Ku Xin, Yan Wei
Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Centre for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
Methods Mol Biol. 2019;1959:151-161. doi: 10.1007/978-1-4939-9164-8_10.
Global proteomics analyses are traditionally performed in data-dependent acquisition (DDA) mode, which results in inadequate reproducibility across large sample cohorts due to the under-sampling inherent to shotgun proteomics. Recently, data-independent acquisition (DIA) strategies were introduced to allow reproducible detection and quantification of thousands of proteins with consistent sensitivity across samples. Here, we present an approach to analyze changes to the protein network in human peripheral blood mononuclear cells (PBMCs) from clinical blood samples, using DIA as a unique platform for biomarker discovery. We describe how to generate spectral PBMC proteome libraries by applying peptide fractionation followed by DDA analysis, and then how to apply DIA methods to PBMC samples from individual patients using a high-resolution Orbitrap Fusion mass spectrometer.
传统上，全局蛋白质组学分析以数据依赖型采集（DDA）模式进行，由于鸟枪法蛋白质组学固有的采样不足，导致在大型样本队列中重现性不足。最近，引入了数据非依赖型采集（DIA）策略，以实现对数千种蛋白质的可重现检测和定量，且在各样本间具有一致的灵敏度。在此，我们提出一种方法，以DIA作为生物标志物发现的独特平台，分析来自临床血液样本的人外周血单核细胞（PBMC）中蛋白质网络的变化。我们描述了如何通过应用肽段分级分离随后进行DDA分析来生成PBMC蛋白质组谱库，然后如何使用高分辨率的Orbitrap Fusion质谱仪将DIA方法应用于个体患者的PBMC样本。
