Deep learning-based segmentation and quantification of podocyte foot process morphology

The kidneys constantly filter enormous amounts of fluid, with almost complete retention of albumin and other macromolecules in the plasma. Diseases of podocytes at the kidney filtration barrier reduce the glomerular capillary surface area available for filtration and alter the intrinsic permeability of the capillary wall resulting in albuminuria, however, direct quantitative assessment of the underlying morphological changes has not been possible so far. Here we developed a deep learning-based approach for segmentation of foot processes in images acquired with super-resolved stimulated emission depletion (STED) microscopy or confocal microscopy. Our method, Automatic Morphological Analysis of Podocytes (AMAP), detected 87-95% manually-annotated foot processes and additionally recognized 1.3 - 2.17-fold more. It also robustly determined morphometric parameters, at a Pearson correlation of r > 0.71 with a previously published semi-automated approach, across a large set of mouse tissue samples. The artificial intelligence algorithm was applied to a set of human kidney disease conditions allowing comprehensive quantifications of various underlying morphometric parameters. These data confirmed that when podocytes are injured, they take on a more simplified architecture and the slit-diaphragm length is much reduced, resulting in a reduction in the filtration slit area and a loss of the buttress force of podocytes which increases the permeability of the GBM to albumin.