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，预计将影响 1,380 万人 如果不开发治疗方法，到 2050 年，美国将损失 1.1 万亿美元的病例。 AD的特点是刻板印象 进行性神经变性和两种错误折叠蛋白在大脑区域的积累对于 认知和记忆。过度磷酸化 tau 蛋白的神经原纤维缠结 (NFT) 遵循以下过程： 神经退行性变，而细胞外淀粉样蛋白 (Aβ) 斑块最初在皮质和深部大脑中检测到 结构。目前尚不清楚这些病理现象是否是其他潜在过程的因果关系或影响 目前尚无抗 tau 蛋白或抗 Aβ 疗法可以阻止或逆转 AD。 AD 的基因表达研究主要是 对组织或细胞群进行，神经元丢失和神经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 相关，尽管甲基化研究产生了相互矛盾的结果 结果表明，尚未出现与 AD 进展直接相关的表观遗传失调的明确模式。 本研究采用最近开发的高通量单细胞方法进行转录组学和 表观遗传分析可识别“临床典型”AD 的分子和基因调控标志，无需 显着的合并症。该项目的目标是在对神经典型成体细胞类型的详细了解的基础上 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 受疾病影响的不同大脑区域的病理学，然后识别基因和染色质的可及性 脆弱细胞群的病理变化。该项目将首先优化单核RNA-seq 和表观遗传学方法，用于不同病理学和组织质量的死后样本，并生成 AD 中待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 然后用于对受 tau 和淀粉样蛋白病理影响不同的区域中的细胞类型进行分类和表征 来自许多跨越 AD 进展的捐赠者，并具有量化的 tau 和 Aβ 病理学。更广泛的大脑 然后将对具有一致 AD 相关表型的病例子集进行调查，以了解是否 整个大脑区域有一个共同的 AD 特征，以及是否可以在 AD 特征之前检测到 AD 特征 神经病理学的出现。最后，更高分辨率的方法将针对转录组和表观遗传学 AD 的变化与特定细胞类型的病理学和疾病诊断相关，旨在实现 AD 表型的机制理解。使用这种设计，该项目可以直接探测失调 首次研究受影响细胞类型内的基因网络，提供遗传或 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 细胞和分子基础的深入了解将通过数据核心公开。

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.