Submicron-Resolution Integrated Spatial Transcriptomics and Proteomics for Studying Kidney Disease

ABSTRACT Kidney diseases, including chronic kidney disease (CKD), acute kidney injury, and glomerulonephritis, impact nearly 850 million people worldwide, leading to significant morbidity, mortality, and rising healthcare costs. While spatial transcriptomics has offered valuable insights by mapping gene expression within specific kidney niches, a comprehensive understanding of kidney disease requires spatial multiomics approaches that can detect both RNA and protein at single-cell or subcellular resolution. However, current spatial omics technologies struggle to analyze the kidney's complex structure and diverse cell types, and integrating RNA and protein assays at high resolution remains both technically challenging and costly. The overall goal of this project is to address these limitations by optimizing Pixel-seqV2, a submicron-resolution spatial transcriptomics assay, and developing ProteoPixel-seq for integrated co-profiling of transcriptomes and proteomes in human kidney disease. Building on Pixel-seqV1, which uses 1-µm-resolution "polony gels", as capture DNA arrays for spatial RNA sequencing, we will enhance its resolution to 0.6 µm through a scalable "polony gel stamping" method, reducing the array fabrication cost by over 100-fold. In Aim 1, Pixel-seqV2 will be optimized for spatial transcriptomics of the kidney at single-cell resolution. Sub-Aim 1A focuses on integrating 0.6-µm-resolution polony gels and optimizing assay conditions to improve RNA capture sensitivity and spatial resolution, while Sub-Aim 1B involves applying the optimized assay to both mouse and human kidney samples, refining cell segmentation algorithms to accurately delineate complex cell boundaries and detect rare, pathologically relevant cell populations. In Aim 2, ProteoPixel- seq will be developed for integrated spatial co-profiling of transcriptomes and proteomes. Sub-Aim 2A aims to expand Pixel-seqV2 with high-plex proteomic analysis using DNA-tagged antibodies, optimizing tissue assay conditions for simultaneous RNA and protein detection. In Sub-Aim 2B, we will apply ProteoPixel-seq to CKD patient biopsy samples, focusing on interactions between kidney stroma and infiltrating immune cells to reveal molecular pathways involved in disease progression and identify potential therapeutic targets. This project will yield catalytic tools for spatial multiomics tissue mapping, offering the first demonstration of integrated spatial transcriptomics and proteomics in kidney disease, significantly enhancing our understanding of kidney pathology and supporting the development of novel diagnostics and therapeutics.