Single-cell epigenomic and trancriptional dissection of sex-specific differences in Alzheimer’s Disease

摘要 阿尔茨海默病(AD)是一种毁灭性的神经退行性疾病，会对 受影响的个人及其家人。虽然对阿尔茨海默病和阿尔茨海默病的遗传贡献的表征 近年来，潜在的分子机制促进了人们对这种疾病的理解 表明性别差异是观察到的风险、进展和严重程度差异的主要原因 在个人之间。在这里，我们直接剖析性别差异对该地区的贡献- 通过系统分析、计算集成和 跨个体、大脑的转录、表观基因组和遗传签名的实验验证 区域和单元格类型。在目标1中，我们使用遗传、表观基因组和转录图谱，产生数百万 使用scRNA-seq和scatac-seq对不同来源的人和小鼠样本进行单细胞(Sc)水平图谱分析 年龄和遗传风险状况。在目标2中，我们在已知AD基因的背景下分析结果数据集 风险变异和潜在的分子机制，使我们能够发现和融合变异，调控 区域、基因、途径、细胞类型和大脑区域，到驱动性相关的功能和因果机制 不同之处。在目标3中，我们使用成熟的小鼠和iPSC模型来测试我们预测的机制 同时进行高通量和细胞类型特异性分析。结果数据集、计算预测和 实验支持的机制将阐明AD的性别差异，并将有助于深化 随着我们在治疗中开发更个性化的治疗方法，我们对疾病的总体理解 广告。

Abstract Alzheimer’s disease (AD) is a devastating neurodegenerative disorder that leads to dramatic effects on the affected individuals and their families. While the characterization of the genetic contribution to AD and underlying molecular mechanisms have advanced the understanding of the disease in recent years, studies show that sex differences account for much of the observed differences in risk, progression, and severity across individuals. Here, we directly dissect the contribution of sex-specific variation down to the region- specific and cell-type-specific molecular basis by systematic profiling, computational integration, and experimental validation of the transcriptional, epigenomic, and genetic signatures across individuals, brain regions, and cell types. In Aim 1, we use genetic, epigenomic, and transcriptional profiles, generating millions of single-cell (sc) level maps using scRNA-seq and scATAC-seq across human and mouse samples of varying ages and genetic risk status. In Aim 2, we analyze the resulting datasets in the context of known AD genetic risk variation and underlying molecular mechanisms, enabling us to discover and converge variants, regulatory regions, genes, pathways, cell types, and brain regions to functional, causal mechanisms that drive sex-related differences. In Aim 3, we use our well-established mouse and iPSC models to test our predicted mechanisms with both high-throughput and cell-type specific assays. The resulting datasets, computational predictions, and experimentally-supported mechanisms will shed light on the sex-related differences of AD and will help deepen our understanding the disease in general as we develop more personalized therapeutic approaches in treating AD.