Project Summary/Abstract There are currently >450,000 patients on dialysis and ~120,000 patients will have to begin dialysis each year in the United States. Along with a marked reduction in quality of patient life, the cost to the US Medicare system is in excess of $114B per year. Therefore, there is an urgent need for developing new platforms that will enhance the drug development process for new therapies to reduce the rate of progression to end-stage kidney disease. Non-invasive biomarkers may be useful but do not directly identify the pathways linked to pathology in the kidney. An approach that integrates tissue structure with functional readouts in a kidney biopsy would be a major advance. We are developing a computational platform that leverages mass spec imaging data coupled with computational pathology for kidney tissue as a technology that will address this unmet need. With our computational platform we can identify signatures linked to normal and abnormal pathology on the same tissue section. This is a powerful approach to understand kidney pathology and will be of great value for drug development for kidney disease. Parameters related to reproducibility across pre-clinical models of diabetic kidney disease interpretation will be optimized during Phase I. Upon completion of the SBIR Phase I project we will have rigorous data to determine consistency of diseased glomerular signatures in diabetic nephropathy. This proof of concept data will make it attractive to pharma and biotech to adopt this platform for application for their therapeutic programs for diabetic kidney disease.