Integrating spatial transcriptomics and snRNA-seq data enhances differential gene expression analysis results of AD-related phenotypes

BackgroundSpatial transcriptomics (ST) data provide spatially-informed gene expression for studying complex diseases such as Alzheimers disease (AD). Existing studies using ST data to identify genes with spatially-informed differential gene expression (DGE) of complex diseases have limited power due to small sample sizes. Conversely, single-nucleus RNA sequencing (snRNA-seq) data offer larger sample sizes for studying cell-type specific (CTS) DGE but lack spatial information. In this study, we integrated ST and snRNA-seq data to enhance the power of spatially-informed CTS DGE analysis of AD-related phenotypes. MethodFirst, we utilized the recently developed deep learning tool CelEry to infer the spatial location of [~]1.5M cells from snRNA-seq data profiled from dorsolateral prefrontal cortex (DLPFC) tissue of 436 postmortem brains in the ROS/MAP cohorts. Spatial locations of six cortical layers that have distinct anatomical structures and biological functions were inferred. Second, we conducted cortical-layer specific (CLS) and CTS DGE analyses for three quantitative AD-related phenotypes -- {beta}-amyloid, tangle density, and cognitive decline. CLS-CTS DGE analyses were conducted based on linear mixed regression models with pseudo-bulk scRNA-seq data and inferred cortical layer locations. ResultsWe identified 450 potential CLS-CTS significant genes with nominal p-values<10-4, including 258 for {beta}-amyloid, 122 for tangle density, and 127 for cognitive decline. Majority of these identified genes, including the ones having known associations with AD (e.g., APOE, KCNIP3, and CTSD), cannot be detected by traditional CTS DGE analyses without considering spatial information. We also identified 8 genes shared across all three phenotypes, 21 between {beta}-amyloid and tangle density, 10 between cognitive decline and tangle density, and 10 between {beta}-amyloid and cognitive density. Particularly, Gene Set Enrichment Analyses with the CLS-CTS DGE results of microglia in cortical layer-6 of {beta}-amyloid identified 12 significant AD-related pathways. ConclusionIncorporating spatial information with snRNA-seq data detected significant genes and pathways for AD-related phenotypes that would not be identified by traditional CTS DGE analyses. These identified CLS-CTS significant genes not only help illustrate the pathogenesis of AD, but also provide potential CLS-CTS targets for developing therapeutics of AD.