Project 1: Discovery of proteins with altered abundance and stability

摘要 尽管脑脊液Aβ42、tau、磷酸化tau水平与潜在的AD之间存在关联 病理学方面，临床诊断的生物标记物准确性的衡量标准在不同研究之间差别很大。考虑到 其他神经退行性疾病可出现类似AD的临床症状，AD患者 经常合并病理，需要额外的标记物来帮助鉴别诊断和 辨别混合的病理。在过去的十年中，许多候选生物标志物已经被识别出来，反映了 包括胆固醇代谢、神经炎症和淀粉样蛋白在内的一系列病理生理过程 正在处理。然而，已在临床实践中采用或在大范围内独立验证的很少。 队列1. MacCoss实验室和其他实验室一直在开创下一代蛋白质组学方法的发展 作为经典随机质谱学方法的替代方法。这些新方法提供了一种 目标蛋白质组学战略和全球蛋白质组学战略的混合体。虽然质谱学数据是在一个 以无偏见的方式，以有针对性的策略分析数据，其中分析特定的多肽，尽管分析的是1000个 利用先验信息。因此，可重复的目标、吞吐量和基于MS/MS的自信 并行反应监测(PRM)的量化可以与经典发现方法的能力相结合 定性检测数以千计的蛋白质。这些新的方法是基于系统收集的质量 光谱数据可以提供类似的量化品质因数，并可以类似于 临床化验。 尽管蛋白质组学技术取得了进步，但大多数试图发现新的脑脊液标志物的努力要么 使用1)随机抽样方法(例如，数据相关采集)，但量化特征不佳 表现，2)小样本队列，3)完全关注总脑脊液，4)忽略蛋白质处理，以及5) 没有考虑蛋白质的错误折叠或稳定性。本项目在合作研究范围内的目的 方案是将我们的方法提升到另一个水平--将真正的定量方法应用于大井 描述了队列的特征，并将其扩展到功能相关的子群体。 我们有一种脑脊液测定仪，可以从完全分离的物质中以数字形式测定&gt；1050蛋白质。 日内/日间精密度、线性度、LOD/LOQ等优点。此外，我们还可以使用这种分析方法来 脑脊液蛋白质组的子集，以评估神经生物学的功能相关方面，包括数量 部分溶液或脑脊液颗粒，完整的蛋白质相对分子质量和蛋白质稳定性。最后，我们有足够的吞吐量 将这些分析应用到足以消除观察是由异常引起的可能性的范围内 亚群。

Abstract Despite the association between the levels of CSF Aβ42, tau, phosphorylated tau and underlying AD pathology, measures of biomarker accuracy for clinical diagnosis vary widely between studies. Given that other neurodegenerative conditions can present with AD-like clinical symptoms, and individuals with AD frequently have comorbid pathologies, additional markers are needed that can aid in differential diagnosis and identify mixed pathologies. Over the last decade, many candidate biomarkers have been identified, reflecting a range of pathophysiological processes including cholesterol metabolism, neuroinflammation and amyloid processing. However, few, have been adopted in clinical practice or been validated in large independent cohorts1. The MacCoss lab and others have been pioneering the development of next generation proteomics methods as an alternative to the classic stochastic mass spectrometry-based methods. These new methods offer a hybrid between a targeted and global proteomics strategy. While mass spectrometry data is collected in an unbiased way, the data is analyzed in a targeted strategy where specific peptides, albeit 1000s are analyzed using prior information. Thus, the reproducible targeting, throughput, and confident MS/MS-based quantification of parallel reaction monitoring (PRM) can be combined with classical discovery methods' ability to qualitatively detect thousands of proteins. These new methods based on systematically collected mass spectrometry data can offer similar quantitative figures of merit and can be validated in analogous fashion to clinical assays. Despite advances in proteomics technologies, most attempts at discovering new CSF markers have either used 1) stochastic sampling methods (e.g. data dependent acquisition) with poorly characterized quantitative performance, 2) small sample cohorts, 3) focused entirely on total CSF, 4) ignored protein processing, and 5) did not consider protein misfolding or stability. The purpose of this project within the cooperative research program is to take our methods to another level – apply true quantitative methods to large well characterized cohorts and extend them to functionally relevant subpopulations. We have a CSF assay that can measure >1050 proteins from completely unfractionated material with figures of merit of within/between day precision, linearity, LOD/LOQ, etc... Furthermore, we can use this assay on subsets of the CSF proteome to assess functionally relevant aspects of the neurobiology including quantity as part of solution or CSF particles, intact protein MW, and protein stability. Finally, we have enough throughput to apply these analyses on a scale sufficient to eliminate the chance that an observation is due to an aberrant subpopulation.