Summary Diabetic kidney disease (DKD) accounts for $42 billion in annual Medicare spending, and >50% of end-stage kidney disease (ESKD) cases. The Renal Pathology Society endorses the use of tissue morphometric features of DKD with discrete classification based on the peak visual semi-quantitative measure of renal tissue morphometry. These features may lack sensitivity to correlate with clinical biometrics (e.g., eGFR) measured at the time of biopsy or during follow-up. A multimodal evidence-based quantitative method is required to deliver continuous scoring by engaging molecular information along with digital histopathology, and summarizing the contributions of each pixel of a renal tissue histopathology image using quantitative morphometry and omics. The investigator team has pioneered the unbiased quantitative morphometry of DKD over the last five years in their existing R01, conducting extensive quantification of novel image pixel features with clinical significance, and focusing on how computational artificial intelligence (AI) improves precision and accuracy, outperforming existing diagnostic standards. An opportunity exists to translate their developed tools in a clinically meaningful form that aids a pathologist in biopsy assessment or a nephrologist in therapy selection. Capitalizing on their extensive work in the field of DKD digital pathology, the investigator team will: 1) translate their developed digital pathology tools to clinicians’ desks in the next five years, 2) extend digital pathology by integrating spatial molecular features to reveal hidden digital image biomarkers, and 3) provide clinicians with additional useful metrics for biopsy assessment to augment the current treatment strategy of DKD. Recently, this team has significantly advanced spatial anchoring of cell types and cell states (e.g., injury, recovery, adaptation) in brightfield histology using paired data from spatial transcriptomics. This effort will be extended to the single-cell level in this application. The team has also delivered to the community the first cloud application of quantitative digital pathology tissue assessment via a single-click, promoting FAIR (findable, accessible, interoperable, reusable) data principles. Based on these preliminary efforts, this renewal application will implement a pipeline for the clinical use of computational pathology in DKD. The central hypothesis is that computational pathology of DKD, integrated with spatial omics, offers transformative tools for DKD diagnostic classification, prognosis, and therapy selection. In particular, the team will: 1) Develop an end-user cloud software for clinical DKD biopsy assessment using continuous risk scoring; 2) Determine the digital histopathology image pixel features corresponding to molecular cell processes with clinical significance; 3) Test whether image pixel and spatial transcriptomics quantitative vectors of glomerular hypertension predict progression and optimal DKD therapy. Wi...