Digital pathology assessment of kidney glomerular filtration barrier ultrastructure in an animal model of podocytopathy

• Published in: Biology methods and protocols Vol. 10; no. 1; p. bpaf024
• Main Authors: Laudon, Aksel, Wang, Zhaoze, Zou, Anqi, Sharma, Richa, Ji, Jiayi, Tan, Winston, Kim, Connor, Qian, Yingzhe, Ye, Qin, Chen, Hui, Henderson, Joel M, Zhang, Chao, Kolachalama, Vijaya B, Lu, Weining
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.01.2025
• Subjects: Animal models; Artificial Intelligence in Biology and Bioinformatics; Automation; Basement membranes; Deep learning; Filtration; ILK protein; Image processing; Kidneys; Methods; Morphology; Pathology; Segmentation; Transmission electron microscopy; Ultrastructure; digital pathology; glomerular basement membrane width; image segmentation; podocytopathy; machine learning; artificial intelligence; podocyte foot process width
• ISSN: 2396-8923; 2396-8923
• DOI: 10.1093/biomethods/bpaf024

Transmission electron microscopy (TEM) images can visualize kidney glomerular filtration barrier ultrastructure, including the glomerular basement membrane (GBM) and podocyte foot processes (PFP). Podocytopathy is associated with glomerular filtration barrier morphological changes observed experimentally and clinically by measuring GBM or PFP width. However, these measurements are currently performed manually. This limits research on podocytopathy disease mechanisms and therapeutics due to labor intensiveness and inter-operator variability. We developed a deep learning-based digital pathology computational method to measure GBM and PFP width in TEM images from the kidneys of Integrin-Linked Kinase (ILK) podocyte-specific conditional knockout (cKO) mouse, an animal model of podocytopathy, compared to wild-type (WT) control mouse. We obtained TEM images from WT and ILK cKO littermate mice at 4 weeks old. Our automated method was composed of two stages: a U-Net model for GBM segmentation, followed by an image processing algorithm for GBM and PFP width measurement. We evaluated its performance with a 4-fold cross-validation study on WT and ILK cKO mouse kidney pairs. Mean [95% confidence interval (CI)] GBM segmentation accuracy, calculated as Jaccard index, was 0.73 (0.70–0.76) for WT and 0.85 (0.83–0.87) for ILK cKO TEM images. Automated and manual GBM width measurements were similar for both WT (P = .49) and ILK cKO (P = .06) specimens. While automated and manual PFP width measurements were similar for WT (P = .89), they differed for ILK cKO (P < .05) specimens. WT and ILK cKO specimens were morphologically distinguishable by manual GBM (P < .05) and PFP (P < .05) width measurements. This phenotypic difference was reflected in the automated GBM (P < .05) more than PFP (P = .06) widths. Our deep learning-based digital pathology tool automated measurements in a mouse model of podocytopathy. This proposed method provides high-throughput, objective morphological analysis and could facilitate podocytopathy research.