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XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and

Colin A Smith1, Elizabeth J Want, Grace O'Maille

  • 1Scripps Center for Mass Spectrometry and Department of Molecular Biology, Scripps Research Institute, La Jolla, California 92037, USA.

Analytical Chemistry
|February 2, 2006
PubMed
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Xenon-chromatography mass spectrometry (XCMS) offers a new approach for analyzing metabolite profiles. This method accurately corrects retention times without internal standards, enabling precise identification of biomarkers and metabolic changes.

Area of Science:

  • Metabolomics
  • Analytical Chemistry
  • Biochemistry

Background:

  • Accurate metabolite profiling is crucial for biomarker discovery and understanding metabolic pathways.
  • Current data preprocessing methods often struggle to correlate metabolites with their biological origins effectively.

Purpose of the Study:

  • To introduce XCMS, a novel LC/MS data analysis approach for improved metabolite profiling.
  • To enable accurate correlation of specific metabolites to their biological origins.

Main Methods:

  • Developed a Liquid Chromatography/Mass Spectrometry (LC/MS) based data analysis approach named XCMS.
  • Incorporated nonlinear retention time alignment, matched filtration, peak detection, and peak matching.
  • Utilized endogenous metabolites for dynamic, in-sample retention time correction without external standards.

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Main Results:

  • Successfully identified hundreds of endogenous metabolites for use as internal standards.
  • Achieved nonlinear retention time correction profiles for each sample.
  • Enabled direct comparison of relative metabolite ion intensities to identify specific endogenous metabolite changes, including potential biomarkers.

Conclusions:

  • XCMS provides a robust method for metabolite profiling and biomarker discovery.
  • The approach enhances the accuracy of correlating metabolites to biological origins.
  • XCMS is a valuable, freely available open-source tool for metabolic research.