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In this work the application of multivariate curve resolution is proposed for the analysis of Mass Spectrometry Imaging (MSI) data. Recently, developments in the ionization of samples have dramatically expanded the number of applications of MSI due to the possibility of collecting the mass spectrum for each pixel of a considered surface in a reasonable time. Using this method, both spatial distribution and spectral information of analyzed samples can be obtained. However, there are major drawbacks inherent to MSI related to the high complexity of the data obtained from real samples and to the extremely huge size of the datasets generated by this technique. Therefore, the potential of chemometrical tools in different steps of the analysis process is unquestionable, from data compression to data resolution of the different components present at each pixel of the image. In this work, this data analysis is carried out by means of the multivariate curve resolution method. The benefits of the application of this method are shown for two examples consisting of a MS image of two platted microbes and a MS image of a mouse lung section. The results show that multivariate curve resolution allows us to obtain distribution maps of different components and their identification from resolved high-resolution mass spectra. ; 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 n. 32073. Also, recognition from the Catalan government (grant 2009SGR45) is acknowledged. JJ acknowledges a CSIC JAE-Doc contract cofounded by the FSE. ; Peer reviewed

[Background] The analysis of LC-MS metabolomic datasets appears to be a challenging task in a wide range of disciplines since it demands the highly extensive processing of a vast amount of data. Different LC-MS data analysis packages have been developed in the last few years to facilitate this analysis. However, most of these strategies involve chromatographic alignment and peak shaping and often associate each "feature" (i.e., chromatographic peak) with a unique m/z measurement. Thus, the development of an alternative data analysis strategy that is applicable to most types of MS datasets and properly addresses these issues is still a challenge in the metabolomics field. ; [Results] Here, we present an alternative approach called ROIMCR to: i) filter and compress massive LC-MS datasets while transforming their original structure into a data matrix of features without losing relevant information through the search of regions of interest (ROIs) in the m/z domain and ii) resolve compressed data to identify their contributing pure components without previous alignment or peak shaping by applying a Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) analysis. In this study, the basics of the ROIMCR method are presented in detail and a detailed description of its implementation is also provided. Data were analyzed using the MATLAB (The MathWorks, Inc., www.mathworks.com) programming and computing environment. The application of the ROIMCR methodology is described in detail, with an example of LC-MS data generated in a lipidomic study and with other examples of recent applications. ; [Conclusions] The methodology presented here combines the benefits of data filtering and compression based on the searching of ROI features, without the loss of spectral accuracy. The method has the benefits of the application of the powerful MCR-ALS data resolution method without the necessity of performing chromatographic peak alignment or modelling. The presented method is a powerful alternative to other existing data analysis approaches that do not use the MCR-ALS method to resolve LC-MS data. The ROIMCR method also represents an improved strategy compared to the direct applications of the MCR-ALS method that use less-powerful data compression strategies such as binning and windowing. Overall, the strategy presented here confirms the usefulness of the ROIMCR chemometrics method for analyzing LC-MS untargeted metabolomics data. ; 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 n. 320737. The first author acknowledges the Spanish Government (Ministerio de Educación, Cultura y Deporte) for a predoctoral FPU scholarship. The authors acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI). Grant support from Generalitat de Catalunya 2017-SGR-753 and Spanish Ministry of Economy, Industry and Competitiveness (project CTQ2015–66254-C2–1-P) is also acknowledged. ; Peer reviewed

Liquid chromatography- mass spectrometry (LC-MS) is a powerful methodology for metabolomics. However, LC-MS data processing comes out as the "bottleneck" of omic sciences due to its complexity. The present protocol, easy to execute in MATLAB environment, covers all data analysis steps (conversion and import, compression and processing) of LC-MS data sets and it is specifically designed for users with limited background in chemometric and data analysis tools. Data conversion and import are described for most important LC-MS manufacturers (i.e., Waters, Thermo Fischer, Agilent, AB Sciex and Bruker), data compression consists on the search of "regions of interest" (ROI) and data processing is based on the use of Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS), a powerful chemometric tool that allows chromatographic resolution. Results are rapidly achieved (usually ˂ 15 min per sample), and they are easy to interpret and evaluate both in terms of chemistry and biology. ; 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 n. 320737. First author acknowledges the Spanish Government (Ministerio de Educación, Cultura y Deporte) for a predoctoral FPU scholarship. ; Peer reviewed

A new Liquid Chromatography Mass Spectrometry (LC-MS) metabolomics strategy coupled to chemometric evaluation, including variable and biomarker selection, has been assessed as a tool to discriminate between control and stressed Saccharomyces cerevisiae yeast samples. Metabolic changes occurring during yeast culture at different temperatures (30 ºC and 42 ºC) were analysed and the complex data generated in profiling experiments were evaluated by different chemometric multivariate approaches. Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) was applied to full spectral scan LC-MS preprocessed data multisets arranged in augmented column-wise data matrices. The results showed that sectioning the MS-chromatograms in different windows and analysing them by MCR-ALS enabled the proper resolution of very complex coeluted chromatographic peaks. The investigation of possible relationships between MCR-ALS resolved chromatographic peak areas and culture temperature was then investigated by Partial Least Squares Discriminant Analysis (PLS-DA). Selection of most relevant resolved chromatographic peaks associated to yeast culture temperature changes was achieved according to PLS-DA- Variable Importance in Projection (VIP) scores. A metabolite identification workflow was developed utilizing MCR-ALS resolved pure MS spectra and high-resolution accurate mass measurements to confirm assigned structures based on entries in metabolite databases. A total of 65 metabolites were identified. A preliminary interpretation of these results indicates that the strategy described in this study can be proposed as a general tool to facilitate biomarker identification and modelling in similar untargeted metabolomic studies. ; 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. 32073. ; Peer reviewed

In this article, we propose the use of the Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS) chemometrics method to resolve the 1H NMR spectra and concentration of the individual metabolites in their mixtures in untargeted metabolomics studies. A decision tree-based strategy is presented to optimally select and implement spectra estimates and equality constraints during MCR-ALS optimization. The proposed method has been satisfactorily evaluated using different 1H NMR metabolomics datasets. In a first study, 1H NMR spectra of the metabolites in a simulated mixture were successfully recovered and assigned. In a second study, more than 30 metabolites were characterized and quantified from an experimental unknown mixture analyzed by 1H NMR. In this work, MCR-ALS is shown to be a convenient tool for metabolite investigation and sample screening using 1H NMR, and it opens a new path for performing metabolomics studies with this chemometric technique. ; 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 n. 320737 and the Spanish Ministry of Economy and Competitiveness (CTQ2015-66254-C2-1-P). ; Peer reviewed

Rhabdomyolysis is a disorder characterized by acute damage of the sarcolemma of the skeletal muscle leading to release of potentially toxic muscle cell components into the circulation, most notably creatine phosphokinase (CK) and myoglobulin, and is frequently accompanied by myoglobinuria. In the present work, we evaluated the toxicity of p-phenylenediamine (PPD), a main component of hair dyes which is reported to induce rhabdomyolysis. We studied the metabolic effect of this compound in vivo with Wistar rats and in vitro with C2C12 muscle cells. To this aim we have combined multi-omic experimental measurements with computational approaches using model-driven methods. The integrative study presented here has unveiled the metabolic disorders associated to PPD exposure that may underlay the aberrant metabolism observed in rhabdomyolys disease. Animals treated with lower doses of PPD (10 and 20 mg/kg) showed depressed activity and myoglobinuria after 10 h of treatment. We measured the serum levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT), and creatine kinase (CK) in rats after 24, 48, and 72 h of PPD exposure. At all times, treatment with PPD at higher doses (40 and 60 mg/kg) showed an increase of AST and ALT, and also an increase of lactate dehydrogenase (LDH) and CK after 24 h. Blood packed cell volume and hemoglobin levels, as well as organs weight at 48 and 72 h, were also measured. No significant differences were observed in these parameters under any condition. PPD induce cell cycle arrest in S phase and apoptosis (40% or early apoptotic cells) on mus musculus mouse C2C12 cells after 24 h of treatment. Incubation of mus musculus mouse C2C12 cells with [1,2-13C2]-glucose during 24 h, subsequent quantification of 13C isotopologues distribution in key metabolites of glucose metabolic network and a computational fluxomic analysis using in-house developed software (Isodyn) showed that PPD is inhibiting glycolysis, non-oxidative pentose phosphate pathway, glycogen turnover, and ATPAse reaction leading to a reduction in ATP synthesis. These findings unveil the glucose metabolism collapse, which is consistent with a decrease in cell viability observed in PPD-treated C2C12 cells and with the myoglubinuria and other effects observed in Wistar Rats treated with PPD. These findings shed new light on muscle dysfunction associated to PPD exposure, opening new avenues for cost-effective therapies in Rhabdomyolysis disease. ; This work has received financial support from the government of Catalunya: ITT program of the Work Community of PyreneesAgència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR)—Generalitat de Catalunya (2014SGR1017). MC acknowledges the support received through the prize "ICREA Academia" for excellence in research, funded by ICREA foundation—Generalitat de Catalunya. IM and RT acknowledge the support received through the European Research Council under the European Union's Seventh Framework Programme (FP/2007–2013)/ERC Grant Agreement no. 320737. ; Peer reviewed ; Peer Reviewed

Epithelial to mesenchymal transition (EMT) is a biological process that plays a crucial role in cancer metastasis. Although studies regarding the EMT mechanisms are usual in terms of gene expression and protein functions, little is known about the involvement of lipids in EMT. In this work, an untargeted lipidomic analysis was performed to reveal which lipids are involved in the EMT process. DU145 prostate cancer cells were treated with TNFα, a well-known EMT inducer. After 6 hours of treatment, a decrease of cell membrane E-cadherin as well as a reduction in its gene expression were observed. Also, the mesenchymal markers Vimentin and Snail were up-regulated, suggesting that EMT started below 6 hours of treatment. Lipid extracts of untreated and TNFα-treated cells at short times were analyzed using ultra-performance liquid chromatography coupled to high-resolution mass spectrometry (UPLC-MS). Multivariate data analysis methods were applied to decipher which lipids presented significant changes after EMT induction. Among the results obtained, a significant increase of twelve unsaturated triacylglycerides (TAGs) was observed. This increase of TAGs was also observed for cells treated with TGFβ (another EMT inducer), suggesting that this feature is a common mechanism in the EMT process. In conclusion, this work reported for the first time a TAG accumulation through EMT induction. These TAG lipids could play a key role in providing cells with the energy, cell membrane components and signaling lipids necessary to guarantee the enhanced cell migration and proliferation of metastatic cells. ; This work was supported by the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement 320737. J.J acknowledges a CSIC JAE-Doc contract cofounded by FSE. ; Peer reviewed

Background: Ultraviolet (UV) radiation is known to be one of the most important environmental hazards acting on the skin. The most part of UV radiation is absorbed in the epidermis, where keratinocytes are the most abundant and exposed cell type. Lipids have an important role in skin biology, not only for their important contribution to the maintenance of the permeability barrier but also for the production and storage of energy, membrane organization and cell signalling functions. However, the effects on the lipid composition of keratinocytes under UV radiation are little explored. Objective: The present work aims to explore the effects on the phenotype and lipid content of primary human keratinocytes exposed to simulated solar UV radiation. Methods: Keratinocytes were exposed to a single (acute exposure) and repeated simulated solar UV irradiations for 4 weeks (chronic exposure). Cell viability and morphology were explored, as well as the production of reactive oxygen species. Then, lipid extracts were analysed through liquid chromatography coupled to mass spectrometry (LC–MS) and the data generated was processed using the ROIMCR chemometric methodology together with partial least squares discriminant analysis (PLS-DA), to finally reveal the most relevant lipid changes that occurred in keratinocytes upon UV irradiation. Also, the potential induction of keratinocyte differentiation was explored by measuring the increase of involucrin. Results: Under acute irradiation, cell viability and morphology were not altered. However, a general increase of phosphatidylcholines (PC) phosphatidylethanolamines (PE) and phosphatidylglycerol (PG) together with a slight sphingomyelin (SM) decrease were found in UV irradiated cells, among other changes. In addition, keratinocyte cultures did not present any differentiation hallmark. Contrary to acute-irradiated cells, in chronic exposures, cell viability was reduced and keratinocytes presented an altered morphology. Also, hallmarks of differentiation, such as the increase of involucrin protein and the autophagy induction were detected. Among the main lipid changes that accompanied this phenotype, the increase of long-chain ceramides, lysoPC and glycerolipid species were found. Conclusion: Important lipid changes were detected under acute and chronic UV irradiation. The lipid profile under chronic exposure may represent a lipid fingerprint of the keratinocyte differentiation phenotype. © 2018 ; 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. We thank Dr. Thierry Levade (Toulouse, France) for critical reading and discussion. ; Peer reviewed

The increasing complexity of omics research has encouraged the development of new instrumental technologies able to deal with these challenging samples. In this way, the rise of multidimensional separations should be highlighted due to the massive amounts of information that provide with an enhanced analyte determination. Both proteomics and metabolomics benefit from this higher separation capacity achieved when different chromatographic dimensions are combined, either in LC or GC. However, this vast quantity of experimental information requires the application of chemometric data analysis strategies to retrieve this hidden knowledge, especially in the case of nontargeted studies. In this work, the most common chemometric tools and approaches for the analysis of this multidimensional chromatographic data are reviewed. First, different options for data preprocessing and enhancement of the instrumental signal are introduced. Next, the most used chemometric methods for the detection of chromatographic peaks and the resolution of chromatographic and spectral contributions (profiling) are presented. The description of these data analysis approaches is complemented with enlightening examples from omics fields that demonstrate the exceptional potential of the combination of multidimensional separation techniques and chemometric tools of data analysis. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. ; The research leading to these results has received funding from the Euro- pean Research Council under the European Union's Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement no. 320737. Also, the authors acknowledge support from both the Ministry of Economy, In- dustry and Competitiveness (Grant CTQ2017-82598-P) and the Catalan Agency for Management of University and Research Grants (AGAUR) (2017SGR753). ; Peer reviewed

In this work the effect of chlorpyrifos exposure on metabolic profiles of zebrafish muscle was evaluated by liquid chromatography coupled to high resolution mass spectrometry. Different chemometric tools based on the selection of Regions of Interest and on Multivariate Curve-Resolution-Alternating Least Squares are proposed for the analysis of the complex data sets generated in the different exposure experiments. Analysis of Variance Simultaneous Component Analysis of changes on metabolite peak profile areas showed significant chlorpyrifos concentration and exposure time-dependent changes, clearly differentiating between exposed and non-exposed samples and between short (2 h) and long exposure times (6 h or 24 h). The changes observed in the concentrations of 50 muscle metabolites are indicative of induction of oxidative stress, of a general disruption of neurotransmitter metabolism, and of muscle exhaustion. These three effects are intimately related to the toxicity of chlorpyrifos. © 2016 Elsevier Ltd ; The research leading to these results has received funding from the European Research Council under European Union's Seven Framework Programme (FP/2007-2013)/ERC Grant Agreement n.320737. ; Peer reviewed

The study of pollutant effects on living organisms provides information about the possible biological and environmental response to a contaminant. Progression of prostate cancer may be related to exposure to pesticides or other chemical substances. In this work, the effect of the pesticide aldrin on human prostate cancer cells (DU145) is studied using Raman spectroscopy and chemometric techniques. Prostate cancer cell line DU145 has been exposed acutely the pesticide aldrin. Individual Raman spectra coming from control and treated cell populations have been acquired. Partial least squares discriminant analysis (PLSDA) has been used to assess differences among treated and control samples and to identify spectral biomarkers associated with pollutant stress. Some preprocessing methodologies have been tested in order to improve the capability of discrimination between fingerprints. Partial least squares discriminant analysis results suggest that the best normalization–scaling preprocessing combination is provided by Euclidean normalization (EN)-SIMPLISMA-based scaling (SBS). SIMPLISMA-based scaling has been proposed as a scaling method focused on the classification objective, which enhances variables with high relative variation among samples. The most relevant spectral variables related to aldrin effect on DU145 seem to be mainly related to lipids, proteins, and variations in nucleic acids. © 2017, © The Author(s) 2017. ; The authors acknowledge the financial support from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement no. 32073. They also belong to the network of recognized research groups by the Catalan government (2014 SGR 1106). ; Peer reviewed

Disruption of specific metabolic pathways constitutes the mode of action of many known toxicants and it is responsible for the adverse phenotypes associated to human genetic defects. Conversely, many industrial applications rely on metabolic alterations of diverse microorganisms, whereas many therapeutic drugs aim to selectively disrupt pathogens' metabolism. In this work we analyzed metabolic changes induced by auxotrophic starvation conditions in yeast in a non-targeted approach, using one-dimensional proton Nuclear Magnetic Resonance spectroscopy (1H NMR) and chemometric analyses. Analysis of the raw spectral datasets showed specific changes linked to the different stages during unrestricted yeast growth, as well as specific changes linked to each of the four tested starvation conditions (L-methionine, L-histidine, L-leucine and uracil). Analysis of changes in concentrations of more than 40 metabolites by Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) showed the normal progression of key metabolites during lag, exponential and stationary unrestricted growth phases, while reflecting the metabolic blockage induced by the starvation conditions. In this case, different metabolic intermediates accumulated over time, allowing identification of the different metabolic pathways specifically affected by each gene disruption. This synergy between NMR metabolomics and molecular biology may have clear implications for both genetic diagnostics and drug development. © The Author(s) 2016. ; 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 n. 320737. We also thank Dr. Yolanda Pérez for her helpful recommendations on setting up the acquisition parameters for some of the NMR experiments. ; Peer reviewed

Effect of growth temperature on the yeast (Saccharomyces cerevisiae) metabolome has been analysed by one-dimensional proton NMR spectroscopy (1H NMR). Potential biomarkers have been first identified by a non-targeted chemometric evaluation of the spectra, followed by a comprehensive analysis of bayesian estimated concentrations of target metabolites in extracts of cells growth either at 30 or 37 °C. Tentative identification of metabolites whose concentrations were affected by this mild heat-shock stress was attempted by partial least squares-discriminant analysis (PLS-DA) on 1H NMR data, combined with Statistical TOtal Correlation SpectroscopY, and further confirmed with empirical data. An extensive assignment for most of the detected NMR signals was performed, with a total number of 38 identified metabolites. Concentrations estimated using automatic BATMAN modelling revealed that bayesian integration is a sufficient approach for obtaining relevant concentration changes of metabolites and biological information of interest. In contrast to when it is applied directly on spectral data, the application of PLS-DA on BATMAN recovered metabolite concentration estimates allowed for a better overview of the investigated samples, since more metabolites were highlighted in the discriminatory model. Observed changes in metabolite concentrations were consistent with the expected process of temperature acclimation, showing alterations in amino acid cellular pools, nucleotide metabolism and lipid composition. The strategy described in this work can thus be proposed as a powerful and easy tool to investigate complex biological processes, from biomarker screening and discovery to the study of metabolite network changes in biological processes. ; 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 n. 320737. ; Peer reviewed