Abstract

Gas Chromatography coupled with Mass Spectrometry (GC-MS) has been broadly used for the detection of changes in metabolite levels in complex samples. Internal Standards (IS) spiked into a complex background at different concentrations help assess the capability of GC-MS in detecting changes in metabolite levels. This study uses a Latin square design to evaluate the ability of GC-MS in full scan and Single Ion Monitoring (SIM) modes to detect changes among IS spiked into human plasma samples at varying concentrations. Statistical analysis of the data demonstrates the potential of GC-MS to detect true differences over a wide range of concentration levels.

Highlights

  • Metabolomics studies small molecules that define the metabolic status of a biological system

  • The data acquired by gas chromatography (GC)-Single Ion Monitoring (SIM)-Mass spectrometry (MS) detected the changes in concentration levels, but they were for the most part marginally significant

  • We did not anticipate any changes in the intensity of the endogenous metabolites since the only compounds varying in concentrations were the spiked-in internal standards, we evaluated the behavior of plasma metabolites in the data acquired by GC-TOF-MS and GC-qMS by calculating their Coefficients of Variation (CVs) across runs within the same subject and across the five subjects

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Summary

Introduction

Metabolomics studies small molecules (molecular weight < 1800 Da) that define the metabolic status of a biological system. This technique provides a simultaneous assessment of numerous metabolites that can help the characterization of phenotypic profiles and the quantification of individual metabolites. Chromatography is often coupled to mass spectrometry to achieve better separation of multiple compounds present in a complex matrix before their ionization. Both gas chromatography (GC) and liquid chromatography (LC) have been used in metabolomics studies to increase the metabolome coverage. An advantage of GC-MS over LC-MS for metabolomics analysis is the availability of commercial spectral libraries and structure databases that can be used for metabolite identification

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