ABSTRACT The presently employed morphology-based classification system used to distinguish unique glomerular diseases is inadequate. This classification system is not derived from an understanding of the molecular basis of these diseases, nor does it well predict observed heterogeneous disease natural history or response to therapy. This project will apply quantitative pathology methods to assess the renal parenchyma as one approach to overcoming this critical barrier to glomerular disease clinical research and patient care. Our goal is to create a well characterized data set that extracts an array of multiple discrete morphological elements from the structural complexity inherent in glomerular diseases. We will exploit this detailed characterization—combined with renal morphometrics and transcriptional and genomic profiling—to explore the hypothesis that inherent in this structural complexity is extractable information that is predictive of underlying disease biology or clinical outcome. The proposed study will use existing detailed clinical data and biomaterials obtained by NEPTUNE, a prospective longitudinal cohort study of proteinuric patients with MCD, FSGS, or MN enrolled at the time of clinically indicated renal biopsy. As part of the NEPTUNE effort, we created the NEPTUNE Digital Pathology Protocol (NDPP), an innovative program that (1) compiled a centralized internet- based digital pathology repository of whole slide digital images, and electron and immunofluorescence micrographs from 500 NEPTUNE subjects, that (2) allows standardization by application of computer- based imaging analytical software for multi-level annotation (enumeration) of all glomeruli across all biopsy levels available, and that (3) enables detailed assessment of renal biopsies using a novel quantitative scoring system. The proposed project will be carried out in three specific aims: We will (1) compile a standardized comprehensive morphologic and morphometric profile of 500 NEPTUNE cohort renal biopsies to test whether these profiles contain reproducible descriptors or groups of descriptors that can be combined with morphometric measures to reliably assess glomerular disease pathology; (2) using a variety of statistical approaches, we will identify quantitative structural parameters that are predictive of outcomes to establish a clinically relevant categorization of proteinuric diseases; and we will (3) identify transcriptomic signatures, molecular pathways, genetic variants, and expression quantitative trait loci (eQTL) that associate with specific morphologic and morphometric profiles.