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Explanatory signal interpretation and metabolite identification strategies for nominal mass FIE-MS metabolite fingerprints

Nature Protocols volume 3, pages 471–485 (2008)Cite this article

Abstract

Flow injection electrospray mass spectrometry (FIE-MS) metabolite fingerprinting is widely used as a 'first pass' screen for compositional differences, where discrimination between samples can be achieved without any preconceptions. Powerful data analysis algorithms can be used to select and rank FIE-MS fingerprint variables highly explanatory of the biological problem under investigation. We describe how to create a species-specific FIE-MS/MSn metabolite database and how to then query the database to predict the identity of highly significant variables within FIE-MS fingerprints. The protocol details how to interpret m/z signals within the explanatory variable list based on a correlation analysis in conjunction with an investigation of mathematical relationships regarding (de)protonated molecular ions, salt adducts, neutral losses and dimeric associations routinely observed in FIE-MS fingerprints. Although designed for use by biologists/analytical chemists, collaboration with data-mining experts is generally advised. The protocol is applicable in any areas of bioscience research involving FIE-MS fingerprinting.

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Figure 1: Flow diagram of steps used to annotate metabolite signals from an FIE-MS fingerprint representing a specific sample matrix.
Figure 2: Flow diagram of steps for the annotation of explanatory signals from FIE-MS fingerprint data-modeling experiments.
Figure 3: FIE-MS fingerprinting of sucrose solutions showing the effect of salts in the sample matrix.
Figure 4: The effect of sodium on the formation of ion clusters.
Figure 5: Examples of neutral losses and chlorine isotopes in metabolite spectra.
Figure 6: FIE-MS/MS3 fragmentation trees of the potato tuber glycoalkaloids chaconine and solanine in positive ion mode showing sequential loss of sugar units.
Figure 7: FIE-MS/MS3 fragmentation tree record of an unknown Brachypodium metabolite (m/z 461) in negative ion mode.
Figure 8: Putative annotation of an arginine [M + H]+ ion from an FIE-MS fingerprint of a crude potato extract by spectrum matching with known standards in the ARMeC database.
Figure 9: Annotation of m/z signals in FIE-MS fingerprints after fragmentation and further chromatography to identify isomers.
Figure 10: Examination of correlation analysis results, isotopic ratios and intensity behavior to discover linked FIE-MS signals.
Figure 11: Correlation analysis of explanatory variables discriminatory for quality traits in a comparison of five different potato cultivars in negative-ion mode FIE-MS.

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Acknowledgements

Research activities were funded by the Food Standards Agency under the project 'Development of unified data models and data pre-processing strategies and the generation of meaningful, standardized statistical analyses of metabolome variability in crop plants' (project code: GO3012).

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  1. David P Overy

    Present address: Present address: CIBE, Merck Sharp & Dohme de España, Madrid 28027, Spain.,

Authors and Affiliations

  1. Institute of Biological Sciences, Aberystwyth University, Aberystwyth, SY23 3DA, Ceredigion, UK

    David P Overy, David P Enot, Kathleen Tailliart, David Parker, Manfred Beckmann & John Draper

  2. Department of Computer Science, Aberystwyth University, Aberystwyth, SY23 3DB, Ceredigion, UK

    Helen Jenkins

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Correspondence to John Draper.

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Overy, D., Enot, D., Tailliart, K. et al. Explanatory signal interpretation and metabolite identification strategies for nominal mass FIE-MS metabolite fingerprints. Nat Protoc 3, 471–485 (2008). https://doi.org/10.1038/nprot.2007.512

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