MIRIAD - Multiplexed Imaging of Resilience In Alzheimers Disease

摘要 阿尔茨海默病（AD）仍然是美国和全球主要公众残疾和死亡的主要原因 由于人口老龄化增加而造成的健康问题造成了难以形容的痛苦，并对健康构成了严峻挑战 经济和社会发展是必然的。解决方案只能来自创新研究。我们的应用程序 高度响应这一迫切的科学需求，提出利用创新的分子成像技术， 斯坦福大学发明的多路离子束成像（MIBI）平台。我们将确定高维 AD相关分子的细胞和亚细胞蛋白水平表达、相互作用和定位 通过基因组和蛋白质组学研究，使用人脑切片在恢复力和病理状态下进行鉴定 一项正在进行的AD蛋白质组学研究我们假设，在深入，阶段特异性表型 从这些独特的群体中提取的签名将提供对支持 大脑储备的保护机制。我们将通过三个具体目标来检验我们的假设：（1）建立 脆弱和抵抗大脑区域的亚细胞表型框架。下一代MIBI设备 将用于同时对30多种蛋白质进行严格成像;目标标记神经元，突触， 将同时测量非神经元细胞、神经炎症和血管成分。(2)揭示 认知弹性复合表型。使用我们成功应用于其他单一 细胞研究，我们将分析多路复用成像数据与统计深度学习方法已经 建立在我们的实验室，以确定拓扑，细胞和分子表型之间的差异弹性，共同， 病态和AD痴呆(3)实施共享数据存储库。多路复用成像在 生物学是其揭示共定位或互斥性以推断调节作用并获得机制的能力。 洞察力.为了传播我们提出的高度多路复用研究中的数百张图像，我们将创建一个 基于网络的门户网站，使用已经建立的基础设施，其中可以从本研究的所有图像 访问，多颜色叠加产生的特设，所有的功能将免费共享。

ABSTRACT Alzheimer's disease (AD) remains a leading cause of disability and death in the US and major global public health problem due to rise in aging population resulting in untold suffering, and severe challenges to health care systems and economies is certain. Solutions will come only from innovative research. Our application is highly responsive to this urgent scientific need by proposing to leverage an innovative molecular imaging platform invented at Stanford, multiplexed ion beam imaging (MIBI). We will determine the high dimensional cellular and subcellular protein-level expression, interaction, and localization for AD-relevant molecules identified by genomic and proteomic studies in resilience and pathologic states using human brain sections from an ongoing proteomic investigation of AD. We hypothesize that in-depth, stage-specific phenotypic signatures extracted from these unique groups will provide insight into the modifiable factors that endorse the protective mechanisms of brain reserve. We will test our hypothesis through three Specific Aims: (1) Establish a subcellular, phenotypic framework of vulnerable and resistant brain areas. Next-generation MIBI equipment will be used to rigorously image 30+ proteins simultaneously; targets marking subtypes of neurons, synapses, non-neuronal cells, neuro-inflammatory, and vascular components will be concurrently measured. (2) Reveal cognitive resilience multiplexed phenotypes. Using approaches we have successfully applied in other single cell studies, we will analyze multiplexed imaging data with statistical deep learning methods already established in our lab to identify topological, cellular, and molecular phenotypic differences among resilient, co- morbid, and AD dementia. (3) Implement a shared data repository. The power of multiplexed imaging in biology is its ability to reveal co-localization or mutual exclusivity to infer regulatory roles and gain mechanistic insight. To disseminate hundreds of these images from our proposed highly multiplexed study, we will create a web-based portal, using already established infrastructure, where all of the images from this study can be accessed, multi-color overlays generated ad hoc, and all the features will be shared freely.