Integration and disentanglement of single-cell and spatial transcriptomics in health and disease

Single-cell and spatial transcriptomics have transformed our understanding of cellular diversity in complex biological systems, enabling detailed exploration of tissue heterogeneity in health and disease. This research focuses on advancing analysis methods for single-cell and spatial transcriptomics, specifically applied to kidney biology and Autosomal Dominant Polycystic Kidney Disease (ADPKD), a genetic disorder marked by progressive cyst formation leading to kidney failure. <br> We developed a comprehensive Mouse Kidney Atlas by integrating multiple single-cell RNA sequencing datasets, creating a hierarchical reference of kidney cell populations. Applying spatial transcriptomics to healthy and ADPKD mouse kidneys, we enhanced cell-type resolution, identifying increases in fibroblasts, injury-repair associated cells, and immune cells. Notably, we discovered failed-repair proximal tubule cells near cyst formations, exclusive to diseased kidneys, and characterized ligand-receptor interactions in cyst microenvironments. <br> Addressing integration challenges, we introduced spVIPES, a computational method distinguishing shared from condition-specific signals in single-cell data, improving integration across conditions and species. Pilot studies optimizing single-nuclei RNA sequencing provided initial insights into early-stage ADPKD cellular and molecular changes. <br> These methodological advancements significantly enhance our understanding of the cellular mechanisms underlying ADPKD.<br>