Laudon Aksel, Wang Zhaoze, Zou Anqi, Sharma Richa, Ji Jiayi, Tan Winston, Kim Connor, Qian Yingzhe, Ye Qin, Chen Hui, Henderson Joel M, Zhang Chao, Kolachalama Vijaya B, Lu Weining
Department of Biomedical Engineering, Boston University, Boston, MA 02215, United States.
Nephrology Section, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston Medical Center, Boston, MA 02118, United States.
Biol Methods Protoc. 2025 Mar 28;10(1):bpaf024. doi: 10.1093/biomethods/bpaf024. eCollection 2025.
Transmission electron microscopy (TEM) images can visualize kidney glomerular filtration barrier ultrastructure, including the glomerular basement membrane (GBM) and podocyte foot processes (PFP). Podocytopathy is associated with glomerular filtration barrier morphological changes observed experimentally and clinically by measuring GBM or PFP width. However, these measurements are currently performed manually. This limits research on podocytopathy disease mechanisms and therapeutics due to labor intensiveness and inter-operator variability. We developed a deep learning-based digital pathology computational method to measure GBM and PFP width in TEM images from the kidneys of Integrin-Linked Kinase (ILK) podocyte-specific conditional knockout (cKO) mouse, an animal model of podocytopathy, compared to wild-type (WT) control mouse. We obtained TEM images from WT and ILK cKO littermate mice at 4 weeks old. Our automated method was composed of two stages: a U-Net model for GBM segmentation, followed by an image processing algorithm for GBM and PFP width measurement. We evaluated its performance with a 4-fold cross-validation study on WT and ILK cKO mouse kidney pairs. Mean [95% confidence interval (CI)] GBM segmentation accuracy, calculated as Jaccard index, was 0.73 (0.70-0.76) for WT and 0.85 (0.83-0.87) for ILK cKO TEM images. Automated and manual GBM width measurements were similar for both WT ( = .49) and ILK cKO ( = .06) specimens. While automated and manual PFP width measurements were similar for WT ( = .89), they differed for ILK cKO ( < .05) specimens. WT and ILK cKO specimens were morphologically distinguishable by manual GBM ( < .05) and PFP ( < .05) width measurements. This phenotypic difference was reflected in the automated GBM ( < .05) more than PFP ( = .06) widths. Our deep learning-based digital pathology tool automated measurements in a mouse model of podocytopathy. This proposed method provides high-throughput, objective morphological analysis and could facilitate podocytopathy research.
透射电子显微镜（TEM）图像可以可视化肾脏肾小球滤过屏障的超微结构，包括肾小球基底膜（GBM）和足细胞足突（PFP）。通过测量GBM或PFP宽度，足细胞病与实验和临床观察到的肾小球滤过屏障形态学变化相关。然而，目前这些测量是手动进行的。由于劳动强度大以及操作人员之间的差异，这限制了对足细胞病发病机制和治疗方法的研究。我们开发了一种基于深度学习的数字病理学计算方法，用于测量整合素连接激酶（ILK）足细胞特异性条件性敲除（cKO）小鼠肾脏TEM图像中的GBM和PFP宽度，该小鼠是足细胞病的动物模型，与野生型（WT）对照小鼠进行比较。我们获取了4周龄WT和ILK cKO同窝小鼠的TEM图像。我们的自动化方法由两个阶段组成：用于GBM分割的U-Net模型，随后是用于GBM和PFP宽度测量的图像处理算法。我们通过对WT和ILK cKO小鼠肾脏对进行4折交叉验证研究来评估其性能。以Jaccard指数计算的平均[95%置信区间（CI）]GBM分割准确率，WT的TEM图像为0.73（0.70 - 0.76），ILK cKO的为0.85（0.83 - 0.87）。WT（P = 0.49）和ILK cKO（P = 0.06）标本的GBM宽度自动测量和手动测量相似。虽然WT的PFP宽度自动测量和手动测量相似（P = 0.89），但ILK cKO标本的两者不同（P < 0.05）。通过手动GBM（P < 0.05）和PFP（P < 0.05）宽度测量，WT和ILK cKO标本在形态上可区分。这种表型差异在自动GBM宽度测量中比PFP宽度测量中更明显（P < 0.05对P = 0.06）。我们基于深度学习的数字病理学工具在足细胞病小鼠模型中实现了自动测量。所提出的方法提供了高通量、客观的形态学分析，并且可以促进足细胞病的研究。
