Understanding the protective and neuroinflammatory role of human brain immune cells in Alzheimer Disease

项目总结 尽管进行了广泛的临床和基因组研究，但肿瘤的发生和发展机制 阿尔茨海默病(AD)仍然难以捉摸。小胶质细胞和其他髓系起源细胞(统称为人类 脑免疫细胞，或HBICs)最近已成为AD发病机制中的关键角色。这是 由遗传关联研究支持，其中许多常见和罕见的风险基因影响 在HBICs中优先或选择性地表达，强调先天免疫系统的关键作用 在公元后。此外，在AD小鼠模型中的单细胞RNA测序分析发现了一种小胶质细胞 出现在神经退行性变部位的亚群。目前尚不清楚HBIC是否具有保护性或 破坏性作用，但这可能会因AD的阶段和进展而不同。因此，进一步分析， 从人脑中纯化的小胶质细胞和其他免疫细胞需要了解HBIC的状态 人类阿尔茨海默病不同阶段的活动。由于HBICs只占脑细胞总数的一小部分， 在人脑组织中基于匀浆的研究不太可能捕捉到HBIC分子的全部光谱 签名，特别是鉴于人们日益认识到大脑中HBIC的多样性。建议数 这项工作解决了以前研究的一些局限性，并集中在：(1)特定细胞类型和单一细胞类型 从人脑组织分离的免疫细胞的细胞研究；以及(2)对 非编码DNA对基因和蛋白质表达的影响，这是必要的，因为大多数 常见的风险变异位于基因组的非编码区。更确切地说，我们的应用程序是 独一无二的设计：(1)应用创新的基因组方法并从HBICs生成多组学数据 从300名献血者中分离到的基因，包括全基因组测序、RNAseq、ATACseq、hic染色体 构象捕获和蛋白质组学；(2)执行最先进的单细胞分析，使我们能够 评估HBIC亚群的多样性，并检测与AD相关的亚群；(3)连接 AD危险基因与HBICs基因和蛋白表达调控机制的变化；以及(4) 组织功能网络中的HBIC多尺度数据，并确定AD的关键驱动因素。我们的总体假设是 HBIC亚群在衰老和AD的早期阶段起到神经保护作用，但作为疾病 随着进展，特定的HBIC亚群转变为神经炎性表型(S)。此转换是 部分由AD风险遗传变异驱动，这些变异影响作为AD关键驱动因素的基因的调节机制 神经炎性HBIC亚群。成功完成拟议的研究将提供：(1) 增加对AD风险基因座功能障碍的机械学理解；(2)确定重要基因座和 未来机制研究的基因；以及(3)获取大规模、多维数据集，以及 对这些数据集进行系统水平的分析，以用于HBICs的转录调控，这是迫切需要的 (且当前缺少)资源。

PROJECT SUMMARY Despite extensive clinical and genomic studies, the mechanisms of development and progression of Alzheimer's disease (AD) remain elusive. Microglia and other myeloid origin cells (collectively called human brain immune cells, or HBICs) have recently emerged as crucial players in the pathogenesis of AD. This is supported through genetic association studies, where many of the common and rare risk loci affect genes that are preferentially or selectively expressed in HBICs, emphasizing the pivotal role of the innate immune system in AD. In addition, single cell RNA sequencing analysis in mouse models of AD has identified a microglia subpopulation that is present at sites of neurodegeneration. It is unclear if HBICs assume a protective or damaging role, but that might vary depending on the stage and progression of AD. Therefore, further analysis of microglia and other immune cells purified from human brains is needed to understand the state of HBIC activity in human AD at different stages of disease. As HBICs constitute a small proportion of total brain cells, homogenate-based studies in human brain tissue are unlikely to capture the full spectrum of HBIC molecular signatures, especially in light of the growing appreciation for the diversity of HBICs in the brain. The proposed work addresses some of the limitations of previous research and is focused on: (1) cell type specific and single cell studies in immune cells isolated from human brain tissue; and (2) a systematic study of the regulatory effects of non-coding DNA on gene and protein expression, which is necessary given that the majority of common risk variants are situated in non-coding regions of the genome. More specifically, our application is uniquely designed to: (1) apply innovative genomic approaches and generate multi-omics data from HBICs isolated from 300 donors, including whole genome sequencing, RNAseq, ATACseq, HiC chromosome conformation capture and proteomics; (2) perform state-of-the-art single cell analysis that will allow us to assess the diversity of HBIC subpopulations, as well as detect those that are associated with AD; (3) connect AD risk loci with changes in the regulatory mechanisms of gene and protein expression in HBICs; and (4) organize HBIC multiscale data in functional networks and identify key drivers for AD. Our overall hypothesis is that HBIC subpopulations assume a neuroprotective role during aging and early stages of AD, but as disease progresses, specific HBIC subpopulations transform to neuroinflammatory phenotype(s). This conversion is partially driven by AD risk genetic variants, which affect regulatory mechanisms of genes that are key drivers of neuroinflammatory HBIC subpopulations. Successful completion of the proposed studies will provide: (1) an increased mechanistic understanding of dysfunction in AD risk loci; (2) prioritization of significant loci and genes for future mechanistic studies; and (3) access to large-scale, multidimensional datasets, together with systems level analyses of these datasets for transcriptional regulation in HBICs, which is an urgently needed (and currently missing) resource.