A comparative study of data-independent acquisition and data-dependent acquisition in liquid chromatography-mass spectrometry-based untargeted metabolomics analysis of Panax genus sample.

IF 3.8 2区化学 Q1 BIOCHEMICAL RESEARCH METHODS

Analytical and Bioanalytical Chemistry Pub Date : 2025-04-02 DOI:10.1007/s00216-025-05861-9

Yi Wu, Yang Wang

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引用次数: 0

Abstract

Data-independent acquisition (DIA) and data-dependent acquisition (DDA) are frequently employed in the execution of tandem mass spectrometry (MS2) analyses. This study explored the application of DIA (MSe) and DDA (fast-DDA) in liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics using Panax genus samples. MSe provided comprehensive sample information, extracting more ion peaks with better peak shape and increased scan points compared to fast-DDA. Features from MSe data are four times more than those from fast-DDA data. Fast-DDA, however, delivered high-quality MS2 data, enhancing compound annotation via the GNPS web tool. Database matches with fast-DDA data were nearly 35 times greater than those using MSe data. Therefore, combining MSe and fast-DDA can improve data analysis and metabolite annotation. An improved workflow integrating DIA and DDA was proposed, requiring additional QC sample injections for DDA analysis but eliminating the need for sample reprocessing and re-analysis, thus saving time and resources. The established workflow was applied to the Panax genus samples analysis to confirm its applicability. This study offers a deeper understanding of DIA and DDA, guiding the selection of data acquisition strategies for LC-MS-based untargeted metabolomics.

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来源期刊

Analytical and Bioanalytical Chemistry 化学-分析化学

CiteScore

8.00

自引率

4.70%

发文量

638

审稿时长

2.1 months

期刊介绍： Analytical and Bioanalytical Chemistry’s mission is the rapid publication of excellent and high-impact research articles on fundamental and applied topics of analytical and bioanalytical measurement science. Its scope is broad, and ranges from novel measurement platforms and their characterization to multidisciplinary approaches that effectively address important scientific problems. The Editors encourage submissions presenting innovative analytical research in concept, instrumentation, methods, and/or applications, including: mass spectrometry, spectroscopy, and electroanalysis; advanced separations; analytical strategies in “-omics” and imaging, bioanalysis, and sampling; miniaturized devices, medical diagnostics, sensors; analytical characterization of nano- and biomaterials; chemometrics and advanced data analysis.