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Automation of Derivatisation Workflows for GC-MS Metabolomics Applications

Application Note

Metabolomics studies often employ gas chromatography-mass spectrometry (GC-MS) as analytical platform because of its reproducibility and chromatographic resolution power. However, many target metabolites, such as organic acids and amino acids, require chemical derivatisation prior to GC-MS analysis to increase their volatility and thermal stability and allow their successful detection. There are a number of protocols used for derivatisation most commonly producing trimethylsilyl (TMS) or tert-butyldimethylsilyl (TBDMS) derivatives.

This application note describes the automation of three derivatisation workflows for metabolomics applications TMS using MSTFA (n-methyl-n-trimethylsilylfluoroacetamide), TMS using TMSCN (trimethylsilylcyanide) and TBDMS using MTBSTFA (n-tertbutyldimethylsilyl-n-methyltrifluoroacetamide). Samples were fully prepared by the MPS and then injected directly on the Agilent GC/Q-TOF MS.

Metabolomics studies usually require the preparation of extensive sample sets to allow significant differentiation between sample types in biological matrices. Control of the analytical variability using automated sample preparation enables potentially larger data sets and ensures the production of the high-quality data required for subsequent statistical analysis, in comparison to manual preparation which can be labour intensive and prone to errors. All three derivatisation methods were successfully and reproducibly performed as shown by the internal standards areas and RSDs%. Due to the poor stability of many derivatives automation of this procedure results in higher sample throughput as well as reducing exposure to chemicals and improving data quality. Each sample can be extracted and derivatised immediately before injection. MSTFA and TMSCN showed similar results for the TMS derivatisation, but TBDMS derivatisation gave a lower total number of derivatives but a higher percentage of hits scoring above 90 match factor (30% for TBDMS against 20% for TMS derivatives). Furthermore, TBDMS profiles showed the presence of several TBDMS derivatives for amino acids and organic acids.

Application Note Downloads
AS165

Automation of Derivatisation Workflows for GC-MS Metabolomics Applications

 

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