Literature DB >> 25654645

Improved data-dependent acquisition for untargeted metabolomics using gas-phase fractionation with staggered mass range.

Zhixiang Yan1, Ru Yan.   

Abstract

The inherently limited MS2 rate of the mass spectrometer is still restricting the performance of data-dependent acquisition (DDA) for untargeted metabolomics. When dealing with the complex metabolome ocean, top-n-based DDA is just scratching the surface as only a small fraction of the ions could be selected for MS2. Here, we report an improved DDA method for untargeted metabolomics using gas-phase fractionation with staggered mass range (sGPF). Unlike the single m/z segment for conventional GPF, the m/z segments for sGPF were narrower, multiplex, and discrete to allow more homogeneous selection of precursor ions in low, medium, and high m/z ranges. This was achieved indirectly by predefining an inclusion list containing multiple discontinuous m/z ranges. Five fraction levels (2, 4, 6, 8, and 10) and two staggering strategies (staggered wide and narrow subsegments (sGPFa and sGPFb)) were compared for characterizing the human urinary metabolites. For both targeted and untargeted comparison, the highest MS2 coverage was obtained by sGPFb8. Targeted comparison of 60 metabolites indicated sGPFb performed the best for 2, 4, 6, and 8 fractions with an increased MS2 triggering rate of 15.0-36.6% over GPF and 6.6-11.7% over sGPFa. For untargeted screening of phase II metabolites and carboxylates, the best performance achieved by sGPFb8 exhibited a 46.9% increase over GPF8 with the increase evenly distributed in glucuronides (54 vs 38), sulfates (55 vs 41), and carboxylates (31 vs 16). Such superiority of sGPF over GPF is mainly due to the reduced number of concurrent precursor ions and increased relative ion intensity ranks.

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Year:  2015        PMID: 25654645     DOI: 10.1021/ac504325x

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  6 in total

1.  High-resolution MS/MS metabolomics by data-independent acquisition reveals urinary metabolic alteration in experimental colitis.

Authors:  Zhixiang Yan; Ting Li; Bin Wei; Panpan Wang; Jianbo Wan; Yitao Wang; Ru Yan
Journal:  Metabolomics       Date:  2019-04-30       Impact factor: 4.290

2.  An integrated strategy for comprehensive characterization of metabolites and metabolic profiles of bufadienolides from Venenum Bufonis in rats.

Authors:  Wen-Long Wei; Hao-Jv Li; Wen-Zhi Yang; Hua Qu; Zhen-Wei Li; Chang-Liang Yao; Jin-Jun Hou; Wan-Ying Wu; De-An Guo
Journal:  J Pharm Anal       Date:  2021-02-12

3.  Enhanced Lipidome Coverage in Shotgun Analyses by using Gas-Phase Fractionation.

Authors:  Milad Nazari; David C Muddiman
Journal:  J Am Soc Mass Spectrom       Date:  2016-08-25       Impact factor: 3.109

4.  LipidFrag: Improving reliability of in silico fragmentation of lipids and application to the Caenorhabditis elegans lipidome.

Authors:  Michael Witting; Christoph Ruttkies; Steffen Neumann; Philippe Schmitt-Kopplin
Journal:  PLoS One       Date:  2017-03-09       Impact factor: 3.240

Review 5.  Existing and Emerging Metabolomic Tools for ALS Research.

Authors:  Christine Germeys; Tijs Vandoorne; Valérie Bercier; Ludo Van Den Bosch
Journal:  Genes (Basel)       Date:  2019-12-05       Impact factor: 4.096

6.  Rapid tryptic peptide mapping of human serum albumin using DI-MS/MSALL.

Authors:  Ke Zhang; Xingcheng Gong; Qian Wang; Pengfei Tu; Jun Li; Yuelin Song
Journal:  RSC Adv       Date:  2022-03-29       Impact factor: 3.361

  6 in total

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