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Metabolomics has become a crucial phenotyping technique in a range of research fields including medicine, the life sciences, biotechnology and the environmental sciences. This necessitates the transfer of experimental information between research groups, as well as potentially to publishers and funders. After the initial efforts of the metabolomics standards initiative, minimum reporting standards were proposed which included the concepts for metabolomics databases. Built by the community, standards and infrastructure for metabolomics are still needed to allow storage, exchange, comparison and re-utilization of metabolomics data. The Framework Programme 7 EU Initiative ‘coordination of standards in metabolomics’ (COSMOS) is developing a robust data infrastructure and exchange standards for metabolomics data and metadata. This is to support workflows for a broad range of metabolomics applications within the European metabolomics community and the wider metabolomics and biomedical communities’ participation. Here we announce our concepts and efforts asking for re-engagement of the metabolomics community, academics and industry, journal publishers, software and hardware vendors, as well as those interested in standardisation worldwide (addressing missing metabolomics ontologies, complex-metadata capturing and XML based open source data exchange format), to join and work towards updating and implementing metabolomics standards.

GC/EI-MS-based metabolite profiling of derivatized polar fractions of crude plant extracts typically reveals several hundred components. Thereof, only up to one half can be identified using mass spectral and retention index libraries, the rest remains unknown. In the present work, the utility of GC/APCI(+)-QTOFMS for the annotation of unknown components was explored. Hence, EI and APCI(+) mass spectra of ~100 known components were extracted from GC/EI-QMS and GC/APCI(+)-QTOFMS profiles obtained from a methoximated and trimethylsilylated root extract of Arabidopsis thaliana. Based on this reference set, adduct and fragment ion formation under APCI(+) conditions was examined and the calculation of elemental compositions evaluated. During these studies, most of the components formed dominating protonated molecular ions. Despite the high mass accuracy (|Δm| ≤ 3 mDa) and isotopic pattern accuracy (mSigma ≤ 30) the determination of a component’s unique native elemental composition requires additional information, namely the number of trimethylsilyl and methoxime moieties as well as the analysis of corresponding collision-induced dissociation (CID) mass spectra. After all, the reference set was used to develop a strategy for the pairwise assignment of EI and APCI(+) mass spectra. Proceeding from these findings, the annotation of unidentified components detected by GC/EI-QMS using GC/APCI(+)-QTOFMS and corresponding deuterated derivatization reagents was attempted. For a total of 25 unknown components, pairs of EI and APCI(+) mass spectra were compiled and elemental compositions determined. Integrative interpretation of EI and CID mass spectra resulted in 14 structural hypotheses, of which seven were confirmed using authenticated standards.

Metabolomics has advanced significantly in the past 10 years with important developments related to hardware, software and methodologies and an increasing complexity of applications. In discovery-based investigations, applying untargeted analytical methods, thousands of metabolites can be detected with no or limited prior knowledge of the metabolite composition of samples. In these cases, metabolite identification is required following data acquisition and processing. Currently, the process of metabolite identification in untargeted metabolomic studies is a significant bottleneck in deriving biological knowledge from metabolomic studies. In this review we highlight the different traditional and emerging tools and strategies applied to identify subsets of metabolites detected in untargeted metabolomic studies applying various mass spectrometry platforms. We indicate the workflows which are routinely applied and highlight the current limitations which need to be overcome to provide efficient, accurate and robust identification of metabolites in untargeted metabolomic studies. These workflows apply to the identification of metabolites, for which the structure can be assigned based on entries in databases, and for those which are not yet stored in databases and which require a de novo structure elucidation.