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.

项目总结