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Blanc L, Ferraro GB, Tuck M, Prideaux B, Dartois V, Jain RK, Desbenoit N. Kendrick Mass Defect Variation to Decipher Isotopic Labeling in Brain Metastases Studied by Mass Spectrometry Imaging. Anal Chem 2021; 93:16314-16319. [PMID: 34860501 PMCID: PMC9841243 DOI: 10.1021/acs.analchem.1c03916] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Besides many other applications, isotopic labeling is commonly used to decipher the metabolism of living biological systems. By giving a stable isotopically labeled compound as a substrate, the biological system will use this labeled nutrient as it would with a regular substrate and incorporate stable heavy atoms into new metabolites. Utilizing mass spectrometry, by comparing heavy atom enriched isotopic profiles and naturally occurring ones, it is possible to identify these metabolites and deduce valuable information about metabolism and biochemical pathways. The coupling of this approach with mass spectrometry imaging (MSI) allows one then to obtain 2D maps of metabolisms used by living specimens. As metabolic networks are convoluted, a global overview of the isotopically labeled data set to detect unexpected metabolites is crucial. Unfortunately, due to the complexity of MSI spectra, such untargeted processing approaches are difficult to decipher. In this technical note, we demonstrate the potential of a variation around the Kendrick analysis concept to detect the incorporation of stable heavy atoms into metabolites. The Kendrick analysis uses as a base unit the difference between the mass of the most abundant isotope and the mass of the corresponding stable isotopic tracer (namely, 12C and 13C). The resulting Kendrick plot offers an alternative method to process the MSI data set with a new perspective allowing for the rapid detection of the 13C-enriched metabolites and separating unrelated compounds. This processing method of MS data could therefore be a useful tool to decipher isotopic labeling and study metabolic networks, especially as it does not require advanced computational capabilities.
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Affiliation(s)
- Landry Blanc
- Univ. Bordeaux, CNRS, CBMN, UMR 5248, F-33600 Pessac, France
| | - Gino B. Ferraro
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, United States
| | - Michael Tuck
- Univ. Bordeaux, CNRS, CBMN, UMR 5248, F-33600 Pessac, France
| | - Brendan Prideaux
- Department of Neuroscience, Cell Biology, and Anatomy, University of Texas Medical Branch (UTMB), Galveston, Texas 77555, United States
| | - Véronique Dartois
- Center for Discovery and Innovation, Hackensack Meridian School of Medicine, Department of Medical Sciences, Hackensack Meridian Health, Nutley, New Jersey 07601, United States
| | - Rakesh K. Jain
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, United States
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6
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Capellades J, Junza A, Samino S, Brunner JS, Schabbauer G, Vinaixa M, Yanes O. Exploring the Use of Gas Chromatography Coupled to Chemical Ionization Mass Spectrometry (GC-CI-MS) for Stable Isotope Labeling in Metabolomics. Anal Chem 2021; 93:1242-1248. [PMID: 33369389 DOI: 10.1021/acs.analchem.0c02998] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Isotopic-labeling experiments have been valuable to monitor the flux of metabolic reactions in biological systems, which is crucial to understand homeostatic alterations with disease. Experimental determination of metabolic fluxes can be inferred from a characteristic rearrangement of stable isotope tracers (e.g., 13C or 15N) that can be detected by mass spectrometry (MS). Metabolites measured are generally members of well-known metabolic pathways, and most of them can be detected using both gas chromatography (GC)-MS and liquid chromatography (LC)-MS. In here, we show that GC methods coupled to chemical ionization (CI) MS have a clear advantage over alternative methodologies due to GC's superior chromatography separation efficiency and the fact that CI is a soft ionization technique that yields identifiable protonated molecular ion peaks. We tested diverse GC-CI-MS setups, including methane and isobutane reagent gases, triple quadrupole (QqQ) MS in SIM mode, or selected ion clusters using optimized narrow windows (∼10 Da) in scan mode, and standard full scan methods using high resolution GC-(q)TOF and GC-Orbitrap systems. Isobutane as a reagent gas in combination with both low-resolution (LR) and high-resolution (HR) MS showed the best performance, enabling precise detection of isotopologues in most metabolic intermediates of central carbon metabolism. Finally, with the aim of overcoming manual operations, we developed an R-based tool called isoSCAN that automatically quantifies all isotopologues of intermediate metabolites of glycolysis, TCA cycle, amino acids, pentose phosphate pathway, and urea cycle, from LRMS and HRMS data.
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Affiliation(s)
- Jordi Capellades
- Department of Electronic Engineering, Universitat Rovira i Virgili, Tarragona, Spain.,CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain.,Institut d'Investigació Sanitària Pere Virgili (IISPV), Metabolomics Platform, Reus, Spain
| | - Alexandra Junza
- CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
| | - Sara Samino
- Department of Electronic Engineering, Universitat Rovira i Virgili, Tarragona, Spain.,CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
| | - Julia S Brunner
- Institute for Vascular Biology, Centre for Physiology and Pharmacology, Medical University Vienna, 1090 Vienna, Austria.,Christian Doppler Laboratory for Arginine Metabolism in Rheumatoid Arthritis and Multiple Sclerosis, 1090 Vienna, Austria
| | - Gernot Schabbauer
- Institute for Vascular Biology, Centre for Physiology and Pharmacology, Medical University Vienna, 1090 Vienna, Austria.,Christian Doppler Laboratory for Arginine Metabolism in Rheumatoid Arthritis and Multiple Sclerosis, 1090 Vienna, Austria
| | - Maria Vinaixa
- Department of Electronic Engineering, Universitat Rovira i Virgili, Tarragona, Spain.,CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain.,Institut d'Investigació Sanitària Pere Virgili (IISPV), Metabolomics Platform, Reus, Spain
| | - Oscar Yanes
- Department of Electronic Engineering, Universitat Rovira i Virgili, Tarragona, Spain.,CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain.,Institut d'Investigació Sanitària Pere Virgili (IISPV), Metabolomics Platform, Reus, Spain
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7
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Selivanov VA, Marin S, Tarragó-Celada J, Lane AN, Higashi RM, Fan TWM, de Atauri P, Cascante M. Software Supporting a Workflow of Quantitative Dynamic Flux Maps Estimation in Central Metabolism from SIRM Experimental Data. Methods Mol Biol 2020; 2088:271-298. [PMID: 31893378 DOI: 10.1007/978-1-0716-0159-4_12] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Stable isotope-resolved metabolomics (SIRM), based on the analysis of biological samples from living cells incubated with artificial isotope enriched substrates, enables mapping the rates of biochemical reactions (metabolic fluxes). We developed software supporting a workflow of analysis of SIRM data obtained with mass spectrometry (MS). The evaluation of fluxes starting from raw MS recordings requires at least three steps of computer support: first, extraction of mass spectra of metabolites of interest, then correction of the spectra for natural isotope abundance, and finally, evaluation of fluxes by simulation of the corrected spectra using a corresponding mathematical model. A kinetic model based on ordinary differential equations (ODEs) for isotopomers of metabolites of the corresponding biochemical network supports the final part of the analysis, which provides a dynamic flux map.
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Affiliation(s)
- Vitaly A Selivanov
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain. .,Institute of Biomedicine of Universitat de Barcelona (IBUB), Barcelona, Spain. .,Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Madrid, Spain. .,INB-Bioinformatics Platform Metabolomics Node, Instituto de Salud Carlos III (ISCIII), Madrid, Spain.
| | - Silvia Marin
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain.,Institute of Biomedicine of Universitat de Barcelona (IBUB), Barcelona, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Josep Tarragó-Celada
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain.,Institute of Biomedicine of Universitat de Barcelona (IBUB), Barcelona, Spain
| | - Andrew N Lane
- Markey Cancer Center, University of Kentucky, Lexington, KY, USA.,Center for Environment and Systems Biochemistry and the Resource Center for Stable Isotope Resolved Metabolomics, University of Kentucky, Lexington, KY, USA.,Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, KY, USA
| | - Richard M Higashi
- Markey Cancer Center, University of Kentucky, Lexington, KY, USA.,Center for Environment and Systems Biochemistry and the Resource Center for Stable Isotope Resolved Metabolomics, University of Kentucky, Lexington, KY, USA.,Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, KY, USA
| | - Teresa W-M Fan
- Markey Cancer Center, University of Kentucky, Lexington, KY, USA.,Center for Environment and Systems Biochemistry and the Resource Center for Stable Isotope Resolved Metabolomics, University of Kentucky, Lexington, KY, USA.,Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, KY, USA
| | - Pedro de Atauri
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain.,Institute of Biomedicine of Universitat de Barcelona (IBUB), Barcelona, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Madrid, Spain.,INB-Bioinformatics Platform Metabolomics Node, Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain. .,Institute of Biomedicine of Universitat de Barcelona (IBUB), Barcelona, Spain. .,Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Madrid, Spain. .,INB-Bioinformatics Platform Metabolomics Node, Instituto de Salud Carlos III (ISCIII), Madrid, Spain.
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9
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Peters K, Bradbury J, Bergmann S, Capuccini M, Cascante M, de Atauri P, Ebbels TMD, Foguet C, Glen R, Gonzalez-Beltran A, Günther UL, Handakas E, Hankemeier T, Haug K, Herman S, Holub P, Izzo M, Jacob D, Johnson D, Jourdan F, Kale N, Karaman I, Khalili B, Emami Khonsari P, Kultima K, Lampa S, Larsson A, Ludwig C, Moreno P, Neumann S, Novella JA, O'Donovan C, Pearce JTM, Peluso A, Piras ME, Pireddu L, Reed MAC, Rocca-Serra P, Roger P, Rosato A, Rueedi R, Ruttkies C, Sadawi N, Salek RM, Sansone SA, Selivanov V, Spjuth O, Schober D, Thévenot EA, Tomasoni M, van Rijswijk M, van Vliet M, Viant MR, Weber RJM, Zanetti G, Steinbeck C. PhenoMeNal: processing and analysis of metabolomics data in the cloud. Gigascience 2019; 8:giy149. [PMID: 30535405 PMCID: PMC6377398 DOI: 10.1093/gigascience/giy149] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 10/19/2018] [Accepted: 11/20/2018] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Metabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological, and many other applied biological domains. Its computationally intensive nature has driven requirements for open data formats, data repositories, and data analysis tools. However, the rapid progress has resulted in a mosaic of independent, and sometimes incompatible, analysis methods that are difficult to connect into a useful and complete data analysis solution. FINDINGS PhenoMeNal (Phenome and Metabolome aNalysis) is an advanced and complete solution to set up Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open-source tools that are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated, and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi, and Pachyderm. CONCLUSIONS PhenoMeNal constitutes a keystone solution in cloud e-infrastructures available for metabolomics. PhenoMeNal is a unique and complete solution for setting up cloud e-infrastructures through easy-to-use web interfaces that can be scaled to any custom public and private cloud environment. By harmonizing and automating software installation and configuration and through ready-to-use scientific workflow user interfaces, PhenoMeNal has succeeded in providing scientists with workflow-driven, reproducible, and shareable metabolomics data analysis platforms that are interfaced through standard data formats, representative datasets, versioned, and have been tested for reproducibility and interoperability. The elastic implementation of PhenoMeNal further allows easy adaptation of the infrastructure to other application areas and 'omics research domains.
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Affiliation(s)
- Kristian Peters
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany
| | - James Bradbury
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | - Sven Bergmann
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Marco Capuccini
- Division of Scientific Computing, Department of Information Technology, Uppsala University, Sweden
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine, Universitat de Barcelona; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Spain
| | - Pedro de Atauri
- Department of Biochemistry and Molecular Biomedicine, Universitat de Barcelona; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Spain
| | - Timothy M D Ebbels
- Department of Surgery & Cancer, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom
| | - Carles Foguet
- Department of Biochemistry and Molecular Biomedicine, Universitat de Barcelona; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Spain
| | - Robert Glen
- Department of Surgery & Cancer, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB21EW, United Kingdom
| | - Alejandra Gonzalez-Beltran
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX1 3QG, Oxford, United Kingdom
| | - Ulrich L Günther
- Institute of Cancer and Genomic Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | - Evangelos Handakas
- Department of Surgery & Cancer, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom
| | - Thomas Hankemeier
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, 2333 CC, The Netherlands
| | - Kenneth Haug
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Stephanie Herman
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, 751 85 Uppsala, Sweden
| | | | - Massimiliano Izzo
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX1 3QG, Oxford, United Kingdom
| | - Daniel Jacob
- INRA, University of Bordeaux, Plateforme Métabolome Bordeaux-MetaboHUB, 33140 Villenave d'Ornon, France
| | - David Johnson
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX1 3QG, Oxford, United Kingdom
- Department of Informatics and Media, Uppsala University, Box 513, 751 20 Uppsala, Sweden
| | - Fabien Jourdan
- INRA - French National Institute for Agricultural Research, UMR1331, Toxalim, Research Centre in Food Toxicology, Toulouse, France
| | - Namrata Kale
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Ibrahim Karaman
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St. Mary's Campus, Norfolk Place, W2 1PG, London, United Kingdom
| | - Bita Khalili
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Payam Emami Khonsari
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, 751 85 Uppsala, Sweden
| | - Kim Kultima
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, 751 85 Uppsala, Sweden
| | - Samuel Lampa
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
| | - Anders Larsson
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
- National Bioinformatics Infrastructure Sweden, Uppsala University, Uppsala, Sweden
| | - Christian Ludwig
- Institute of Metabolism and Systems Research (IMSR), University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | - Pablo Moreno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Steffen Neumann
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany
| | - Jon Ander Novella
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
- National Bioinformatics Infrastructure Sweden, Uppsala University, Uppsala, Sweden
| | - Claire O'Donovan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Jake T M Pearce
- Department of Surgery & Cancer, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom
| | - Alina Peluso
- Department of Surgery & Cancer, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom
| | | | | | - Michelle A C Reed
- Institute of Cancer and Genomic Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | - Philippe Rocca-Serra
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX1 3QG, Oxford, United Kingdom
| | - Pierrick Roger
- CEA, LIST, Laboratory for Data Analysis and Systems’ Intelligence, MetaboHUB, Gif-Sur-Yvette F-91191, France
| | - Antonio Rosato
- Magnetic Resonance Center (CERM) and Department of Chemistry, University of Florence and CIRMMP, 50019 Sesto Fiorentino, Florence, Italy
| | - Rico Rueedi
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Christoph Ruttkies
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany
| | - Noureddin Sadawi
- Department of Computer Science, College of Engineering, Design and Physical Sciences, Brunel University, London, UK
- Department of Surgery & Cancer, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom
| | - Reza M Salek
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Susanna-Assunta Sansone
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX1 3QG, Oxford, United Kingdom
| | - Vitaly Selivanov
- Department of Biochemistry and Molecular Biomedicine, Universitat de Barcelona; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Spain
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
| | - Daniel Schober
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany
| | - Etienne A Thévenot
- CEA, LIST, Laboratory for Data Analysis and Systems’ Intelligence, MetaboHUB, Gif-Sur-Yvette F-91191, France
| | - Mattia Tomasoni
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Merlijn van Rijswijk
- Netherlands Metabolomics Center, Leiden, 2333 CC, Netherlands
- ELIXIR-NL, Dutch Techcentre for Life Sciences, Utrecht, 3503 RM, Netherlands
| | - Michael van Vliet
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, 2333 CC, The Netherlands
| | - Mark R Viant
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
- Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | - Ralf J M Weber
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
- Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | | | - Christoph Steinbeck
- Cheminformatics and Computational Metabolomics, Institute for Analytical Chemistry, Lessingstr. 8, 07743 Jena, Germany
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