Bruderer Roland, Sondermann Julia, Tsou Chih-Chiang, Barrantes-Freer Alonso, Stadelmann Christine, Nesvizhskii Alexey I, Schmidt Manuela, Reiter Lukas, Gomez-Varela David
Biognosys AG, Schlieren, Switzerland.
Somatosensory Signaling and Systems Biology Research Group, Max Planck Institute of Experimental Medicine, Goettingen, Germany.
Proteomics. 2017 May;17(9). doi: 10.1002/pmic.201700021.
The use of data-independent acquisition (DIA) approaches for the reproducible and precise quantification of complex protein samples has increased in the last years. The protein information arising from DIA analysis is stored in digital protein maps (DIA maps) that can be interrogated in a targeted way by using ad hoc or publically available peptide spectral libraries generated on the same sample species as for the generation of the DIA maps. The restricted availability of certain difficult-to-obtain human tissues (i.e., brain) together with the caveats of using spectral libraries generated under variable experimental conditions limits the potential of DIA. Therefore, DIA workflows would benefit from high-quality and extended spectral libraries that could be generated without the need of using valuable samples for library production. We describe here two new targeted approaches, using either classical data-dependent acquisition repositories (not specifically built for DIA) or ad hoc mouse spectral libraries, which enable the profiling of human brain DIA data set. The comparison of our results to both the most extended publically available human spectral library and to a state-of-the-art untargeted method supports the use of these new strategies to improve future DIA profiling efforts.
近年来，数据非依赖型采集（DIA）方法在复杂蛋白质样品的可重复且精确的定量分析中的应用日益增多。DIA分析产生的蛋白质信息存储在数字蛋白质图谱（DIA图谱）中，通过使用与生成DIA图谱相同的样本物种生成的特定或公开可用的肽谱库，可以对其进行靶向查询。某些难以获取的人体组织（如大脑）的可用性有限，以及使用在可变实验条件下生成的谱库所存在的问题，限制了DIA的潜力。因此，DIA工作流程将受益于高质量且扩展的谱库，这些谱库可以在无需使用珍贵样本进行库构建的情况下生成。我们在此描述了两种新的靶向方法，一种使用经典的数据依赖型采集数据库（并非专门为DIA构建），另一种使用特定的小鼠谱库，这两种方法都能够对人脑DIA数据集进行分析。将我们的结果与最全面的公开可用人类谱库以及一种先进的非靶向方法进行比较，支持使用这些新策略来改进未来的DIA分析工作。
