Discrepancies in Biomarker Identification in Peak Picking Strategies in Untargeted Metabolomics Analyses of Cells, Tissues, and Biofluids

Different software and algorithms are available for peak
picking
in nontargeted metabolomics, and each may have its own strengths and
limitations. The choice of the peak picking method can significantly
influence the results obtained, including the number and identity
of metabolites detected, their quantification, and subsequent biomarker
analysis. The impact of peak picking by different tools in an untargeted
metabolomics-based biomarker study is largely understated. This study
compares two popular open-source software tools for peak picking in
untargeted metabolomics of cancer cells, tissues, and biofluids: XCMS
and MZmine 2. The investigation evaluates the impact of these peak
picking algorithms on biomarker identification after careful noise
filtering by blank feature filtering (BFF). We found significant discrepancy
between the results obtained from XCMS and MZmine 2, regardless of
the sample types, solvent gradient phases, retention time, or mass-to-charge
ratio (m/z) tolerances used. Notably,
this study revealed significant disagreement between peak picking
tools in the context of metabolite-based biomarker study after BFF
and highlighted the importance of carefully evaluating and selecting
appropriate peak picking tools to ensure reliable and accurate results
in untargeted metabolomics research.