A new strategy for MS/MS data acquisition applying multiple data dependent experiments on Orbitrap mass spectrometers in non-targeted metabolomic applications View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2014-12-20

AUTHORS

Graham Mullard, James W. Allwood, Ralf Weber, Marie Brown, Paul Begley, Katherine A. Hollywood, Martin Jones, Richard D. Unwin, Paul N. Bishop, Garth J. S. Cooper, Warwick B. Dunn

ABSTRACT

The annotation and identification of metabolites is a current bottleneck in non-targeted metabolomics studies. Although accurate measurement of m/z is applied routinely in UPLC–MS applications to putatively annotate metabolites, the acquisition of MS/MS or MSn data is required to deduce structural information, further reducing the chemical search-space and providing greater confidence in metabolite annotation by comparison to mass spectral databases. Here we propose an innovative new strategy for data dependent acquisition (DDA) for on-line MS/MS data acquisition in parallel to full-scan metabolite profiling on LTQ-Orbitrap mass spectrometers. We recommend the application of different and integrated DDA MS/MS experiments to increase the coverage of unique metabolites for which MS/MS data were acquired. We demonstrate, for the first time, that the integrated application of both CID and higher-energy collisional dissociation ion activation methods, multiple different activation energies and narrow precursor ion m/z ranges of 100 or 300 for acquisition of MS/MS spectra provide complementary information and increases the number of unique metabolites for which MS/MS data is acquired. The strategies herein provide a different approach for data acquisition to increase the number of unique MS/MS mass spectra acquired for metabolite annotation purposes in non-targeted metabolomics studies. MS/MS data are available on request from the corresponding author. More... »

PAGES

1068-1080

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11306-014-0763-6

DOI

http://dx.doi.org/10.1007/s11306-014-0763-6

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https://app.dimensions.ai/details/publication/pub.1002502555


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