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J Proteome Res. 2015 Apr 3;14(4):1810-7. doi: 10.1021/pr5011923. Epub 2015 Mar 16.

Comparison of GC-MS and GC×GC-MS in the analysis of human serum samples for biomarker discovery.

Author information

1
⊥David H. Murdock Research Institute, Kannapolis, North Carolina 28081, United States.
2
‡Duke Molecular Physiology Institute, Department of Medicine, Duke University, Durham, North Carolina 27701, United States.

Abstract

We compared the performance of gas chromatography time-of-flight mass spectrometry (GC-MS) and comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC-MS) for metabolite biomarker discovery. Metabolite extracts from 109 human serum samples were analyzed on both platforms with a pooled serum sample analyzed after every 9 biological samples for the purpose of quality control (QC). The experimental data derived from the pooled QC samples showed that the GC×GC-MS platform detected about three times as many peaks as the GC-MS platform at a signal-to-noise ratio SNR ≥ 50, and three times the number of metabolites were identified by mass spectrum matching with a spectral similarity score Rsim ≥ 600. Twenty-three metabolites had statistically significant abundance changes between the patient samples and the control samples in the GC-MS data set while 34 metabolites in the GC×GC-MS data set showed statistically significant differences. Among these two groups of metabolite biomarkers, nine metabolites were detected in both the GC-MS and GC×GC-MS data sets with the same direction and similar magnitude of abundance changes between the control and patient sample groups. Manual verification indicated that the difference in the number of the biomarkers discovered using these two platforms was mainly due to the limited resolution of chromatographic peaks by the GC-MS platform, which can result in severe peak overlap making subsequent spectrum deconvolution for metabolite identification and quantification difficult.

KEYWORDS:

GC-MS; GC×GC-MS; biomarker discovery; metabolomics; peak capacity

PMID:
25735966
PMCID:
PMC4849136
DOI:
10.1021/pr5011923
[Indexed for MEDLINE]
Free PMC Article

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