Combining DI-ESI-MS and NMR datasets for metabolic profiling.
Combining DI-ESI-MS and NMR datasets for metabolic profiling.
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结合DI-ESI-MS和NMR数据集用于代谢分析。
DOI：
10.1007/s11306-014-0704-4
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发表时间：
2015-04
期刊：
影响因子：
3.6
通讯作者：
Powers, Robert
中科院分区：
文献类型：
--
作者：
Marshall, Darrell D.;Lei, Shulei;Worley, Bradley;Huang, Yuting;Garcia-Garcia, Aracely;Franco, Rodrigo;Dodds, Eric D.;Powers, Robert
代谢组学数据集通常通过质谱学(MS)或核磁共振波谱(核磁共振)获得，尽管它们在根本上具有互补性。事实上，将MS和核磁共振数据集结合在一起，大大提高了代谢组的覆盖范围，提高了代谢物识别的准确性，提供了因疾病、药物治疗或各种其他环境刺激而引起的代谢变化的详细和高通量分析。理想情况下，一个代谢组学样本将同时用于MS和核磁共振分析，最大限度地减少两个数据集之间的变异性。这就需要优化样品制备、数据收集和数据处理方案，以有效地将直接注入的MS数据与一维(1D)1H核磁共振谱相结合。为了实现这一目标，我们首次报告了(I)用于核磁共振和质谱仪双重分析的代谢组学样品制备的优化，(Ii)用于复杂代谢物混合物分析的高通量、正离子直接进样电喷雾电离质谱仪(DI-ESI-MS)，以及(Iii)使用多块双线性分解同时分析DI-ESI-MS和1H核磁共振谱数据的数据处理方案，即多块主成分分析(MB-PCA)和多块偏最小二乘(MB-PLS)。最后，我们演示了组合使用反向加载、准确的质量测量和串联MS实验来识别对MB-PLS-DA评分中的类别分离有显著贡献的代谢物。我们表明，核磁共振和DI-ESI-MS数据集的整合在神经毒素参与多巴胺能细胞死亡的分析中产生了实质性的改进。
Metabolomics datasets are commonly acquired by either mass spectrometry (MS) or nuclear magnetic resonance spectroscopy (NMR), despite their fundamental complementarity. In fact, combining MS and NMR datasets greatly improves the coverage of the metabolome and enhances the accuracy of metabolite identification, providing a detailed and high-throughput analysis of metabolic changes due to disease, drug treatment, or a variety of other environmental stimuli. Ideally, a single metabolomics sample would be simultaneously used for both MS and NMR analyses, minimizing the potential for variability between the two datasets. This necessitates the optimization of sample preparation, data collection and data handling protocols to effectively integrate direct-infusion MS data with one-dimensional (1D) 1H NMR spectra. To achieve this goal, we report for the first time the optimization of (i) metabolomics sample preparation for dual analysis by NMR and MS, (ii) high throughput, positive-ion direct infusion electrospray ionization mass spectrometry (DI-ESI-MS) for the analysis of complex metabolite mixtures, and (iii) data handling protocols to simultaneously analyze DI-ESI-MS and 1D 1H NMR spectral data using multiblock bilinear factorizations, namely multiblock principal component analysis (MB-PCA) and multiblock partial least squares (MB-PLS). Finally, we demonstrate the combined use of backscaled loadings, accurate mass measurements and tandem MS experiments to identify metabolites significantly contributing to class separation in MB-PLS-DA scores. We show that integration of NMR and DI-ESI-MS datasets yields a substantial improvement in the analysis of neurotoxin involvement in dopaminergic cell death.
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影响因子：
3
作者：
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通讯作者：
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影响因子：
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