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<p>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 <i>Panax</i> 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&#xa0;data&#xa0;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 <i>Panax</i> 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.</p> Graphical Abstract <p></p>

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

  • 期刊论文

  • 发表日期：

    2025年4月2日

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  • Yi Wu,
  • Yang Wang

摘要

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.

Graphical Abstract