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.

Preclinical Studies with Glioblastoma Brain Organoid Co-Cultures Show Efficient 5-ALA Photodynamic Therapy.

BACKGROUND: The high recurrence of glioblastoma (GB) that occurs adjacent to the resection cavity within two years of diagnosis urges an improvement of therapies oriented to GB local control. Photodynamic therapy (PDT) has been proposed to cleanse infiltrating tumor cells from parenchyma to ameliorate short long-term progression-free survival. We examined 5-aminolevulinic acid (5-ALA)-mediated PDT effects as therapeutical treatment and determined optimal conditions for PDT efficacy without causing phototoxic injury to the normal brain tissue. METHODS: We used a platform of Glioma Initiation Cells (GICs) infiltrating cerebral organoids with two different glioblastoma cells, GIC7 and PG88. We measured GICs-5-ALA uptake and PDT/5-ALA activity in dose-response curves and the efficacy of the treatment by measuring proliferative activity and apoptosis. RESULTS: 5-ALA (50 and 100 µg/mL) was applied, and the release of protoporphyrin IX (PpIX) fluorescence measures demonstrated that the emission of PpIX increases progressively until its stabilization at 24 h. Moreover, decreased proliferation and increased apoptosis corroborated the effect of 5-ALA/PDT on cancer cells without altering normal cells. CONCLUSIONS: We provide evidence about the effectiveness of PDT to treat high proliferative GB cells in a complex in vitro system, which combines normal and cancer cells and is a useful tool to standardize new strategic therapies.

A Multi-Level Systems Biology Analysis of Aldrin's Metabolic Effects on Prostate Cancer Cells.

Although numerous studies support a dose-effect relationship between Endocrine disruptors (EDs) and the progression and malignancy of tumors, the impact of a chronic exposure to non-lethal concentrations of EDs in cancer remains unknown. More specifically, a number of studies have reported the impact of Aldrin on a variety of cancer types, including prostate cancer. In previous studies, we demonstrated the induction of the malignant phenotype in DU145 prostate cancer (PCa) cells after a chronic exposure to Aldrin (an ED). Proteins are pivotal in the regulation and control of a variety of cellular processes. However, the mechanisms responsible for the impact of ED on PCa and the role of proteins in this process are not yet well understood. Here, two complementary computational approaches have been employed to investigate the molecular processes underlying the acquisition of malignancy in prostate cancer. First, the metabolic reprogramming associated with the chronic exposure to Aldrin in DU145 cells was studied by integrating transcriptomics and metabolomics via constraint-based metabolic modeling. Second, gene set enrichment analysis was applied to determine (i) altered regulatory pathways and (ii) the correlation between changes in the transcriptomic profile of Aldrin-exposed cells and tumor progression in various types of cancer. Experimental validation confirmed predictions revealing a disruption in metabolic and regulatory pathways. This alteration results in the modification of protein levels crucial in regulating triacylglyceride/cholesterol, linked to the malignant phenotype observed in Aldrin-exposed cells.

A biochemical and lipidomic approach to perceive Halimione portulacoides (L.) response to mercury: An environmental perspective.

The impact of hazardous materials, such as Hg, on life is far from being understood and due to the high number of polluted sites it has generated great concern. A biochemical and lipidomic approach was used to assess the effects of Hg on the saltmarsh halophyte Halimione portulacoides. Plants were collected at two sites of a Hg contaminated saltmarsh. Hg accumulation and distribution in the plant, biochemical parameters (antioxidant and metabolic) and lipid profiles were determined and compared between plant organs and sites (s1 and s2). Hg did not induce antioxidant enzyme activity. Lipid profiles changed under Hg exposure, especially in leaves, decreasing the unsaturation level, the membrane fluidity and stability, and evidencing that membrane lipid remodeling influences plant tolerance to Hg. This knowledge can help select the most appropriate methodologies for the restoration of Hg polluted hotspots, curtailing a serious environmental problem threatening saltmarshes.

Phenotypic and Metabolomic Characterization of 3D Lung Cell Cultures Exposed to Airborne Particulate Matter from Three Air Quality Network Stations in Catalonia.

Air pollution constitutes an environmental problem that it is known to cause many serious adverse effects on the cardiovascular and respiratory systems. The chemical characterization of particulate matter (PM) is key for a better understanding of the associations between chemistry and toxicological effects. In this work, the chemical composition and biological effects of fifteen PM10 air filter samples from three air quality stations in Catalonia with contrasting air quality backgrounds were investigated. Three-dimensional (3D) lung cancer cell cultures were exposed to these sample extracts, and cytotoxicity, reactive oxygen species (ROS) induction, metabolomics, and lipidomics were explored. The factor analysis method Multivariate Curve Resolution-Alternating Least-Squares (MCR-ALS) was employed for an integrated interpretation of the associations between chemical composition and biological effects, which could be related to urban traffic emission, biomass burning smoke, and secondary aerosols. In this pilot study, a novel strategy combining new approach methodologies and chemometrics provided new insights into the biomolecular changes in lung cells associated with different sources of air pollution. This approach can be applied in further research on air pollution toxicity to improve our understanding of the causality between chemistry and its effects.

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.

Untargeted metabolomics of prostate cancer zwitterionic and positively charged compounds in urine.

Prostate cancer, a leading cause of cancer-related deaths worldwide, principally occurs in over 50-year-old men. Nowadays there is urgency to discover biomarkers alternative to prostate-specific antigen, as it cannot discriminate patients with benign prostatic hyperplasia from clinically significant forms of prostatic cancer. In the present paper, 32 benign prostatic hyperplasia and 41 prostatic cancer urine samples were collected and analyzed. Polar and positively charged metabolites were therein investigated using an analytical platform comprising an up to 40-fold analyte enrichment step by graphitized carbon black solid-phase extraction, HILIC separation, and untargeted high-resolution mass spectrometry analysis. These classes of compounds are often neglected in common metabolomics experiments even though previous studies reported their significance in cancer biomarker discovery. The complex metabolomics big datasets, generated by the UHPLC-HRMS, were analyzed with the ROIMCR procedure, based on the selection of the MS regions of interest data and their analysis by the Multivariate Curve-Resolution Alternating Least Squares chemometrics method. This approach allowed the resolution and tentative identification of the metabolites differentially expressed by the two data sets. Among these, amino acids and carnitine derivatives were tentatively identified highlighting the importance of the proposed methodology for cancer biomarker research.

Application of chemometric methods to the analysis of multimodal chemical images of biological tissues.

Current histology techniques, such as tissue staining or histochemistry protocols, provide very limited chemical information about the tissues. Chemical imaging technologies such as infrared, Raman, and mass spectrometry imaging, are powerful analytical techniques with a huge potential in describing the chemical composition of sample surfaces. In this work, three images of the same tissue slice using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry, infrared microspectroscopy, and an RGB picture from a conventional hematoxylin/eosin (H/E) staining are simultaneously analyzed. These fused images were analyzed by multivariate curve resolution-alternating least squares (MCR-ALS), which provided, for each component, its distribution within the tissue surface, its IR spectrum fingerprint, its characteristic mass values, and the contribution of the RGB channels of the H/E staining. Compared with the individual analysis of each of the images alone, the fusion of the three images showed the relationship between the different types of chemical/biological information and enabled a better interpretation of the tissue under study. In addition, the least-squares projection of the MCR-ALS resolved spectra of components at low spatial resolution onto the IR and RBG images at high spatial resolution, provided a better delimitation of the sample constituents on the image, giving a more precise description of their distribution on the investigated tissue. The application of this procedure can be of interest in different research areas in which a good description of the spatial distribution of the chemical constituents of the samples is needed, such as in biomedicine, food, or environmental research.

GM2-GM3 gangliosides ratio is dependent on GRP94 through down-regulation of GM2-AP cofactor in brain metastasis cells.

GRP94 is an ATP-dependent chaperone able to regulate pro-oncogenic signaling pathways. Previous studies have shown a critical role of GRP94 in brain metastasis (BrM) pathogenesis and progression. In this work, an untargeted lipidomic analysis revealed that some lipid species were altered in GRP94-deficient cells, specially GM2 and GM3 gangliosides. The catalytic pathway of GM2 is affected by the low enzymatic activity of ß-Hexosaminidase (HexA), responsible for the hydrolysis of GM2 to GM3. Moreover, a deficiency of the GM2-activator protein (GM2-AP), the cofactor of HexA, is observed without alteration of gene expression, indicating a post-transcriptional alteration of GM2-AP in the GRP94-ablated cells. One plausible explanation of these observations is that GM2-AP is a client of GRP94, resulting in defective GM2 catabolic processing and lysosomal accumulation of GM2 in GRP94-ablated cells. Overall, given the role of gangliosides in cell surface dynamics and signaling, their imbalance might be linked to modifications of cell behaviour acquired in BrM progression. This work indicates that GM2-AP could be an important factor in ganglioside balance maintenance. These findings highlight the relevance of GM3 and GM2 gangliosides in BrM and reveal GM2-AP as a promising diagnosis and therapeutic target in BrM research.

Stoichiometric gene-to-reaction associations enhance model-driven analysis performance: Metabolic response to chronic exposure to Aldrin in prostate cancer.

BACKGROUND: Genome-scale metabolic models (GSMM) integrating transcriptomics have been widely used to study cancer metabolism. This integration is achieved through logical rules that describe the association between genes, proteins, and reactions (GPRs). However, current gene-to-reaction formulation lacks the stoichiometry describing the transcript copies necessary to generate an active catalytic unit, which limits our understanding of how genes modulate metabolism. The present work introduces a new state-of-the-art GPR formulation that considers the stoichiometry of the transcripts (S-GPR). As case of concept, this novel gene-to-reaction formulation was applied to investigate the metabolic effects of the chronic exposure to Aldrin, an endocrine disruptor, on DU145 prostate cancer cells. To this aim we integrated the transcriptomic data from Aldrin-exposed and non-exposed DU145 cells through S-GPR or GPR into a human GSMM by applying different constraint-based-methods. RESULTS: Our study revealed a significant improvement of metabolite consumption/production predictions when S-GPRs are implemented. Furthermore, our computational analysis unveiled important alterations in carnitine shuttle and prostaglandine biosynthesis in Aldrin-exposed DU145 cells that is supported by bibliographic evidences of enhanced malignant phenotype. CONCLUSIONS: The method developed in this work enables a more accurate integration of gene expression data into model-driven methods. Thus, the presented approach is conceptually new and paves the way for more in-depth studies of aberrant cancer metabolism and other diseases with strong metabolic component with important environmental and clinical implications.

Phenotypic and lipidomic characterization of primary human epidermal keratinocytes exposed to simulated solar UV radiation.

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.

Deciphering the Underlying Metabolomic and Lipidomic Patterns Linked to Thermal Acclimation in Saccharomyces cerevisiae.

Temperature is one of the most critical parameters for yeast growth, and it has deep consequences in many industrial processes where yeast is involved. Nevertheless, the metabolic changes required to accommodate yeast cells at high or low temperatures are still poorly understood. In this work, the ultimate responses of these induced transcriptomic effects have been examined using metabolomics-derived strategies. The yeast metabolome and lipidome have been characterized by 1D proton nuclear magnetic resonance spectroscopy and ultra-high-performance liquid chromatography-mass spectrometry at four temperatures, corresponding to low, optimal, high, and extreme thermal conditions. The underlying pathways that drive the acclimation response of yeast to these nonoptimal temperatures were evaluated using multivariate curve resolution-alternating least-squares. The analysis revealed three different thermal profiles (cold, optimal, and high temperature), which include changes in the lipid composition, secondary metabolic pathways, and energy metabolism, and we propose that they reflect the acclimation strategy of yeast cells to low and high temperatures. The data suggest that yeast adjusts membrane fluidity by changing the relative proportions of the different lipid families (acylglycerides, phospholipids, and ceramides, among others) rather than modifying the average length and unsaturation levels of the corresponding fatty acids.

Validation of the Regions of Interest Multivariate Curve Resolution (ROIMCR) procedure for untargeted LC-MS lipidomic analysis.

Untargeted liquid chromatography coupled to mass spectrometry (LC-MS) analysis generates massive amounts of information-rich mass data which presents storage and processing challenges. In this work, the validation of a recently proposed procedure for LC-MS data compression and processing is presented, using as example the analysis of lipid mixtures. This method consists of a preliminary selection of the Regions of Interest of the LC-MS data (MSROI) coupled to their throughout chemometric analysis by the Multivariate Curve Resolution Alternating Least Squares method (MCR-ALS). The proposed data selection procedure is based on the search of the most significant mass traces regions with high mass densities. This allows for a drastic reduction of the MS data size and of the computer storage requirements, without any significant loss neither of spectral resolution nor of accuracy on m/z measures. The combination of the MSROI data compression and MCR-ALS data analysis procedures in the new ROIMCR procedure has the main advantage of not requiring neither the chromatographic peak alignment nor the chromatographic peak shape modelling used in many other procedures as a pre-treatment step of the data analysis. The proposed ROIMCR procedure is tested in the analysis of the LC-MS experimental data coming from different lipid mixtures and of a melanoma cell line culture sample with satisfactory results. The proposed strategy is shown to be a general, fast, reliable and easy to use method for general untargeted LC-MS metabolic and lipidomic data analysis type of studies.

Untargeted lipidomic analysis of primary human epidermal melanocytes acutely and chronically exposed to UV radiation.

Ultraviolet (UV) radiation present in sunlight has been related to harmful effects on skin such as premature aging and skin cancer. In order to study the effects of UV radiation on skin, many investigations have been carried out at transcriptomic and proteomic levels. However, studies on the effects of UV radiation on lipid composition are scarce. In this work, primary cultures of melanocytes were exposed to UV radiation in a similar UVA/UVB ratio to that found in solar light. The UV exposure was carried out twice a week and different endpoints were investigated at 0.5 (acute exposure), 1.5 and 3 weeks. As a result, dendrite formation and a progressive reduction in cell viability were observed. Also, cell cycle arrest and a reduced E-cadherin content were detected at 0.5 and 1.5 weeks. In the second stage of the study, lipid extracts of melanocytes were analysed by liquid chromatography coupled to mass spectrometry (LC-MS) and subjected to an untargeted lipidomic approach using the ROIMCR chemometric method. Among the most important changes observed under UV irradiation, lipid raft components such as sphingomyelins and GM3 gangliosides as well as other signalling molecules such as phosphatidylinositols decreased progressively with time. These modifications indicated strong effects on important functions such as cell signalling and recognition. In contrast, triacylglycerol species, associated with energy storage, increased progressively, which could be interpreted as a survival mechanism under adverse conditions. Further studies are needed to better understand the functional implications of the changes observed.

Handling Different Spatial Resolutions in Image Fusion by Multivariate Curve Resolution-Alternating Least Squares for Incomplete Image Multisets.

Data fusion of different imaging techniques allows a comprehensive description of chemical and biological systems. Yet, joining images acquired with different spectroscopic platforms is complex because of the different sample orientation and image spatial resolution. Whereas matching sample orientation is often solved by performing suitable affine transformations of rotation, translation, and scaling among images, the main difficulty in image fusion is preserving the spatial detail of the highest spatial resolution image during multitechnique image analysis. In this work, a special variant of the unmixing algorithm Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) for incomplete multisets is proposed to provide a solution for this kind of problem. This algorithm allows analyzing simultaneously images collected with different spectroscopic platforms without losing spatial resolution and ensuring spatial coherence among the images treated. The incomplete multiset structure concatenates images of the two platforms at the lowest spatial resolution with the image acquired with the highest spatial resolution. As a result, the constituents of the sample analyzed are defined by a single set of distribution maps, common to all platforms used and with the highest spatial resolution, and their related extended spectral signatures, covering the signals provided by each of the fused techniques. We demonstrate the potential of the new variant of MCR-ALS for multitechnique analysis on three case studies: (i) a model example of MIR and Raman images of pharmaceutical mixture, (ii) FT-IR and Raman images of palatine tonsil tissue, and (iii) mass spectrometry and Raman images of bean tissue.

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.

Exposure to chlorpyrifos induces morphometric, biochemical and lipidomic alterations in green beans (Phaseolus vulgaris).

Chlorpyrifos (CPF) is a worldwide used pesticide that raises concerns from the environmental and human health perspectives. The presence of pesticides such as CPF in edible vegetables has been already reported, but little is known about the effects induced by this pesticide stress on the morphology, oxidative response and lipid composition of treated plants. In this work, green bean plants (Phaseolus vulgaris) were exposed to increasing concentrations of CPF and the different plant parts (roots, stem bases, stem, leaves, pods and beans) were subjected to different analyses. First, morphometric parameters and the oxidative response caused by CPF were explored. In a second phase of the study, an untargeted lipidomic analysis of the different tissue extracts was performed and MALDI-TOF mass spectrometry images of pods and beans were recorded and analysed to illustrate the spatial distribution of the changes observed. As a result of CPF treatment, plants showed a significant decrease in their height, leaf length, and pod number. The biochemical analysis showed lipid peroxidation and the activation of antioxidant mechanisms in roots, stem and leaves. Regarding the lipidomic results, changes in lipid levels were observed, mainly in leaves, pods and seeds. The main changes observed were a reduction of photosynthetic pigments and lipids in leaves and a decrease of triacylglycerols levels in pods and seeds. This last point was confirmed by the analysis of mass spectrometry images of the pods. These observations suggest that CPF would affect the yield of green bean crops as well as the nutritional value of pods and beans. This work represents a step forward in the knowledge of the effects of CPF, one of the most used pesticides worldwide, in plants.

Application of a sparseness constraint in multivariate curve resolution - Alternating least squares.

The use of sparseness in chemometrics is a concept that has increased in popularity. The advantage is, above all, a better interpretability of the results obtained. In this work, sparseness is implemented as a constraint in multivariate curve resolution - alternating least squares (MCR-ALS), which aims at reproducing raw (mixed) data by a bilinear model of chemically meaningful profiles. In many cases, the mixed raw data analyzed are not sparse by nature, but their decomposition profiles can be, as it is the case in some instrumental responses, such as mass spectra, or in concentration profiles linked to scattered distribution maps of powdered samples in hyperspectral images. To induce sparseness in the constrained profiles, one-dimensional and/or two-dimensional numerical arrays can be fitted using a basis of Gaussian functions with a penalty on the coefficients. In this work, a least squares regression framework with L0-norm penalty is applied. This L0-norm penalty constrains the number of non-null coefficients in the fit of the array constrained without having an a priori on the number and their positions. It has been shown that the sparseness constraint induces the suppression of values linked to uninformative channels and noise in MS spectra and improves the location of scattered compounds in distribution maps, resulting in a better interpretability of the constrained profiles. An additional benefit of the sparseness constraint is a lower ambiguity in the bilinear model, since the major presence of null coefficients in the constrained profiles also helps to limit the solutions for the profiles in the counterpart matrix of the MCR bilinear model.

Preprocessing Tools Applied to Improve the Assessment of Aldrin Effects on Prostate Cancer Cells Using Raman Spectroscopy.

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.

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.