NMR Metabonomics Laboratory, Laboratory of Chemistry, Department of Biosciences, University of Kuopio, Kuopio, Finland.
Analyst. 2009 Sep;134(9):1781-5. doi: 10.1039/b910205a. Epub 2009 Jul 30.
A high-throughput proton (1H) nuclear magnetic resonance (NMR) metabonomics approach is introduced to characterise systemic metabolic phenotypes. The methodology combines two molecular windows that contain the majority of the metabolic information available by 1H NMR from native serum, e.g. serum lipids, lipoprotein subclasses as well as various low-molecular-weight metabolites. The experimentation is robotics-controlled and fully automated with a capacity of about 150-180 samples in 24 h. To the best of our knowledge, the presented set-up is unique in the sense of experimental high-throughput, cost-effectiveness, and automated multi-metabolic data analyses. As an example, we demonstrate that the NMR data as such reveal associations between systemic metabolic phenotypes and the metabolic syndrome (n = 4407). The high-throughput of up to 50,000 serum samples per year is also paving the way for this technology in large-scale clinical and epidemiological studies. In contradiction to single 'biomarkers', the application of this holistic NMR approach and the integrated computational methods provides a data-driven systems biology approach to biomedical research.
一种高通量质子（1H）核磁共振（NMR）代谢组学方法被引入来描述系统代谢表型。该方法结合了两个分子窗口，包含了通过 1H NMR 从天然血清中获得的大多数代谢信息，例如血清脂质、脂蛋白亚类以及各种低分子量代谢物。该实验是机器人控制的，完全自动化，每 24 小时可处理约 150-180 个样本。据我们所知，就实验高通量、成本效益和自动化多代谢数据分析而言，所提出的方案是独特的。例如，我们证明了 NMR 数据本身揭示了系统代谢表型与代谢综合征之间的关联（n = 4407）。每年高达 50,000 个血清样本的高通量也为这项技术在大规模临床和流行病学研究中的应用铺平了道路。与单个“生物标志物”相反，这种整体 NMR 方法的应用和集成计算方法为生物医学研究提供了一种数据驱动的系统生物学方法。
