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

• Authors: Quinlyn A. Soltow, Emory University; Frederick Strobel, Emory University; Keith G. Mansfield, Harvard University; Lynn Wachtman, Harvard University; Youngja Park, Emory University; Dean P Jones, Emory University
• Journal Title: Metabolomics
• Volume: Volume 9, Number 1
• Publisher: Springer Verlag (Germany) | 2013-03-01, Pages S132-S143
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1007/s11306-011-0332-1
• Subjects: Health Sciences, Medicine and Surgery
• Keywords: Science & Technology; Life Sciences & Biomedicine; Endocrinology & Metabolism; Metabolomics; LC/MS; Anion exchange; Reverse phase; Exposome; Personalized medicine; Predictive health; FT-ICR; Plasma; HUMAN PLASMA; POLLUTANTS; EXPOSURE; STRATEGY; PLATFORM; HEALTH

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.