PodoCount: A Robust, Fully Automated, Whole-Slide Podocyte Quantification Tool

Abstract

Introduction: Podocyte depletion is a histomorphologic indicator of glomerular injury and predicts clinical outcomes. Podocyte estimation methods or podometrics are semiquantitative, technically involved, 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.

Methods: Whole-slide images (WSIs) of tissues immunostained with a podocyte nuclear marker and periodic acid-Schiff counterstain were acquired. 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. Computational performance evaluation and statistical testing were performed to validate podometric and associated image features. PodoCount was disbursed as an open-source, cloud-based computational tool.

Results: PodoCount produced highly accurate podocyte quantification when benchmarked against existing methods. Podocyte nuclear profiles were identified with 0.98 accuracy and segmented with 0.85 sensitivity and 0.99 specificity. Errors in podocyte count were bounded by 1 podocyte per glomerulus. Podocyte-specific image features were found to be significant predictors of disease state, proteinuria, and clinical outcome.

Conclusion: 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. Our cloud-based tool will provide end users with a standardized approach for automated podometrics from gigapixel-sized WSIs.

Keywords: chronic kidney disease; digital pathology; gigapixel size images; glomerular disease; podocyte; podometrics.

Graphical abstract

Figure 1

Summary of data sets. The image data set contains light microscopic images of kidney tissues from 6 mouse models of glomerular disease and 5 stages of human DN. (a) The murine cohort was composed of tissues from 135 mice with control and diseased specimens for each model. Two distinct models of type II diabetes mellitus were studied (db/db and KKAy). The SAND intervention (saline, angiotensin II, uninephrectomy, and deoxycortisone) models postadaptive FSGS (FSGS [SAND]). Samples from SAND, HIVAN, and Progeroid syndrome models included male and female mice; those from the db/db, KKAy, and Aging mouse models consisted only of males. (b) The human DN study consisted of 45 patients (n = 35 male and n = 10 female subjects). Representative glomerular p57-PAS image from (c) each mouse model and (d) each Tervaert stage of the human DN cohort. DN, diabetic nephropathy; FSGS (SAND), a postadaptive model of FSGS, focal segmental glomerular sclerosis; HIVAN, HIV-associated nephropathy.

Figure 2

Comparison of podometric estimates by PodoCount and the single-section method. PodoCount estimates of corrected podocyte count and podocyte density were compared against those from manual ground truth measurements using the single-section method. (a) Error in automated counts was bounded by 1 podocyte. Tendency toward over- or under- estimation was cohort dependent. (b) The modified Bland-Altman plot highlights the departure in PodoCount podocyte density estimates from ground truth. No., number; FSGS (SAND), focal segmental glomerular sclerosis; HIVAN, HIV-associated nephropathy.

Figure 3

Podocyte and glomerular morphometrics of control and disease mice across murine models. Distribution of podocyte feature values across disease states with each black dot corresponding to a single mouse in the (a) db/db model of type II diabetes mellitus, (b) KKAy model of type II diabetes mellitus, (c) FSGS model, (d) HIVAN model, (e) Aging model, and (f) Progeroid (Ercc^−/Δ) model. All podometric values are based on 2D quantification from glomerulus profiles in whole kidney sections. ∗q < 0.05. 2D, 2-dimensional; CTRL, control; FSGS, focal segmental glomerular sclerosis; Glom =, glomerulus; HIVAN, HIV-associated nephropathy; Pod, podocyte; WT, wild type.

Figure 4

Podocyte and glomerular morphometrics in diabetic nephropathy kidney biopsy specimens predict outcome. Distribution of podocyte feature values comparing those with diabetes with progression to ESKD to those without with each black dot corresponding to a single patient (a) or glomerulus (b). All podometric values are based on 2D quantification from glomerulus profiles in whole kidney sections. ∗q < 0.05. 2D, 2-dimensional; ESKD, end-stage kidney disease; Pod, podocyte; Glom, glomerulus.