Papadimitropoulos Matthaios-Emmanouil P, Vasilopoulou Catherine G, Maga-Nteve Christoniki, Klapa Maria I
Metabolic Engineering and Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research & Technology - Hellas (FORTH/ICE-HT), Patras, 26504, Greece.
Division of Genetics, Cell & Developmental Biology, Department of Biology, University of Patras, Patras, 26500, Greece.
Methods Mol Biol. 2018;1738:133-147. doi: 10.1007/978-1-4939-7643-0_9.
Untargeted metabolomics refers to the high-throughput analysis of the metabolic state of a biological system (e.g., tissue, biological fluid, cell culture) based on the concentration profile of all measurable free low molecular weight metabolites. Gas chromatography-mass spectrometry (GC-MS), being a highly sensitive and high-throughput analytical platform, has been proven a useful tool for untargeted studies of primary metabolism in a variety of applications. As an omic analysis, GC-MS metabolomics is a multistep procedure; thus, standardization of an untargeted GC-MS metabolomics protocol requires the integrated optimization of pre-analytical, analytical, and computational steps. The main difference of GC-MS metabolomics compared to other metabolomics analytical platforms, including liquid chromatography-MS, is the need for the derivatization of the metabolite extracts into volatile and thermally stable derivatives, the latter being quantified in the metabolic profiles. This analytical step requires special care in the optimization of the untargeted GC-MS metabolomics experimental protocol. Moreover, both the derivatization of the original sample and the compound fragmentation that takes place in GC-MS impose specialized GC-MS metabolomic data identification, quantification, normalization and filtering methods. In this chapter, we describe the integrated protocol of untargeted GC-MS metabolomics with both the analytical and computational steps, focusing on the GC-MS specific parts, and provide details on any sample depending differences.
非靶向代谢组学是指基于所有可测量的游离低分子量代谢物的浓度分布，对生物系统（如组织、生物流体、细胞培养物）的代谢状态进行高通量分析。气相色谱-质谱联用（GC-MS）作为一种高灵敏度和高通量的分析平台，已被证明是在各种应用中对初级代谢进行非靶向研究的有用工具。作为一种组学分析，GC-MS代谢组学是一个多步骤过程；因此，非靶向GC-MS代谢组学方案的标准化需要对分析前、分析和计算步骤进行综合优化。与其他代谢组学分析平台（包括液相色谱-质谱联用）相比，GC-MS代谢组学的主要区别在于需要将代谢物提取物衍生化为挥发性和热稳定的衍生物，后者在代谢谱中进行定量。这一分析步骤在优化非靶向GC-MS代谢组学实验方案时需要特别注意。此外，原始样品的衍生化以及GC-MS中发生的化合物碎片化都需要专门的GC-MS代谢组学数据识别、定量、归一化和过滤方法。在本章中，我们描述了非靶向GC-MS代谢组学的综合方案，包括分析和计算步骤，重点关注GC-MS特定部分，并提供了因样品而异的详细信息。
