Kraut Alexandra, Louwagie Mathilde, Bruley Christophe, Masselon Christophe, Couté Yohann, Brun Virginie, Hesse Anne-Marie
Université Grenoble Alpes, CEA, Inserm, BGE U1038, Grenoble, France.
Methods Mol Biol. 2019;1959:129-150. doi: 10.1007/978-1-4939-9164-8_9.
In discovery proteomics experiments, tandem mass spectrometry and data-dependent acquisition (DDA) are classically used to identify and quantify peptides and proteins through database searching. This strategy suffers from known limitations such as under-sampling and lack of reproducibility of precursor ion selection in complex proteomics samples, leading to somewhat inconsistent analytical results across large datasets. Data-independent acquisition (DIA) based on fragmentation of all the precursors detected in predetermined isolation windows can potentially overcome this limitation. DIA promises reproducible peptide and protein quantification with deeper proteome coverage and fewer missing values than DDA strategies. This approach is particularly attractive in the field of clinical biomarker discovery, where large numbers of samples must be analyzed. Here, we describe a DIA workflow for non-depleted serum analysis including a straightforward approach through which to construct a dedicated spectral library, and indications on how to optimize chromatographic and mass spectrometry analytical methods to produce high-quality DIA data and results.
在发现蛋白质组学实验中，串联质谱和数据依赖型采集（DDA）传统上用于通过数据库搜索来鉴定和定量肽段与蛋白质。这种策略存在一些已知的局限性，例如在复杂蛋白质组样本中存在采样不足以及前体离子选择缺乏重现性，导致在大型数据集中的分析结果有些不一致。基于在预定隔离窗口中检测到的所有前体的碎裂的数据非依赖型采集（DIA）有可能克服这一局限性。与DDA策略相比，DIA有望实现可重现的肽段和蛋白质定量，具有更深的蛋白质组覆盖范围和更少的缺失值。这种方法在临床生物标志物发现领域特别有吸引力，因为在该领域必须分析大量样本。在此，我们描述了一种用于非耗尽血清分析的DIA工作流程，包括一种构建专用光谱库的直接方法，以及关于如何优化色谱和质谱分析方法以产生高质量DIA数据和结果的说明。
