A1379
Title: Mapping disease critical spatially variable gene programs by integrating spatial transcriptomics with human genetics
Authors: Kushal Dey - Memorial Sloan Kettering Cancer Center (United States) [presenting]
Abstract: The purpose is to present Spacelink as a statistical framework that models spatial variability of a gene at both whole-tissue and cell-type resolution using an adaptive mixture of data-driven spatial kernels and summarizes it using an Effective Spatial Variability (ESV) metric. Across extensive simulations, Spacelink achieved up to 3.2x higher detection power over eight existing global SVG and cell-type SVG methods, while exhibiting controlled FDR. Applied to 3 healthy CosMx human tissues (brain cortex, lymph node, liver), Spacelink showed up to 2.2x higher disease informativeness over competing methods in tissue-relevant complex diseases and traits. Applied to a mouse organogenesis Stereo-seq atlas (8 developmental stages), Spacelink identified 145 genes with stage-associated ESV within brain independent of mean expression, that are enriched in pathways like Wnt signaling and Rap1 signaling characterizing early and late development, respectively. In neurodegeneration, analysis of 32 Visium dorsolateral prefrontal cortex samples spanning Alzheimer's disease (AD) pathology stages identified 334 genes with decreasing ESV along amyloid burden (enriched for glycolysis) and 216 genes with decreasing ESV along tau tangle accumulation (enriched for apoptotic pathways). These results highlight the utility of Spacelink in decoding spatially variable gene programs that connect tissue architecture to disease genetics.
