Automated detection and quantification of Wilms' Tumor 1-positive cells in murine diabetic kidney disease.
Automated detection and quantification of Wilms' Tumor 1-positive cells in murine diabetic kidney disease.
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小鼠糖尿病肾病中肾母细胞瘤 1 阳性细胞的自动检测和定量。
DOI：
10.1117/12.2581387
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Sarder,Pinaki
中科院分区：
文献类型：
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作者：
Govind,Darshana;Santo,BrianaA;Ginley,Brandon;Yacoub,Rabi;Rosenberg,AviZ;Jen,Kuang-Yu;Walavalkar,Vignesh;Wilding,GregoryE;Worral,AmberM;Mohammad,Imtiaz;Sarder,Pinaki
在糖尿病肾病 (DKD) 中，足细胞耗竭以及随后的壁上皮细胞 (PEC) 向肾簇迁移是进行性肾小球损伤的先兆，但明场显微镜的局限性目前无法对这些细胞进行直接病理定量。在这里，我们提出了一种足细胞和 PEC 检测的自动化方法，该方法使用模拟 DKD 的小鼠模型的肾切片开发，首先通过免疫荧光对肾母细胞瘤 1 (WT1)（足细胞和 PEC 标记）进行染色，然后用高碘酸希夫 (PAS) 进行后染色。使用基于生成对抗网络 (GAN) 的流程将这些 PAS 染色切片转换为 WT1 标记的 IF 图像，从而在计算机上实现无标记足细胞和明场图像中的 PEC 识别。我们的方法以高灵敏度/特异性 (0.87/0.92) 检测 WT1 阳性细胞。此外，我们的算法的 Cohen kappa (0.85) 比三位肾脏病理学家的平均手动识别 (0.78) 更高。我们建议该管道将能够在研究应用中准确检测 WT1 阳性细胞。
In diabetic kidney disease (DKD), podocyte depletion, and the subsequent migration of parietal epithelial cells (PECs) to the tuft, is a precursor to progressive glomerular damage, but the limitations of brightfield microscopy currently preclude direct pathological quantitation of these cells. Here we present an automated approach to podocyte and PEC detection developed using kidney sections from mouse model emulating DKD, stained first for Wilms’ Tumor 1 (WT1) (podocyte and PEC marker) by immunofluorescence, then post-stained with periodic acid-Schiff (PAS). A generative adversarial network (GAN)-based pipeline was used to translate these PAS-stained sections into WT1-labeled IF images, enabling in silico label-free podocyte and PEC identification in brightfield images. Our method detected WT1-positive cells with high sensitivity/specificity (0.87/0.92). Additionally, our algorithm performed with a higher Cohen’s kappa (0.85) than the average manual identification by three renal pathologists (0.78). We propose that this pipeline will enable accurate detection of WT1-positive cells in research applications.