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.
Highlights
Identification of unknowns is one of the largest challenges in metabolomics
We analyzed a set of samples taken from an environmental metabolomics study, representing 205 spectra of tissue extracts of Lumbricus rubellus earthworms
We make no attempt here at biological interpretation of these spectra, but use them only to investigate the statistical correlation of the N uclear magnetic resonance (NMR) and direct-infusion mass spectrometry (DIMS) data
Summary
Identification of unknowns is one of the largest challenges in metabolomics. While individual unknown peaks can, be assigned using classical analytical approaches, the process is time-consuming, costly, and success is unpredictable. Statistical approaches have been shown to help greatly in the identification process, for example, by highlighting signals which are highly correlated to each other and might arise from the same molecule. This has been formalized for NMR studies as “statistical total correlation spectroscopy” (STOCSY),[4,5] to the point where it We demonstrate that using these correlations can greatly inform identification, with the most highly correlated ion being structurally related to the known NMR signal in the majority of cases
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