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Anal Chem. 2012 Jul 3;84(13):5524-34. doi: 10.1021/ac2034216. Epub 2012 Jun 22.

Metabolite identification using automated comparison of high-resolution multistage mass spectral trees.

Author information

1
Netherlands Metabolomics Centre, Leiden, The Netherlands. m.rojas@lacdr.leidenuniv.nl

Abstract

Multistage mass spectrometry (MS(n)) generating so-called spectral trees is a powerful tool in the annotation and structural elucidation of metabolites and is increasingly used in the area of accurate mass LC/MS-based metabolomics to identify unknown, but biologically relevant, compounds. As a consequence, there is a growing need for computational tools specifically designed for the processing and interpretation of MS(n) data. Here, we present a novel approach to represent and calculate the similarity between high-resolution mass spectral fragmentation trees. This approach can be used to query multiple-stage mass spectra in MS spectral libraries. Additionally the method can be used to calculate structure-spectrum correlations and potentially deduce substructures from spectra of unknown compounds. The approach was tested using two different spectral libraries composed of either human or plant metabolites which currently contain 872 MS(n) spectra acquired from 549 metabolites using Orbitrap FTMS(n). For validation purposes, for 282 of these 549 metabolites, 765 additional replicate MS(n) spectra acquired with the same instrument were used. Both the dereplication and de novo identification functionalities of the comparison approach are discussed. This novel MS(n) spectral processing and comparison approach increases the probability to assign the correct identity to an experimentally obtained fragmentation tree. Ultimately, this tool may pave the way for constructing and populating large MS(n) spectral libraries that can be used for searching and matching experimental MS(n) spectra for annotation and structural elucidation of unknown metabolites detected in untargeted metabolomics studies.

PMID:
22612383
DOI:
10.1021/ac2034216
[Indexed for MEDLINE]

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