Zarate Erica, Boyle Veronica, Rupprecht Udo, Green Saras, Villas-Boas Silas G, Baker Philip, Pinu Farhana R
School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland 1010, New Zealand.
The Liggins Institute, University of Auckland, Private Bag 92019, Auckland 1010, New Zealand.
Metabolites. 2016 Dec 29;7(1):1. doi: 10.3390/metabo7010001.
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.
气相色谱 - 质谱联用技术（GC-MS）长期以来一直用于对各种生物样品进行代谢物谱分析。许多衍生化方案已经存在，其中三甲基硅烷基（TMS）衍生化是代谢组学中使用最广泛的方法之一。然而，大多数TMS方法依赖于GC-MS分析前的离线衍生化。在手动离线TMS衍生化的情况下，生成的衍生物不稳定，因此回收率会随着时间的推移而降低。因此，衍生化是分批进行的。在此，我们提出了一种使用机器人自动进样器的全自动TMS衍生化方案，并且我们还评估了一款来自Gerstel GmbH的商业软件Maestro。由于自动化，衍生化样品在自动进样器上无需等待时间，从而减少了不稳定代谢物的降解。此外，这种方法使我们能够重叠样品并提高通量。我们比较了在三种不同基质（包括标准混合物、葡萄酒和血浆样品）上进行的手动和自动TMS方法获得的数据。与手动衍生化方法相比，自动TMS方法对大多数已鉴定的代谢物显示出更好的重现性和更高的峰强度。我们还使用114个质量控制血浆样品验证了该自动化方法。此外，我们表明这种在线方法对于大多数检测和鉴定出的代谢物具有高度重现性（RSD < 20），并且特别在糖类、糖醇类和一些有机酸方面取得了优异的结果。据我们所知，这是首次将自动化TMS方法应用于分析大量复杂的血浆样品。此外，我们发现这种方法非常适用于任何代谢组学实验室的常规代谢物谱分析（靶向和非靶向）。
