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The Renaissance of GC-MS as an Analytical Platform for Metabolomics

What is Metabolomics?

Metabolomics (also known as metabonomics or metabolic phenotyping) is core discipline in modern bioscience. Metabolomics involves the measurement of the metabolome (the set of metabolites present within a metabolic system, organism, cell or tissue) and relating changes in the metabolome to endogenous (genetic) or exogenous (environmental) inputs into a metabolic system.

Any input will have both predictable and unpredictable changes on the metabolome, so metabolomics uses both targeted (profiling) and untargeted (fingerprinting) approaches to measure quantitative or semi-quantitative changes in the metabolome. Due to the large number of variables and samples needed to ensure statistical robustness of the experimental design, powerful multivariate statistical algorithms are used to determine which metabolic changes are due to the experimental variable being tested.

Metabolomics contributes to research in clinical studies, clinical diagnostics, health, nutrition, functional genomics, agriscience, pharmacology and synthetic biology.


Current Platforms for Metabolomics

Metabolomics is currently dominated by two analytical platforms: LC-MS and 1H-NMR.

These are both great techniques, but they are both best suited in the analysis of small polar metabolites. Even if we use both platforms there are still whole sections of the metabolome jigsaw that we’re missing.

But, if we’re missing sections, how can we hope to complete our picture? Wouldn’t it be great if there was a way to measure these metabolites?


The Missing Jigsaw Piece

Back in the early days of metabolomics (late 1990s and early 2000s) Gas Chromatography-Mass Spectrometry (GC-MS) was used routinely in metabolomics studies; having been popularized by Professor Oliver Fiehn at the Max Planck Institute in Golm.

GC-MS did a great job of analyzing small non-polar compounds and, with MOX-TMS derivatization, many of the polar ones too! What’s more, the analytical precision, chromatographic resolution and retention time stability were all better than LC-MS.


What Went Wrong?

To answer this question we have to consider the metabolomics data processing workflow:

  • Acquire our dataset
  • Extract (chemical shift or RT:m/z) features from the raw data
  • Combine feature abundances into a data array (features x sample)
  • Model our data using multivariate statistics.

The simple fact is that there was nothing wrong with GC-MS as a technique, and the instrumentation was robust and reliable.

The downfall of GC-MS was that the available feature extraction and array alignment algorithms couldn’t cope with the high chromatographic resolution and mass spectral data density of GC-MS datasets.

GC-MS never went away but it didn’t become a mainstream platform for most labs because the data processing algorithms that worked for LC-MS and 1H-NMR were easier to use, more robust and readily available, either as commercial or open-source software products.

As new entrants to the field of metabolomics platforms bought into these platforms, where feature extraction was easier, untargeted fingerprinting using GC-MS was confined to labs with their own chemometrics groups.


What’s Changed?

The great news for the metabolomics community is that GC-MS is back and, what’s more it’s ready for your metabolomics lab. The reason for this is that we now have all the tools we need:

  • Agilent 7200B High Resolution-Accurate Mass (HRAM), fast scanning, full scan GC/Q-TOF GC-MS System with uniform data density and sensitivity.
  • Agilent MassHunter Software with:
      • Retention time locking
      • Personal Compound Database Libraries
      • RT/MS based feature extraction
      • Quant for targeted metabolomics workflows
      • Unknowns for semi-targeted and untargeted metabolomics workflows


  • New Profinder for GC-MS feature extraction.
  • Automated tools for unknown ID (Molecular Formula Generator and Molecular Structure Correlator).
  • Agilent GeneSpring Mass Profiler Professional for GC-MS.
  • GERSTEL Automated Sample Extraction and Derivatization solutions for just-in-time sample preparation – essential for derivatization based methods.
  • A whole new set of fully automated metabolomics and metabolite profiling applications.
  • GERSTEL advanced sample introduction techniques to extend the range of what’s possible.

Anatune can build metabolomics solutions for GC-MS based metabolomics, incorporating fully automated, integrated, sample preparation. Our solutions are based upon instrumentation from Agilent Technologies and GERSTEL.

Anatune will be sponsoring Metabolomics 2016 in the Conference Centre Dublin and will be there with our Partners GERSTEL and Agilent.

If you’re there too, come to our booth to find out how GC-MS can help you fill in more of your jigsaw and see more of the picture.

If you can’t meet us there and would like to know more call us on +44 (0)1223 279210 or email:

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