Structure Annotation of All Mass Spectra in Untargeted Metabolomics.

Urine metabolites are used in many clinical and biomedical studies but usually only for a few classic compounds. Metabolomics detects vastly more metabolic signals that may be used to precisely define the health status of individuals. However, many compounds remain unidentified, hampering biochemical conclusions. Here, we annotate all metabolites detected by two untargeted metabolomic assays, hydrophilic interaction chromatography (HILIC)-Q Exactive HF mass spectrometry and charged surface hybrid (CSH)-Q Exactive HF mass spectrometry. Over 9,000 unique metabolite signals were detected, of which 42% triggered MS/MS fragmentations in data-dependent mode. On the highest Metabolomics Standards Initiative (MSI) confidence level 1, we identified 175 compounds using authentic standards with precursor mass, retention time, and MS/MS matching. An additional 578 compounds were annotated by precursor accurate mass and MS/MS matching alone, MSI level 2, including a novel library specifically geared at acylcarnitines (CarniBlast). The rest of the metabolome is usually left unannotated. To fill this gap, we used the in silico fragmentation tool CSI:FingerID and the new NIST hybrid search to annotate all further compounds (MSI level 3). Testing the top-ranked metabolites in CSI:Finger ID annotations yielded 40% accuracy when applied to the MSI level 1 identified compounds. We classified all MSI level 3 annotations by the NIST hybrid search using the ClassyFire ontology into 21 superclasses that were further distinguished into 184 chemical classes. ClassyFire annotations showed that the previously unannotated urine metabolome consists of 28% derivatives of organic acids, 16% heterocyclics, and 16% lipids as major classes.

Comprehensive comparison of in silico MS/MS fragmentation tools of the CASMI contest: database boosting is needed to achieve 93% accuracy.

In mass spectrometry-based untargeted metabolomics, rarely more than 30% of the compounds are identified. Without the true identity of these molecules it is impossible to draw conclusions about the biological mechanisms, pathway relationships and provenance of compounds. The only way at present to address this discrepancy is to use in silico fragmentation software to identify unknown compounds by comparing and ranking theoretical MS/MS fragmentations from target structures to experimental tandem mass spectra (MS/MS). We compared the performance of four publicly available in silico fragmentation algorithms (MetFragCL, CFM-ID, MAGMa+ and MS-FINDER) that participated in the 2016 CASMI challenge. We found that optimizing the use of metadata, weighting factors and the manner of combining different tools eventually defined the ultimate outcomes of each method. We comprehensively analysed how outcomes of different tools could be combined and reached a final success rate of 93% for the training data, and 87% for the challenge data, using a combination of MAGMa+, CFM-ID and compound importance information along with MS/MS matching. Matching MS/MS spectra against the MS/MS libraries without using any in silico tool yielded 60% correct hits, showing that the use of in silico methods is still important.