Going beyond segmentation in discovering early pixel level changes in diabetic nephropathy using histology image and urinary proteomics data
Pinaki Sarder   (Gainesville, FL)
In the USA, approximately 34.2 million people have diabetes. Twenty-five percent of this population will develop diabetic nephropathy (DN). Diabetes is the most common cause of end stage renal disease (ESRD), accounting for approximately 45% of cases. In DN, manual scoring of renal microstructural damage often does not correlate with disease progression. Moreover, while glucose control is known to be the most acceptable treatment for diabetes, intensive control of glucose may not reduce the risk of DN. This gap in the literature prompts investigation of two overarching and interlinked questions in diabetes: (i) what are the early digital biomarkers that can be measured from histologically stained renal biopsies which predict DN and (ii) can we identify links between early image biomarkers and molecular pathways in DN. Answering these questions will help to better stratify DN patients into different risk categories, and aid precise strategic targeting of new and hopefully better treatment strategies for preventing or treating DN. The biggest investigative challenge is that there exist no open-source databases of well-curated digital images of DN renal biopsies with matching high throughput molecular data. While the generation of spatial transcriptomics or single cell/ nuclei RNA sequencing (scRNAseq / snRNAseq) data matched with renal biopsy of early DN cases is becoming technically possible, the sample sizes available from earlier efforts are still too low to answer the proposed questions. In this study, we will collaborate with nephropathologists, nephrologists, and molecular biologists to answer the proposed questions using matching renal tissue image and urinary proteomic (as molecular) data collected from patients with early and late DN stages. Our primary aim is to identify biological correlations between renal microstructural image patterns and urinary proteins in DN patients. Over the last six years, the PI has generated numerous results discussing objective quantification of renal micro-compartments, computational classification of renal diseases, and computational prediction of clinical biometrics from renal tissue images. In this study, the PI is refocusing his team’s efforts to investigate the molecular biomarkers from urinary proteomics and correlating/integrating the resulting information with matched histomorphometrics. This newly developed framework will answer the outlined questions pertaining to DN diagnosis and prognostication. The developed framework can be extended to conduct similar studies using other molecular omics data such as scRNAseq, snRNAseq, spatial transcriptomics, spatial metabolomics, and laser dissected spatial seq data.