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

摘要 阿尔茨海默氏病（AD）是一种破坏性的神经退行性疾病，其导致对大脑皮层的显著影响。 受影响的个人及其家庭。虽然对AD的遗传贡献的表征和 近年来，研究表明，潜在的分子机制促进了对疾病的理解， 表明性别差异解释了观察到的风险、进展和严重程度的差异 在不同的个体之间。在这里，我们直接剖析了性别特异性变异对区域的贡献- 特异性和细胞类型特异性的分子基础，通过系统分析，计算积分， 实验验证的转录，表观基因组，和遗传签名跨越个人，大脑 区域和细胞类型。在目标1中，我们使用遗传、表观基因组和转录谱， 使用scRNA-seq和scATAC-seq的单细胞（sc）水平图，其跨越具有不同细胞的人类和小鼠样品。 年龄和遗传风险状况。在目标2中，我们在已知的AD遗传背景下分析所得数据集。 风险变异和潜在的分子机制，使我们能够发现和汇聚变异，监管 区域，基因，途径，细胞类型和大脑区域的功能，因果机制，驱动性相关的 差异在目标3中，我们使用我们完善的小鼠和iPSC模型来测试我们预测的机制。 同时具有高通量和细胞类型特异性测定。由此产生的数据集，计算预测， 实验支持的机制将阐明AD的性别相关差异，并有助于加深 我们对这种疾病的总体理解，因为我们开发了更个性化的治疗方法， ad.

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.