Plubell Deanna L, Huang Eric, Spencer Sandra E, Poston Kathleen L, Montine Thomas J, MacCoss Michael J
University of Washington, Department of Genome Sciences, Seattle, Washington 98195, United States.
Stanford University, Department of Neurology & Neurological Sciences, Stanford, California 94305, United States.
J Proteome Res. 2025 Jun 6;24(6):2885-2891. doi: 10.1021/acs.jproteome.5c00016. Epub 2025 May 6.
Mass spectrometry based targeted proteomics methods provide a sensitive and high-throughput analysis of selected proteins. To develop a targeted bottom-up proteomics assay, peptides must be evaluated as proxies for the measurement of a protein or proteoform in a biological matrix. Candidate peptide selection typically relies on predetermined biochemical properties, data from semistochastic sampling, or empirical measurements. These strategies require extensive testing and method refinement due to the difficulties associated with prediction of the peptide response in the biological matrix of interest. Gas-phase fractionated (GPF) narrow window data-independent acquisition (DIA) aids in the development of reproducible selected reaction monitoring (SRM) assays by providing matrix-specific information on peptide detectability and quantification by mass spectrometry. To demonstrate the suitability of DIA data for selecting peptide targets, we reimplement a portion of an existing assay to measure 98 Alzheimer's disease proteins in cerebrospinal fluid (CSF). Peptides were selected from GPF-DIA based on signal intensity and reproducibility. The resulting SRM assay exhibits a quantitative precision similar to that of published data, despite the inclusion of different peptides between the assays. This workflow enables development of new assays without additional upfront data acquisition, demonstrated here through generation of a separate assay for an unrelated set of proteins in CSF from the same data set.
基于质谱的靶向蛋白质组学方法能够对选定的蛋白质进行灵敏且高通量的分析。要开发一种靶向自下而上的蛋白质组学检测方法,必须将肽段作为生物基质中蛋白质或蛋白质变体测量的替代物进行评估。候选肽段的选择通常依赖于预先确定的生化特性、半随机采样的数据或经验测量。由于难以预测目标生物基质中肽段的响应情况,这些策略需要进行广泛的测试和方法优化。气相分级(GPF)窄窗口数据非依赖采集(DIA)通过提供关于肽段可检测性和质谱定量的基质特异性信息,有助于开发可重现的选择反应监测(SRM)检测方法。为了证明DIA数据用于选择肽段靶点的适用性,我们重新实施了现有检测方法的一部分,以测量脑脊液(CSF)中的98种阿尔茨海默病相关蛋白。基于信号强度和可重复性从GPF-DIA中选择肽段。尽管两种检测方法所包含的肽段不同,但最终的SRM检测方法显示出与已发表数据相似的定量精度。这种工作流程能够在无需额外前期数据采集的情况下开发新的检测方法,本文通过利用同一数据集为CSF中另一组不相关蛋白质生成单独的检测方法进行了展示。