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

Abstract

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.

Global-Scale Metabolomic Profiling of Human Hair for Simultaneous Monitoring of Endogenous Metabolome, Short- and Long-Term Exposome

“…Following metabolite extraction, we applied the LC-MS platform to profile metabolites in both ESI(+) and ESI(–) modes. The collected metabolomic data were further processed using recently developed feature extraction program targeting low-abundant metabolic features (Hu et al, 2019 ) and metabolite annotation software (Xing et al, 2020 ) aiming to annotate “unknown unknown” metabolites that are not archived in a spectral database. The annotated metabolic feature table can then be used for downstream data interpretation to understand how short- and long-term exposures as well as endogenous metabolites are changed over different exposure situations.…”

“…Raw LC-MS data are available on MetaboLights (ID: MTBLS2577). Raw LC-MS data were preprocessed using a recently published integrated feature extraction (Hu et al, 2019 ), which has better sensitivity in detecting low-abundant metabolic features. Detailed statistical analysis was performed using MetaboAnalyst (Xia et al, 2015 ) following the in-depth spectral annotation.…”

“…In addition, endogenous metabolites and exposure chemicals present diverse chemical structures with wide concentration ranges. Many of the features do not have good peak shapes and are difficult to identify using conventional peak-fitting based feature extraction algorithms (Smith et al, 2006 ; Hu et al, 2019 ). Moreover, many exposure chemicals have not been reported in literature, and their chemical identities are completely unknown (i.e., the “unknown unknown”; Stein, 2012 ).…”

“…Orthogonal LC-MS analyses were performed to detect and quantify a wide range of metabolites. To effectively process LC-MS data, we applied a recently developed integrated feature extraction algorithm to capture both high and low abundant metabolic features (Hu et al, 2019 ), followed by metabolite annotation using McSearch to capture novel metabolites that are not archived in a spectral library (Xing et al, 2020 ). For data interpretation, we manually sourced and resolved the chemicals on hair surfaces and in hair strands by searching against the latest version of Human Metabolome Database (HMDB; Wishart et al, 2018 ).…”

From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data

“…The pitfall of this strategy is the time lapse (and thus possible sample alterations) between the first batch of analyses in MS mode only, for relative quantification, and the targeted run to acquire MS/MS data on ions of interest for their identification. Although DDA is still the most popular simultaneous MS/MS acquisition mode used, DIA is gaining attention following the development of MS/MS data deconvolution algorithms (to link precursor and product ions) and improved coverage for low abundant precursor ions [60][61][62]. In general, the quality and the amount of acquired MS/MS data depend on instrument acquisition speed and sensitivity (also related to metabolite ionization efficiency).…”