Original Article

Metabolomics

, Volume 9, Issue 1, pp 132-143

High-performance metabolic profiling with dual chromatography-Fourier-transform mass spectrometry (DC-FTMS) for study of the exposome

  • Quinlyn A. SoltowAffiliated withDepartment of Medicine, Division of Pulmonary, Allergy and Critical Care, Emory University
  • , Frederick H. StrobelAffiliated withMass Spectrometry Center, Emory University
  • , Keith G. MansfieldAffiliated withHarvard Medical School, New England Primate Research Center
  • , Lynn WachtmanAffiliated withHarvard Medical School, New England Primate Research Center
  • , Youngja ParkAffiliated withDepartment of Medicine, Division of Pulmonary, Allergy and Critical Care, Emory UniversityClinical Biomarkers Laboratory, Emory University
  • , Dean P. JonesAffiliated withDepartment of Medicine, Division of Pulmonary, Allergy and Critical Care, Emory UniversityClinical Biomarkers Laboratory, Emory University Email author 

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Abstract

Studies of gene–environment (G × E) interactions require effective characterization of all environmental exposures from conception to death, termed the exposome. The exposome includes environmental exposures that impact health. Improved metabolic profiling methods are needed to characterize these exposures for use in personalized medicine. In the present study, we compared the analytic capability of dual chromatography-Fourier-transform mass spectrometry (DC-FTMS) to previously used liquid chromatography-FTMS (LC-FTMS) analysis for high-throughput, top-down metabolic profiling. For DC-FTMS, we combined data from sequential LC-FTMS analyses using reverse phase (C18) chromatography and anion exchange (AE) chromatography. Each analysis was performed with electrospray ionization in the positive ion mode and detection from m/z 85 to 850. Run time for each column was 10 min with gradient elution; 10 μl extracts of plasma from humans and common marmosets were used for analysis. In comparison to analysis with the AE column alone, addition of the second LC-FTMS analysis with the C18 column increased m/z feature detection by 23–36%, yielding a total number of features up to 7,000 for individual samples. Approximately 50% of the m/z matched to known chemicals in metabolomic databases, and 23% of the m/z were common to analyses on both columns. Database matches included insecticides, herbicides, flame retardants, and plasticizers. Modularity clustering algorithms applied to MS-data showed the ability to detection clusters and ion interactions. DC-FTMS thus provides improved capability for high-performance metabolic profiling of the exposome and development of personalized medicine.

Keywords

Metabolomics LC/MS Anion exchange Reverse phase Exposome Personalized medicine Predictive health FT-ICR Plasma