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

项目摘要 阿尔茨海默病（Alzheimer's disease，AD）是一种严重影响人类生活质量的神经退行性疾病， 为受影响的人及其亲属提供社会和经济援助。尽管有广泛的临床和基因组研究， AD的发展和进展的确切机制仍然难以捉摸。小胶质细胞和其他髓样起源 细胞，统称为人脑免疫细胞（HBIC），已被确定在免疫系统中发挥关键作用。 AD的发病机制这是通过遗传关联研究支持的，其中许多常见和罕见的 风险基因座影响HBIC中优先或选择性表达的基因，强调了HBIC的关键作用。 先天免疫系统在AD中的作用在母授权1 R 01 AG 065582中，我们利用荧光激活细胞分选 从新鲜人脑组织中分离CD 45 +/CD 11b + HBIC。然后，我们应用创新的神经基因组学， 单细胞方法产生全面的、高通量的、多组学的HBIC分子谱 来自300位不同阶段的捐赠者这些非凡的资源可以提供关键的见解的作用 通过增加我们对AD风险基因座功能障碍的机制理解，一个关键 目前在母基金中没有涉及的一个组成部分是应用创新的基因组方法， AI/ML技术，可以协调来自不同组学模式的信号，并提供一种新的见解， 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，为提高数据的效用，我们建议开发和维护一个共享资源， 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，以展示资源的效用， 作为其他人的基准，提供业绩的量化衡量标准。这个模型将帮助我们 鉴定保护性和神经炎性HBIC亚群并共定位转录组和调节性HBIC亚群 在AD的不同阶段签名。拟议的工作将解决发展中的潜在挑战， AI/ML应用。我们建议：（1）确定和消除技术变化的潜在来源， 规范化数据（2）在自定义的数据库中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表单（3）使用开放的 论坛使用AD知识库门户。成功完成拟议的研究将：（1）促进获得 为AI/ML应用提供HBIC上的大规模多维数据集;（2）加速研究 增加对AD发病和进展的机制理解;（3）提供系统水平的见解 利用整合AI/ML模型研究HBICs的转录调控和AD的发病机制;（4）提供一个 优先列表的重要位点和基因，为未来的机制研究，在AD。与示范系统一起- 水平分析和注释这些数据集的AI/ML为基础的研究，它将提供给科学 这是一个急需的资源。

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.