Abstract
Background and aims
Optimizing metabolomics data processing parameters is a challenging and fundamental task to obtain reliable results. Automated tools have been developed to assist this optimization for LC-MS data. GC-MS data require substantial modifications in processing parameters, as the chromatographic profiles are more robust, with more symmetrical and Gaussian peaks. This work compared an automated XCMS parameter optimization using the Isotopologue Parameter Optimization (IPO) software with manual optimization of GC-MS metabolomics data. Additionally, the results were compared to online XCMS platform.
Methods
GC-MS data from control and test groups of intracellular metabolites from Trypanosoma cruzi trypomastigotes were used. Optimizations were performed on the quality control (QC) samples.
Results
The results in terms of the number of molecular features extracted, repeatability, missing values, and the search for significant metabolites showed the importance of optimizing the parameters for peak detection, alignment, and grouping, especially those related to peak width (fwhm, bw) and noise ratio (snthresh).
Conclusion
This is the first time that a systematic optimization using IPO has been performed on GC-MS data. The results demonstrate that there is no universal approach for optimization but automated tools are valuable at this stage of the metabolomics workflow. The online XCMS proves to be an interesting processing tool, helping, above all, in the choice of parameters as a starting point for adjustments and optimizations. Although the tools are easy to use, there is still a need for technical knowledge about the analytical methods and instruments used.
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Data availability
Raw data are publicly available in the link: https://github.com/gicanuto/GC-MS-data.
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Acknowledgements
E.K.P. dos Santos is thankful to the Fundação de Amparo a Pesquisa do Estado da Bahia (PIBIC-FAPESB) for the undergraduate research.
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EKPS carried out the formal analysis, and GABC performed the conceptualization, project administration, supervision, and writing. All authors have given approval of the final version of the manuscript.
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dos Santos, E.K., Canuto, G.A.B. Optimizing XCMS parameters for GC-MS metabolomics data processing: a case study. Metabolomics 19, 26 (2023). https://doi.org/10.1007/s11306-023-01992-1
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DOI: https://doi.org/10.1007/s11306-023-01992-1