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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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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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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