Towards automatic metabolomic profiling of high-resolution one-dimensional proton NMR spectra
Towards automatic metabolomic profiling of high-resolution one-dimensional proton NMR spectra
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DOI：
10.1007/s10858-011-9480-x
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发表时间：
2011-04-01
影响因子：
2.7
通讯作者：
Wishart, David S.
中科院分区：
文献类型：
--
作者：
Mercier, Pascal;Lewis, Michael J.;Wishart, David S.
核磁共振（NMR）和质谱（MS）是代谢组学中最常用的两种光谱分析技术。NMR和MS生成的大型光谱数据集通常使用数据简化技术（如主成分分析（PCA））进行分析。尽管快速，但这些方法易受溶剂和基质效应的影响，假阳性率高，缺乏重现性，并且从一个平台到下一个平台的数据可转移性有限。鉴于这些限制，基于NMR和MS的代谢组学的增长趋势是朝向靶向分析或“定量”代谢组学，其中化合物在任何统计分析之前通过光谱拟合来鉴定和定量。尽管这种方法有明显的优点，但目标分析受到执行手动或计算机辅助光谱拟合所需时间的阻碍。为了提高基于NMR的代谢组学的数据分析吞吐量，我们开发了一种用于识别和定量一维（1D）质子NMR谱中代谢物的自动方法。这种新算法能够使用精心构建的参考光谱和优化数千个变量，使用来自物理化学和NMR理论的规则和概念来重建生物流体的实验NMR光谱。自动分析程序已被测试对合成混合物的光谱，以及尿液，血清和脑脊液（CSF）的生物光谱。我们的研究结果表明，该算法可以正确地识别化合物与高保真度在每个生物流体样本（尿液除外）。此外，代谢物浓度与模拟值和手动检测值均表现出非常高的相关性。
Nuclear magnetic resonance (NMR) and Mass Spectroscopy (MS) are the two most common spectroscopic analytical techniques employed in metabolomics. The large spectral datasets generated by NMR and MS are often analyzed using data reduction techniques like Principal Component Analysis (PCA). Although rapid, these methods are susceptible to solvent and matrix effects, high rates of false positives, lack of reproducibility and limited data transferability from one platform to the next. Given these limitations, a growing trend in both NMR and MS-based metabolomics is towards targeted profiling or "quantitative" metabolomics, wherein compounds are identified and quantified via spectral fitting prior to any statistical analysis. Despite the obvious advantages of this method, targeted profiling is hindered by the time required to perform manual or computer-assisted spectral fitting. In an effort to increase data analysis throughput for NMR-based metabolomics, we have developed an automatic method for identifying and quantifying metabolites in one-dimensional (1D) proton NMR spectra. This new algorithm is capable of using carefully constructed reference spectra and optimizing thousands of variables to reconstruct experimental NMR spectra of biofluids using rules and concepts derived from physical chemistry and NMR theory. The automated profiling program has been tested against spectra of synthetic mixtures as well as biological spectra of urine, serum and cerebral spinal fluid (CSF). Our results indicate that the algorithm can correctly identify compounds with high fidelity in each biofluid sample (except for urine). Furthermore, the metabolite concentrations exhibit a very high correlation with both simulated and manually-detected values.