Enhancing Metabolome Coverage in Data-Dependent LC–MS/MS Analysis through an Integrated Feature Extraction Strategy

In untargeted metabolomics,
conventional data preprocessing software
(e.g., XCMS, MZmine 2, MS-DIAL) are used extensively due to their
high efficiency in metabolic feature extraction. However, these programs
present limitations in recognizing low-abundance metabolic features,
thus hindering complete metabolome coverage from the analysis. In
this work, we explored the possibility of enhancing the metabolome
coverage of data-dependent liquid chromatography–tandem mass
spectrometry (LC–MS/MS) results by rescuing metabolic features
that are missed by conventional software. To achieve this goal, we
first categorized the metabolic features into four confidence levels
based on their chromatographic peak shapes and the presence of corresponding
MS/MS spectra. We then assessed the false positives and quantitative
accuracy of the metabolic features that contain MS/MS spectra but
are not recognized by conventional software. Our results indicate
that these missed features contain valid and important metabolic information
and should be integrated into the conventional metabolomics results.
Thus, we developed a data-preprocessing pipeline to extract low-abundance
metabolic features and integrate them with the results from conventional
programs. This integrated feature extraction strategy was tested on
a set of fecal metabolomic data retrieved from mice who have undergone
normal diet vs high-fat diet treatments. In our test data set, the
integrated feature extraction approach increased the number of significant
features being extracted by 24.4% and identified five additional metabolites
bearing critical biological meanings. Our results show that this integrated
feature extraction strategy remarkably improves the metabolome coverage
beyond that of conventional data preprocessing, therefore facilitating
the confirmation of metabolites of interest and accomplishment of
a higher success rate in de novo metabolite identification.