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

项目摘要 阿尔茨海默病（AD）影响美国85岁以上人口的一半，其特征是认知功能障碍。 损伤和预期寿命缩短。尽管有广泛的临床和基因组研究， AD的发展和进展仍然难以捉摸。小胶质细胞和其他髓样起源细胞（统称为 人脑免疫细胞或HBIC）最近已成为AD发病机制中的关键参与者。这 通过遗传关联研究得到支持，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。因此，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的活性状态 在人类AD的不同阶段。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）调节作用的系统研究 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 变异体位于基因组的非编码区。更具体地说，我们的应用程序设计独特， （1）应用创新的基因组学方法，从300个分离的HBIC中生成多组学数据 供体，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的多样性 亚群，以及检测那些与AD相关的;（3）连接AD风险基因座的变化， HBIC中基因和蛋白质表达的调控机制;以及（4）将HBIC多尺度数据组织成 功能网络并确定AD的关键驱动因素。我们的总体假设是HBIC亚群假设 在衰老和AD早期阶段具有神经保护作用，但随着疾病的进展，特异性HBIC 亚群转化为神经炎性表型。这种转换部分是由AD风险驱动的 遗传变异，影响作为神经炎性HBIC关键驱动因素的基因的调节机制 亚群成功完成拟议的研究将提供：（1）增加机械 了解AD风险位点的功能障碍;（2）优先考虑未来机制的重要位点和基因 研究;（3）访问大规模，多维数据集，以及这些数据集的系统级分析 HBIC中转录调控的数据集，这是一个迫切需要的（目前缺失的）资源。

PROJECT SUMMARY Alzheimer’s disease (AD) affects half the US population over the age of 85 and is characterized by cognitive impairment and reduced life expectancy. Despite extensive clinical and genomic studies, the mechanisms of development and progression of 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.