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

项目摘要 尽管进行了广泛的临床和基因组研究，但其发展和进展的机制仍不清楚。 阿尔茨海默病（AD）仍然难以捉摸。小胶质细胞和其他髓样起源细胞（统称为人类 脑免疫细胞或HBIC）最近已成为AD发病机制中的关键参与者。这是 通过遗传关联研究支持，其中许多常见和罕见的风险位点影响基因， 优先或选择性地在HBIC中表达，强调先天免疫系统的关键作用 在AD中。此外，在AD小鼠模型中的单细胞RNA测序分析已经鉴定出小胶质细胞， 存在于神经变性部位的亚群。目前尚不清楚HBIC是否承担保护性或 这可能是有害的，但这可能取决于AD的阶段和进展。进一步分析 需要从人脑中纯化的小胶质细胞和其他免疫细胞来了解HBIC的状态 在人类AD不同阶段的活性。由于HBIC占总脑细胞的一小部分， 在人脑组织中基于匀浆的研究不太可能捕获HBIC分子的全谱 签名，特别是鉴于对大脑中HBIC多样性的日益重视。拟议 工作解决了以前研究的一些局限性，并集中在：（1）细胞类型特异性和单一 从人脑组织中分离的免疫细胞的细胞研究;和（2）调节性的系统研究 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。更具体地说，我们的应用程序是 独特设计：（1）应用创新的基因组学方法，从HBIC生成多组学数据 从300名供体中分离，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）进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 HBIC中基因和蛋白质表达调节机制改变的AD风险位点;和（4） 在功能网络中组织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早期阶段发挥神经保护作用，但作为疾病 当疾病进展时，特异性HBIC亚群转化为神经炎性表型。此转化反应是 部分由AD风险遗传变异驱动，这些变异影响基因的调控机制，这些基因是AD的关键驱动因素。 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制理解;（2）重要基因座的优先级， 用于未来机制研究的基因;以及（3）访问大规模，多维数据集，以及 系统水平分析这些数据集的转录调控在HBIC，这是一个迫切需要的 (and目前缺少资源。

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.