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Patterned Signal Ratio Biases in Mass Spectrometry-Based Quantitative Metabolomics
1Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1, British Columbia, Canada.
Analytical Chemistry
|January 5, 2021
Summary
Nonlinear electrospray ionization (ESI) in mass spectrometry (MS) complicates quantitative comparisons. A new metabolic ratio correction (MRC) strategy uses polynomial regression to accurately adjust biased signal ratios in untargeted metabolomics.
Area of Science:
- Analytical Chemistry
- Biochemistry
- Metabolomics
Background:
- Electrospray ionization (ESI) in mass spectrometry (MS) exhibits nonlinear responses, impacting quantitative accuracy.
- The feature-dependent nature of ESI nonlinearity and its effect on signal ratios are not fully understood.
- Existing methods struggle to correct for ESI-induced ratio bias, especially within purported linear ranges.
Purpose of the Study:
- To investigate the feature-dependent nonlinear response patterns of ESI-MS.
- To develop and validate a novel strategy for correcting biased signal ratios in untargeted metabolomics.
- To improve the accuracy of quantitative comparisons and downstream biological interpretation.
Main Methods:
- Analysis of serially diluted human urine samples using ESI-MS.
- Characterization of signal ratio compression and inflation across metabolic features.
- Development of a metabolic ratio correction (MRC) strategy employing polynomial regression models.
- Cross-validation to select optimal regression models (linear vs. polynomial) for individual features.
Main Results:
- Over 72% of metabolic features showed ratio compression and 16% showed ratio inflation, with only 12% reflecting true concentration ratios.
- Biases persisted even within the commonly assumed linear ESI response ranges.
- Polynomial regression models outperformed linear models in correcting biased ratios and improving data prediction.
- The MRC strategy effectively corrected biased signal ratios in human urine samples and a broader metabolomics application.
Conclusions:
- ESI nonlinearity creates significant, patterned biases in signal ratios that are not resolved by simple dilution.
- The proposed metabolic ratio correction (MRC) workflow, utilizing feature-specific regression models, accurately corrects these biases.
- MRC enhances the reliability of quantitative comparisons and biological interpretations in untargeted metabolomics.
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