diaPASEF:平行累积-串联碎片化与数据非依赖性采集的组合。

diaPASEF: parallel accumulation-serial fragmentation combined with data-independent acquisition.

机构信息

Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.

Functional Proteomics, Jena University Hospital, Jena, Germany.

出版信息

Nat Methods. 2020 Dec;17(12):1229-1236. doi: 10.1038/s41592-020-00998-0. Epub 2020 Nov 30.

Abstract

Data-independent acquisition modes isolate and concurrently fragment populations of different precursors by cycling through segments of a predefined precursor m/z range. Although these selection windows collectively cover the entire m/z range, overall, only a few per cent of all incoming ions are isolated for mass analysis. Here, we make use of the correlation of molecular weight and ion mobility in a trapped ion mobility device (timsTOF Pro) to devise a scan mode that samples up to 100% of the peptide precursor ion current in m/z and mobility windows. We extend an established targeted data extraction workflow by inclusion of the ion mobility dimension for both signal extraction and scoring and thereby increase the specificity for precursor identification. Data acquired from whole proteome digests and mixed organism samples demonstrate deep proteome coverage and a high degree of reproducibility as well as quantitative accuracy, even from 10 ng sample amounts.

摘要

数据非依赖性采集模式通过在预定义的前体质荷比范围的片段中循环,来分离和同时碎片化不同前体的离子群体。尽管这些选择窗口共同涵盖了整个质荷比范围,但总体上只有少数百分之几的入射离子被隔离用于质谱分析。在这里,我们利用在俘获离子淌度装置(timsTOF Pro)中的分子量和离子淌度的相关性,设计了一种扫描模式,该模式可以在质荷比和淌度窗口中对高达 100%的肽前体离子电流进行采样。我们通过包括离子淌度维度来扩展现有的靶向数据提取工作流程,用于信号提取和评分,从而提高前体识别的特异性。从全蛋白质组消化物和混合生物样本中获得的数据证明了深度蛋白质组覆盖度和高度重现性,以及定量准确性,即使在 10ng 样品量的情况下也是如此。

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