Weljie Aalim M, Newton Jack, Mercier Pascal, Carlson Erin, Slupsky Carolyn M
Chenomx Inc., Edmonton, Alberta, Canada, and Metabolomics Research Centre, University of Calgary, Calgary, Canada.
Anal Chem. 2006 Jul 1;78(13):4430-42. doi: 10.1021/ac060209g.
Extracting meaningful information from complex spectroscopic data of metabolite mixtures is an area of active research in the emerging field of "metabolomics", which combines metabolism, spectroscopy, and multivariate statistical analysis (pattern recognition) methods. Chemometric analysis and comparison of 1H NMR1 spectra is commonly hampered by intersample peak position and line width variation due to matrix effects (pH, ionic strength, etc.). Here a novel method for mixture analysis is presented, defined as "targeted profiling". Individual NMR resonances of interest are mathematically modeled from pure compound spectra. This database is then interrogated to identify and quantify metabolites in complex spectra of mixtures, such as biofluids. The technique is validated against a traditional "spectral binning" analysis on the basis of sensitivity to water suppression (presaturation, NOESY-presaturation, WET, and CPMG), relaxation effects, and NMR spectral acquisition times (3, 4, 5, and 6 s/scan) using PCA pattern recognition analysis. In addition, a quantitative validation is performed against various metabolites at physiological concentrations (9 microM-8 mM). "Targeted profiling" is highly stable in PCA-based pattern recognition, insensitive to water suppression, relaxation times (within the ranges examined), and scaling factors; hence, direct comparison of data acquired under varying conditions is made possible. In particular, analysis of metabolites at low concentration and overlapping regions are well suited to this analysis. We discuss how targeted profiling can be applied for mixture analysis and examine the effect of various acquisition parameters on the accuracy of quantification.
从代谢物混合物的复杂光谱数据中提取有意义的信息，是新兴的“代谢组学”领域中一个活跃的研究方向，该领域结合了代谢、光谱学和多元统计分析（模式识别）方法。由于基质效应（pH值、离子强度等）导致的样品间峰位置和线宽变化，常常妨碍了化学计量学对1H NMR光谱的分析和比较。本文提出了一种用于混合物分析的新方法，定义为“靶向分析”。通过纯化合物光谱对感兴趣的各个核磁共振共振进行数学建模。然后查询该数据库，以识别和定量混合物（如生物流体）复杂光谱中的代谢物。基于主成分分析模式识别分析，通过对水抑制（预饱和、NOESY预饱和、WET和CPMG）、弛豫效应以及核磁共振光谱采集时间（3、4、5和6秒/扫描）的敏感性，对该技术与传统的“光谱分箱”分析进行了验证。此外，还针对生理浓度（9微摩尔-8毫摩尔）的各种代谢物进行了定量验证。“靶向分析”在基于主成分分析的模式识别中高度稳定，对水抑制、弛豫时间（在所研究的范围内）和比例因子不敏感；因此，可以直接比较在不同条件下获取的数据。特别是，对低浓度和重叠区域代谢物的分析非常适合这种分析方法。我们讨论了靶向分析如何应用于混合物分析，并研究了各种采集参数对定量准确性的影响。
