Data processing of product ion spectra: Quality improvement by averaging multiple similar spectra of small molecules. Saccharomyces cerevisiae

In metabolomics studies using high-resolution mass spectrometry (MS), a set of product ion spectra is comprehensively acquired from observed ions using the data-dependent acquisition (DDA) mode of various tandem MS. However, especially for low-intensity signals, it is sometimes difficult to distinguish artifact signals from true fragment ions derived from a precursor ion. Moreover, inadequate precision in the measured m/z value is one of the bottlenecks in narrowing down the candidate compositional formula. In this study, it was demonstrated that averaging multiple product ion spectra can improve m/z precision as well as the reliability of fragment ion observation. A graph-based method was applied to cluster a set of similar spectra from multiple DDA data files to create an averaged product-ion spectrum. The error levels of m/z values declined following the central limit theorem, which allowed us to reduce the number of candidate compositional formulas. The improved reliability and precision of the averaged spectra would contribute to a more efficient annotation of the product ion spectral data.