Fully automated trimethylsilyl (TMS) derivatisation protocol for metabolite profiling by GC-MS

Zarate, Erica, Boyle, Veronica, Rupprecht, Udo, Green, Saras, Villas-Boas, Silas G., Baker, Philip and Pinu, Farhana R. (2017) Fully automated trimethylsilyl (TMS) derivatisation protocol for metabolite profiling by GC-MS. Metabolites, 7 (1). ISSN 2218-1989

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Abstract

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 laboratory.

Item Type:	Article
Additional Information:	We thank the Mass spectrometry facility, School of Biological Sciences, University of Auckland (UoA) and Science Publishing Office (SPO), New Zealand Institute for Plant and Food Research Ltd. (PFR). We also thank Dr Elizabeth Mckenzie (UoA) for insightful discussion during method set up, Damian Martin and Megan Jones (PFR) for their support during the study. Wine and plasma samples were provided by PFR and MAVIDOS, respectively.
Uncontrolled Keywords:	amino acids,automation,matrix,metabolomics,organic acids,sample preparation,sugars,endocrinology, diabetes and metabolism,biochemistry,molecular biology ,/dk/atira/pure/subjectarea/asjc/2700/2712
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	05 Feb 2026 14:37
Last Modified:	05 Feb 2026 14:38
URI:	https://ueaeprints.uea.ac.uk/id/eprint/101844
DOI:	10.3390/metabo7010001

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