PodoCount: A Robust, Fully Automated, Whole-Slide Podocyte Quantification Tool.
PodoCount: A Robust, Fully Automated, Whole-Slide Podocyte Quantification Tool.
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DOI：
10.1016/j.ekir.2022.03.004
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发表时间：
2022-06
影响因子：
6
通讯作者：
Sarder, Pinaki
中科院分区：
文献类型：
--
作者：
Santo, Briana A.;Govind, Darshana;Daneshpajouhnejad, Parnaz;Yang, Xiaoping;Wang, Xiaoxin X.;Myakala, Komuraiah;Jones, Bryce A.;Levi, Moshe;Kopp, Jeffrey B.;Yoshida, Teruhiko;Niedernhofer, Laura J.;Manthey, David;Moon, Kyung Chul;Han, Seung Seok;Zee, Jarcy;Rosenberg, Avi Z.;Sarder, Pinaki
关键词：
足细胞耗竭是肾小球损伤的组织形态学指标，可预测临床结局。足细胞估计方法或足测量是半定量的，技术上涉及，和费力。在实验和临床工作流程中实现高通量的podometrics需要一个自动化的podometrics管道。认识到计算图像分析提供了一个强大的方法来研究细胞和组织结构，我们开发和验证PodoCount（一种计算工具，用于自动化足细胞定量的化学标记的组织）使用不同的数据集。获得用足细胞核标记物和高碘酸-希夫复染免疫染色的组织的全载玻片图像（WSIs）。数据集由来自6种疾病模型的鼠全肾切片（n = 135）和来自糖尿病肾病（DN）患者的人肾活检标本（n = 45）组成。在分割的肾小球内，提取足细胞，并应用图像分析来计算足细胞耗竭和核形态计量学的测量值。进行计算性能评价和统计检验，以验证podometric和相关的图像功能。PodoCount是作为一个开源的、基于云的计算工具分发的。PodoCount产生高度准确的足细胞定量基准时，对现有的方法。足细胞核的配置文件，确定与0.98的准确性和分割与0.85的灵敏度和0.99的特异性。足细胞计数的误差以每个肾小球1个足细胞为界。足细胞特异性影像学特征被认为是疾病状态、蛋白尿和临床结果的重要预测因子。PodoCount在多种鼠疾病模型和人肾活检标本中提供高性能的足细胞定量。结果特征提供了与关联元数据和结果的显著相关性。我们基于云的工具将为最终用户提供一种标准化的方法，用于从千兆像素大小的WSIs进行自动化的podometrics。
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. 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. 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. 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.
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