The Southeast Resource Center for Integrated Metabolomics (SECIM) integrates existing strengths to create a comprehensive resource for basic and clinical scientists to obtain state-of-the-art metabolomics data and analyses. Advanced Mass Spectrometry (MS) Core 3, led by Prof. Yost, will provide advanced MS services to SECIM users. Core 3 will provide biomarker identification, will help to improve protocols for global metabolomics services in Core 1 (MS Services), will provide imaging mass spectrometry (IMS) to users, and in collaboration with Dr. Chris Beecher will provide Isotopic Ratio Outlier Analysis (IROA), a patented technology that can greatly simplify metabolomics studies. The biomarker identification service builds on a long tradition in the UF Department of Chemistry of MS identification of unknown molecules, and it will coordinate closely with Core 2 (NMR). The Yost group has developed MALDI imaging techniques that allow metabolite detection and localization in heterogeneous biological tissues with unprecedented sensitivity. When biomarkers are discovered in Cores 1 and/or 2 with global metabolomics. Core 3 will be able collect IMS data on tissues to show the distribution of biomarkers in normal vs. diseased or treated tissue. IMS will capture information that is completely inaccessible through conventional extracts, and these studies will bridge to high-resolution magic angle spinning (HRMAS) tissue studies in Core 2. Core 3 will closely interface with Core 1, with identical Q-Exactive instruments obtained through a SECIM partnership with Thermo Fisher located side-by-side for joint protocol development and redundancy in the global metabolomics pipeline. The IROA technology will be developed and deployed on the Q-Exactive platform, following up on preliminary studies between SECIM investigators, NextGen Metabolomics, and Thermo Fisher that demonstrate IROA. Core 3 will also coordinate with Core 2 and Bioinformatics Core 4 to expand the IROA study design and integrate NMR into the IROA workflow.

Public Health Relevance

The major goal of metabolomics is the measurement and characterization of metabolites from living organisms, including people. Small molecule metabolites are sensitive to disease, treatment, and many environmental factors, and they can be used as indicators of disease in diagnosis or as markers to assess the outcome of treatment.

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Hattori, Ayuna; Tsunoda, Makoto; Konuma, Takaaki et al. (2017) Cancer progression by reprogrammed BCAA metabolism in myeloid leukaemia. Nature 545:500-504
Le Guennec, Adrien; Tayyari, Fariba; Edison, Arthur S (2017) Alternatives to Nuclear Overhauser Enhancement Spectroscopy Presat and Carr-Purcell-Meiboom-Gill Presat for NMR-Based Metabolomics. Anal Chem 89:8582-8588
Ulmer, Candice Z; Koelmel, Jeremy P; Ragland, Jared M et al. (2017) LipidPioneer : A Comprehensive User-Generated Exact Mass Template for Lipidomics. J Am Soc Mass Spectrom 28:562-565
Markley, John L; Br├╝schweiler, Rafael; Edison, Arthur S et al. (2017) The future of NMR-based metabolomics. Curr Opin Biotechnol 43:34-40
Reed, Laura K; Baer, Charles F; Edison, Arthur S (2017) Considerations when choosing a genetic model organism for metabolomics studies. Curr Opin Chem Biol 36:7-14
Liu, Haiyan; Garrett, Timothy J; Su, Zhihua et al. (2017) UHPLC-Q-Orbitrap-HRMS-based global metabolomics reveal metabolome modifications in plasma of young women after cranberry juice consumption. J Nutr Biochem 45:67-76
DeRatt, Barbara N; Ralat, Maria A; Lysne, Vegard et al. (2017) Metabolomic Evaluation of the Consequences of Plasma Cystathionine Elevation in Adults with Stable Angina Pectoris. J Nutr 147:1658-1668
Koelmel, Jeremy P; Kroeger, Nicholas M; Gill, Emily L et al. (2017) Expanding Lipidome Coverage Using LC-MS/MS Data-Dependent Acquisition with Automated Exclusion List Generation. J Am Soc Mass Spectrom 28:908-917
Koelmel, Jeremy P; Ulmer, Candice Z; Jones, Christina M et al. (2017) Common cases of improper lipid annotation using high-resolution tandem mass spectrometry data and corresponding limitations in biological interpretation. Biochim Biophys Acta 1862:766-770
Wang, Cheng; He, Lidong; Li, Da-Wei et al. (2017) Accurate Identification of Unknown and Known Metabolic Mixture Components by Combining 3D NMR with Fourier Transform Ion Cyclotron Resonance Tandem Mass Spectrometry. J Proteome Res 16:3774-3786

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