Deep learning-supported image quantification of epithelial cell shapes and its application to polycystic kidney disease.

Cell shape is a fundamental determinant of tissue architecture and organ function. In epithelial tissues, cytoskeletal organization and apical junctions regulate cell geometry, shaping functional tissue units. Disruption of these mechanisms is associated with diseases such as autosomal dominant polycystic kidney disease (ADPKD), in which epithelial organization is altered leading to cyst formation. Quantitative analysis of epithelial morphology can provide mechanistic insight, but existing approaches are often manual, low-throughput, and difficult to standardize. Here, we present a fully automated, deep learning-supported image analysis workflow for quantifying epithelial morphology in immunofluorescence images of zonula occludens protein 1 (ZO-1)-stained monolayers. Using a U-Net-based segmentation approach designed to mitigate out-of-focus regions, we extract standard cell shape features together with readouts tailored to the phenotype under study, including the R-index for junctional meandering and a border-based proxy for intercellular force transmission at shared cell-cell interfaces. We apply this workflow to genetically modified Madin-Darby canine kidney (MDCK) cell models of ADPKD and show that it captures genotype-associated differences in junctional organization that are not fully described by conventional shape descriptors alone. The workflow enables standardized, high-throughput phenotyping across large image datasets, reduces observer dependence, and supports analysis of mixed-cell experiments with genotype-resolved shared-border behavior. Together, these results establish a scalable framework for assay-specific quantification of epithelial morphology and junctional organization in defined experimental systems.