Peak Annotation and Verification Engine for Untargeted LC–MS Metabolomics

Untargeted
metabolomics can detect more than 10 000 peaks
in a single LC–MS run. The correspondence between these peaks
and metabolites, however, remains unclear. Here, we introduce a Peak
Annotation and Verification Engine (PAVE) for annotating untargeted
microbial metabolomics data. The workflow involves growing cells in 13C and 15N isotope-labeled media to identify peaks
from biological compounds and their carbon and nitrogen atom counts.
Improved deisotoping and deadducting are enabled by algorithms that
integrate positive mode, negative mode, and labeling data. To distinguish
metabolites and their fragments, PAVE experimentally measures the
response of each peak to weak in-source collision induced dissociation,
which increases the peak intensity for fragments while decreasing
it for their parent ions. The molecular formulas of the putative metabolites
are then assigned based on database searching using both m/z and C/N atom counts. Application of this procedure
to Saccharomyces cerevisiae and Escherichia
coli revealed that more than 80% of peaks do not label, i.e.,
are environmental contaminants. More than 70% of the biological peaks
are isotopic variants, adducts, fragments, or mass spectrometry artifacts
yielding ∼2000 apparent metabolites across the two organisms.
About 650 match to a known metabolite formula based on m/z and C/N atom counts, with 220 assigned structures
based on MS/MS and/or retention time to match to authenticated standards.
Thus, PAVE enables systematic annotation of LC–MS metabolomics
data with only ∼4% of peaks annotated as apparent metabolites.