TY - JOUR
T1 - PodoSighter
T2 - A cloud-based tool for label-free podocyte detection in kidney whole-slide images
AU - Govind, Darshana
AU - Becker, Jan U.
AU - Miecznikowski, Jeffrey
AU - Rosenberg, Avi Z.
AU - Dang, Julien
AU - Tharaux, Pierre Louis
AU - Yacoub, Rabi
AU - Thaiss, Friedrich
AU - Hoyer, Peter F.
AU - Manthey, David
AU - Lutnick, Brendon
AU - Worral, Amber M.
AU - Mohammad, Imtiaz
AU - Walavalkar, Vighnesh
AU - Tomaszewski, John E.
AU - Jen, Kuang Yu
AU - Sarder, Pinaki
N1 - Funding Information:
This work was supported by NIDDK grant R01 DK114485, National Institutes of Health OD (Office of Director) grants R01 DK114485 02S1 and R01 DK114485 03S1, NIDDK CKD Biomarker Consortium grant U01 DK103225, NIDDK Kidney Precision Medicine Project grant U2C DK114886, and the Deutsche Forschungsgemeinschaft (BE-3801 to J.U. Becker).
Funding Information:
A.Z. Rosenberg reports receiving research funding from the National Institutes of Health and the National Kidney Foundation; reports receiving honoraria from Georgetown University, Ichilov Hospital (Tel Aviv, Israel), and Stony Brook University; and reports being a scientific advisor or member of Escala. D. Manthey reports being employed by Kitware Inc. F. Thaiss reports having consultancy agreements with B. Braun Viamedis, Novartis, Sanofi, and Union Chimique Belge; reports receiving honoraria from Alexion, Bristol Myers Squibb, Chiesi, Hexal, Novartis, Pfizer, and Sanofi; and reports being a scientific advisor or member of Novartis and Sanofi. J.E. Tomaszewski reports having an ownership interest in AXA, General Electric, and Neurovascular Diagnostics, Inc.; reports receiving honoraria from the American College of Veterinary Pathologists, Dakota Cancer Collaborative on Translational Activity, and the University of Washington; reports being a scientific advisor or member of Neurovascular Diagnostics, Inc.; and reports having other interests/relationships with the Board of Directors, Kidney Precision Medicine Project (National Institute of Diabetes and Digestive and Kidney Diseases [NIDDK]) external evaluation panel through May 2021, National Kidney Foundation, SPIE (the Society of Photo-Optical Instrumentation Engineers) Medical Imaging, Western New York Chapter, and the Editorial Boards of SPIE Medical Imaging; Dakota Cancer Collaborative on Translational Activity, External Evaluation Panel, and the Journal Pathology Informatics. J.U. Becker reports having consultancy agreements with Sanofi. P.F. Hoyer reports having consultancy agreements with Boehringer Ingelheim; and reports being a scientific advisor or member of the Archives of Disease in Childhood. P.L. Tharaux reports having consultancy agreements with and receiving honoraria from Travere Therapeutics; reports being a scientific advisor or member as an Advisory Board Member for Nature Review Nephrology, Associate Editor for Kidney International, the French National Institute for Medical Research, the French Society of Cardiology, and the French Society of Hypertension. P. Sarder reports receiving research funding from the CKD Biomarker Consortium, Clinical and Translational Science Institute at the University at Buffalo, Kidney Precision Medicine Project, NIDDK, the State University of New York, and the University at Buffalo; reports being a scientific advisor or member as Associate Editor of PLoS One, and Editorial Board Member of the Journal of American Society of Nephrology. All remaining authors have nothing to disclose.
Publisher Copyright:
Copyright ß 2021 by the American Society of Nephrology
PY - 2021/11
Y1 - 2021/11
N2 - 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.
AB - 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.
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U2 - 10.1681/ASN.2021050630
DO - 10.1681/ASN.2021050630
M3 - Article
C2 - 34479966
AN - SCOPUS:85119052286
SN - 1046-6673
VL - 32
SP - 2795
EP - 2813
JO - Journal of the American Society of Nephrology : JASN
JF - Journal of the American Society of Nephrology : JASN
IS - 11
ER -