Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London , London SW7 2AZ, U.K.
Life Science Research Center, Technology Research Laboratory, Shimadzu Corporation , Kyoto 604-8511, Japan.
Anal Chem. 2017 Nov 7;89(21):11405-11412. doi: 10.1021/acs.analchem.7b02374. Epub 2017 Oct 13.
H nuclear magnetic resonance (NMR) spectroscopy-based metabolic phenotyping is now widely used for large-scale epidemiological applications. To minimize signal overlap present in 1D H NMR spectra, we have investigated the use of 2D J-resolved (JRES) H NMR spectroscopy for large-scale phenotyping studies. In particular, we have evaluated the use of the 1D projections of the 2D JRES spectra (pJRES), which provide single peaks for each of the J-coupled multiplets, using 705 human plasma samples from the FGENTCARD cohort. On the basis of the assessment of several objective analytical criteria (spectral dispersion, attenuation of macromolecular signals, cross-spectral correlation with GC-MS metabolites, analytical reproducibility and biomarker discovery potential), we concluded that the pJRES approach exhibits suitable properties for implementation in large-scale molecular epidemiology workflows.
基于氢核磁共振(NMR)光谱的代谢表型分析现在被广泛应用于大规模的流行病学研究。为了最小化 1D H NMR 光谱中存在的信号重叠,我们研究了使用二维 J 分辨(JRES)H NMR 光谱进行大规模表型研究。特别是,我们评估了使用二维 JRES 光谱的一维投影(pJRES)的方法,该方法为每个 J 偶合多峰提供了单个峰,使用了来自 FGENTCARD 队列的 705 个人类血浆样本。基于对几种客观分析标准(光谱分散度、大分子信号衰减、与 GC-MS 代谢物的交叉光谱相关性、分析重现性和生物标志物发现潜力)的评估,我们得出结论,pJRES 方法具有适合于大规模分子流行病学工作流程的特性。