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

了解人脑免疫细胞在阿尔茨海默病中的保护和神经炎症作用

• 批准号: 10412322
• 负责人: VAHRAM HAROUTUNIAN
• 金额: $ 33.8万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-15 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10412322
• 关键词: Address; Adopted; Affect; Age; Alzheimer' s Disease; Alzheimer' s disease risk; American; Artificial Intelligence; Benchmarking; Biological Phenomena; Brain; CRISPR/Cas technology; Cells; Clinical; Communities; Complex; Data; Data Set; Development; Disease; Documentation; Etiology; Fluorescence-Activated Cell Sorting; Fresh Tissue; Functional disorder; Future; Genes; Genomic approach; Genomics; Goals; Human; ITGAM gene; Immune; Innate Immune System; Knowledge Portal; Libraries; Machine Learning; Manuals; Measures; Metadata; Methods; Microglia; Modality; Modeling; Modernization; Molecular Profiling; Myelogenous; Neurodegenerative Disorders; PTPRC gene; Pathogenesis; Pathogenicity; Performance; Play; Population; Process; Publishing; Quality of life; Readability; Research; Research Personnel; Resolution; Resource Sharing; Resources; Role; Signal Transduction; Source; Synapses; System; Techniques; Time; Transcriptional Regulation; Update; Variant; Work; brain tissue; cloud storage; cohort; data format; data sharing networks; deep learning; genetic association; high dimensionality; induced pluripotent stem cell; innovation; insight; machine learning method; multidimensional data; multiple omics; neurogenomics; neuroinflammation; novel; parent grant; prototype; research based learning; risk variant; selective expression; social; tool; transcriptome; transcriptomics

PROJECT SUMMARY Alzheimer's disease (AD) is a devastating neurodegenerative disease that deeply impacts the quality of life both socially and financially for affected ones and their relatives. Despite extensive clinical and genomic studies, the exact mechanisms of development and progression of AD remain elusive. Microglia and other myeloid origin cells, collectively known as human brain immune cells (HBICs), have been identified to play crucial roles 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 the parent grant 1R01AG065582, we utilize fluorescence-activated cell sorting to isolate CD45+/CD11b+ HBICs from human brain fresh tissue. We then apply innovative neurogenomics and single-cell approaches to generate comprehensive, high-throughput, multi-omics molecular profiles of HBICs from 300 donors at different stages of AD. These remarkable resources can provide critical insights into the role of immune cells in AD by increasing our mechanistic understanding of dysfunction in AD risk loci. One critical component that is currently not addressed in the parent grant is to apply innovative genomic approaches using AI/ML techniques, which can harmonize the signals from different omics modalities and offer a novel insight into the role of microglia and other immune cells in AD. In this Supplement, to increase the utility of the data, we propose to develop and maintain a shared resource of high-dimensional HBIC omics data for AI/ML applications. In addition, we propose to build a multi-scale integrative deep learning model leveraging single-cell omics data, to demonstrate the utility of the resource and serve as a benchmark for others to provide a quantitative measure of performance. This model will help us to identify protective and neuroinflammatory HBIC subpopulations and colocalize transcriptomic and regulatory signatures at different stages of AD. The proposed work will address potential challenges in the development of AI/ML applications. We propose: (1) identifying and removing potential sources of technical variations and normalize the data (2) uniformly processing and preparing fully annotated AI/ML-ready resource in a self- contained form for rapid prototyping with modern AI/ML tools (3) sharing and collaborating ideas using an open forum using AD knowledge base portal. Successful completion of the proposed studies will: (1) facilitate access to large-scale, multidimensional datasets on HBICs for AI/ML applications; (2) accelerate researches for an increased mechanistic understanding of the onset and progression of AD; (3) provide systems-level insights about transcriptional regulation in HBICs and AD pathogenesis using integrative AI/ML model; (4) provide a prioritized list of significant loci and genes for future mechanistic studies in AD. Together with exemplary systems- level analyses and annotations of these datasets for AI/ML-based research, it will provide to the scientific community an urgently needed resource.

项目概要 阿尔茨海默病 (AD) 是一种毁灭性的神经退行性疾病，严重影响人们的生活质量 受影响者及其亲属的社会和经济利益。尽管进行了大量的临床和基因组研究， AD 发生和进展的确切机制仍然难以捉摸。小胶质细胞和其他骨髓来源 细胞，统称为人脑免疫细胞（HBIC），已被确定在 AD的发病机制。这得到了遗传关联研究的支持，其中许多常见和罕见的 风险位点影响在 HBIC 中优先或选择性表达的基因，强调了 HBIC 的关键作用 AD 中的先天免疫系统。在母基金 1R01AG065582 中，我们利用荧光激活细胞分选 从人脑新鲜组织中分离 CD45+/CD11b+ HBIC。然后我们应用创新的神经基因组学和 单细胞方法生成 HBIC 的全面、高通量、多组学分子谱 来自 AD 不同阶段的 300 名捐赠者。这些非凡的资源可以提供对该角色的重要见解 通过增加我们对 AD 风险位点功能障碍的机制理解，研究 AD 中免疫细胞的作用。一位批评者 目前父资助中未涉及的部分是应用创新的基因组方法 人工智能/机器学习技术，可以协调来自不同组学模式的信号，并提供新颖的见解 小胶质细胞和其他免疫细胞在 AD 中的作用。 在本补充文件中，为了提高数据的效用，我们建议开发和维护以下数据的共享资源： 用于 AI/ML 应用的高维 HBIC 组学数据。此外，我们建议建立一个多尺度的 利用单细胞组学数据的综合深度学习模型，展示资源的实用性和 作为其他人的基准，提供绩效的定量衡量标准。这个模型将帮助我们 识别保护性和神经炎症性 HBIC 亚群，并共定位转录组和调节组 AD不同阶段的签名。拟议的工作将解决发展中的潜在挑战 人工智能/机器学习应用。我们建议：(1) 识别并消除技术变化的潜在来源， 标准化数据 (2) 统一处理和准备完全注释的 AI/ML 就绪资源 使用现代 AI/ML 工具进行快速原型设计的包含形式 (3) 使用开放式平台共享和协作想法 使用AD知识库门户的论坛。成功完成拟议研究将：(1) 促进获取 用于 AI/ML 应用的 HBIC 上的大规模多维数据集； （二）加快研究 增加对 AD 发病和进展机制的了解； (3) 提供系统级洞察 使用综合 AI/ML 模型研究 HBIC 的转录调控和 AD 发病机制； (4) 提供一个 用于未来 AD 机制研究的重要位点和基因的优先列表。与示范系统一起- 对这些数据集进行水平分析和注释，用于基于人工智能/机器学习的研究，它将为科学界提供 社区急需的资源。