Alseekh Saleh, Wu Si, Brotman Yariv, Fernie Alisdair R
Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.
Center of Plant System Biology and Biotechnology, Plovdiv, Bulgaria.
Methods Mol Biol. 2018;1778:33-46. doi: 10.1007/978-1-4939-7819-9_3.
Recent methodological advances in both liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) have facilitated the profiling highly complex mixtures of primary and secondary metabolites in order to investigate a diverse range of biological questions. These techniques usually face a large number of potential sources of technical and biological variation. In this chapter we describe guidelines and normalization procedures to reduce the analytical variation, which are essential for the high-throughput evaluation of metabolic variance used in broad genetic populations which commonly entail the evaluation of hundreds or thousands of samples. This chapter specifically deals with handling of large-scale plant samples for metabolomics analysis of quantitative trait loci (mQTL) in order to reduce analytical error as well as batch-to-batch variation.
液相色谱 - 质谱联用（LC-MS）和气相色谱 - 质谱联用（GC-MS）技术在方法学上的最新进展，有助于对初级和次级代谢产物的高度复杂混合物进行剖析，从而研究各种各样的生物学问题。这些技术通常面临大量潜在的技术和生物学变异来源。在本章中，我们描述了减少分析变异的指南和标准化程序，这对于高通量评估广泛遗传群体中的代谢变异至关重要，因为这些群体通常需要评估数百或数千个样本。本章专门论述了为减少分析误差以及批次间变异而对用于数量性状位点代谢组学分析（mQTL）的大规模植物样本的处理。
