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.

项目摘要 阿尔茨海默氏病（AD）是一种毁灭性的神经退行性疾病，对生活质量产生了深远的影响 在受影响的人及其亲戚的社会和财务上。尽管进行了广泛的临床和基因组研究，但 确切的AD发展和进展的机制仍然难以捉摸。小胶质细胞和其他髓样起源 已经鉴定出细胞，统称为人脑免疫细胞（HBIC），在 AD发病机理。这是通过遗传关联研究来支持的，其中许多常见和罕见 风险基因座影响在HBIC中优先或选择性表达的基因，强调了关键的作用 AD中的先天免疫系统。在父授予1R01AG065582中，我们利用荧光激活的细胞分类 将CD45+/CD11b+ HBIC分离与人脑新鲜组织。然后，我们应用创新的神经基因组学和 单细胞的方法来生成HBICS的全面，高通量，多摩西分子剖面 来自AD不同阶段的300个捐助者。这些非凡的资源可以为角色提供关键的见解 通过增加AD风险基因座功能障碍的机械理解，AD中的免疫细胞的免疫细胞。一个关键 父母赠款中目前尚未解决的组件是使用创新的基因组方法 AI/ML技术，可以协调不同的OMIC模式的信号，并为您提供新颖的见解 小胶质细胞和其他免疫细胞在AD中的作用。 在此补充中，为了增加数据的效用，我们建议开发和维护 AI/ML应用的高维HBIC OMICS数据。此外，我们建议建立一个多尺度 利用单细胞OMIC数据的集成深度学习模型，以证明资源的实用性和 作为其他人提供定量性能度量的基准。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 在AD的不同阶段的签名。拟议的工作将应对发展的潜在挑战 AI/ML应用程序。我们建议：（1）识别和消除技术变化的潜在来源和 将数据（2）均匀处理和准备完全注释的AI/ML准备资源的数据归一化（2） 包含使用现代AI/ML工具（3）共享和协作想法的快速原型制作的形式 使用广告知识基本门户网站论坛。成功完成拟议的研究将：（1）促进访问 到有关AI/ML应用的HBIC的大规模的多维数据集； （2）加速研究 对AD的发作和进展的机械理解增加了； （3）提供系统级别的见解 关于使用集成AI/ML模型在HBIC和AD发病机理中的转录调节； （4）提供 优先考虑的重要基因座和基因清单，用于未来的AD机械研究。与模范系统一起 这些数据集的水平分析和注释用于基于AI/ML的研究，它将提供科学 社区急需的资源。