The expression and location of proteins in tissues represent key determinants of health and disease. Although recent advances in multiplexed imaging have expanded the number of spatially accessible proteins1-3, the integration of biological layers (that is, cell structure, subcellular domains and signalling activity) remains challenging. This is due to limitations in the compositions of antibody panels and image resolution, which together restrict the scope of image analysis. Here we present pathology-oriented multiplexing (PathoPlex), a scalable, quality-controlled and interpretable framework. It combines highly multiplexed imaging at subcellular resolution with a software package to extract and interpret protein co-expression patterns (clusters) across biological layers. PathoPlex was optimized to map more than 140 commercial antibodies at 80 nm per pixel across 95 iterative imaging cycles and provides pragmatic solutions to enable the simultaneous processing of at least 40 archival biopsy specimens. In a proof-of-concept experiment, we identified epithelial JUN activity as a key switch in immune-mediated kidney disease, thereby demonstrating that clusters can capture relevant pathological features. PathoPlex was then used to analyse human diabetic kidney disease. The framework linked patient-level clusters to organ disfunction and identified disease traits with therapeutic potential (that is, calcium-mediated tubular stress). Finally, PathoPlex was used to reveal renal stress-related clusters in individuals with type 2 diabetes without histological kidney disease. Moreover, tissue-based readouts were generated to assess responses to inhibitors of the glucose cotransporter SGLT2. In summary, PathoPlex paves the way towards democratizing multiplexed imaging and establishing integrative image analysis tools in complex tissues to support the development of next-generation pathology atlases.