Integrated spatial and single‐cell transcriptomic analysis of genetic and sporadic forms of Alzheimer’s disease

Abstract

AbstractBackgroundThe pathogenesis of Alzheimer’s disease (AD) varies greatly depending on environmental and heritable factors. Neuropathological scores aid in the understanding of disease severity in postmortem tissue, but fail to provide insights into disease onset and progression. Conversely, mouse models of AD like 5XFAD offer predictable timelines of amyloid accumulation due to specific human AD risk signals, but have limitations due to species differences. Studying AD in Down Syndrome (AD in DS) patients provides an opportunity to study the transcriptomics of AD progression due to overexpression of APP, which results in a more predictable disease time course compared to the more stochastic disease progression in the general population.MethodWe present a rigorous transcriptomic analysis of AD using single‐nucleus RNA‐seq (snRNA‐seq) and spatial transcriptomics (ST) in cortical samples from donors with early‐stage AD, late‐stage AD, AD in DS, and cognitively normal controls. We performed ST (10X Genomics Visium) in these groups, and additionally performed snRNA‐seq (Parse Biosciences) in tissue from AD in DS donors and controls. Finally, we profiled 5XFAD and wild type mice at four time points (4, 6, 8, and 12 months) using ST to facilitate cross‐species comparisons. We applied state‐of‐the‐art bioinformatic and statistical approaches to integrate these datasets with published AD snRNA‐seq data and to perform systematic analyses of gene expression and gene networks in these groups.ResultWe found shared and distinct differentially expressed genes (DEGs) between AD and AD in DS in the ST and snRNA‐seq datasets, with greater concordance in neuronal cell types compared to glia. Cell‐cell communication network analysis of integrated snRNA‐seq and ST data revealed vast network remodeling in disease, highlighting an increase in WNT and ANGPTL signaling and a decrease in CD99 signaling among other disrupted pathways. Using geospatial statistical analysis, we identified amyloid plaque proximal DEGs in human and mouse ST data, which were largely species‐specific with little overlap.ConclusionThe primary data generated here will be a valuable resource for the AD community, and our study provides new insights into the transcriptomics of AD and amyloid pathology by performing a holistic analysis considering sporadic and genetic forms of AD.