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Disentangling method-induced differences in metabolomics: single- versus dual-phase extraction.

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This study compares methanol-based single-phase and methyl tertiary-butyl ether (MTBE)-based dual-phase metabolite extraction for untargeted metabolomics. Results show partitioning and matrix effects, not pipetting, drive differences, impacting metabolite coverage and data quality in LC-MS analysis.

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Area of Science:

  • Metabolomics
  • Analytical Chemistry
  • Biochemistry

Background:

  • Efficient metabolite extraction is crucial for comprehensive metabolome coverage and high-quality data in untargeted metabolomics.
  • Two common strategies, methanol-based single-phase and methyl tertiary-butyl ether (MTBE)-based dual-phase extraction, are widely used but their comparative performance is not fully understood.

Purpose of the Study:

  • To systematically compare methanol-based single-phase and MTBE-based dual-phase extraction strategies for untargeted metabolomics.
  • To evaluate their performance in common biological matrices like urine and plasma using liquid chromatography-mass spectrometry (LC-MS).
  • To dissect the multifactorial influences of pipetting, partitioning, and matrix effects on metabolite extraction efficiency.

Main Methods:

  • Systematic comparison of single-phase (methanol) and dual-phase (MTBE) extraction methods.
  • Evaluation across urine and plasma samples analyzed by LC-MS.
  • Utilized isotopically labeled internal metabolite standards (¹³C-labeled yeast extract) to quantify extraction-induced changes.
  • Disentangled extraction differences into pipetting, partitioning, and matrix effects.

Main Results:

  • Pipetting variability was minimal, with polar standards showing minimal fold changes.
  • Partitioning effects were significantly influenced by solvent composition and phase volume ratios.
  • Dual-phase extraction in urine with a high organic-to-aqueous ratio led to nonpolar metabolite redistribution.
  • Matrix effects contributed to selective differences, with ion suppression observed in specific LC-MS retention time regions.

Conclusions:

  • Metabolite extraction efficiency in LC-MS-based metabolomics is a complex interplay of partitioning behavior and matrix effects, not pipetting variability.
  • The study provides a framework for systematic evaluation of extraction methods, aiding future metabolomics method development.
  • Understanding these factors is essential for optimizing metabolite coverage and ensuring data quality in untargeted metabolomics studies.