Statistical Correlations between NMR Spectroscopy and Direct Infusion FT-ICR Mass Spectrometry Aid Annotation of Unknowns in Metabolomics

Jie Hao, Manuel Liebeke, Ulf Sommer, Mark R. Viant, Jacob G. Bundy*, Timothy M D Ebbels

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)2583-2589
Number of pages7
JournalAnalytical Chemistry
Volume88
Issue number5
Early online date29 Jan 2016
DOIs
Publication statusPublished - 1 Mar 2016

ASJC Scopus subject areas

  • Analytical Chemistry

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