Metabolomics Platform, IISPV, DEEEA, Universitat Rovira i Virgili, Campus Sescelades, Carretera de Valls, s/n, 43007, Tarragona, Catalonia, Spain.
European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom.
Sci Rep. 2018 Aug 8;8(1):11886. doi: 10.1038/s41598-018-30351-7.
NMR spectroscopy is a technology that is widely used in metabolomic studies. The information that these studies most commonly use from NMR spectra is the metabolite concentration. However, as well as concentration, pH and ionic strength information are also made available by the chemical shift of metabolite signals. This information is typically not used even though it can enhance sample discrimination, since many conditions show pH or ionic imbalance. Here, we demonstrate how chemical shift information can be used to improve the quality of the discrimination between case and control samples in three public datasets of different human matrices. In two of these datasets, chemical shift information helped to provide an AUROC value higher than 0.9 during sample classification. In the other dataset, the chemical shift also showed discriminant potential (AUROC 0.831). These results are consistent with the pH imbalance characteristic of the condition studied in the datasets. In addition, we show that this signal misalignment dependent on sample class can alter the results of fingerprinting approaches in the three datasets. Our results show that it is possible to use chemical shift information to enhance the diagnostic and predictive properties of NMR.
NMR 光谱学是一种广泛应用于代谢组学研究的技术。这些研究最常从 NMR 谱中获取的信息是代谢物浓度。然而，除了浓度之外，代谢物信号的化学位移也提供了 pH 值和离子强度信息。尽管这些信息可以增强样本的区分度，但由于许多情况下存在 pH 值或离子失衡，因此通常不会使用这些信息。在这里，我们展示了如何在三个不同人体基质的公共数据集之间使用化学位移信息来提高病例和对照样本之间区分的质量。在其中两个数据集中，化学位移信息有助于在样本分类期间提供高于 0.9 的 AUC 值。在另一个数据集中，化学位移也显示出了区分潜力（AUC 值为 0.831）。这些结果与数据集中研究条件的 pH 值失衡特征一致。此外，我们还表明，这种依赖于样本类别的信号偏移会改变三个数据集中指纹图谱方法的结果。我们的结果表明，有可能利用化学位移信息来增强 NMR 的诊断和预测特性。
