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Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS
Published on: May 20, 2013
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Accuracy, linearity, and statistical differences in comparative quantification in untargeted plant metabolomics using
Christina Maisl1, Rainer Schuhmacher1, Christoph Bueschl2
1Department of Agrobiotechnology IFA-Tulln, Institute of Bioanalytics and Agro-Metabolomics, BOKU University, Vienna, Tulln, 3430, Austria.
Analytical and Bioanalytical Chemistry
|March 11, 2025
Summary
Untargeted metabolomics using Orbitrap mass spectrometry reveals widespread non-linear responses. Method validation is crucial, as overestimations in less concentrated samples can increase false negatives in metabolite discovery.
Area of Science:
- Analytical Chemistry
- Biochemistry
- Metabolomics
Background:
- High-resolution mass spectrometry, especially Orbitrap instruments coupled with liquid chromatography, is vital for untargeted metabolomics.
- While offering high sensitivity and mass accuracy, these instruments present quantification challenges, necessitating rigorous method validation.
- Untargeted metabolomics requires reliable detection and quantification of numerous metabolites across a wide dynamic range.
Purpose of the Study:
- To evaluate the suitability of untargeted metabolomics methods for discovery-based research.
- To assess the linearity and reliability of metabolite quantification using a Q Exactive HF Orbitrap.
- To investigate the impact of non-linear responses on data interpretation in metabolomics.
Main Methods:
- Utilized a stable isotope-assisted strategy for quantitative analysis.
- Analyzed wheat extracts using a Q Exactive HF Orbitrap mass spectrometer.
- Employed nine dilution levels to assess metabolite response linearity across a broad concentration range.
Main Results:
- A significant portion (70%) of 1327 detected metabolites exhibited non-linear behavior across nine dilution levels.
- Linearity was observed in at least four dilution levels for 47% of metabolites (8-fold difference).
- Non-linear responses often led to overestimation of metabolite abundance in lower concentrations, potentially increasing false negatives.
Conclusions:
- Widespread non-linearity in metabolite quantification necessitates careful method validation for untargeted metabolomics.
- Overestimation of abundances in samples outside the linear range can impact statistical analysis, potentially increasing false negatives.
- Non-linear behavior is not easily predictable based on compound class or polarity, highlighting the need for empirical assessment.
