Topographic, cell type and molecular pathway characterization ofAlzheimer's disease using single cell transcriptomics and epigenomics

摘要(项目2) 阿尔茨海默病(AD)是一种高度渗透性的神经退行性疾病，预计影响1380万人 如果不开发治疗方法，到2050年美国将出现病例，成本为1.1万亿美元。广告的特点是千篇一律 两个错误折叠的蛋白质在脑内重要区域的进行性神经变性和积聚 认知和记忆。过度磷酸化tau的神经原纤维缠结(NFT)遵循如下过程 神经变性，而细胞外淀粉样β蛋白(Aβ)斑块最初在大脑皮质和深层检测到 结构。目前尚不清楚这些病理是因果关系还是其他潜在过程的结果 目前还没有抗tau或抗A-β疗法可以阻止或逆转AD。阿尔茨海默病的基因表达研究在很大程度上 在组织或细胞群体上进行，神经元丢失和胶质增生对这些结果的影响尚不清楚。 表观遗传修饰也与AD有关，尽管甲基化研究产生了相互矛盾的结果 结果，没有出现与AD进展直接相关的表观遗传失调的明确模式。 本研究采用新近发展的高通量、单细胞方法进行转录和 表观遗传学分析识别无临床典型AD的分子和基因调控特征 严重的共病。基于对神经典型成体细胞类型的详细了解，该项目旨在 识别与AD严重程度增加相关的特定细胞类型或类别的转录变化 病理观察不同脑区疾病的影响，进而确定基因和染色质的可及性 脆弱细胞群体中的病理变化。该项目将初步优化单核rna-seq。 和表观遗传学方法，用于不同病理和组织质量的尸检样本，并产生 AD中要分析的大脑区域的参考数据集。低成本、基于液滴的单核RNA-seq将 然后用于分类和表征受到tau和淀粉样蛋白病理影响的不同区域的细胞类型 来自许多患有量化tau和Aβ病理的AD进展的供者。更广泛的一组大脑 然后将对AD相关表型一致的病例子集进行地区调查，以了解 有一个共同的AD特征跨大脑区域，以及AD的特征是否可以在 神经病理学的出现。最后，更高分辨率的方法将针对转录和表观遗传 与特定细胞类型的病理和疾病诊断相关的AD的变化，旨在实现 对阿尔茨海默病表型的机械理解。使用该设计，本项目可以直接探测失调 首次在受影响的细胞类型中建立基因网络，在基因或基因之间提供了潜在的因果联系 表观遗传状态和由此产生的基因表达。由此产生的数据集和平台将产生有价值的 对AD的细胞和分子基础的洞察，并将通过数据核心公开访问。

ABSTRACT (PROJECT 2) Alzheimer's disease (AD) is a highly penetrant neurodegenerative disease projected to affect 13.8 million cases in the US by 2050 at a cost of $1.1 trillion if no treatment is developed. AD is characterized by stereotyped progressive neurodegeneration and accumulation of two misfolded proteins in brain regions important for cognition and memory. Neurofibrillary tangles (NFTs) of hyperphosphorylated tau follow a progression like neurodegeneration, while extracellular amyloid beta (Aβ) plaques are initially detected in cortical and deep brain structures. It is unclear whether these pathologies are causal or effects of other underlying processes and currently no anti-tau or anti- Aβ therapies stop or reverse AD. Gene expression studies of AD have largely been performed on tissue or cell populations, and impact of neuronal loss and gliosis on these results is unknown. Epigenetic modifications are also associated with AD, though methylation studies have produced conflicting results and no clear pattern of epigenetic dysregulation associated directly with AD progression has emerged. The present study adapts recently developed high-throughput, single-cell methods for transcriptomic and epigenetic analysis to the identify molecular and gene regulatory hallmarks of “clinically typical” AD without significant co-morbidities. Building off a detailed understanding of neurotypical adult cell types, the project aims to identify transcriptional changes in specific cell types or classes correlated with increasing severity of AD pathology in different brain regions affected by the disease, and then identify gene and chromatin accessibility changes with pathology in vulnerable cell populations. This project will initially optimize single nucleus RNA-seq and epigenetics methods for use with postmortem samples of varying pathology and tissue quality, and generate reference datasets for brain regions to be analyzed in AD. Low-cost, droplet-based single nucleus RNA-seq will then be used to classify and characterize cell types in regions differentially affected by tau and amyloid pathology from many donors spanning AD progression with quantified tau and Aβ pathologies. A broader set of brain regions will then be surveyed on a subset of cases with consistent AD-related phenotypes to understand whether there is a common AD signature across brain regions, and whether signatures of AD can be detected prior to the emergence of neuropathology. Finally, higher-resolution methods will target transcriptomic and epigenetic changes in AD associated with pathology and disease diagnosis in specific cell types, aimed at achieving a mechanistic understanding of AD phenotypes. Using this design, this project can directly probe dysregulated gene networks within affected cell types for the first time, providing a potential causal link between genetic or epigenetic states and resulting gene expression. The resulting datasets and platform will produce valuable insights into the cellular and molecular basis of AD and will be made publicly accessible through the Data Core.