Department of Biochemistry and Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, USA.
Biomolecular Analysis Facility, University of Virginia School of Medicine, Charlottesville, VA, USA.
Methods Mol Biol. 2022;2539:235-260. doi: 10.1007/978-1-0716-2537-8_19.
Metabolite profiling provides insights into the metabolic signatures, which themselves are considered as phonotypes closely related to the agronomic and phenotypic traits such as yield, nutritional values, stress resistance, and nutrient use efficiency. GC-MS is a sensitive and high-throughput analytical platform and has been proved to be a vital tool for the analysis of primary metabolism to provide an overview of cellular and organismal metabolic status. The potential of GC-MS metabolite profiling as a tool for detecting metabolic changes in plants grown in a high-throughput plant phenotyping platform was explored. In this chapter, we describe an integrated workflow of semi-targeted GC-high-resolution (HR)-time-of-flight (TOF)-MS metabolomics with both the analytical and computational steps, focusing mainly on the sample preparation, GC-HR-TOF-MS analysis part, and data analysis for plant phenotyping efforts.
代谢物分析可深入了解代谢特征，这些特征本身被认为是与农艺和表型特征（如产量、营养价值、抗逆性和养分利用效率）密切相关的表型。GC-MS 是一种敏感且高通量的分析平台，已被证明是分析初级代谢物的重要工具，可全面了解细胞和生物体的代谢状态。本研究探索了 GC-MS 代谢物分析作为一种工具在高通量植物表型平台中检测植物代谢变化的潜力。在本章中，我们描述了一种集成的半靶向 GC-高分辨率（HR）-飞行时间（TOF）-MS 代谢组学工作流程，包括分析和计算步骤，主要侧重于样品制备、GC-HR-TOF-MS 分析部分和用于植物表型研究的数据分析。
