Correlation of glomerular histomorphometry changes with spatially resolved transcriptomic profiles in diabetic nephropathy

Diabetic nephropathy (DN), a common complication of diabetes mellitus, remains a leading cause of endstage renal disease. Histopathological assessment of renal biopsy remains the gold standard for diagnosis. Accurate diagnosis is crucial for timely intervention and personalized management plans. Machine learning (ML) models can analyze digital pathology slides, learn DN biomarkers, and aid in DN staging. Developing ML models can be challenging for the limited availability of annotated images, subjectivity in histopathology interpretation, and histology artifacts. Molecular profiling such as single-cell RNA sequencing (SC) and spatial transcriptomics (ST) can contribute to better understanding of cellular heterogeneity and molecular pathways. Clinical use of molecular tests is limited due to the absence of well-established protocols specific to DN diagnosis. In this study, we propose a framework for correlating glomerular histomorphometry with spatially resolved transcriptomics to better understand the histologic spectrum of DN. The framework uses manual tissue labels by experienced users, and hybrid labels by combining user input and unsupervised clustering of molecular data. Clustering is performed on the gene expression levels of disease biomarkers and on the cell type decomposition of tissue by integration with SC reference data from KPMP. We used a dataset of 6 DN and 3 normal cases, with frozen section histology, ST, and SC collected at Seoul National University Hospital, Seoul, South Korea. Our initial experiments identified a correlation between the imaging features histomorphometry and disease label. Our cloud-based prototype visualizes both gene markers and cell type decomposition as a heatmap on histology, enables molecular-informed validation of structures, enables adding manual labels, and visualizes the clusters on histology. In conclusion, our framework can analyze the correlation between histomorphometry and tissue labels generated in a molecular-informed environment. Our cloud-based prototype can aid the diagnosis process by visualizing these correlations overlaid on digital slides.