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García-Pérez P, Tomas M, Rivera-Pérez A, Patrone V, Giuberti G, Capanoglu E, Lucini L. Exploring the bioaccessibility of polyphenols and glucosinolates from Brassicaceae microgreens by combining metabolomics profiling and computational chemometrics. Food Chem 2024; 452:139565. [PMID: 38759437 DOI: 10.1016/j.foodchem.2024.139565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 04/23/2024] [Accepted: 05/03/2024] [Indexed: 05/19/2024]
Abstract
Microgreens constitute natural-based foods with health-promoting properties mediated by the accumulation of glucosinolates (GLs) and phenolic compounds (PCs), although their bioaccessibility may limit their nutritional potential. This work subjected eight Brassicaceae microgreens to in vitro gastrointestinal digestion and large intestine fermentation before the metabolomics profiling of PCs and GLs. The application of multivariate statistics effectively discriminated among species and their interaction with in vitro digestion phases. The flavonoids associated with arugula and the aliphatic GLs related to red cabbage and cauliflower were identified as discriminant markers among microgreen species. The multi-omics integration along in vitro digestion and fermentation predicted bioaccessible markers, featuring potential candidates that may eventually be responsible for these functional foods' nutritional properties. This combined analytical and computational framework provided a promising platform to predict the nutritional metabolome-wide outcome of functional food consumption, as in the case of microgreens.
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Affiliation(s)
- Pascual García-Pérez
- Department for Sustainable Food Process - DiSTAS, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122 Piacenza, Italy
| | - Merve Tomas
- Department of Food Engineering, Faculty of Chemical and Metallurgical Engineering, Istanbul Technical University, 34469 Maslak, Istanbul, Turkiye
| | - Araceli Rivera-Pérez
- Department for Sustainable Food Process - DiSTAS, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122 Piacenza, Italy; Research Group "Analytical Chemistry of Contaminants", Department of Chemistry and Physics, Research Centre for Mediterranean Intensive Agrosystems and Agrifood Biotechnology (CIAIMBITAL), Agrifood Campus of International Excellence (ceiA3), University of Almeria, 04120 Almeria, Spain
| | - Vania Patrone
- Department for Sustainable Food Process - DiSTAS, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122 Piacenza, Italy
| | - Gianluca Giuberti
- Department for Sustainable Food Process - DiSTAS, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122 Piacenza, Italy
| | - Esra Capanoglu
- Department of Food Engineering, Faculty of Chemical and Metallurgical Engineering, Istanbul Technical University, 34469 Maslak, Istanbul, Turkiye.
| | - Luigi Lucini
- Department for Sustainable Food Process - DiSTAS, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122 Piacenza, Italy
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Märtens A, Holle J, Mollenhauer B, Wegner A, Kirwan J, Hiller K. Instrumental Drift in Untargeted Metabolomics: Optimizing Data Quality with Intrastudy QC Samples. Metabolites 2023; 13:metabo13050665. [PMID: 37233706 DOI: 10.3390/metabo13050665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 05/08/2023] [Accepted: 05/12/2023] [Indexed: 05/27/2023] Open
Abstract
Untargeted metabolomics is an important tool in studying health and disease and is employed in fields such as biomarker discovery and drug development, as well as precision medicine. Although significant technical advances were made in the field of mass-spectrometry driven metabolomics, instrumental drifts, such as fluctuations in retention time and signal intensity, remain a challenge, particularly in large untargeted metabolomics studies. Therefore, it is crucial to consider these variations during data processing to ensure high-quality data. Here, we will provide recommendations for an optimal data processing workflow using intrastudy quality control (QC) samples that identifies errors resulting from instrumental drifts, such as shifts in retention time and metabolite intensities. Furthermore, we provide an in-depth comparison of the performance of three popular batch-effect correction methods of different complexity. By using different evaluation metrics based on QC samples and a machine learning approach based on biological samples, the performance of the batch-effect correction methods were evaluated. Here, the method TIGER demonstrated the overall best performance by reducing the relative standard deviation of the QCs and dispersion-ratio the most, as well as demonstrating the highest area under the receiver operating characteristic with three different probabilistic classifiers (Logistic regression, Random Forest, and Support Vector Machine). In summary, our recommendations will help to generate high-quality data that are suitable for further downstream processing, leading to more accurate and meaningful insights into the underlying biological processes.
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Affiliation(s)
- Andre Märtens
- Department of Bioinformatics and Biochemistry, Braunschweig Integrated Centre of Systems Biology, Technische Universität Braunschweig, 38118 Braunschweig, Germany
- Physikalisch-Technische Bundesanstalt, 38116 Braunschweig, Germany
| | - Johannes Holle
- Department of Pediatric Gastroenterology, Nephrology and Metabolic Diseases, Universitätsmedizin Berlin, 13353 Berlin, Germany
| | - Brit Mollenhauer
- Department of Neurology, University Medical Center Göttingen, 37073 Göttingen, Germany
- Paracelsus-Elena-Klinik, 34128 Kassel, Germany
| | - Andre Wegner
- Department of Bioinformatics and Biochemistry, Braunschweig Integrated Centre of Systems Biology, Technische Universität Braunschweig, 38118 Braunschweig, Germany
| | - Jennifer Kirwan
- Berlin Institute of Health at Charité, Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Karsten Hiller
- Department of Bioinformatics and Biochemistry, Braunschweig Integrated Centre of Systems Biology, Technische Universität Braunschweig, 38118 Braunschweig, Germany
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3
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Hattaway ME, Black GP, Young TM. Batch correction methods for nontarget chemical analysis data: application to a municipal wastewater collection system. Anal Bioanal Chem 2023; 415:1321-1331. [PMID: 36627378 PMCID: PMC9928919 DOI: 10.1007/s00216-023-04511-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 12/08/2022] [Accepted: 01/02/2023] [Indexed: 01/12/2023]
Abstract
Nontarget chemical analysis using high-resolution mass spectrometry has increasingly been used to discern spatial patterns and temporal trends in anthropogenic chemical abundance in natural and engineered systems. A critical experimental design consideration in such applications, especially those monitoring complex matrices over long time periods, is a choice between analyzing samples in multiple batches as they are collected, or in one batch after all samples have been processed. While datasets acquired in multiple analytical batches can include the effects of instrumental variability over time, datasets acquired in a single batch risk compound degradation during sample storage. To assess the influence of batch effects on the analysis and interpretation of nontarget data, this study examined a set of 56 samples collected from a municipal wastewater system over 7 months. Each month's samples included 6 from sites within the collection system, one combined influent, and one treated effluent sample. Samples were analyzed using liquid chromatography high-resolution mass spectrometry in positive electrospray ionization mode in multiple batches as the samples were collected and in a single batch at the conclusion of the study. Data were aligned and normalized using internal standard scaling and ComBat, an empirical Bayes method developed for estimating and removing batch effects in microarrays. As judged by multiple lines of evidence, including comparing principal variance component analysis between single and multi-batch datasets and through patterns in principal components and hierarchical clustering analyses, ComBat appeared to significantly reduce the influence of batch effects. For this reason, we recommend the use of more, small batches with an appropriate batch correction step rather than acquisition in one large batch.
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Affiliation(s)
- Madison E. Hattaway
- grid.27860.3b0000 0004 1936 9684Department of Civil and Environmental Engineering, University of California, Davis, Davis, CA 95616 USA
| | - Gabrielle P. Black
- grid.27860.3b0000 0004 1936 9684Department of Civil and Environmental Engineering, University of California, Davis, Davis, CA 95616 USA
| | - Thomas M. Young
- grid.27860.3b0000 0004 1936 9684Department of Civil and Environmental Engineering, University of California, Davis, Davis, CA 95616 USA
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Han W, Li L. Evaluating and minimizing batch effects in metabolomics. MASS SPECTROMETRY REVIEWS 2022; 41:421-442. [PMID: 33238061 DOI: 10.1002/mas.21672] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 10/27/2020] [Accepted: 10/29/2020] [Indexed: 06/11/2023]
Abstract
Determining metabolomic differences among samples of different phenotypes is a critical component of metabolomics research. With the rapid advances in analytical tools such as ultrahigh-resolution chromatography and mass spectrometry, an increasing number of metabolites can now be profiled with high quantification accuracy. The increased detectability and accuracy raise the level of stringiness required to reduce or control any experimental artifacts that can interfere with the measurement of phenotype-related metabolome changes. One of the artifacts is the batch effect that can be caused by multiple sources. In this review, we discuss the origins of batch effects, approaches to detect interbatch variations, and methods to correct unwanted data variability due to batch effects. We recognize that minimizing batch effects is currently an active research area, yet a very challenging task from both experimental and data processing perspectives. Thus, we try to be critical in describing the performance of a reported method with the hope of stimulating further studies for improving existing methods or developing new methods.
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Affiliation(s)
- Wei Han
- Department of Chemistry, University of Alberta, Edmonton, Alberta, Canada
| | - Liang Li
- Department of Chemistry, University of Alberta, Edmonton, Alberta, Canada
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Marie B. Disentangling of the ecotoxicological signal using "omics" analyses, a lesson from the survey of the impact of cyanobacterial proliferations on fishes. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 736:139701. [PMID: 32497891 DOI: 10.1016/j.scitotenv.2020.139701] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 05/16/2020] [Accepted: 05/23/2020] [Indexed: 06/11/2023]
Abstract
Omics technologies offer unprecedented perspectives for the rational investigation of complex biological systems. Indeed, omics present the ability of offering an extensive perception of the biochemistry and physiology of the cell and of any perturbing consequences of contaminants through the joint investigation of thousands of molecular responses simultaneously; then it has recently conducted to a fervent attention by research ecotoxicologists. Beyond the presentation of latest advances, exemplified here by omics investigation of cyanobacterial deleterious effects on various fishes (at various experimental and biological scales and with various analytical tools and pipeline), the present review paper re-explores the promising perspectives and also the pitfalls of such holistic investigations of the ecotoxicological response of organisms for environmental assessment.
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Affiliation(s)
- Benjamin Marie
- Muséum National d'Histoire Naturelle, UMR 7245, CNRS, MNHN Molécules de Communication et Adaptation des Micro-organismes (MCAM), équipe "Cyanobactéries, Cyanotoxines et Environnement", 12 rue Buffon, CP 39, 75231 Paris Cedex 05, France.
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Multifactorial Analysis of Environmental Metabolomic Data in Ecotoxicology: Wild Marine Mussel Exposed to WWTP Effluent as a Case Study. Metabolites 2020; 10:metabo10070269. [PMID: 32610679 PMCID: PMC7407289 DOI: 10.3390/metabo10070269] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 06/21/2020] [Accepted: 06/27/2020] [Indexed: 01/22/2023] Open
Abstract
Environmental metabolomics is a powerful approach to investigate the response of organisms to contaminant exposure at a molecular scale. However, metabolomic responses to realistic environmental conditions can be hindered by factors intrinsic to the environment and the organism. Hence, a well-designed experimental exposure associated with adequate statistical analysis could be helpful to better characterize and relate the observed variability to its different origins. In the current study, we applied a multifactorial experiment combined to Analysis of variance Multiblock Orthogonal Partial Least Squares (AMOPLS), to assess the metabolic response of wild marine mussels, Mytilus galloprovincialis, exposed to a wastewater treatment plant effluent, considering gender as an experimental factor. First, the total observed variability was decomposed to highlight the contribution of each effect related to the experimental factors. Both the exposure and the interaction gender × exposure had a statistically significant impact on the observed metabolic alteration. Then, these metabolic patterns were further characterized by analyzing the individual variable contributions to each effect. A main change in glycerophospholipid levels was highlighted in both males and females as a common response, possibly caused by oxidative stress, which could lead to reproductive disorders, whereas metabolic alterations in some polar lipids and kynurenine pathway were rather gender-specific. This may indicate a disturbance in the energy metabolism and immune system only in males. Finally, AMOPLS is a useful tool facilitating the interpretation of complex metabolomic data and is expected to have a broad application in the field of ecotoxicology.
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Xi Y, Yang X, Zhang H, Liu H, Watson P, Yang F. Binding interactions of halo-benzoic acids, halo-benzenesulfonic acids and halo-phenylboronic acids with human transthyretin. CHEMOSPHERE 2020; 242:125135. [PMID: 31669991 DOI: 10.1016/j.chemosphere.2019.125135] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 10/15/2019] [Accepted: 10/16/2019] [Indexed: 06/10/2023]
Abstract
The anionic form-dependent binding interaction of halo-phenolic substances with human transthyretin (hTTR) has been observed previously. This indicates that ionizable compounds should be the primary focus in screening potential hTTR disruptors. Here, the potential binding potency of halo-benzoic acids, halo-benzenesulfonic acids/sulfates and halo-phenylboronic acids with hTTR was determined and analyzed by competitive fluorescence displacement assay integrated with computational methods. The laboratorial results indicated that the three test groups of model compounds exhibited a distinct binding affinity to hTTR. All the tested halo-phenylboronic acids, some of the tested halo-benzoic acids and halo-benzenesulfonic acids/sulfates were shown to be inactive with hTTR. Other halo-benzoic acids and halo-benzenesulfonic acids/sulfates were moderate and/or weak hTTR binders. The binding affinity of halo-benzoic acids and halo-benzenesulfonic acids/sulfates with hTTR was similar. The low distribution ability of the model compounds from water to hTTR may be the reason why they exhibited the binding potency observed with hTTR. By introducing other highly hydrophobic compounds, we observed that the binding affinity between compounds and hTTR increased with increasing molecular hydrophobicity. Those results indicated that the highly hydrophobic halo-benzoic acids and halo-benzenesulfonic acids/sulfates may be high-priority hTTR disruptors. Finally, a binary classification model was constructed employing three predictive variables. The sensitivity (Sn), specificity (Sp), predictive accuracy (Q) values of the training set and validation set were >0.83, indicating that the model had good classification performance. Thus, the binary classification model developed here could be used to distinguish whether a given ionizable compound is a potential hTTR binder or not.
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Affiliation(s)
- Yue Xi
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Xianhai Yang
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
| | - Hongyu Zhang
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Huihui Liu
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
| | - Peter Watson
- Department of Civil and Environmental Engineering, University of Connecticut, Storrs, 06268, CT, United States
| | - Feifei Yang
- Department of Civil and Environmental Engineering, University of Connecticut, Storrs, 06268, CT, United States
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Pezzatti J, Boccard J, Codesido S, Gagnebin Y, Joshi A, Picard D, González-Ruiz V, Rudaz S. Implementation of liquid chromatography-high resolution mass spectrometry methods for untargeted metabolomic analyses of biological samples: A tutorial. Anal Chim Acta 2020; 1105:28-44. [PMID: 32138924 DOI: 10.1016/j.aca.2019.12.062] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 11/18/2019] [Accepted: 12/20/2019] [Indexed: 12/23/2022]
Abstract
Untargeted metabolomics is now widely recognized as a useful tool for exploring metabolic changes taking place in biological systems under different conditions. By its nature, this is a highly interdisciplinary field of research, and mastering all of the steps comprised in the pipeline can be a challenging task, especially for those researchers new to the topic. In this tutorial, we aim to provide an overview of the most widely adopted methods of performing LC-HRMS-based untargeted metabolomics of biological samples. A detailed protocol is provided in the Supplementary Information for rapidly implementing a basic screening workflow in a laboratory setting. This tutorial covers experimental design, sample preparation and analysis, signal processing and data treatment, and, finally, data analysis and its biological interpretation. Each section is accompanied by up-to-date literature to guide readers through the preparation and optimization of such a workflow, as well as practical information for avoiding or fixing some of the most frequently encountered pitfalls.
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Affiliation(s)
- Julian Pezzatti
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Rue Michel-Servet 1, 1211, Geneva, Switzerland
| | - Julien Boccard
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Rue Michel-Servet 1, 1211, Geneva, Switzerland; Swiss Centre for Applied Human Toxicology (SCAHT), Switzerland
| | - Santiago Codesido
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Rue Michel-Servet 1, 1211, Geneva, Switzerland
| | - Yoric Gagnebin
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Rue Michel-Servet 1, 1211, Geneva, Switzerland
| | - Abhinav Joshi
- Department of Cell Biology, Faculty of Science, University of Geneva, 1211, Geneva, Switzerland
| | - Didier Picard
- Department of Cell Biology, Faculty of Science, University of Geneva, 1211, Geneva, Switzerland
| | - Víctor González-Ruiz
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Rue Michel-Servet 1, 1211, Geneva, Switzerland; Swiss Centre for Applied Human Toxicology (SCAHT), Switzerland
| | - Serge Rudaz
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Rue Michel-Servet 1, 1211, Geneva, Switzerland; Swiss Centre for Applied Human Toxicology (SCAHT), Switzerland.
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González-Ruiz V, Schvartz D, Sandström J, Pezzatti J, Jeanneret F, Tonoli D, Boccard J, Monnet-Tschudi F, Sanchez JC, Rudaz S. An Integrative Multi-Omics Workflow to Address Multifactorial Toxicology Experiments. Metabolites 2019; 9:E79. [PMID: 31022902 PMCID: PMC6523777 DOI: 10.3390/metabo9040079] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 04/03/2019] [Accepted: 04/21/2019] [Indexed: 01/02/2023] Open
Abstract
Toxicology studies can take advantage of omics approaches to better understand the phenomena underlying the phenotypic alterations induced by different types of exposure to certain toxicants. Nevertheless, in order to analyse the data generated from multifactorial omics studies, dedicated data analysis tools are needed. In this work, we propose a new workflow comprising both factor deconvolution and data integration from multiple analytical platforms. As a case study, 3D neural cell cultures were exposed to trimethyltin (TMT) and the relevance of the culture maturation state, the exposure duration, as well as the TMT concentration were simultaneously studied using a metabolomic approach combining four complementary analytical techniques (reversed-phase LC and hydrophilic interaction LC, hyphenated to mass spectrometry in positive and negative ionization modes). The ANOVA multiblock OPLS (AMOPLS) method allowed us to decompose and quantify the contribution of the different experimental factors on the outcome of the TMT exposure. Results showed that the most important contribution to the overall metabolic variability came from the maturation state and treatment duration. Even though the contribution of TMT effects represented the smallest observed modulation among the three factors, it was highly statistically significant. The MetaCore™ pathway analysis tool revealed TMT-induced alterations in biosynthetic pathways and in neuronal differentiation and signaling processes, with a predominant deleterious effect on GABAergic and glutamatergic neurons. This was confirmed by combining proteomic data, increasing the confidence on the mechanistic understanding of such a toxicant exposure.
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Affiliation(s)
- Víctor González-Ruiz
- Analytical Sciences, School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, 1206 Geneva, Switzerland.
- Swiss Centre for Applied Human Toxicology, 4055 Basel, Switzerland.
| | - Domitille Schvartz
- Swiss Centre for Applied Human Toxicology, 4055 Basel, Switzerland.
- Translational Biomarker Group, Department of Internal Medicine Specialties, University of Geneva, 1206 Geneva, Switzerland.
| | - Jenny Sandström
- Swiss Centre for Applied Human Toxicology, 4055 Basel, Switzerland.
- Department of Physiology, University of Lausanne, 1005 Lausanne, Switzerland.
| | - Julian Pezzatti
- Analytical Sciences, School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, 1206 Geneva, Switzerland.
- Swiss Centre for Applied Human Toxicology, 4055 Basel, Switzerland.
| | - Fabienne Jeanneret
- Analytical Sciences, School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, 1206 Geneva, Switzerland.
- Swiss Centre for Applied Human Toxicology, 4055 Basel, Switzerland.
| | - David Tonoli
- Analytical Sciences, School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, 1206 Geneva, Switzerland.
- Swiss Centre for Applied Human Toxicology, 4055 Basel, Switzerland.
| | - Julien Boccard
- Analytical Sciences, School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, 1206 Geneva, Switzerland.
- Swiss Centre for Applied Human Toxicology, 4055 Basel, Switzerland.
| | - Florianne Monnet-Tschudi
- Swiss Centre for Applied Human Toxicology, 4055 Basel, Switzerland.
- Department of Physiology, University of Lausanne, 1005 Lausanne, Switzerland.
| | - Jean-Charles Sanchez
- Swiss Centre for Applied Human Toxicology, 4055 Basel, Switzerland.
- Translational Biomarker Group, Department of Internal Medicine Specialties, University of Geneva, 1206 Geneva, Switzerland.
| | - Serge Rudaz
- Analytical Sciences, School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, 1206 Geneva, Switzerland.
- Swiss Centre for Applied Human Toxicology, 4055 Basel, Switzerland.
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