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Govind D, Becker JU, Miecznikowski J, et al. PodoSighter: A Cloud-Based Tool for Label-Free Podocyte Detection in Kidney Whole-Slide Images. J Am Soc Nephrol. 2021;32(11):2795-2813doi: 10.1681/ASN.2021050630.
Govind, D., Becker, J. U., Miecznikowski, J., Rosenberg, A. Z., Dang, J., Tharaux, P. L., Yacoub, R., Thaiss, F., Hoyer, P. F., Manthey, D., Lutnick, B., Worral, A. M., Mohammad, I., Walavalkar, V., Tomaszewski, J. E., Jen, K. Y., & Sarder, P. (2021). PodoSighter: A Cloud-Based Tool for Label-Free Podocyte Detection in Kidney Whole-Slide Images. Journal of the American Society of Nephrology : JASN, 32(11), 2795-2813. https://doi.org/10.1681/ASN.2021050630
Govind, Darshana, et al. "PodoSighter: A Cloud-Based Tool for Label-Free Podocyte Detection in Kidney Whole-Slide Images." Journal of the American Society of Nephrology : JASN vol. 32,11 (2021): 2795-2813. doi: https://doi.org/10.1681/ASN.2021050630
Govind D, Becker JU, Miecznikowski J, Rosenberg AZ, Dang J, Tharaux PL, Yacoub R, Thaiss F, Hoyer PF, Manthey D, Lutnick B, Worral AM, Mohammad I, Walavalkar V, Tomaszewski JE, Jen KY, Sarder P. PodoSighter: A Cloud-Based Tool for Label-Free Podocyte Detection in Kidney Whole-Slide Images. J Am Soc Nephrol. 2021 Nov;32(11):2795-2813. doi: 10.1681/ASN.2021050630. Epub 2021 Sep 03. PMID: 34479966.
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J Am Soc Nephrol. 2021 Nov;32(11):2795-2813. doi: 10.1681/ASN.2021050630. Epub 2021 Sep 03.

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

Journal of the American Society of Nephrology : JASN

Darshana Govind, Jan U Becker, Jeffrey Miecznikowski, Avi Z Rosenberg, Julien Dang, Pierre Louis Tharaux, Rabi Yacoub, Friedrich Thaiss, Peter F Hoyer, David Manthey, Brendon Lutnick, Amber M Worral, Imtiaz Mohammad, Vighnesh Walavalkar, John E Tomaszewski, Kuang-Yu Jen, Pinaki Sarder

Affiliations

  1. Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, New York.
  2. Institute of Pathology, University Hospital of Cologne, Cologne, Germany.
  3. Department of Biostatistics, University at Buffalo, Buffalo, New York.
  4. Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
  5. Paris Cardiovascular Center, Paris, France.
  6. Department of Internal Medicine, University at Buffalo, Buffalo, New York.
  7. Third Medical Department of Clinical Medicine, University Hospital Hamburg Eppendorf, Hamburg, Germany.
  8. Pediatric Nephrology, University Hospital Essen, Essen, Germany.
  9. Kitware Incorporated, Clifton Park, New York.
  10. Department of Pathology, University of California San Francisco, San Francisco, California.
  11. Department of Pathology and Laboratory Medicine, University of California, Sacramento, California.
  12. Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, New York [email protected].

PMID: 34479966 DOI: 10.1681/ASN.2021050630

Abstract

BACKGROUND: Podocyte depletion precedes progressive glomerular damage in several kidney 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 the 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 GN, and dose-dependent direct podocyte toxicity and depletion, and 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.

CONCLUSIONS: 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.

Copyright © 2021 by the American Society of Nephrology.

Keywords: CNN; Deeplab; cloud; cloud computing; deep learning; pix2pix GAN; podocyte detection; podocytes; urinary tract; viscera

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Grant support