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).

Comprehensive lipidomic analysis of human plasma using multidimensional liquid- and gas-phase separations: Two-dimensional liquid chromatography-mass spectrometry vs. liquid chromatography-trapped-ion-mobility-mass spectrometry.

Recent advancements in separation science have resulted in the commercialization of multidimensional separation systems that provide higher peak capacities and, hence, enable a more-detailed characterization of complex mixtures. In particular, two powerful analytical tools are increasingly used by analytical scientists, namely online comprehensive two-dimensional liquid chromatography (LC×LC, having a second-dimension separation in the liquid phase) and liquid chromatography-ion mobility-spectrometry (LC-IMS, second dimension separation in the gas phase). The goal of the current study was a general assessment of the liquid-chromatography-trapped-ion-mobility-mass spectrometry (LC-TIMS-MS) and comprehensive two-dimensional liquid chromatography-mass spectrometry (LC×LC-MS) platforms for untargeted lipid mapping in human plasma. For the first time trapped-ion-mobility spectrometry (TIMS) was employed for the separation of the major lipid classes and ion-mobility-derived collision-cross-section values were determined for a number of lipid standards. The general effects of a number of influencing parameters have been inspected and possible directions for improvements are discussed. We aimed to provide a general indication and practical guidelines for the analyst to choose an efficient multidimensional separation platform according to the particular requirements of the application. Analysis time, orthogonality, peak capacity, and an indicative measure for the resolving power are discussed as main characteristics for multidimensional separation systems.

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.