Data Processing of Product Ion Spectra: Redundancy of Product Ion Spectra of Small Molecules in Data-Dependent Acquisition Dataset

Abstract

Non-targeted metabolome analysis studies comprehensively acquire product ion spectra from the observed ions by the data-dependent acquisition (DDA) mode of tandem mass spectrometry (MS). A DDA dataset redundantly contains closely similar product ion spectra of metabolites commonly existing among the biological samples analyzed in a metabolome study. Moreover, a single DDA data file often includes two or more closely similar raw spectra obtained from an identical precursor ion. The redundancy of product ion spectra has been used to generate an averaged product ion spectrum from a set of similar product ion spectra recorded in a DDA dataset. The spectral averaging improved the accuracy of m/z values and signal-to-noise levels of product ion spectra. However, the origins of redundancy, variations among datasets, and these effects on the spectral averaging procedure needed to be better characterized. This study investigated the nature of the redundancy by comparing the averaging results of eight DDA datasets of non-targeted metabolomics studies. The comparison revealed a significant variation in redundancy among datasets. The DDA datasets obtained by the quadrupole (Q)-Orbitrap-MS datasets had more significant intrafile redundancy than that of the Q-time-of-flight-MS. For evaluating the similarity score between two production spectra, the optimal threshold level of the cosine-product method was approximately 0.8–0.9. Moreover, contamination of biological samples such as plasticizers was another origin of spectral redundancy. The results will be the basis for further development of methods for processing of product ion spectra data. Copyright © 2023 Fumio Matsuda. This is an open-access article distributed under the terms of Creative Commons Attribution Non-Commercial 4.0 International License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. Please cite this article as: Mass Spectrom (Tokyo) 2023; 12(1): A0138