Targeted profiling:: Quantitative analysis of 1H NMR metabolomics data
Targeted profiling:: Quantitative analysis of 1H NMR metabolomics data
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DOI：
10.1021/ac060209g
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发表时间：
2006-07-01
影响因子：
7.4
通讯作者：
Slupsky, Carolyn M.
中科院分区：
文献类型：
--
作者：
Weljie, Aalim M.;Newton, Jack;Slupsky, Carolyn M.
从代谢物混合物的复杂光谱数据中提取有意义的信息是“代谢组学”这一新兴领域的一个活跃研究领域，它结合了代谢、光谱和多元统计分析（模式识别）方法。H-1 NMR1光谱的化学计量分析和比较通常受到样品间峰位置和由于基质效应（pH、离子强度等）引起的线宽变化的阻碍。本文提出了一种新的混合分析方法，定义为“目标分析”。单个核磁共振感兴趣的数学模型从纯化合物光谱。然后对该数据库进行查询，以确定和量化混合物（如生物流体）中复杂光谱中的代谢物。基于对水抑制（预饱和、nosy -预饱和、WET和CPMG）的敏感性，以及使用PCA模式识别分析的核磁共振光谱采集时间（3、4、5和6秒/扫描），该技术与传统的“光谱分割”分析进行了验证。此外，还对生理浓度（9 μ M-8 mM）下的各种代谢物进行了定量验证。“目标剖面”在基于PCA的模式识别中高度稳定，对水抑制、松弛时间（在检测范围内）和比例因子不敏感；因此，可以直接比较在不同条件下获得的数据。特别是，低浓度和重叠区域的代谢物分析非常适合这种分析。我们讨论了如何将目标分析应用于混合物分析，并检查了各种采集参数对定量准确性的影响。
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 H-1 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 mu M-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.