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

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.