Department II of Internal Medicine, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany; Center for Molecular Medicine Cologne (CMMC), University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany; Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases, University of Cologne, Cologne, Germany; MedTechLabs, Karolinska University Hospital, Solna, Sweden; Division of Renal Medicine, Department of Clinical Sciences, Intervention and Technology, Karolinska Institute, Stockholm, Sweden; Science for Life Laboratory, Department of Applied Physics, Royal Institute of Technology, Solna, Sweden.
Department II of Internal Medicine, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany; Center for Molecular Medicine Cologne (CMMC), University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany; Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases, University of Cologne, Cologne, Germany.
Kidney Int. 2023 Jun;103(6):1120-1130. doi: 10.1016/j.kint.2023.03.013. Epub 2023 Mar 27.
Morphological alterations at the kidney filtration barrier increase intrinsic capillary wall permeability resulting in albuminuria. However, automated, quantitative assessment of these morphological changes has not been possible with electron or light microscopy. Here we present a deep learning-based approach for segmentation and quantitative analysis of foot processes in images acquired with confocal and super-resolution fluorescence microscopy. Our method, Automatic Morphological Analysis of Podocytes (AMAP), accurately segments podocyte foot processes and quantifies their morphology. AMAP applied to a set of kidney diseases in patient biopsies and a mouse model of focal segmental glomerulosclerosis allowed for accurate and comprehensive quantification of various morphometric features. With the use of AMAP, detailed morphology of podocyte foot process effacement was found to differ between categories of kidney pathologies, showed detailed variability between diverse patients with the same clinical diagnosis, and correlated with levels of proteinuria. AMAP could potentially complement other readouts such as various omics, standard histologic/electron microscopy and blood/urine assays for future personalized diagnosis and treatment of kidney disease. Thus, our novel finding could have implications to afford an understanding of early phases of kidney disease progression and may provide supplemental information in precision diagnostics.
肾脏滤过屏障的形态改变会增加固有毛细血管壁的通透性，导致白蛋白尿。然而，电子显微镜或光学显微镜都无法对这些形态变化进行自动、定量评估。在这里，我们提出了一种基于深度学习的方法，用于对共聚焦和超分辨率荧光显微镜采集的图像中的足细胞足突进行分割和定量分析。我们的方法，即自动足细胞形态分析（AMAP），可以准确地分割足细胞足突并定量它们的形态。AMAP 应用于一组患者活检和局灶节段性肾小球硬化症的小鼠模型中，能够准确和全面地量化各种形态特征。使用 AMAP，发现不同肾脏病理类别的足细胞足突消失的详细形态存在差异，在具有相同临床诊断的不同患者之间存在详细的可变性，并且与蛋白尿水平相关。AMAP 可能会补充其他读数，如各种组学、标准组织学/电子显微镜以及血液/尿液检测，以用于未来的肾脏疾病个性化诊断和治疗。因此，我们的新发现可能对理解肾脏疾病进展的早期阶段具有重要意义，并可能在精准诊断中提供补充信息。
