表观遗传学
基因组
计算生物学
生物
签名(拓扑)
后生
反褶积
遗传学
DNA甲基化
计算机科学
基因
基因表达
算法
几何学
数学
作者
Eloïse Berson,Anjali Sreenivas,Thanaphong Phongpreecha,Amalia Perna,Fiorella C. Grandi,Lei Xue,Neal G. Ravindra,Neelufar Payrovnaziri,Samson Mataraso,Yeasul Kim,Camilo Espinosa,Alan L. Chang,Martin Becker,Kathleen S. Montine,Edward Fox,Howard Y. Chang,M. Ryan Corces,Nima Aghaeepour,Thomas J. Montine
标识
DOI:10.1038/s41467-023-40611-4
摘要
Abstract Assay for Transposase Accessible Chromatin by sequencing (ATAC-seq) accurately depicts 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. To address this, we developed Cellformer, a deep learning method that deconvolutes bulk ATAC-seq into cell type-specific expression across the whole genome. Cellformer enables cost-effective cell type-specific open chromatin profiling in large cohorts. Applied to 191 bulk samples from 3 brain regions, Cellformer identifies cell type-specific gene regulatory mechanisms involved in resilience to Alzheimer’s disease, an uncommon group of cognitively healthy individuals that harbor a high pathological load of Alzheimer’s disease. Cell type-resolved chromatin profiling unveils cell type-specific pathways and nominates potential epigenetic mediators underlying resilience that may illuminate therapeutic opportunities to limit the cognitive impact of the disease. Cellformer is freely available to facilitate future investigations using high-throughput bulk ATAC-seq data.
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