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.