H OH metabolites OH Article Enhanced Isotopic Ratio Outlier Analysis (IROA) Peak Detection and Identification with Ultra-High Resolution GC-Orbitrap/MS: Potential Application for Investigation of Model Organism Metabolomes Yunping Qiu 1 ID , Robyn D. Moir 2, Ian M. Willis 2, Suresh Seethapathy 3, Robert C. Biniakewitz 3 and Irwin J. Kurland 1,* 1 Stable Isotope and Metabolomics Core Facility, Diabetes Center, Department of Medicine, Albert Einstein College of Medicine, Bronx, NY 10461, USA;
[email protected] 2 Department of Biochemistry, Albert Einstein College of Medicine, Bronx, NY 10461, USA;
[email protected] (R.D.M.);
[email protected] (I.M.W.) 3 Thermo Fisher Scientific, Somerset, NJ 08873, USA; suresh.seethapathy@thermofisher.com (S.S.);
[email protected] (R.C.B.) * Correspondence:
[email protected]; Tel.: +1-718-678-1091 Received: 7 November 2017; Accepted: 10 January 2018; Published: 18 January 2018 Abstract: Identifying non-annotated peaks may have a significant impact on the understanding of biological systems. In silico methodologies have focused on ESI LC/MS/MS for identifying non-annotated MS peaks. In this study, we employed in silico methodology to develop an Isotopic Ratio Outlier Analysis (IROA) workflow using enhanced mass spectrometric data acquired with the ultra-high resolution GC-Orbitrap/MS to determine the identity of non-annotated metabolites. The higher resolution of the GC-Orbitrap/MS, together with its wide dynamic range, resulted in more IROA peak pairs detected, and increased reliability of chemical formulae generation (CFG). IROA uses two different 13C-enriched carbon sources (randomized 95% 12C and 95% 13C) to produce mirror image isotopologue pairs, whose mass difference reveals the carbon chain length (n), which aids in the identification of endogenous metabolites.