The comprehensive analysis of untargeted metabolomics data acquired using LC-MS is still a major challenge. Different data analysis tools have been developed in recent years such as XCMS (various forms (X) of chromatography mass spectrometry) and multivariate curve resolution alternating least squares (MCR-ALS)-based strategies. In this work, metabolites extracted from rice tissues cultivated in an environmental test chamber were subjected to untargeted full-scan LC-MS analysis, and the obtained data sets were analyzed using XCMS and MCR-ALS. These approaches were compared in the investigation of the effects of copper and cadmium exposure on rice tissue (roots and aerial parts) samples. Both methods give, as a result of their application, the whole set of resolved elution and spectra profiles of the extracted metabolites in control and metal-treated samples, as well as the values of their corresponding chromatographic peak areas. The effects caused by the two considered metals on rice samples were assessed by further chemometric analysis and statistical evaluation of these peak area values. Results showed that there was a statistically significant interaction between the considered factors (type of metal of treatment and tissue). Also, the discrimination of the samples according to both factors was possible. A tentative identification of the most discriminant metabolites (biomarkers) was assessed. It is finally concluded that both XCMS- and MCR-ALS-based strategies provided similar results in all the considered cases despite the completely different approaches used by these two methods in the chromatographic peak resolution and detection strategies. Finally, advantages and disadvantages of using these two methods are discussed. Graphical Abstract Summary of the workflow for untargeted metabolomics using the compared approaches.

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.

While the knowledge of plant metabolomes has increased in the last few years, their response to the presence of toxicants is still poorly understood. Here, we analyse the metabolomic changes in Japanese rice (Oryza sativa var. Japonica) upon exposure to heavy metals (Cd(ii) and Cu(ii)) in concentrations from 10 to 1000 μM. After harvesting, rice metabolites were extracted from aerial parts of the plants and analysed by HPLC (HILIC TSK gel amide-80 column) coupled to a mass spectrometer quadrupole-Orbitrap (Q-Exactive). Full scan and all ion fragmentation (AIF) mass spectrometry modes were used during the analysis. The proposed untargeted metabolomics data analysis strategy is based on the application of the multivariate curve resolution alternating least squares (MCR-ALS) method for feature detection, allowing the simultaneous resolution of pure chromatographic profiles and mass spectra of all metabolites present in the analysed rice extracts. All-ion fragmentation data were used to confirm the identification of MCR-ALS resolved metabolites. A total of 112 metabolites were detected, and 97 of them were subsequently identified and confirmed. Pathway analysis of the observed metabolic changes suggested an underlying similarity of the responses of the plant to Cd(ii) and Cu(ii), although the former treatment appeared to be the more severe of the two. In both cases, secondary metabolism and amino acid-, purine-, carbon- and glycerolipid-metabolism pathways were affected, in a pattern consistent with reduction in plant growth and/or photosynthetic capacity and with induction of defence mechanisms to reduce cell damage.

Untargeted lipidomic samples are extremely complex and often exceed the limits of peak capacity achievable by one-dimensional liquid chromatography (LC). Comprehensive two-dimensional liquid chromatography (LC × LC) appears as a promising alternative to overcome this drawback. Unfortunately, this approach generates highly complex datasets which untargeted analysis is challenging. In this work, a global methodological strategy combining LC × LC-MS/MS with chemometric data analysis is proposed for untargeted lipidomic studies. The feasibility of the proposed methodology is demonstrated by its application to assess the effects of arsenic exposure on the lipidome of growing rice samples. A two-dimensional chromatographic setup coupling reversed phase (RP) and hydrophilic interaction liquid chromatography (HILIC) modes together with a triple quadrupole mass detector (TQD) is proposed to analyze lipid extracts from rice samples at different experimental conditions. Chemometric tools were used for data compression, spectral and elution profiles resolution, feature detection and statistical analysis of the multidimensional LC × LC-MS/MS data. The obtained results revealed that the proposed methodology was useful to gather relevant information from untargeted lipidomic studies and detect potential biomarkers.