RT Journal Article
SR Electronic
T1 PodoCount: A robust, fully automated whole-slide podocyte quantification tool
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.04.27.441689
DO 10.1101/2021.04.27.441689
A1 Briana A. Santo
A1 Darshana Govind
A1 Parnaz Daneshpajouhnejad
A1 Xiaoping Yang
A1 Xiaoxin X. Wang
A1 Komuraiah Myakala
A1 Bryce A. Jones
A1 Moshe Levi
A1 Jeffrey B. Kopp
A1 Laura J. Niedernhofer
A1 David Manthey
A1 Kyung Chul Moon
A1 Seung Seok Han
A1 Avi Z. Rosenberg
A1 Pinaki Sarder
YR 2021
UL http://biorxiv.org/content/early/2021/05/19/2021.04.27.441689.abstract
AB Background Podocyte depletion is an established indicator of glomerular injury and predicts clinical outcomes. The semi-quantitative nature of existing podocyte estimation methods or podometrics hinders incorporation of such analysis into experimental and clinical pathologic workflows. Computational image analysis offers a robust approach to automate podometrics through objective quantification of cell and tissue structure. Toward this goal, we developed PodoCount, a computational tool for quantitative analysis of podocytes, and validated the generalizability of the tool across a diverse dataset.Methods Podocyte nuclei and glomerular boundaries were labeled in murine whole kidney sections, n = 135, from six disease models and human kidney biopsies, n = 45, from diabetic nephropathy (DN) patients. Digital whole slide images (WSIs) of tissues were then acquired. Classical image analysis was applied to obtain podocyte nuclear and glomerular morphometrics. Statistically significant morphometric features, which correlated with each murine disease, were identified. Engineered features were also assessed for their ability to predict outcomes in human DN. PodoCount has been disbursed for other researchers as an open-source, cloud-based computational tool.Results PodoCount offers highly accurate quantification of podocytes. Engineered podometric features were benchmarked against routine glomerular histopathology and were found to be significant predictors of disease diagnosis, proteinuria level, and clinical outcomes.Conclusions PodoCount offers high quantification performance in diverse murine disease models as well as in human DN. Resultant podometric features offers significant correlation with associated metadata as well as outcome. Our cloud-based end-user tool will provide a standardized approach for podometric analysis from gigapixel size WSIs in basic research and clinical practice.Competing Interest StatementThe authors have declared no competing interest.