Effects of Indoor Dust Exposure on Lung Cells: Association of Chemical Composition with Phenotypic and Lipid Changes in a 3D Lung Cancer Cell Model.

Indoor dust is a key contributor to the global human exposome in urban areas since the population develops most of its activities in private and public buildings. To gain insight into the health risks associated with this chronic exposure, it is necessary to characterize the chemical composition of dust and understand its biological impacts using reliable physiological models. The present study investigated the biological effects of chemically characterized indoor dust extracts using three-dimensional (3D) lung cancer cell cultures combining phenotypic and lipidomic analyses. Apart from the assessment of cell viability, reactive oxygen species (ROS) induction, and interleukin-8 release, lipidomics was applied to capture the main lipid changes induced as a cellular response to the extracted dust compounds. The application of chemometric tools enabled the finding of associations between chemical compounds present in dust and lipidic and phenotypic profiles in the cells. This study contributes to a better understanding of the toxicity mechanisms associated with exposure to chemical pollutants present in indoor dust.

Quantification strategies for two-dimensional liquid chromatography datasets using regions of interest and multivariate curve resolution approaches.

In this work, three chemometrics-based approaches are compared for quantification purposes when using two-dimensional liquid chromatography (LC×LC-MS), taking as a study case the quantification of amino acids in commercial drug mixtures. Although the approaches have been already used for one-dimensional gas or liquid chromatography, the main novelty of this work is the demonstration of their applicability to LC×LC-MS datasets. Besides, steps such as peak alignment and modelling, commonly applied in this type of data analysis, are not required with the approaches proposed here. In a first step, regions of interest (ROI) strategy is used for the spectral compression of the LC×LC-MS datasets. Then the first strategy consists of building a calibration curve from the areas obtained in this ROI compression step. Alternatively, the ROI intensity matrices can be used as input for a second analysis step employing the multivariate curve resolution alternating least squares (MCR-ALS) method. The main benefit of MCR-ALS is the resolution of elution and spectral profiles for each of the analytes in the mixture, even in the case of strong coelutions and high signal overlapping. Classical MCR-ALS based calibration curve from the peak areas resolved only applying non-negativity constraints (second strategy) is compared to the results obtained when an area correlation constraint is imposed during the ALS optimization (third strategy). All in all, similar quantification results were achieved by the three approaches but, especially in prediction studies, the more accurate quantification is obtained when the calibration curve is built from the peak areas obtained with MCR-ALS when the area correlation constraint is imposed.

Comparison of Multivariate ANOVA-Based Approaches for the Determination of Relevant Variables in Experimentally Designed Metabolomic Studies.

The use of chemometric methods based on the analysis of variances (ANOVA) allows evaluation of the statistical significance of the experimental factors used in a study. However, classical multivariate ANOVA (MANOVA) has a number of requirements that make it impractical for dealing with metabolomics data. For this reason, in recent years, different options have appeared that overcome these limitations. In this work, we evaluate the performance of three of these multivariate ANOVA-based methods (ANOVA simultaneous component analysis-ASCA, regularized MANOVA-rMANOVA, and Group-wise ANOVA-simultaneous component analysis-GASCA) in the framework of metabolomics studies. Our main goals are to compare these various ANOVA-based approaches and evaluate their performance on experimentally designed metabolomic studies to find the significant factors and identify the most relevant variables (potential markers) from the obtained results. Two experimental data sets were generated employing liquid chromatography coupled to mass spectrometry (LC-MS) with different complexity in the design to evaluate the performance of the statistical approaches. Results show that the three considered ANOVA-based methods have a similar performance in detecting statistically significant factors. However, relevant variables pointed by GASCA seem to be more reliable as there is a strong similarity with those variables detected by the widely used partial least squares discriminant analysis (PLS-DA) method.

Lipidomic analysis of single and combined effects of polyethylene microplastics and polychlorinated biphenyls on human hepatoma cells.

Microplastics are an emerging environmental issue as a result of their ubiquity, persistence, and intrinsic toxic potential. In addition, their ability to sorb and transport a wide variety of environmental pollutants (i.e. "Trojan Horse" effect) exerts significant adverse impacts upon ecosystems. The toxicological evaluation of the single and combined effects produced by polyethylene microplastics and two polychlorinated biphenyl congeners was performed on the human hepatoma cell line HepG2 by cell viability assessment and an untargeted lipidomic study. The cell lethality evaluation evinced that MPs did not induce relevant cell lethality at any of the concentration range tested, while both PCBs presented a hormetic behavior. The lipidomic analysis suggested that both single PCB exposures induced significant lipidomic changes, especially for glycerophospholipids and glycerolipids. In contrast, for MPs single exposure, the most remarkable change was the substantial enhancement of triglyceride content. Regarding combined exposures, results showed that MPs could induce even more harmful effects than those produced intrinsically as a result of desorbing previously sorbed toxic pollutants. To the best of our knowledge, this is the first study assessing the toxicity of microplastics and their possible "Trojan Horse" effect by applying an untargeted lipidomic methodology.

Interaction of Environmental Pollutants with Microplastics: A Critical Review of Sorption Factors, Bioaccumulation and Ecotoxicological Effects.

Microplastics have become one of the leading environmental threats due to their persistence, ubiquity and intrinsic toxic potential. The potential harm that microplastics impose on ecosystems varies from direct effects (i.e., entanglement and ingestion) to their ability to sorb a diversity of environmental pollutants (e.g., heavy metals, persistent organic compounds or pharmaceuticals). Therefore, the toxicological assessment of the combined effects of microplastics and sorbed pollutants can produce in biota is one of the hottest topics on the environmental toxicology field. This review aims to clarify the main impacts that this interaction could have on ecosystems by (1) highlighting the principal factors that influence the microplastics sorption capacities; (2) discussing the potential scenarios in which microplastics may have an essential role on the bioaccumulation and transfer of chemicals; and (3) reviewing the recently published studies describing toxicological effects caused by the combination of microplastics and their sorbed chemicals. Finally, a discussion regarding the need for a new generation of toxicological studies is presented.

Partial characterization of the lipidome of the cold-water scallop, Chlamys islandica.

Fingerprinting of the main lipid components of the digestive gland of the Icelandic scallop-Chlamys islandica-has been performed by ultra-high-performance liquid chromatography coupled with time of flight high-resolution mass spectrometry, UHPLC-HRMS/ToF. This method allowed the identification of 224 lipids, including phosphatidylcholines (PC), plasmanyl (PC-O)/plasmenyl (PC-P) phosphatidylcholines, lyso-phosphatidylcholines (LPC), and their plasmanyl/plasmenyl forms (LPC-O/LPC-P). Diacylglycerols (DG), triacylglycerols (TG), and cholesteryl esters (CE) were the neutral lipids (NL) analyzed. While all of the lipids showed a strong seasonal dependence in terms of quantity, only NLs presented significant qualitative changes. Principal component analysis (PCA) of TG and DG profiles evidenced a prevalence of low unsaturated TGs and DGs in spring, which were replaced by species with a higher degree of unsaturations in summer. In autumn, long and highly unsaturated TGs constitute the lipid fraction of the digestive gland of the scallop, while DG species offer a mixed profile. This study contributes to the characterization and the elucidation of the lipidome of Chlamys islandica and provides baseline data for further study of the effects of pollutants on the lipidome of the Icelandic scallop, often used as a sentinel species in biomonitoring programs.

ROIMCR: a powerful analysis strategy for LC-MS metabolomic datasets.

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.

Untargeted lipidomic evaluation of hydric and heat stresses on rice growth.

Environmental stresses are the major factors that limit the geographical distribution of plants. As a consequence, plants have developed different strategies to adapt to these environmental changes among which can be outlined the maintenance of membranes' integrity and fluidity. Lipids are key molecules for this environmental adaptation and a comprehensive understand of the molecular mechanisms underlying is still required. Here, lipidome changes in Japanese rice (Oryza sativa var. Japonica) upon heat and hydric stresses are assessed using an untargeted approach based on liquid chromatography coupled with mass spectrometry (LC-MS). The obtained data were analyzed using different multivariate data analysis tools. A total number of 298 lipids responded to these abiotic stresses, and 128 of them were tentatively identified. Diacylglycerols (DG), triacylglycerols (TG), phosphatidylcholines (PC) and phosphatidylethanolamines (PE) were the most altered lipid families heat and hydric stress. Interpretation of the obtained results showed relevant changes related to the unsaturation degree in the identified lipids. In the case of heat stress, a decrease in the unsaturation degree of lipids can be linked to an increase in the cell membranes' rigidity. In contrast, the hydric stress produced an increase in the lipids unsaturation degree causing an increase in the cell membranes' fluidity, in an attempt to adapt to these non-optimal conditions.

Identification of antihypertensive peptides in nutraceuticals by capillary electrophoresis-mass spectrometry.

We present capillary electrophoresis-mass spectrometry (CE-MS) in combination with advanced chemometric tools for the analysis of bioactive compounds in food, in particular for the identification of antihypertensive peptides in a nutraceutical derived from a bovine milk protein hydrolysate. Different extracts of the nutraceutical were analyzed by CE-MS, and the electropherograms were processed using a novel data analysis workflow that included regions of interest (ROIs) compression and multivariate curve resolution alternating least squares (MCR-ALS). MCR-ALS permitted the description of the nutraceutical extract as ten characteristic components with their electrophoretic profiles and mass spectra. Twenty-two compounds were tentatively identified as antihypertensive bovine casein fragments through a mass search in a database of bioactive peptides. The identity of 17 antihypertensive peptides was reliably confirmed by capillary electrophoresis-tandem mass spectrometry. The proposed analytical approach demonstrated the potential to obtain a characteristic and activity-related fingerprint for quality control and authentication of the antihypertensive nutraceutical.

An untargeted lipidomic strategy combining comprehensive two-dimensional liquid chromatography and chemometric analysis.

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.

Chemometric Strategies for Peak Detection and Profiling from Multidimensional Chromatography.

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.

Assessment of endocrine disruptors effects on zebrafish (Danio rerio) embryos by untargeted LC-HRMS metabolomic analysis.

Bisphenol A (BPA), perfluorooctane sulfonate (PFOS), and tributyltin (TBT) are emerging endocrine disruptors (EDCs) with still poorly defined mechanisms of toxicity and metabolic effects in aquatic organisms. We used an untargeted liquid chromatography-high resolution mass spectrometry (LC-HRMS) metabolomic approach to study the effects of sub-lethal doses of these three EDCs on the metabolic profiles of zebrafish embryos exposed from 48 to 120hpf (hours post fertilization). Advanced chemometric data analysis methods were used to reveal effects on the subjacent regulatory pathways. EDC treatments induced changes in concentrations of about 50 metabolites for TBT and BPA, and of 25 metabolites for PFOS. The analysis of the corresponding metabolic changes suggested the presence of similar underlying zebrafish responses to BPA, TBT and PFOS affecting the metabolism of glycerophospholipids, amino acids, purines and 2-oxocarboxylic acids. We related the changes in glycerophospholipid metabolism to alterations in absorption of the yolk sack, the main source of nutrients (including lipids) for the developing embryo, linking the molecular markers with adverse phenotypic effects. We propose a general mode of action for all three chemical compounds, probably related to their already described interaction with the PPAR/RXR complex, combined with specific effects on different signaling pathways resulting in particular alterations in the zebrafish embryos metabolism.

A chemometric approach for characterization of serum transthyretin in familial amyloidotic polyneuropathy type I (FAP-I) by electrospray ionization-ion mobility mass spectrometry.

In this study, we describe a chemometric data analysis approach to assist in the interpretation of the complex datasets from the analysis of high-molecular mass oligomeric proteins by ion mobility mass spectrometry (IM-MS). The homotetrameric protein transthyretin (TTR) is involved in familial amyloidotic polyneuropathy type I (FAP-I). FAP-I is associated with a specific TTR mutant variant (TTR(Met30)) that can be easily detected analyzing the monomeric forms of the mutant protein. However, the mechanism of protein misfolding and aggregation onset, which could be triggered by structural changes in the native tetrameric protein, remains under investigation. Serum TTR from healthy controls and FAP-I patients was purified under non-denaturing conditions by conventional immunoprecipitation in solution and analyzed by IM-MS. IM-MS allowed separation and characterization of several tetrameric, trimeric and dimeric TTR gas ions due to their differential drift time. After an appropriate data pre-processing, multivariate curve resolution alternating least squares (MCR-ALS) was applied to the complex datasets. A group of seven independent components being characterized by their ion mobility profiles and mass spectra were resolved to explain the observed data variance in control and patient samples. Then, principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were considered for exploration and classification. Only four out of the seven resolved components were enough for an accurate differentiation. Furthermore, the specific TTR ions identified in the mass spectra of these components and the resolved ion mobility profiles provided a straightforward insight into the most relevant oligomeric TTR proteoforms for the disease.

Modelling of Hydrophilic Interaction Liquid Chromatography Stationary Phases Using Chemometric Approaches.

Metabolomics is a powerful and widely used approach that aims to screen endogenous small molecules (metabolites) of different families present in biological samples. The large variety of compounds to be determined and their wide diversity of physical and chemical properties have promoted the development of different types of hydrophilic interaction liquid chromatography (HILIC) stationary phases. However, the selection of the most suitable HILIC stationary phase is not straightforward. In this work, four different HILIC stationary phases have been compared to evaluate their potential application for the analysis of a complex mixture of metabolites, a situation similar to that found in non-targeted metabolomics studies. The obtained chromatographic data were analyzed by different chemometric methods to explore the behavior of the considered stationary phases. ANOVA-simultaneous component analysis (ASCA), principal component analysis (PCA) and partial least squares regression (PLS) were used to explore the experimental factors affecting the stationary phase performance, the main similarities and differences among chromatographic conditions used (stationary phase and pH) and the molecular descriptors most useful to understand the behavior of each stationary phase.

Metabolic disruption of zebrafish (Danio rerio) embryos by bisphenol A. An integrated metabolomic and transcriptomic approach.

Although bisphenol A (BPA) is commonly recognized as an endocrine disruptor, the metabolic consequences of its exposure are still poorly understood. In this study, we present a non-targeted LC-MS based metabolomic analysis in combination with a full-genome, high-throughput RNA sequencing (RNA-Seq) to reveal the metabolic effects and the subjacent regulatory pathways of exposing zebrafish embryos to BPA during the first 120 hours post-fertilization. We applied multivariate data analysis methods to extract biochemical information from the LC-MS and RNA-Seq complex datasets and to perform testable predictions of the phenotypic adverse effects. Metabolomic and transcriptomic data revealed a similar subset of altered pathways, despite the large difference in the number of identified biomarkers (around 50 metabolites and more than 1000 genes). These results suggest that even a moderate coverage of zebrafish metabolome may be representative of the global metabolic changes. These multi-omic responses indicate a specific metabolic disruption by BPA affecting different signaling pathways, such as retinoid and prostaglandin metabolism. The combination of transcriptomic and metabolomic data allowed a dynamic interpretation of the results that could not be drawn from either single dataset. These results illustrate the utility of -omic integrative analyses for characterizing the physiological effects of toxicants beyond the mere indication of the affected pathways.

Analysis of multiple mass spectrometry images from different Phaseolus vulgaris samples by multivariate curve resolution.

A new procedure based on the simultaneous analysis of multiple mass spectrometry images using multivariate curve resolution is presented in this work. Advantages of the application of the proposed approach are shown for three cases of plant studies demonstrating its potential usefulness in metabolomics studies, particularly in lipidomics. In the first dataset, a three stage germination time course process of green bean seeds is presented. The second example is a dose-response study where the stem bases of a non-exposed plant are compared to those of plants exposed to increasing concentrations of the pesticide chlorpyrifos. Finally, the third study is the simultaneous analysis of several sequential transversal and longitudinal cuts of the same green bean plant stem segment. The analysis of these three examples required the comprehensive adaptation of different chemometric methodologies including data compression by selection of the regions of interest (ROI strategy), appropriate data normalization and baseline correction, all of them before MCR-ALS simultaneous image analysis of multiple samples and post processing of the achieved results. MCR-ALS resolved components provided spatial information about the changes in the spatial composition and distribution of the different lipids on the surface of the investigated samples. These results enabled the identification of single lipids and the clustering of those lipids that behaved similarly in the different images simultaneously analyzed. The proposed strategy for MSI analysis represents a step forward in the simultaneous analysis of multiple sets of images providing an improved recovery of both spatial and structural information in environmental and biomedical studies.

Knowledge integration strategies for untargeted metabolomics based on MCR-ALS analysis of CE-MS and LC-MS data.

In this work, two knowledge integration strategies based on multivariate curve resolution alternating least squares (MCR-ALS) were used for the simultaneous analysis of data from two metabolomic platforms. The benefits and the suitability of these integration strategies were demonstrated in a comparative study of the metabolite profiles from yeast (Saccharomyces cerevisiae) samples grown in non-fermentable (acetate) and fermentable (glucose) carbon source. Untargeted metabolomics data acquired by capillary electrophoresis-mass spectrometry (CE-MS) and liquid chromatography-mass spectrometry (LC-MS) were jointly analysed. On the one hand, features obtained by independent MCR-ALS analysis of each dataset were joined to obtain a biological interpretation based on the combined metabolic network visualization. On the other hand, taking advantage of the common spectral mode, a low-level data fusion strategy was proposed merging CE-MS and LC-MS data before the MCR-ALS analysis to extract the most relevant features for further biological interpretation. Then, results obtained by the two presented methods were compared. Overall, the study highlights the ability of MCR-ALS to be used in any of both knowledge integration strategies for untargeted metabolomics. Furthermore, enhanced metabolite identification and differential carbon source response detection were achieved when considering a combination of LC-MS and CE-MS based platforms.

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.

Metabolomic analysis of the effects of cadmium and copper treatment in Oryza sativa L. using untargeted liquid chromatography coupled to high resolution mass spectrometry and all-ion fragmentation.

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.