title: Fully Automated Trimethylsilyl (TMS) Derivatisation Protocol for Metabolite Profiling by GC-MS.
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.
authors.affiliation: School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland 1010, New Zealand. e.zarate@auckland.ac.nz.
authors.forename: Erica
authors.initials: E
authors.lastname: Zarate
authors.affiliation: The Liggins Institute, University of Auckland, Private Bag 92019, Auckland 1010, New Zealand. v.boyle@auckland.ac.nz.
authors.forename: Veronica
authors.initials: V
authors.lastname: Boyle
authors.affiliation: Lasersan Australasia Pty Ltd., Robina QLD 4226, Australia. udo@lasersan.com.
authors.forename: Udo
authors.initials: U
authors.lastname: Rupprecht
authors.affiliation: School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland 1010, New Zealand. saras.green@auckland.ac.nz.
authors.forename: Saras
authors.initials: S
authors.lastname: Green
authors.affiliation: School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland 1010, New Zealand. s.villas-boas@auckland.ac.nz.
authors.forename: Silas G
authors.initials: SG
authors.lastname: Villas-Boas
authors.affiliation: The Liggins Institute, University of Auckland, Private Bag 92019, Auckland 1010, New Zealand. philip.baker@le.ac.uk.|College of Medicine, Biological Sciences and Psychology, University of Leicester, Leicester LE1 7RH, UK. philip.baker@le.ac.uk.
authors.forename: Philip
authors.initials: P
authors.lastname: Baker
authors.affiliation: Sustainable Production, The New Zealand Institute for Plant & Food Research Limited, Private Bag 92169, Auckland 1142, New Zealand. farhana.pinu@plantandfood.co.nz.
authors.forename: Farhana R
authors.initials: FR
authors.lastname: Pinu
keywords: amino acids; automation; matrix; metabolomics; organic acids; sample preparation; sugars