Kanani Harin, Chrysanthopoulos Panagiotis K, Klapa Maria I
Metabolic Engineering and Systems Biology Laboratory, Department of Chemical and Biomolecular Engineering, University of Maryland, MD 20742, USA.
J Chromatogr B Analyt Technol Biomed Life Sci. 2008 Aug 15;871(2):191-201. doi: 10.1016/j.jchromb.2008.04.049. Epub 2008 May 21.
Metabolomics being the most recently introduced "omic" analytical platform is currently at its development phase. For the metabolomics to be broadly deployed to biological and clinical research and practice, issues regarding data validation and reproducibility need to be resolved. Gas chromatography-mass spectrometry (GC-MS) will remain integral part of the metabolomics laboratory. In this paper, the sources of biases in GC-MS metabolomics are discussed and experimental evidence for their occurrence and impact on the final results is provided. When available, methods to correct or account for these biases are presented towards the standardization of a systematic methodology for quantitative GC-MS metabolomics.
代谢组学作为最新引入的“组学”分析平台，目前正处于发展阶段。为了使代谢组学广泛应用于生物学和临床研究及实践，需要解决数据验证和可重复性方面的问题。气相色谱 - 质谱联用仪（GC-MS）仍将是代谢组学实验室不可或缺的一部分。本文讨论了GC-MS代谢组学中偏差的来源，并提供了这些偏差发生的实验证据及其对最终结果的影响。在可行的情况下，提出了校正或考虑这些偏差的方法，以实现定量GC-MS代谢组学系统方法的标准化。
