Untargeted Comprehensive Two-Dimensional Liquid Chromatography Coupled with High-Resolution Mass Spectrometry Analysis of Rice Metabolome Using Multivariate Curve Resolution

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. © 2017 American Chemical Society.The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement No. 320737. The authors would like to thank CRAG for kindly supplying Japanese rice seeds. CTQ2015- 66254-C2-1-P project from MINCO (Spain) is also acknowledged.Peer reviewe

Enhancing detectability of anabolic-steroid residues in bovine urine by actively modulated online comprehensive two-dimensional liquid chromatography - high-resolution mass spectrometry

In this study we describe an approach to enhance the sensitivity of an online comprehensive two-dimensional liquid chromatography (LC × LC) high-resolution mass spectrometry method for the separation and detection of trace levels of anabolic-steroid residues in complex urine matrices.Compared to one-dimensional liquid chromatography (1D-LC), LC × LC methods offer higher separation power, thanks to the combined effect of two different selectivities and a higher peak capacity. However, when using state-of-the-art LC × LC instrumentation, the price paid for the increase in separation power is a decrease in sensitivity and detectability of trace-level analytes. This can be ascribed to the sample dilution that takes place during each of the two chromatographic steps. The way in which fractions are collected and transferred from the first to the second column is also of paramount importance, especially the volume and the solvent composition of the fractions injected in the second column.To overcome the detection limitation, we present an active-modulation strategy, based on concentrating the fractions of the first-dimension effluent using a modulation interface that employs trap columns. We obtained a signal enhancement for anabolic-steroid compounds in a bovine-urine sample by a factor of 2.4-7.6 and an increase in the signal-to-noise ratio up to a factor of 7 in comparison with a standard loop-based modulation interface. In addition, thanks to the increased sensitivity of our method, a substantially larger number of peaks were detected (76 vs. 36). Moreover, we could reduce the solvent consumption by a factor of three (160 mL vs. 500 mL per run)