Digital Spatial Profiling of Human Kidney Biopsies from Patients with Minimal Change Disease

Spatial transcriptomic profiling enables precise quantification of gene expression with simultaneous localization of expression profiles onto tissue structures. This new technology promises to improve our understanding of the disease mechanisms. Therefore, there is intense interest in applying these methods in clinical trials or as laboratory developed tests to aid in diagnosis of disease. Before these applications can been more broadly deployed in clinical research and diagnostics, it is necessary to thoroughly understand the technology’s performance in real world conditions. In this study, we vet technical reproducibility, data normalization methods and assay sensitivity of digital spatial profiling, one widely used spatial transcriptomic methodology. Using clinically sourced human tissue specimens, we find that digital spatial profiling exhibits high rigor and reproducibility. Our approach lays the foundation for incorporation of digital spatial profiling methods into clinical workflows. We applied whole transcriptome atlas (WTA) digital spatial profiling (>18,000 genes) to understand the gene expression profiles of glomeruli from n=10 patients with MCD and n=4 controls. We had previously generated cancer transcriptome atlas (CTA, 1,825 genes) measurements from n=12 glomeruli of 3 individuals with histologically normal kidney biopsies. In this study, we performed WTA measurements on n=20 glomeruli from those same 3 individuals. After performing QC and Q3 normalization, we identified a subset of 451 genes that were measured above the limit of quantification (LOQ) in the glomeruli hybridized with CTA probes. We then compared the average counts of these genes (across the 3 donors) against the average Q3 normalized counts generated using the WTA probe mix on the same 3 donors’ kidney biopsy tissues.