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致残和死亡的主要原因 人口老龄化加剧造成的健康问题，给健康带来了难以形容的痛苦，并对健康提出了严峻挑战 护理系统和经济是肯定的。解决方案只会来自创新研究。我们的应用程序是 通过提议利用创新的分子成像技术来高度响应这一紧迫的科学需求 斯坦福大学发明的多路离子束成像(MIBI)平台。我们将确定高维空间 AD相关分子的细胞和亚细胞蛋白水平的表达、相互作用和定位 利用人脑切片通过基因组和蛋白质组学研究确定弹性和病理状态 来自一项正在进行的AD蛋白质组学研究。我们假设，深入的、特定阶段的表型 从这些独特的组中提取的签名将提供对支持 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 一种亚细胞的表型结构，由脆弱和抵抗的脑区组成。下一代MIBI设备 将被用来同时对30种蛋白质进行严格成像；目标标记神经元、突触、 将同时测量非神经细胞、神经炎性和血管成分。(2)揭示 认知韧性使表型多样化。使用我们在其他单曲中成功应用的方法 细胞研究，我们将使用统计深度学习方法分析多路成像数据 在我们的实验室建立，以确定拓扑，细胞和分子表型的差异，弹性，共同 病态和阿尔茨海默病。(3)实现共享数据仓库。多路复用成像的威力 生物学是揭示共同定位或相互排他性的能力，以推断监管角色并获得机械性 洞察力。为了传播我们提议的高度多元化研究中的数百张这样的图像，我们将创建一个 基于Web的门户，使用已建立的基础设施，可以在其中放置本研究的所有图像 访问，多色覆盖生成即席，所有功能将自由共享。

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.