In this project we adopt machine learning approach to comprehensively analyze and quantify morphological changes of podocytes related to the kidney filtration decline. FPs form intricate patterns difficult to describe manually or with a set of hand-designed visual parameters. Not only size and shape of individual FPs might play a role in filtration but also their collective configurations within the tissue. Although differences between healthy and diseased adult tissues are striking, these differences are less apparent between young, pre-symptomatic individuals. Here we combine machine learning methods with super-resolution microscopy and the unique mouse model to search for morphological markers of the disease.