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Kidney Week

Abstract: TH-PO028

Automated, Scalable, and Comprehensive Three-Dimensional Analysis of Glomeruli and Whole Nephrons in Kidney Tissues

Session Information

Category: Augmented Intelligence, Digital Health, and Data Science

  • 300 Augmented Intelligence, Digital Health, and Data Science

Authors

  • Poudel, Chetan, University of Washington, Seattle, Washington, United States
  • Brenes, David, University of Washington, Seattle, Washington, United States
  • Sandoval, Ruben M., Indiana University School of Medicine, Indianapolis, Indiana, United States
  • Martinez-Irizarry, Michelle M., Indiana University School of Medicine, Indianapolis, Indiana, United States
  • Dunn, Ken, Indiana University School of Medicine, Indianapolis, Indiana, United States
  • Dagher, Pierre C., Indiana University School of Medicine, Indianapolis, Indiana, United States
  • Liu, Jonathan T. C., University of Washington, Seattle, Washington, United States
  • Vaughan, Joshua C., University of Washington, Seattle, Washington, United States
Background

Studying intact functional units or nephrons in 3D has so far been challenging as it requires organ-scale imaging at cellular resolution. The entire field has relied mostly on 2D assays with sparse sampling and thin tissue sections. 2D imaging, however, obscures the relationships between different parts of the same nephron, between nearby nephrons, and between nephrons and other units (e.g. vasculature, lymphatics). There is a great need for advanced imaging and analytical tools to study whole functional units in 3D.

Methods

We developed an end-to-end pipeline to stain, optically clear, image, and analyze thick kidney tissues (both mouse and human) in 3D. We used fluorescent FLARE stains and an open-top lightsheet microscope. For analysis, we trained machine learning models to segment all glomeruli in the tissue. We developed new algorithms with deep learning to dynamically travel through nephrons and accurately extract their entire paths. We can also untwist nephrons to visualize sub-segments and compare between nephrons. This enables 3D morphology analysis (location, trajectory, curvature, lengths, diameters) and in situ cytometry.

Results

We demonstrate orders of magnitude (>100X) faster analysis over manual 3D segmentation methods. The speed and scalability allow us to study a hundred nephrons in a single tissue, and extract 3D models and highly quantitative information. We applied this method to study glomeruli and nephrons in young, mid-aged, and aged mouse kidneys and correlate morphology to functional attributes. We note several interesting changes to glomeruli, glom-tubule connections, and tubule morphology in aged mice.

Conclusion

We established an automated pipeline using artificial intelligence to comprehensively assess 3D morphology of mouse and human kidney tissues. This analysis could assist organ atlas efforts by providing 3D data complementary to existing 2D assays. It could pave the way for 3D renal pathology of human biopsies.

Funding

  • NIDDK Support