Integrating comprehensive two-dimensional gas chromatography mass spectrometry and parallel two-dimensional liquid chromatography mass spectrometry for untargeted metabolomics - PubMed (original) (raw)
. 2019 Jul 21;144(14):4331-4341.
doi: 10.1039/c9an00560a. Epub 2019 Jun 13.
Affiliations
- PMID: 31192319
- PMCID: PMC6677244
- DOI: 10.1039/c9an00560a
Integrating comprehensive two-dimensional gas chromatography mass spectrometry and parallel two-dimensional liquid chromatography mass spectrometry for untargeted metabolomics
Md Aminul Islam Prodhan et al. Analyst. 2019.
Abstract
The diverse characteristics and large number of entities make metabolite separation challenging in metabolomics. To date, there is not a singular instrument capable of analyzing all types of metabolites. In order to achieve a better separation for higher peak capacity and accurate metabolite identification and quantification, we integrated GC × GC-MS and parallel 2DLC-MS for analysis of polar metabolites. To test the performance of the developed system, 13 rats were fed different diets to form two animal groups. Polar metabolites extracted from rat livers were analyzed by GC × GC-MS, parallel 2DLC-MS (-) and parallel 2DLC-MS (+), respectively. By integrating all data together, 58 metabolites were detected with significant change in their abundance levels between groups (p≤ 0.05). Of the 58 metabolites, three metabolites were detected in two platforms and two in all three platforms. Manual examination showed that discrepancy of metabolite regulation measured by different platforms was mainly caused by the poor shape of chromatographic peaks resulting from low instrument response. Pathway analysis demonstrated that integrating the results from multiple platforms increased the confidence of metabolic pathway assignment.
Figures
Figure 1.
Overlap of metabolite identification. Metabolites were identified from GC×GC-MS by EI mass spectrum matching and retention index matching. (A) The metabolite identification in analysis of 2DLC-MS data was done by parent ion m/z matching. (B) The metabolite identification in analysis of 2DLC-MS/MS data by MS/MS spectrum matching with or without retention time match.
Figure 1.
Overlap of metabolite identification. Metabolites were identified from GC×GC-MS by EI mass spectrum matching and retention index matching. (A) The metabolite identification in analysis of 2DLC-MS data was done by parent ion m/z matching. (B) The metabolite identification in analysis of 2DLC-MS/MS data by MS/MS spectrum matching with or without retention time match.
Figure 2.
Overlap of metabolites detected with significant changes in their abundance levels between groups by three platforms.
Figure 3.
Samples of instrument response of a metabolite affecting the quantification accuracy of that metabolite. (A) three dimensional chromatographic peak of
l-
ornithine detected by GC×GC-MS. (B) extracted ion chromatograms of
l-
ornithine in a randomly selected biological samples detected by 2DLC-MS (−). (C) Extracted ion chromatograms of
l-
ornithine in a randomly selected biological samples detected by 2DLC-MS (+). (D) three dimensional chromatographic peak of taurine detected by GC×GC-MS. (E) extracted ion chromatograms of taurine in a randomly selected biological samples detected by 2DLC-MS (−). (F) Extracted ion chromatograms of taurine in a randomly selected biological samples detected by 2DLC-MS (+).
Figure 4.
Overlap of the pathways that were affected using different set of significant metabolites for metabolic pathway analysis.
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