title: Statistical Correlations between NMR Spectroscopy and Direct Infusion FT-ICR Mass Spectrometry Aid Annotation of Unknowns in Metabolomics.
abstract: NMR spectroscopy and mass spectrometry are the two major analytical platforms for metabolomics, and both generate substantial data with hundreds to thousands of observed peaks for a single sample. Many of these are unknown, and peak assignment is generally complex and time-consuming. Statistical correlations between data types have proven useful in expediting this process, for example, in prioritizing candidate assignments. However, this approach has not been formally assessed for the comparison of direct-infusion mass spectrometry (DIMS) and NMR data. Here, we present a systematic analysis of a sample set (tissue extracts), and the utility of a simple correlation threshold to aid metabolite identification. The correlations were surprisingly successful in linking structurally related signals, with 15 of 26 NMR-detectable metabolites having their highest correlation to a cognate MS ion. However, we found that the distribution of the correlations was highly dependent on the nature of the MS ion, such as the adduct type. This approach should help to alleviate this important bottleneck where both 1D NMR and DIMS data sets have been collected.
authors.affiliation: Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London , London SW7 2AZ, United Kingdom.
authors.forename: Jie
authors.initials: J
authors.lastname: Hao
authors.affiliation: Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London , London SW7 2AZ, United Kingdom.
authors.forename: Manuel
authors.initials: M
authors.lastname: Liebeke
authors.affiliation: NERC Biomolecular Analysis Facility - Metabolomics Node (NBAF-B), School of Biosciences, University of Birmingham , Edgbaston, Birmingham B15 2TT, United Kingdom.
authors.forename: Ulf
authors.initials: U
authors.lastname: Sommer
authors.affiliation: NERC Biomolecular Analysis Facility - Metabolomics Node (NBAF-B), School of Biosciences, University of Birmingham , Edgbaston, Birmingham B15 2TT, United Kingdom.
authors.forename: Mark R
authors.initials: MR
authors.lastname: Viant
authors.affiliation: Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London , London SW7 2AZ, United Kingdom.
authors.forename: Jacob G
authors.initials: JG
authors.lastname: Bundy
authors.affiliation: Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London , London SW7 2AZ, United Kingdom.
authors.forename: Timothy M D
authors.initials: TM
authors.lastname: Ebbels
mesh_terms: D000818:Animals; D008239:Lysine / Q000031:analogs & derivatives / Q000032:analysis; D009682:Magnetic Resonance Spectroscopy; D013058:Mass Spectrometry; D055432:Metabolomics; D009835:Oligochaeta / Q000737:chemistry / Q000378:metabolism*; D012694:Serine / Q000031:analogs & derivatives / Q000032:analysis; D013223:Statistics as Topic; D019802:Succinic Acid / Q000032:analysis; D014020:Tissue Extracts / Q000032:analysis*
chemical_list: D014020:Tissue Extracts; C003712:trimethyllysine; D012694:Serine; C008295:lombricine; D019802:Succinic Acid; D008239:Lysine