Whole genome deconvolution unveils Alzheimer's resilient epigenetic signature
Assay for Transposase Accessible Chromatin by sequencing (ATAC-seq) provides an accurate way to depict the chromatin regulatory state and altered mechanisms guiding gene expression in disease. However bulk sequencing entangles information from different cell types and obscures cellular heterogeneity. Here, we develop and validate Cellformer, a novel deep learning method, that deconvolutes bulk ATAC-seq into cell type-specific expression across the whole genome. Cellformer enhances the bulk ATAC-seq resolution and allows an efficient cell type specific open chromatin profiling on large size cohorts at a low cost. Applied to 191 bulk samples from 3 brain regions, Cellformer identifies cell type-specific gene regulatory mechanisms and putative mediators involved in resilient to Alzheimer's disease (RAD), an uncommon group of cognitively healthy individuals that harbor a high pathological load of Alzheimer's disease (AD). Cell type-resolved chromatin profiling unveils cell type specific pathways and nominates potential epigenetic mediators underlying RAD that may illuminate therapeutic opportunities to limit the cognitive impact of this highly prevalent yet incurable disease. Cellformer has been made freely and publicly available to advance analysis of high-throughput bulk ATAC-seq in future investigations. Overall design: Bulk ATAC-seq