Department of Environmental Chemistry, IDAEA-CSIC , Jordi Girona 18-26, 08034 Barcelona, Spain.
Van't Hoff Institute for Molecular Science, University of Amsterdam , 1090 XH Amsterdam, The Netherlands.
Anal Chem. 2017 Jul 18;89(14):7675-7683. doi: 10.1021/acs.analchem.7b01648. Epub 2017 Jul 7.
In this work, a new strategy for the chemometric analysis of two-dimensional liquid chromatography-high-resolution mass spectrometry (LC × LC-HRMS) data is proposed. This approach consists of a preliminary compression step along the mass spectrometry (MS) spectral dimension based on the selection of the regions of interest (ROI), followed by a further data compression along the chromatographic dimension by wavelet transforms. In a secondary step, the multivariate curve resolution alternating least squares (MCR-ALS) method is applied to previously compressed data sets obtained in the simultaneous analysis of multiple LC × LC-HRMS chromatographic runs from multiple samples. The feasibility of the proposed approach is demonstrated by its application to a large experimental data set obtained in the untargeted LC × LC-HRMS study of the effects of different environmental conditions (watering and harvesting time) on the metabolism of multiple rice samples. An untargeted chromatographic setup coupling two different liquid chromatography (LC) columns [hydrophilic interaction liquid chromatography (HILIC) and reversed-phase liquid chromatography (RPLC)] together with an HRMS detector was developed and applied to analyze the metabolites extracted from rice samples at the different experimental conditions. In the case of the metabolomics study taken as example in this work, a total number of 154 metabolites from 15 different families were properly resolved after the application of MCR-ALS. A total of 139 of these metabolites could be identified by their HRMS spectra. Statistical analysis of their concentration changes showed that both watering and harvest time experimental factors had significant effects on rice metabolism. The biochemical insight of the effects of watering and harvesting experimental factors on the changes in concentration of these detected metabolites in the investigated rice samples is attempted.
在这项工作中,提出了一种用于二维液相色谱-高分辨质谱(LC×LC-HRMS)数据化学计量分析的新策略。该方法包括基于选择感兴趣区域(ROI)沿质谱(MS)光谱维度进行初步压缩,然后沿色谱维度通过小波变换进一步进行数据压缩。在第二步中,多元曲线分辨交替最小二乘法(MCR-ALS)方法应用于从多个样品同时分析多个 LC×LC-HRMS 色谱运行中获得的先前压缩数据集。通过将其应用于在不同环境条件(浇水和收获时间)对多个水稻样品代谢的非靶向 LC×LC-HRMS 研究中获得的大型实验数据集,证明了该方法的可行性。开发并应用了一种非靶向色谱装置,将两种不同的液相色谱(LC)柱[亲水相互作用液相色谱(HILIC)和反相液相色谱(RPLC)]与 HRMS 检测器耦合,以分析在不同实验条件下从水稻样品中提取的代谢物。在本文作为示例的代谢组学研究中,在应用 MCR-ALS 后,可以正确分辨来自 15 个不同家族的 154 种代谢物。其中 139 种代谢物可以通过它们的 HRMS 光谱进行鉴定。对它们浓度变化的统计分析表明,浇水和收获时间这两个实验因素对水稻代谢都有显著影响。尝试从生化角度解释浇水和收获实验因素对研究中水稻样品中这些检测到的代谢物浓度变化的影响。