Fully Automated Trimethylsilyl (TMS) Derivatisation Protocol for Metabolite Profiling by GC-MS

Gas Chromatography-Mass Spectrometry (GC-MS) has long been used for metabolite profiling of a wide range of biological samples. Many derivatisation protocols are already available and among these, trimethylsilyl (TMS) derivatisation is one of the most widely used in metabolomics. However, most TMS methods rely on off-line derivatisation prior to GC-MS analysis. In the case of manual off-line TMS derivatisation, the derivative created is unstable, so reduction in recoveries occurs over time. Thus, derivatisation is carried out in small batches. Here, we present a fully automated TMS derivatisation protocol using robotic autosamplers and we also evaluate a commercial software, Maestro available from Gerstel GmbH. Because of automation, there was no waiting time of derivatised samples on the autosamplers, thus reducing degradation of unstable metabolites. Moreover, this method allowed us to overlap samples and improved throughputs. We compared data obtained from both manual and automated TMS methods performed on three different matrices, including standard mix, wine, and plasma samples. The automated TMS method showed better reproducibility and higher peak intensity for most of the identified metabolites than the manual derivatisation method. We also validated the automated method using 114 quality control plasma samples. Additionally, we showed that this online method was highly reproducible for most of the metabolites detected and identified (RSD < 20) and specifically achieved excellent results for sugars, sugar alcohols, and some organic acids. To the very best of our knowledge, this is the first time that the automated TMS method has been applied to analyse a large number of complex plasma samples. Furthermore, we found that this method was highly applicable for routine metabolite profiling (both targeted and untargeted) in any metabolomics laborator

Trace biomarkers associated with spontaneous preterm birth from the maternal serum metabolome of asymptomatic nulliparous women - parallel case-control studies from the SCOPE cohort.

Prediction of spontaneous preterm birth (sPTB) in asymptomatic women remains a great challenge; accurate and reproducible screening tools are still not available in clinical practice. We aimed to investigate whether the maternal serum metabolome together with clinical factors could be used to identify asymptomatic women at risk of sPTB. We conducted two case-control studies using gas chromatography-mass spectrometry to analyse maternal serum samples collected at 15- and 20-weeks' gestation from 164 nulliparous women from Cork, and 157 from Auckland. Smoking and vaginal bleeding before 15 weeks were the only significant clinical predictors of sPTB for Auckland and Cork subsets, respectively. Decane, undecane, and dodecane were significantly associated with sPTB (FDR < 0.05) in the Cork subset. An odds ratio of 1.9 was associated with a one standard deviation increase in log (undecane) in a multiple logistic regression which also included vaginal bleeding as a predictor. In summary, elevated serum levels of the alkanes decane, undecane, and dodecane were associated with sPTB in asymptomatic nulliparous women from Cork, but not in the Auckland cohort. The association is not strong enough to be a useful clinical predictor, but suggests that further investigation of the association between oxidative stress processes and sPTB risk is warranted