Evaluation of Recent Data Processing Strategies on Q-TOF LC/MS Based Untargeted Metabolomics


MASS SPECTROMETRY LETTERS, vol.11, no.1, pp.1-5, 2020 (ESCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 11 Issue: 1
  • Publication Date: 2020
  • Doi Number: 10.5478/msl.2020.11.1.1
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus, Directory of Open Access Journals
  • Page Numbers: pp.1-5
  • Keywords: Metabolomics, metabolite profiling, Q-TOF LC/MS, XCMS, metabolic pathway analysis, data processing, BIOLOGICAL SAMPLES, XCMS
  • Hacettepe University Affiliated: Yes


In this study, some of the recently reported data processing strategies were evaluated and modified based on their capabilities and a brief workflow for data mining was redefined for Q-TOF LC-MS based untargeted metabolomics. Commercial pooled human plasma samples were used for this purpose. An ultrafiltration procedure was applied on sample preparation. Sample set was analyzed through Q-TOF LC/MS. A C18 column (Agilent Zorbax 1.8 mu M, 50 x 2.1 mm) was used for chromatographic separation. Raw chromatograms were processed using XCMS - R programming language edition and Isotopologue Parameter Optimization (IPO) was used to optimize XCMS parameters. The raw XCMS table was processed using MS Excel to find reliable and reproducible peaks. Totally 1650 reliable and reproducible potential metabolite peaks were found based on the data processing procedures given in this paper. The redefined dataset was upload into MetaboAnalyst platform and the identified metabolites were matched with 86 metabolic pathways. Thus, two list were obtained and presented in this study as supplement files. The first list is to present the retention times and m/z values of detected metabolite peaks. The second list is the metabolic pathways related with the identified metabolites. The briefly described data processing strategies and dataset presented in this study could be beneficial for the researchers working on untargeted metabolomics for processing their data and validating their results.