Enabling Efficient and Confident Annotation of LC−MS Metabolomics Data through MS1 Spectrum and Time Prediction

Liquid chromatography coupled to
electrospray ionization-mass spectrometry
(LC–ESI-MS) is a versatile and robust platform for metabolomic
analysis. However, while ESI is a soft ionization technique, in-source
phenomena including multimerization, nonproton cation adduction, and
in-source fragmentation complicate interpretation of MS data. Here,
we report chromatographic and mass spectrometric behavior of 904 authentic
standards collected under conditions identical to a typical nontargeted
profiling experiment. The data illustrate that the often high level
of complexity in MS spectra is likely to result in misinterpretation
during the annotation phase of the experiment and a large overestimation
of the number of compounds detected. However, our analysis of this
MS spectral library data indicates that in-source phenomena are not
random but depend at least in part on chemical structure. These nonrandom
patterns enabled predictions to be made as to which in-source signals
are likely to be observed for a given compound. Using the authentic
standard spectra as a training set, we modeled the in-source phenomena
for all compounds in the Human Metabolome Database to generate a theoretical
in-source spectrum and retention time library. A novel spectral similarity
matching platform was developed to facilitate efficient spectral searching
for nontargeted profiling applications. Taken together, this collection
of experimental spectral data, predictive modeling, and informatic
tools enables more efficient, reliable, and transparent metabolite
annotation.