SCH: Graph-based Spatial Transcriptomics Computational Methods in Kidney Diseases - Chronic Kidney Disease (CKD) and Acute Kidney Injury (AKI) are two common intersecting kidney diseases. CKD has been recognized as a leading public health problem worldwide, affecting about 15% of the global population. AKI can lead to CKD and affects more than 200,000 individuals across the US annually, with sequelae in distant organs such as the brain, heart, and lungs. To better understand the pathogenesis of kidney disease and potentially prevent the transition of AKI into CKD, it is necessary to define the heterogeneity of cell types and states, their associated molecular signatures, and complex interactions within the microenvironment. Emerging spatial transcriptomic technologies (e.g., 10X Genomics Visium) generate high-throughput spatial transcriptome data, which provides insights into the heterogeneous cell types within kidney health and disease. However, in contrast to organs with larger structural features like the brain, the kidney is organized into over a million nephrons with representation from more than 100 cell types arranged in close proximity. There are still tremendous computational challenges in identifying the colocalizing cell types and elucidating mechanisms in fibrosis, immune interactions, and epithelial repair. To fill such gaps, we propose to develop AI-based computational methods for studying kidney diseases based on spatial transcriptome data. First, we will build a deep learning framework to address heterogeneous, sparse, and mosaic-like cell type distribution in kidney injury, empowered by graph neural networks in a self-supervised learning training style. Second, we will compare the healthy and injured cell states to illustrate the inherent mechanism beneath the injury of CKD and AKI. Third, we will predict the effects of kidney injury with an interpretable generative process. We will evaluate the methods’ performances using the multi-omics data from the cell atlas of the healthy and injured kidneys in the Human Cell Atlas (HCA), Human Biomolecular Atlas Program (HuBMAP), and Kidney Precision Medicine project (KPMP). Our long-term goal is to create an eco-community for analyzing, sharing, and disseminating spatial transcriptomics data for physicians and bioinformaticians in kidney research.
