in injured kidneys as compared to healthy reference tissue, with greatest reduction detected in samples from patients with AKI.
Funding
- NIDDK Support