1
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Essex M, Millet Pascual-Leone B, Löber U, Kuhring M, Zhang B, Brüning U, Fritsche-Guenther R, Krzanowski M, Fiocca Vernengo F, Brumhard S, Röwekamp I, Anna Bielecka A, Lesker TR, Wyler E, Landthaler M, Mantei A, Meisel C, Caesar S, Thibeault C, Corman VM, Marko L, Suttorp N, Strowig T, Kurth F, Sander LE, Li Y, Kirwan JA, Forslund SK, Opitz B. Gut microbiota dysbiosis is associated with altered tryptophan metabolism and dysregulated inflammatory response in COVID-19. NPJ Biofilms Microbiomes 2024; 10:66. [PMID: 39085233 PMCID: PMC11291933 DOI: 10.1038/s41522-024-00538-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 07/22/2024] [Indexed: 08/02/2024] Open
Abstract
The clinical course of COVID-19 is variable and often unpredictable. To test the hypothesis that disease progression and inflammatory responses associate with alterations in the microbiome and metabolome, we analyzed metagenome, metabolome, cytokine, and transcriptome profiles of repeated samples from hospitalized COVID-19 patients and uninfected controls, and leveraged clinical information and post-hoc confounder analysis. Severe COVID-19 was associated with a depletion of beneficial intestinal microbes, whereas oropharyngeal microbiota disturbance was mainly linked to antibiotic use. COVID-19 severity was also associated with enhanced plasma concentrations of kynurenine and reduced levels of several other tryptophan metabolites, lysophosphatidylcholines, and secondary bile acids. Moreover, reduced concentrations of various tryptophan metabolites were associated with depletion of Faecalibacterium, and tryptophan decrease and kynurenine increase were linked to enhanced production of inflammatory cytokines. Collectively, our study identifies correlated microbiome and metabolome alterations as a potential contributor to inflammatory dysregulation in severe COVID-19.
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Affiliation(s)
- Morgan Essex
- Experimental and Clinical Research Center (ECRC), a cooperation of the Max Delbrück Center and Charité-Universitätsmedizin, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Charité-Universitätsmedizin Berlin, a corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Belén Millet Pascual-Leone
- Department of Infectious Diseases, Respiratory Medicine and Critical Care, Charité-Universitätsmedizin Berlin, a corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Ulrike Löber
- Experimental and Clinical Research Center (ECRC), a cooperation of the Max Delbrück Center and Charité-Universitätsmedizin, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Charité-Universitätsmedizin Berlin, a corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Mathias Kuhring
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Berlin Institute of Health (BIH) at Charité, BIH Metabolomics Platform, Berlin, Germany
- Berlin Institute of Health (BIH) at Charité, Core Unit Bioinformatics, Berlin, Germany
| | - Bowen Zhang
- Department of Computational Biology for Individualized Infection Medicine, Center for Individualized Infection Medicine (CiiM), a joint venture between the Helmholtz-Center for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany
- TWINCORE, joint ventures between the Helmholtz Center for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany
- College of Life Sciences, Beijing Normal University, Beijing, China
| | - Ulrike Brüning
- Berlin Institute of Health (BIH) at Charité, BIH Metabolomics Platform, Berlin, Germany
| | | | - Marta Krzanowski
- Department of Infectious Diseases, Respiratory Medicine and Critical Care, Charité-Universitätsmedizin Berlin, a corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Facundo Fiocca Vernengo
- Department of Infectious Diseases, Respiratory Medicine and Critical Care, Charité-Universitätsmedizin Berlin, a corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sophia Brumhard
- Department of Infectious Diseases, Respiratory Medicine and Critical Care, Charité-Universitätsmedizin Berlin, a corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Ivo Röwekamp
- Department of Infectious Diseases, Respiratory Medicine and Critical Care, Charité-Universitätsmedizin Berlin, a corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Agata Anna Bielecka
- Department of Microbial Immune Regulation, Helmholtz Center for Infection Research (HZI), Braunschweig, Germany
- German Center for Infection Research (DZIF), partner site Hannover-Braunschweig, Braunschweig, Germany
| | - Till Robin Lesker
- Department of Microbial Immune Regulation, Helmholtz Center for Infection Research (HZI), Braunschweig, Germany
| | - Emanuel Wyler
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Markus Landthaler
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | | | - Christian Meisel
- Labor Berlin-Charité Vivantes GmbH, Berlin, Germany
- Institute of Medical Immunology, Charité-Universitätsmedizin Berlin, a corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sandra Caesar
- Department of Infectious Diseases, Respiratory Medicine and Critical Care, Charité-Universitätsmedizin Berlin, a corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Charlotte Thibeault
- Department of Infectious Diseases, Respiratory Medicine and Critical Care, Charité-Universitätsmedizin Berlin, a corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Victor M Corman
- Labor Berlin-Charité Vivantes GmbH, Berlin, Germany
- Institute of Virology, Charité-Universitätsmedizin Berlin, a corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- German Center for Infection Research (DZIF), Berlin, Germany
| | - Lajos Marko
- Experimental and Clinical Research Center (ECRC), a cooperation of the Max Delbrück Center and Charité-Universitätsmedizin, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Charité-Universitätsmedizin Berlin, a corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- German Center for Cardiovascular Research (DZHK), partner site Berlin, Berlin, Germany
| | - Norbert Suttorp
- Department of Infectious Diseases, Respiratory Medicine and Critical Care, Charité-Universitätsmedizin Berlin, a corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- German Center for Lung Research (DZL), Berlin, Germany
| | - Till Strowig
- Department of Computational Biology for Individualized Infection Medicine, Center for Individualized Infection Medicine (CiiM), a joint venture between the Helmholtz-Center for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany
- Department of Microbial Immune Regulation, Helmholtz Center for Infection Research (HZI), Braunschweig, Germany
- German Center for Infection Research (DZIF), partner site Hannover-Braunschweig, Braunschweig, Germany
| | - Florian Kurth
- Department of Infectious Diseases, Respiratory Medicine and Critical Care, Charité-Universitätsmedizin Berlin, a corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Leif E Sander
- Department of Infectious Diseases, Respiratory Medicine and Critical Care, Charité-Universitätsmedizin Berlin, a corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- German Center for Lung Research (DZL), Berlin, Germany
| | - Yang Li
- Department of Computational Biology for Individualized Infection Medicine, Center for Individualized Infection Medicine (CiiM), a joint venture between the Helmholtz-Center for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany
- TWINCORE, joint ventures between the Helmholtz Center for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany
| | - Jennifer A Kirwan
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Berlin Institute of Health (BIH) at Charité, BIH Metabolomics Platform, Berlin, Germany
- University of Nottingham School of Veterinary Medicine and Science, Loughborough, UK
| | - Sofia K Forslund
- Experimental and Clinical Research Center (ECRC), a cooperation of the Max Delbrück Center and Charité-Universitätsmedizin, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Charité-Universitätsmedizin Berlin, a corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- German Center for Cardiovascular Research (DZHK), partner site Berlin, Berlin, Germany
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Bastian Opitz
- Department of Infectious Diseases, Respiratory Medicine and Critical Care, Charité-Universitätsmedizin Berlin, a corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
- Labor Berlin-Charité Vivantes GmbH, Berlin, Germany.
- German Center for Lung Research (DZL), Berlin, Germany.
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Ghafari N, Sleno L. Challenges and recent advances in quantitative mass spectrometry-based metabolomics. ANALYTICAL SCIENCE ADVANCES 2024; 5:e2400007. [PMID: 38948317 PMCID: PMC11210748 DOI: 10.1002/ansa.202400007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 06/03/2024] [Accepted: 06/08/2024] [Indexed: 07/02/2024]
Abstract
The field of metabolomics has gained tremendous interest in recent years. Whether the goal is to discover biomarkers related to certain pathologies or to better understand the impact of a drug or contaminant, numerous studies have demonstrated how crucial it is to understand variations in metabolism. Detailed knowledge of metabolic variabilities can lead to more effective treatments, as well as faster or less invasive diagnostics. Exploratory approaches are often employed in metabolomics, using relative quantitation to look at perturbations between groups of samples. Most metabolomics studies have been based on metabolite profiling using relative quantitation, with very few studies using an approach for absolute quantitation. Using accurate quantitation facilitates the comparison between different studies, as well as enabling longitudinal studies. In this review, we discuss the most widely used techniques for quantitative metabolomics using mass spectrometry (MS). Various aspects will be addressed, such as the use of external and/or internal standards, derivatization techniques, in vivo isotopic labelling, or quantitative MS imaging. The principles, as well as the associated limitations and challenges, will be described for each approach.
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Affiliation(s)
- Nathan Ghafari
- Chemistry Department/CERMO‐FCUniversity of Quebec in Montreal (UQAM)MontrealCanada
| | - Lekha Sleno
- Chemistry Department/CERMO‐FCUniversity of Quebec in Montreal (UQAM)MontrealCanada
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González-Domínguez Á, Estanyol-Torres N, Brunius C, Landberg R, González-Domínguez R. QC omics: Recommendations and Guidelines for Robust, Easily Implementable and Reportable Quality Control of Metabolomics Data. Anal Chem 2024; 96:1064-1072. [PMID: 38179935 PMCID: PMC10809278 DOI: 10.1021/acs.analchem.3c03660] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 11/03/2023] [Accepted: 12/21/2023] [Indexed: 01/06/2024]
Abstract
The implementation of quality control strategies is crucial to ensure the reproducibility, accuracy, and meaningfulness of metabolomics data. However, this pivotal step is often overlooked within the metabolomics workflow and frequently relies on the use of nonstandardized and poorly reported protocols. To address current limitations in this respect, we have developed QComics, a robust, easily implementable and reportable method for monitoring and controlling data quality. The protocol operates in various sequential steps aimed to (i) correct for background noise and carryover, (ii) detect signal drifts and "out-of-control" observations, (iii) deal with missing data, (iv) remove outliers, (v) monitor quality markers to identify samples affected by improper collection, preprocessing, or storage, and (vi) assess overall data quality in terms of precision and accuracy. Notably, this tool considers important issues often neglected along quality control, such as the need of separately handling missing values and truly absent data to avoid losing relevant biological information, as well as the large impact that preanalytical factors may elicit on metabolomics results. Altogether, the guidelines compiled in QComics might contribute to establishing gold standard recommendations and best practices for quality control within the metabolomics community.
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Affiliation(s)
- Álvaro González-Domínguez
- Instituto
de Investigación e Innovación Biomédica de Cádiz
(INiBICA), Hospital Universitario Puerta del Mar, Universidad de Cádiz, Cádiz 11009, Spain
| | - Núria Estanyol-Torres
- Division
of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology,SE-412 96Gothenburg ,Sweden
| | - Carl Brunius
- Division
of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology,SE-412 96Gothenburg ,Sweden
| | - Rikard Landberg
- Division
of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology,SE-412 96Gothenburg ,Sweden
| | - Raúl González-Domínguez
- Instituto
de Investigación e Innovación Biomédica de Cádiz
(INiBICA), Hospital Universitario Puerta del Mar, Universidad de Cádiz, Cádiz 11009, Spain
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Courraud J, Russo F, Themudo GE, Laursen SS, Ingason A, Hougaard DM, Cohen AS, Werge T, Ernst M. Metabolic signature of the pathogenic 22q11.2 deletion identifies carriers and provides insight into systemic dysregulation. Transl Psychiatry 2023; 13:391. [PMID: 38097559 PMCID: PMC10721888 DOI: 10.1038/s41398-023-02697-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 11/22/2023] [Accepted: 11/29/2023] [Indexed: 12/17/2023] Open
Abstract
Large deletions at chromosome 22q11.2 are known to cause severe clinical conditions collectively known as 22q11.2 deletion syndrome. Notwithstanding the pathogenicity of these deletions, affected individuals are typically diagnosed in late childhood or early adolescence, and little is known of the molecular signaling cascades and biological consequences immediately downstream of the deleted genes. Here, we used targeted metabolomics to compare neonatal dried blood spot samples from 203 individuals clinically identified as carriers of a deletion at chromosome 22q11.2 with 203 unaffected individuals. A total of 173 metabolites were successfully identified and used to inform on systemic dysregulation caused by the genomic lesion and to discriminate carriers from non-carriers. We found 84 metabolites to be differentially abundant between carriers and non-carriers of the 22q11.2 deletion. A predictive model based on all 173 metabolites achieved high Accuracy (89%), Area Under the Curve (93%), F1 (88%), Positive Predictive Value (94%), and Negative Predictive Value (84%) with tyrosine and proline having the highest individual contributions to the model as well as the highest interaction strength. Targeted metabolomics provides insight into the molecular consequences possibly contributing to the pathology underlying the clinical manifestations of the 22q11 deletion and is an easily applicable approach to first-pass screening for carrier status of the 22q11 to prompt subsequent verification of the genomic diagnosis.
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Affiliation(s)
- Julie Courraud
- Section for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Artillerivej 5, DK-2300, Copenhagen S, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771, Athens, Greece
- Department of Clinical Therapeutics, School of Medicine, National and Kapodistrian University of Athens, Alexandra Hospital, Leof. Vasilissis Sofias 80, Athens, 11528, Greece
| | - Francesco Russo
- Section for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Artillerivej 5, DK-2300, Copenhagen S, Denmark
| | - Gonçalo Espregueira Themudo
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Institute of Biological Psychiatry, Copenhagen University Hospital, Copenhagen Mental Health Services, Kristineberg 3, DK-2100, Copenhagen Ø, Denmark
- CIIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Avenida General Norton de Matos, S/N, 4450-208, Matosinhos, Portugal
- Centre for Ecology, Evolution and Environmental Changes (CE3C), Faculdade de Ciências da Universidade de Lisboa, Campo Grande, 1749-016, Lisboa, Portugal
| | - Susan Svane Laursen
- Section for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Artillerivej 5, DK-2300, Copenhagen S, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
| | - Andrés Ingason
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Institute of Biological Psychiatry, Mental Health Center Sankt Hans, DK-4000, Roskilde, Denmark
| | - David M Hougaard
- Section for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Artillerivej 5, DK-2300, Copenhagen S, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
| | - Arieh S Cohen
- Section for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Artillerivej 5, DK-2300, Copenhagen S, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
| | - Thomas Werge
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark.
- Institute of Biological Psychiatry, Copenhagen University Hospital, Copenhagen Mental Health Services, Kristineberg 3, DK-2100, Copenhagen Ø, Denmark.
- Department of Clinical Sciences, Faculty of Health, University of Copenhagen, Blegdamsvej 3, DK-2200, København N, Denmark.
- GLOBE Institute, LF Center for GeoGenetics, Faculty of Health, University of Copenhagen, Oester Voldgade 5-7, 1350, Copenhagen K, Denmark.
| | - Madeleine Ernst
- Section for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Artillerivej 5, DK-2300, Copenhagen S, Denmark.
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark.
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Amer B, Deshpande RR, Bird SS. Simultaneous Quantitation and Discovery (SQUAD) Analysis: Combining the Best of Targeted and Untargeted Mass Spectrometry-Based Metabolomics. Metabolites 2023; 13:metabo13050648. [PMID: 37233689 DOI: 10.3390/metabo13050648] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 04/27/2023] [Accepted: 05/05/2023] [Indexed: 05/27/2023] Open
Abstract
Untargeted and targeted approaches are the traditional metabolomics workflows acquired for a wider understanding of the metabolome under focus. Both approaches have their strengths and weaknesses. The untargeted, for example, is maximizing the detection and accurate identification of thousands of metabolites, while the targeted is maximizing the linear dynamic range and quantification sensitivity. These workflows, however, are acquired separately, so researchers compromise either a low-accuracy overview of total molecular changes (i.e., untargeted analysis) or a detailed yet blinkered snapshot of a selected group of metabolites (i.e., targeted analysis) by selecting one of the workflows over the other. In this review, we present a novel single injection simultaneous quantitation and discovery (SQUAD) metabolomics that combines targeted and untargeted workflows. It is used to identify and accurately quantify a targeted set of metabolites. It also allows data retro-mining to look for global metabolic changes that were not part of the original focus. This offers a way to strike the balance between targeted and untargeted approaches in one single experiment and address the two approaches' limitations. This simultaneous acquisition of hypothesis-led and discovery-led datasets allows scientists to gain more knowledge about biological systems in a single experiment.
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Affiliation(s)
- Bashar Amer
- Thermo Fisher Scientific, San Jose, 95134 CA, USA
| | | | - Susan S Bird
- Thermo Fisher Scientific, San Jose, 95134 CA, USA
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Dmitrenko A, Reid M, Zamboni N. A system suitability testing platform for untargeted, high-resolution mass spectrometry. Front Mol Biosci 2022; 9:1026184. [PMID: 36304928 PMCID: PMC9592825 DOI: 10.3389/fmolb.2022.1026184] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 09/26/2022] [Indexed: 11/25/2022] Open
Abstract
The broad coverage of untargeted metabolomics poses fundamental challenges for the harmonization of measurements along time, even if they originate from the very same instrument. Internal isotopic standards can hardly cover the chemical complexity of study samples. Therefore, they are insufficient for normalizing data a posteriori as done for targeted metabolomics. Instead, it is crucial to verify instrument’s performance a priori, that is, before samples are injected. Here, we propose a system suitability testing platform for time-of-flight mass spectrometers independent of liquid chromatography. It includes a chemically defined quality control mixture, a fast acquisition method, software for extracting ca. 3,000 numerical features from profile data, and a simple web service for monitoring. We ran a pilot for 21 months and present illustrative results for anomaly detection or learning causal relationships between the spectral features and machine settings. Beyond mere detection of anomalies, our results highlight several future applications such as 1) recommending instrument retuning strategies to achieve desired values of quality indicators, 2) driving preventive maintenance, and 3) using the obtained, detailed spectral features for posterior data harmonization.
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Affiliation(s)
- Andrei Dmitrenko
- Institute of Molecular Systems Biology, Department of Biology, ETH Zürich, Zürich, Switzerland
- Life Science Zurich PhD Program on Systems Biology, Zürich, Switzerland
| | - Michelle Reid
- Institute of Molecular Systems Biology, Department of Biology, ETH Zürich, Zürich, Switzerland
| | - Nicola Zamboni
- Institute of Molecular Systems Biology, Department of Biology, ETH Zürich, Zürich, Switzerland
- *Correspondence: Nicola Zamboni,
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Dehghani F, Yousefinejad S, Walker DI, Omidi F. Metabolomics for exposure assessment and toxicity effects of occupational pollutants: current status and future perspectives. Metabolomics 2022; 18:73. [PMID: 36083566 DOI: 10.1007/s11306-022-01930-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 08/19/2022] [Indexed: 11/24/2022]
Abstract
INTRODUCTION Work-related exposures to harmful agents or factors are associated with an increase in incidence of occupational diseases. These exposures often represent a complex mixture of different stressors, challenging the ability to delineate the mechanisms and risk factors underlying exposure-disease relationships. The use of omics measurement approaches that enable characterization of biological marker patterns provide internal indicators of molecular alterations, which could be used to identify bioeffects following exposure to a toxicant. Metabolomics is the comprehensive analysis of small molecule present in biological samples, and allows identification of potential modes of action and altered pathways by systematic measurement of metabolites. OBJECTIVES The aim of this study is to review the application of metabolomics studies for use in occupational health, with a focus on applying metabolomics for exposure monitoring and its relationship to occupational diseases. METHODS PubMed, Web of Science, Embase and Scopus electronic databases were systematically searched for relevant studies published up to 2021. RESULTS Most of reviewed studies included worker populations exposed to heavy metals such as As, Cd, Pb, Cr, Ni, Mn and organic compounds such as tetrachlorodibenzo-p-dioxin, trichloroethylene, polyfluoroalkyl, acrylamide, polyvinyl chloride. Occupational exposures were associated with changes in metabolites and pathways, and provided novel insight into the relationship between exposure and disease outcomes. The reviewed studies demonstrate that metabolomics provides a powerful ability to identify metabolic phenotypes and bioeffect of occupational exposures. CONCLUSION Continued application to worker populations has the potential to enable characterization of thousands of chemical signals in biological samples, which could lead to discovery of new biomarkers of exposure for chemicals, identify possible toxicological mechanisms, and improved understanding of biological effects increasing disease risk associated with occupational exposure.
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Affiliation(s)
- Fatemeh Dehghani
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
- Research Center for Health Sciences, Research Institute for Health, Department of Occupational Health and Safety Engineering, School of Health Shiraz, University of Medical Sciences, Shiraz, Iran
| | - Saeed Yousefinejad
- Research Center for Health Sciences, Research Institute for Health, Department of Occupational Health and Safety Engineering, School of Health Shiraz, University of Medical Sciences, Shiraz, Iran.
| | - Douglas I Walker
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Fariborz Omidi
- Research Center for Environmental Determinants of Health (RCEDH), Health Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran
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Diaz-Uriarte R, Gómez de Lope E, Giugno R, Fröhlich H, Nazarov PV, Nepomuceno-Chamorro IA, Rauschenberger A, Glaab E. Ten quick tips for biomarker discovery and validation analyses using machine learning. PLoS Comput Biol 2022; 18:e1010357. [PMID: 35951526 PMCID: PMC9371329 DOI: 10.1371/journal.pcbi.1010357] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
- Ramon Diaz-Uriarte
- Department of Biochemistry, School of Medicine, Universidad Autónoma de Madrid, Instituto de Investigaciones Biomédicas ‘Alberto Sols’ (UAM-CSIC), Madrid, Spain
| | - Elisa Gómez de Lope
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg
| | - Rosalba Giugno
- Department of Computer Science, University of Verona, Verona, Italy
| | - Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Centre for IT (b-it), Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Petr V. Nazarov
- Department of Cancer Research, Luxembourg Institute of Health, Strassen, Luxembourg
| | | | - Armin Rauschenberger
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg
| | - Enrico Glaab
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg
- * E-mail:
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9
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Fritsche-Guenther R, Gloaguen Y, Eisenberger A, Kirwan JA. Analysis of adherent cell culture lysates with low metabolite concentrations using the Biocrates AbsoluteIDQ p400 HR kit. Sci Rep 2022; 12:7933. [PMID: 35562573 PMCID: PMC9106690 DOI: 10.1038/s41598-022-11118-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 03/07/2022] [Indexed: 11/09/2022] Open
Abstract
The AbsoluteIDQ p400 HR kit is a commercial product for targeted metabolomics. While the kit has been validated for human plasma and serum, adherent cell lysates have not yet been evaluated. We have optimized the detection of polar and lipid metabolites in cell lysates using the kit to enable robust and repeatable analysis of the detected metabolites. Parameters optimized include total cell mass, loading volume and extraction solvent. We present a cell preparation and analytical method and report on the performance of the kit with regard to detectability of the targeted metabolites and their repeatability. The kit can be successfully used for a relative quantification analysis of cell lysates from adherent cells although validated only for human plasma and serum. Most metabolites are below the limit of the Biocrates' set quantification limits and we confirmed that this relative quantification can be used for further statistical analysis. Using this approach, up to 45% of the total metabolites in the kit can be detected with a reasonable analytical performance (lowest median RSD 9% and 13% for LC and FIA, respectively) dependent on the method used. We recommend using ethanol as the extraction solvent for cell lysates of osteosarcoma cell lines for the broadest metabolite coverage and 25 mg of cell mass with a loading volume of 20 µL per sample.
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Affiliation(s)
- Raphaela Fritsche-Guenther
- Berlin Institute of Health (BIH) @ Charité, BIH Metabolomics Platform, Anna-Louisa-Karsch-Str. 2, 10178, Berlin, Germany
| | - Yoann Gloaguen
- Berlin Institute of Health (BIH) @ Charité, BIH Metabolomics Platform, Anna-Louisa-Karsch-Str. 2, 10178, Berlin, Germany.,Core Unit Bioinformatics, Berlin Institute of Health (BIH) @ Charité, Chariteplatz 1, 10117, Berlin, Germany
| | - Alina Eisenberger
- Berlin Institute of Health (BIH) @ Charité, BIH Metabolomics Platform, Anna-Louisa-Karsch-Str. 2, 10178, Berlin, Germany
| | - Jennifer A Kirwan
- Berlin Institute of Health (BIH) @ Charité, BIH Metabolomics Platform, Anna-Louisa-Karsch-Str. 2, 10178, Berlin, Germany.
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10
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Dong Y, Kazachkova Y, Gou M, Morgan L, Wachsman T, Gazit E, Birkler RID. RawHummus: an R Shiny app for automated raw data quality control in metabolomics. Bioinformatics 2022; 38:2072-2074. [PMID: 35080628 DOI: 10.1093/bioinformatics/btac040] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 01/16/2022] [Accepted: 01/24/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Robust and reproducible data is essential to ensure high-quality analytical results and is particularly important for large-scale metabolomics studies where detector sensitivity drifts, retention time and mass accuracy shifts frequently occur. Therefore, raw data need to be inspected before data processing to detect measurement bias and verify system consistency. RESULTS Here, we present RawHummus, an R Shiny app for an automated raw data quality control (QC) in metabolomics studies. It produces a comprehensive QC report, which contains interactive plots and tables, summary statistics and detailed explanations. The versatility and limitations of RawHummus are tested with 13 metabolomics/lipidomics datasets and 1 proteomics dataset obtained from 5 different liquid chromatography mass spectrometry platforms. AVAILABILITY AND IMPLEMENTATION RawHummus is released on CRAN repository (https://cran.r-project.org/web/packages/RawHummus), with source code being available on GitHub (https://github.com/YonghuiDong/RawHummus). The web application can be executed locally from the R console using the command 'runGui()'. Alternatively, it can be freely accessed at https://bcdd.shinyapps.io/RawHummus/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yonghui Dong
- Metabolite Medicine Division, BLAVATNIK CENTER for Drug Discovery, Tel Aviv University, Tel Aviv 69978, Israel
| | - Yana Kazachkova
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Meng Gou
- College of Life Science, Liaoning Normal University, Dalian 116081, China
| | - Liat Morgan
- Metabolite Medicine Division, BLAVATNIK CENTER for Drug Discovery, Tel Aviv University, Tel Aviv 69978, Israel
| | - Tal Wachsman
- Metabolite Medicine Division, BLAVATNIK CENTER for Drug Discovery, Tel Aviv University, Tel Aviv 69978, Israel
| | - Ehud Gazit
- Metabolite Medicine Division, BLAVATNIK CENTER for Drug Discovery, Tel Aviv University, Tel Aviv 69978, Israel
| | - Rune Isak Dupont Birkler
- Metabolite Medicine Division, BLAVATNIK CENTER for Drug Discovery, Tel Aviv University, Tel Aviv 69978, Israel
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Gupta P, Verma A, Rai N, Singh AK, Singh SK, Kumar B, Kumar R, Gautam V. Mass Spectrometry-Based Technology and Workflows for Studying the Chemistry of Fungal Endophyte Derived Bioactive Compounds. ACS Chem Biol 2021; 16:2068-2086. [PMID: 34724607 DOI: 10.1021/acschembio.1c00581] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Bioactive compounds have gained substantial attention in research and have conferred great advancements in the industrial and pharmacological fields. Highly diverse fungi and their metabolome serve as a big platform to be explored for their diverse bioactive compounds. Omics tools coupled with bioinformatics, statistical, and well-developed algorithm tools have elucidated immense knowledge about fungal endophyte derived bioactive compounds. Further, these compounds are subjected to chromatography-gas chromatography and liquid chromatography (LC), spectroscopy-nuclear magnetic resonance (NMR), and "soft ionization" technique-matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) based analytical techniques for structural characterization. The mass spectrometry (MS)-based approach, being highly sensitive, reproducible, and reliable, produces quick and high-profile identification. Coupling these techniques with MS has resulted in a descriptive account of the identification and quantification of fungal endophyte derived bioactive compounds. This paper emphasizes the workflows of the above-mentioned techniques, their advancement, and future directions to study the unraveled area of chemistry of fungal endophyte-derived bioactive compounds.
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Affiliation(s)
- Priyamvada Gupta
- Centre of Experimental Medicine and Surgery, Institute of Medical Sciences, Banaras Hindu University, Varanasi-221005, India
| | - Ashish Verma
- Centre of Experimental Medicine and Surgery, Institute of Medical Sciences, Banaras Hindu University, Varanasi-221005, India
| | - Nilesh Rai
- Centre of Experimental Medicine and Surgery, Institute of Medical Sciences, Banaras Hindu University, Varanasi-221005, India
| | - Anurag Kumar Singh
- Centre of Experimental Medicine and Surgery, Institute of Medical Sciences, Banaras Hindu University, Varanasi-221005, India
| | - Santosh Kumar Singh
- Centre of Experimental Medicine and Surgery, Institute of Medical Sciences, Banaras Hindu University, Varanasi-221005, India
| | - Brijesh Kumar
- Department of Pharmacology, Institute of Medical Sciences, Banaras Hindu University, Varanasi-221005, India
| | - Rajiv Kumar
- Centre of Experimental Medicine and Surgery, Institute of Medical Sciences, Banaras Hindu University, Varanasi-221005, India
| | - Vibhav Gautam
- Centre of Experimental Medicine and Surgery, Institute of Medical Sciences, Banaras Hindu University, Varanasi-221005, India
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12
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Naake T, Huber W. MatrixQCvis: shiny-based interactive data quality exploration for omics data. Bioinformatics 2021; 38:1181-1182. [PMID: 34788796 PMCID: PMC8796383 DOI: 10.1093/bioinformatics/btab748] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 10/06/2021] [Accepted: 11/04/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION First-line data quality assessment and exploratory data analysis are integral parts of any data analysis workflow. In high-throughput quantitative omics experiments (e.g. transcriptomics, proteomics and metabolomics), after initial processing, the data are typically presented as a matrix of numbers (feature IDs × samples). Efficient and standardized data quality metrics calculation and visualization are key to track the within-experiment quality of these rectangular data types and to guarantee for high-quality datasets and subsequent biological question-driven inference. RESULTS We present MatrixQCvis, which provides interactive visualization of data quality metrics at the per-sample and per-feature level using R's shiny framework. It provides efficient and standardized ways to analyze data quality of quantitative omics data types that come in a matrix-like format (features IDs × samples). MatrixQCvis builds upon the Bioconductor SummarizedExperiment S4 class and thus facilitates the integration into existing workflows. AVAILABILITY AND IMPLEMENTATION MatrixQCVis is implemented in R. It is available via Bioconductor and released under the GPL v3.0 license. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Wolfgang Huber
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
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Abstract
BACKGROUND Precision medicine, space exploration, drug discovery to characterization of dark chemical space of habitats and organisms, metabolomics takes a centre stage in providing answers to diverse biological, biomedical, and environmental questions. With technological advances in mass-spectrometry and spectroscopy platforms that aid in generation of information rich datasets that are complex big-data, data analytics tend to co-evolve to match the pace of analytical instrumentation. Software tools, resources, databases, and solutions help in harnessing the concealed information in the generated data for eventual translational success. AIM OF THE REVIEW In this review, ~ 85 metabolomics software resources, packages, tools, databases, and other utilities that appeared in 2020 are introduced to the research community. KEY SCIENTIFIC CONCEPTS OF REVIEW In Table 1 the computational dependencies and downloadable links of the tools are provided, and the resources are categorized based on their utility. The review aims to keep the community of metabolomics researchers updated with all the resources developed in 2020 at a collated avenue, in line with efforts form 2015 onwards to help them find these at one place for further referencing and use.
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Mabwi HA, Kim E, Song DG, Yoon HS, Pan CH, Komba E, Ko G, Cha KH. Synthetic gut microbiome: Advances and challenges. Comput Struct Biotechnol J 2020; 19:363-371. [PMID: 33489006 PMCID: PMC7787941 DOI: 10.1016/j.csbj.2020.12.029] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 12/19/2020] [Accepted: 12/20/2020] [Indexed: 12/16/2022] Open
Abstract
An exponential rise in studies regarding the association among human gut microbial communities, human health, and diseases is currently attracting the attention of researchers to focus on human gut microbiome research. However, even with the ever-growing number of studies on the human gut microbiome, translation into improved health is progressing slowly. This hampering is due to the complexities of the human gut microbiome, which is composed of >1,000 species of microorganisms, such as bacteria, archaea, viruses, and fungi. To overcome this complexity, it is necessary to reduce the gut microbiome, which can help simplify experimental variables to an extent, such that they can be deliberately manipulated and controlled. Reconstruction of synthetic or established gut microbial communities would make it easier to understand the structure, stability, and functional activities of the complex microbial community of the human gut. Here, we provide an overview of the developments and challenges of the synthetic human gut microbiome, and propose the incorporation of multi-omics and mathematical methods in a better synthetic gut ecosystem design, for easy translation of microbiome information to therapies.
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Affiliation(s)
- Humphrey A. Mabwi
- KIST Gangneung Institute of Natural Products, Gangneung 25451, Republic of Korea
- SACIDS Foundation for One Health, College of Veterinary Medicine and Biomedical Sciences, Sokoine University of Agriculture, Morogoro 25523, Tanzania
| | - Eunjung Kim
- KIST Gangneung Institute of Natural Products, Gangneung 25451, Republic of Korea
| | - Dae-Geun Song
- KIST Gangneung Institute of Natural Products, Gangneung 25451, Republic of Korea
| | - Hyo Shin Yoon
- KIST Gangneung Institute of Natural Products, Gangneung 25451, Republic of Korea
| | - Cheol-Ho Pan
- KIST Gangneung Institute of Natural Products, Gangneung 25451, Republic of Korea
| | - Erick.V.G. Komba
- SACIDS Foundation for One Health, College of Veterinary Medicine and Biomedical Sciences, Sokoine University of Agriculture, Morogoro 25523, Tanzania
| | - GwangPyo Ko
- Department of Environmental Health Sciences, School of Public Health, Seoul National University, Seoul 08826, Republic of Korea
- Center for Human and Environmental Microbiome, Seoul National University, Seoul 08826, Republic of Korea
- KoBioLabs, Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Kwang Hyun Cha
- KIST Gangneung Institute of Natural Products, Gangneung 25451, Republic of Korea
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Abstract
Introduction: Saliva is an ideal biofluid that can be collected in a noninvasive manner, enabling safe and frequent screening of various diseases. Recent studies have revealed that salivary metabolomics analysis has the potential to detect both oral and systemic cancers. Area covered: We reviewed the technical aspects, as well as applications, of salivary metabolomics for cancer detection. The topics include the effects of preconditioning and the method of sample collection, sample storage, processing, measurement, data analysis, and validation of the results. We also examined the rational relationship between salivary biomarkers and tumors distant from the oral cavity. A strategy to establish standard operating protocols for obtaining reproducible quantification data is also discussed Expert opinion: Salivary metabolomics reflects oral and systematic health status, which potently enables cancer detection. The sensitivity and specificity of each marker and their combinations have been well evaluated, but a validation study is required. Further, the standard operating protocol for each procedure should be established to obtain reproducible data before clinical usage.
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Affiliation(s)
- Masahiro Sugimoto
- Research and Development Centre for Minimally Invasive Therapies, Medical Research Institute, Tokyo Medical University , Tokyo, Japan.,Institute for Advanced Biosciences, Keio University , Yamagata, Japan
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16
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Chen L, Zhong F, Zhu J. Bridging Targeted and Untargeted Mass Spectrometry-Based Metabolomics via Hybrid Approaches. Metabolites 2020; 10:E348. [PMID: 32867165 PMCID: PMC7570162 DOI: 10.3390/metabo10090348] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 08/19/2020] [Accepted: 08/23/2020] [Indexed: 01/11/2023] Open
Abstract
This mini-review aims to discuss the development and applications of mass spectrometry (MS)-based hybrid approaches in metabolomics. Several recently developed hybrid approaches are introduced. Then, the overall workflow, frequently used instruments, data handling strategies, and applications are compared and their pros and cons are summarized. Overall, the improved repeatability and quantitative capability in large-scale MS-based metabolomics studies are demonstrated, in comparison to either targeted or untargeted metabolomics approaches alone. In summary, we expect this review to serve as a first attempt to highlight the development and applications of emerging hybrid approaches in metabolomics, and we believe that hybrid metabolomics approaches could have great potential in many future studies.
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Affiliation(s)
- Li Chen
- Department of Human Sciences, The Ohio State University, Columbus, OH 43210, USA;
- James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| | - Fanyi Zhong
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA;
| | - Jiangjiang Zhu
- Department of Human Sciences, The Ohio State University, Columbus, OH 43210, USA;
- James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
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