PodoSighter: A Cloud-Based Tool for Label-Free Podocyte Detection in Kidney Whole Slide Images.

Abstract

Background: Podocyte depletion precedes progressive glomerular damage in several renal diseases. However, the current standard of visual detection and quantification of podocyte nuclei from brightfield microscopy images is laborious and imprecise. Methods: We have developed PodoSighter, an online cloud-based tool, to automatically identify and quantify podocyte nuclei from giga-pixel brightfield whole-slide images (WSIs) using deep learning. Ground-truth to train the tool used immunohistochemically or immunofluorescence-labeled images from a multi-institutional cohort of 122 histologic sections from mouse, rat, and human kidneys. To demonstrate generalizability of our tool in investigating podocyte loss in clinically relevant samples, we tested it in rodent models of glomerular diseases, including diabetic kidney disease, crescentic glomerulonephritis, and dose-dependent direct podocyte toxicity and depletion, as well as in human biopsies from steroid resistant nephrotic syndrome and from human autopsy tissues. Results: The optimal model yielded high sensitivity/specificity of 0.80/0.80, 0.81/0.86, and 0.80/0.91, in mouse, rat, and human images, respectively, from periodic-acid Schiff-stained WSIs. Furthermore, the podocyte nuclear morphometrics extracted using PodoSighter were informative in identifying diseased glomeruli. We have made PodoSighter freely available to the general public as turnkey plugins in a cloud-based web application for end-users. Conclusion: Our study demonstrates an automated computational approach to detect and quantify podocyte nuclei in standard histologically-stained WSIs, facilitating podocyte research and enabling possible future clinical applications.