Universität zu Köln

Govind, Darshana, Becker, Jan U., Miecznikowski, Jeffrey, Rosenberg, Avi Z., Dang, Julien ORCID: 0000-0002-5690-7424, Tharaux, Pierre Louis, Yacoub, Rabi, Thaiss, Friedrich, Hoyer, Peter F., Manthey, David, Lutnick, Brendon, Worral, Amber M., Mohammad, Imtiaz, Walavalkar, Vighnesh, Tomaszewski, John E., Jen, Kuang-Yu and Sarder, Pinaki (2021). PodoSighter: A Cloud-Based Tool for Label-Free Podocyte Detection in Kidney Whole-Slide Images. J. Am. Soc. Nephrol., 32 (11). S. 2795 - 2814. WASHINGTON: AMER SOC NEPHROLOGY. ISSN 1533-3450

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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.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Govind, Darshana UNSPECIFIED UNSPECIFIED UNSPECIFIED Becker, Jan U. UNSPECIFIED UNSPECIFIED UNSPECIFIED Miecznikowski, Jeffrey UNSPECIFIED UNSPECIFIED UNSPECIFIED Rosenberg, Avi Z. UNSPECIFIED UNSPECIFIED UNSPECIFIED Dang, Julien UNSPECIFIED orcid.org/0000-0002-5690-7424 UNSPECIFIED Tharaux, Pierre Louis UNSPECIFIED UNSPECIFIED UNSPECIFIED Yacoub, Rabi UNSPECIFIED UNSPECIFIED UNSPECIFIED Thaiss, Friedrich UNSPECIFIED UNSPECIFIED UNSPECIFIED Hoyer, Peter F. UNSPECIFIED UNSPECIFIED UNSPECIFIED Manthey, David UNSPECIFIED UNSPECIFIED UNSPECIFIED Lutnick, Brendon UNSPECIFIED UNSPECIFIED UNSPECIFIED Worral, Amber M. UNSPECIFIED UNSPECIFIED UNSPECIFIED Mohammad, Imtiaz UNSPECIFIED UNSPECIFIED UNSPECIFIED Walavalkar, Vighnesh UNSPECIFIED UNSPECIFIED UNSPECIFIED Tomaszewski, John E. UNSPECIFIED UNSPECIFIED UNSPECIFIED Jen, Kuang-Yu UNSPECIFIED UNSPECIFIED UNSPECIFIED Sarder, Pinaki UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-600064
DOI:	10.1681/ASN.2021050630
Journal or Publication Title:	J. Am. Soc. Nephrol.
Volume:	32
Number:	11
Page Range:	S. 2795 - 2814
Date:	2021
Publisher:	AMER SOC NEPHROLOGY
Place of Publication:	WASHINGTON
ISSN:	1533-3450
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language TURNOVER; MODELS; NUMBER Multiple languages Urology & Nephrology Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/60006