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Updated: Feb 11, 2026

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
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Untargeted GC-MS Metabolomics.

Matthaios-Emmanouil P Papadimitropoulos1,2, Catherine G Vasilopoulou1,3, Christoniki Maga-Nteve1,4

  • 1Metabolic Engineering and Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research & Technology - Hellas (FORTH/ICE-HT), Patras, 26504, Greece.

Methods in Molecular Biology (Clifton, N.J.)
|April 15, 2018
PubMed
Summary

Untargeted metabolomics using gas chromatography-mass spectrometry (GC-MS) requires optimizing pre-analytical, analytical, and computational steps. Special attention to metabolite derivatization and GC-MS specific data processing is crucial for accurate results.

Keywords:
Gas chromatography-mass spectrometry (GC-MS) metabolomicsMetabolic network analysisMetabolic profilingPrimary metabolismUntargeted metabolomics

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Area of Science:

  • Metabolomics
  • Analytical Chemistry
  • Biochemistry

Background:

  • Untargeted metabolomics analyzes the complete metabolic profile of biological systems.
  • Gas chromatography-mass spectrometry (GC-MS) is a sensitive, high-throughput tool for primary metabolism studies.
  • Standardization is key for reliable GC-MS metabolomics protocols.

Purpose of the Study:

  • To describe an integrated protocol for untargeted GC-MS metabolomics.
  • To highlight GC-MS specific analytical and computational considerations.
  • To address sample-dependent variations in the protocol.

Main Methods:

  • High-throughput analysis of low molecular weight metabolites.
  • Gas chromatography-mass spectrometry (GC-MS) for metabolite quantification.
  • Metabolite derivatization to enhance volatility and thermal stability.
  • Specialized data identification, quantification, normalization, and filtering methods for GC-MS data.

Main Results:

  • GC-MS metabolomics involves pre-analytical, analytical, and computational optimization.
  • Metabolite derivatization is a critical GC-MS specific step.
  • Specialized computational methods are needed for GC-MS metabolomic data.

Conclusions:

  • An optimized, integrated protocol is essential for untargeted GC-MS metabolomics.
  • Addressing GC-MS specific challenges, like derivatization, improves data quality.
  • The protocol considers sample-specific differences for broader applicability.