Podocytes are depleted in several renal parenchymal processes. The current gold standard to identify podocytes considers histopathological staining of nuclei using specific antibodies and manual enumeration, which is expensive and laborious. Implementation of high-throughput podometrics in experimental and clinical workflows necessitates an automated podometrics pipeline. Recognizing that computational image analysis offers a robust approach to study cell and tissue structure, we developed and validated PodoCount (a computational tool for automated podocyte quantification in immunohistochemically labeled tissues) using a diverse data set. The data set consisted of murine whole kidney sections (n = 135) from 6 disease models and human kidney biopsy specimens from patients with diabetic nephropathy (DN) (n = 45). Within segmented glomeruli, podocytes were extracted and image analysis was applied to compute measures of podocyte depletion and nuclear morphometry.
PodoCount offers high-performance podocyte quantitation in diverse murine disease models and in human kidney biopsy specimens. Resultant features offer significant correlation with associated metadata and outcome.
B. A. Santo†, D. Govind†, P. Daneshpajouhnejad, X. Yang, X. X. Wang, K. Myakala, B. A. Jones, M. Levi, J. B. Kopp, L. J. Niedernhofer, D. Manthey, K. C. Moon, S. S. Han, J. Zee, A. Z. Rosenberg, and P. Sarder, “PodoCount: A robust, fully automated whole-slide podocyte quantification tool,” Kidney International Reports, vol. 7, no. 6, pp. 1377-92, Jun. 2022.