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Using a Cyclic Ion Mobility Spectrometer for Tandem Ion Mobility Experiments

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XCMS2: processing tandem mass spectrometry data for metabolite identification and structural characterization.

H P Benton¹, D M Wong, S A Trauger

¹Department of Molecular Biology and The Center for Mass Spectrometry, The Scripps Research Institute 10550 North Torrey Pines Road, La Jolla, California 92037, USA.

Analytical Chemistry

|July 17, 2008

Summary

XCMS(2) is a new bioinformatics tool for mass spectrometry metabolomics. It identifies known metabolites using tandem mass spectrometry (MS/MS) and can determine structural information for unknown compounds.

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Area of Science:

Bioinformatics
Computational Biology
Analytical Chemistry

Background:

Mass spectrometry-based metabolomics is an emerging field requiring advanced bioinformatics tools.
Current tools like XCMS statistically analyze liquid chromatography-mass spectrometry (LC-MS) data but lack structural identification capabilities.
Accurate structural identification is crucial for understanding metabolic pathways and biological functions.

Purpose of the Study:

To introduce XCMS(2), an open-source software package designed to enhance metabolite identification in metabolomics.
To enable automatic searching of tandem mass spectrometry (MS/MS) data against a reference library of known metabolites.
To provide methods for elucidating the structures of unknown metabolites not present in existing databases.

Main Methods:

XCMS(2) utilizes tandem mass spectrometry (MS/MS) data for metabolite identification.
It searches analytical MS/MS spectra against the METLIN reference library of experimental MS/MS data from known metabolites.
A "shared peak count" method scores matches based on common fragment ion masses, and a "similarity search" algorithm identifies structural motifs for unknown compounds.

Main Results:

XCMS(2) automates the identification of known metabolites by matching MS/MS spectra.
The software provides structural information for unknown metabolites by comparing fragmentation patterns and neutral losses to reference compounds in METLIN.
This enables the characterization of metabolites even when precursor masses do not exactly match.

Conclusions:

XCMS(2) significantly advances metabolomics by integrating MS/MS spectral matching and structural elucidation.
The software offers a powerful solution for identifying both known and unknown metabolites, thereby improving the depth of metabolomic analysis.
XCMS(2) facilitates a deeper understanding of biological systems through comprehensive metabolite profiling.