1
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McCullagh J, Probert F. New analytical methods focusing on polar metabolite analysis in mass spectrometry and NMR-based metabolomics. Curr Opin Chem Biol 2024; 80:102466. [PMID: 38772215 DOI: 10.1016/j.cbpa.2024.102466] [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: 11/09/2023] [Revised: 03/19/2024] [Accepted: 04/26/2024] [Indexed: 05/23/2024]
Abstract
Following in the footsteps of genomics and proteomics, metabolomics has revolutionised the way we investigate and understand biological systems. Rapid development in the last 25 years has been driven largely by technical innovations in mass spectrometry and nuclear magnetic resonance spectroscopy. However, despite the modest size of metabolomes relative to proteomes and genomes, methodological capabilities for robust, comprehensive metabolite analysis remain a major challenge. Therefore, development of new methods and techniques remains vital for progress in the field. Here, we review developments in LC-MS, GC-MS and NMR methods in the last few years that have enhanced quantitative and comprehensive metabolome coverage, highlighting the techniques involved, their technical capabilities, relative performance, and potential impact.
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Affiliation(s)
- James McCullagh
- Department of Chemistry, University of Oxford, Mansfield Road, Oxford, OX1 3TA, UK.
| | - Fay Probert
- Department of Chemistry, University of Oxford, Mansfield Road, Oxford, OX1 3TA, UK
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2
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Praud C, Ribay V, Dey A, Charrier B, Mandral J, Farjon J, Dumez JN, Giraudeau P. Optimization of heteronuclear ultrafast 2D NMR for the study of complex mixtures hyperpolarized by dynamic nuclear polarization. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:6209-6219. [PMID: 37942549 DOI: 10.1039/d3ay01681a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
Hyperpolarized 13C NMR at natural abundance, based on dissolution dynamic nuclear polarization (d-DNP), provides rich, sensitive and repeatable 13C NMR fingerprints of complex mixtures. However, the sensitivity enhancement is associated with challenges such as peak overlap and the difficulty to assign hyperpolarized 13C signals. Ultrafast (UF) 2D NMR spectroscopy makes it possible to record heteronuclear 2D maps of d-DNP hyperpolarized samples. Heteronuclear UF 2D NMR can provide correlation peaks that link quaternary carbons and protons through long-range scalar couplings. Here, we report the analytical assessment of an optimized UF long-range HETCOR pulse sequence, applied to the detection of metabolic mixtures at natural abundance and hyperpolarized by d-DNP, based on repeatability and sensitivity considerations. We show that metabolite-dependent limits of quantification in the range of 1-50 mM (in the sample before dissolution) can be achieved, with a repeatability close to 10% and a very good linearity. We provide a detailed comparison of such analytical performance in two different dissolution solvents, D2O and MeOD. The reported pulse sequence appears as an useful analytical tool to facilitate the assignment and integration of metabolite signals in hyperpolarized complex mixtures.
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Affiliation(s)
- Clément Praud
- Nantes Université, CNRS, CEISAM, UMR 6230, F-44000 Nantes, France.
| | - Victor Ribay
- Nantes Université, CNRS, CEISAM, UMR 6230, F-44000 Nantes, France.
| | - Arnab Dey
- Nantes Université, CNRS, CEISAM, UMR 6230, F-44000 Nantes, France.
| | - Benoît Charrier
- Nantes Université, CNRS, CEISAM, UMR 6230, F-44000 Nantes, France.
| | - Joris Mandral
- Nantes Université, CNRS, CEISAM, UMR 6230, F-44000 Nantes, France.
| | - Jonathan Farjon
- Nantes Université, CNRS, CEISAM, UMR 6230, F-44000 Nantes, France.
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3
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Shi C, Zi Y, Huang S, Chen J, Wang X, Zhong J. Development and application of lipidomics for food research. ADVANCES IN FOOD AND NUTRITION RESEARCH 2023; 104:1-42. [PMID: 37236729 DOI: 10.1016/bs.afnr.2022.10.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Lipidomics is an emerging and promising omics derived from metabolomics to comprehensively analyze all of lipid molecules in biological matrices. The purpose of this chapter is to introduce the development and application of lipidomics for food research. First, three aspects of sample preparation are introduced: food sampling, lipid extraction, and transportation and storage. Second, five types of instruments for data acquisition are summarized: direct infusion-mass spectrometry (MS), chromatographic separation-MS, ion mobility-MS, MS imaging, and nuclear magnetic resonance spectroscopy. Third, data acquisition and analysis software are described for the lipidomics software development. Fourth, the application of lipidomics for food research is discussed such as food origin and adulteration analysis, food processing research, food preservation research, and food nutrition and health research. All the contents suggest that lipidomics is a powerful tool for food research based on its ability of lipid component profile analysis.
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Affiliation(s)
- Cuiping Shi
- Xinhua Hospital, Shanghai Institute for Pediatric Research, Shanghai Key Laboratory of Pediatric Gastroenterology and Nutrition, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ye Zi
- Xinhua Hospital, Shanghai Institute for Pediatric Research, Shanghai Key Laboratory of Pediatric Gastroenterology and Nutrition, Shanghai Jiao Tong University School of Medicine, Shanghai, China; National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China
| | - Shudan Huang
- Xinhua Hospital, Shanghai Institute for Pediatric Research, Shanghai Key Laboratory of Pediatric Gastroenterology and Nutrition, Shanghai Jiao Tong University School of Medicine, Shanghai, China; National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China
| | - Jiahui Chen
- Xinhua Hospital, Shanghai Institute for Pediatric Research, Shanghai Key Laboratory of Pediatric Gastroenterology and Nutrition, Shanghai Jiao Tong University School of Medicine, Shanghai, China; National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China
| | - Xichang Wang
- National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China
| | - Jian Zhong
- Xinhua Hospital, Shanghai Institute for Pediatric Research, Shanghai Key Laboratory of Pediatric Gastroenterology and Nutrition, Shanghai Jiao Tong University School of Medicine, Shanghai, China; National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China.
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4
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From targeted methods to metabolomics based strategies to screen for growth promoters misuse in horseracing and livestock: A review. Food Control 2023. [DOI: 10.1016/j.foodcont.2023.109601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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5
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Dumez JN. NMR methods for the analysis of mixtures. Chem Commun (Camb) 2022; 58:13855-13872. [PMID: 36458684 PMCID: PMC9753098 DOI: 10.1039/d2cc05053f] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 10/19/2022] [Indexed: 07/31/2023]
Abstract
NMR spectroscopy is a powerful approach for the analysis of mixtures. Its usefulness arises in large part from the vast landscape of methods, and corresponding pulse sequences, that have been and are being designed to tackle the specific properties of mixtures of small molecules. This feature article describes a selection of methods that aim to address the complexity, the low concentrations, and the changing nature that mixtures can display. These notably include pure-shift and diffusion NMR methods, hyperpolarisation methods, and fast 2D NMR methods such as ultrafast 2D NMR and non-uniform sampling. Examples or applications are also described, in fields such as reaction monitoring and metabolomics, to illustrate the relevance and limitations of different methods.
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6
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Marie B, Gallet A. Fish metabolome from sub-urban lakes of the Paris area (France) and potential influence of noxious metabolites produced by cyanobacteria. CHEMOSPHERE 2022; 296:134035. [PMID: 35183584 DOI: 10.1016/j.chemosphere.2022.134035] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 02/03/2022] [Accepted: 02/15/2022] [Indexed: 06/14/2023]
Abstract
The recent democratization of high-throughput molecular phenotyping allows the rapid expansion of promising untargeted multi-dimensional approaches (e.g. epigenomics, transcriptomics, proteomics, and/or metabolomics). Indeed, these emerging omics tools, processed for ecologically relevant species, may present innovative perspectives for environmental assessments, that could provide early warning of eco(toxico)logical impairments. In a previous pilot study (Sotton et al., Chemosphere 2019), we explore by 1H NMR the bio-indicative potential of metabolomics analyses on the liver of 2 sentinel fish species (Perca fluviatilis and Lepomis gibbosus) collected in 8 water bodies of the peri-urban Paris' area (France). In the present study, we further investigate on the same samples the high potential of high-throughput UHPLC-HRMS/MS analyses. We show that the LC-MS metabolome investigation allows a clear separation of individuals according to the species, but also according to their respective sampling lakes. Interestingly, similar variations of Perca and Lepomis metabolomes occur locally indicating that site-specific environmental constraints drive the metabolome variations which seem to be influenced by the production of noxious molecules by cyanobacterial blooms in certain lakes. Thus, the development of such reliable environmental metabolomics approaches appears to constitute an innovative bio-indicative tool for the assessment of ecological stress, such as toxigenic cyanobacterial blooms, and aim at being further follow up.
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Affiliation(s)
- Benjamin Marie
- 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.
| | - Alison Gallet
- 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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7
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Lhoste C, Lorandel B, Praud C, Marchand A, Mishra R, Dey A, Bernard A, Dumez JN, Giraudeau P. Ultrafast 2D NMR for the analysis of complex mixtures. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2022; 130-131:1-46. [PMID: 36113916 DOI: 10.1016/j.pnmrs.2022.01.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 01/21/2022] [Accepted: 01/23/2022] [Indexed: 06/15/2023]
Abstract
2D NMR is extensively used in many different fields, and its potential for the study of complex biochemical or chemical mixtures has been widely demonstrated. 2D NMR gives the ability to resolve peaks that overlap in 1D spectra, while providing both structural and quantitative information. However, complex mixtures are often analysed in situations where the data acquisition time is a crucial limitation, due to an ongoing chemical reaction or a moving sample from a hyphenated technique, or to the high-throughput requirement associated with large sample collections. Among the great diversity of available fast 2D methods, ultrafast (or single-scan) 2D NMR is probably the most general and versatile approach for complex mixture analysis. Indeed, ultrafast NMR has undergone an impressive number of methodological developments that have helped turn it into an efficient analytical tool, and numerous applications to the analysis of mixtures have been reported. This review first summarizes the main concepts, features and practical limitations of ultrafast 2D NMR, as well as the methodological developments that improved its analytical potential. Then, a detailed description of the main applications of ultrafast 2D NMR to mixture analysis is given. The two major application fields of ultrafast 2D NMR are first covered, i.e., reaction/process monitoring and metabolomics. Then, the potential of ultrafast 2D NMR for the analysis of hyperpolarized mixtures is described, as well as recent developments in oriented media. This review focuses on high-resolution liquid-state 2D experiments (including benchtop NMR) that include at least one spectroscopic dimension (i.e., 2D spectroscopy and DOSY) but does not cover in depth applications without spectral resolution and/or in inhomogeneous fields.
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Affiliation(s)
- Célia Lhoste
- Nantes Université, CNRS, CEISAM UMR 6230, Nantes F-44000, France
| | | | - Clément Praud
- Nantes Université, CNRS, CEISAM UMR 6230, Nantes F-44000, France
| | - Achille Marchand
- Nantes Université, CNRS, CEISAM UMR 6230, Nantes F-44000, France
| | - Rituraj Mishra
- Nantes Université, CNRS, CEISAM UMR 6230, Nantes F-44000, France
| | - Arnab Dey
- Nantes Université, CNRS, CEISAM UMR 6230, Nantes F-44000, France
| | - Aurélie Bernard
- Nantes Université, CNRS, CEISAM UMR 6230, Nantes F-44000, France
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Du X, Yang L, Kong L, Sun Y, Shen K, Cai Y, Sun H, Zhang B, Guo S, Zhang A, Wang X. Metabolomics of various samples advancing biomarker discovery and pathogenesis elucidation for diabetic retinopathy. Front Endocrinol (Lausanne) 2022; 13:1037164. [PMID: 36387907 PMCID: PMC9646596 DOI: 10.3389/fendo.2022.1037164] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 10/10/2022] [Indexed: 11/28/2022] Open
Abstract
Diabetic retinopathy (DR) is a universal microvascular complication of diabetes mellitus (DM), which is the main reason for global sight damage/loss in middle-aged and/or older people. Current clinical analyses, like hemoglobin A1c, possess some importance as prognostic indicators for DR severity, but no effective circulating biomarkers are used for DR in the clinic currently, and studies on the latent pathophysiology remain lacking. Recent developments in omics, especially metabolomics, continue to disclose novel potential biomarkers in several fields, including but not limited to DR. Therefore, based on the overview of metabolomics, we reviewed progress in analytical technology of metabolomics, the prominent roles and the current status of biomarkers in DR, and the update of potential biomarkers in various DR-related samples via metabolomics, including tear as well as vitreous humor, aqueous humor, retina, plasma, serum, cerebrospinal fluid, urine, and feces. In this review, we underscored the in-depth analysis and elucidation of the common biomarkers in different biological samples based on integrated results, namely, alanine, lactate, and glutamine. Alanine may participate in and regulate glucose metabolism through stimulating N-methyl-D-aspartate receptors and subsequently suppressing insulin secretion, which is the potential pathogenesis of DR. Abnormal lactate could cause extensive oxidative stress and neuroinflammation, eventually leading to retinal hypoxia and metabolic dysfunction; on the other hand, high-level lactate may damage the structure and function of the retinal endothelial cell barrier via the G protein-coupled receptor 81. Abnormal glutamine indicates a disturbance of glutamate recycling, which may affect the activation of Müller cells and proliferation via the PPP1CA-YAP-GS-Gln-mTORC1 pathway.
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Affiliation(s)
- Xiaohui Du
- National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Le Yang
- State Key Laboratory of Dampness Syndrome, the Second Affiliated Hospital Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Ling Kong
- National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Ye Sun
- National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Harbin, China
- State Key Laboratory of Dampness Syndrome, the Second Affiliated Hospital Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Kunshuang Shen
- National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Ying Cai
- National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Hui Sun
- National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Harbin, China
- *Correspondence: Hui Sun, ; Xijun Wang,
| | - Bo Zhang
- National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Sifan Guo
- National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Aihua Zhang
- National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Xijun Wang
- National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Harbin, China
- State Key Laboratory of Dampness Syndrome, the Second Affiliated Hospital Guangzhou University of Chinese Medicine, Guangzhou, China
- State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Macau, Macau SAR, China
- *Correspondence: Hui Sun, ; Xijun Wang,
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Letertre MPM, Giraudeau P, de Tullio P. Nuclear Magnetic Resonance Spectroscopy in Clinical Metabolomics and Personalized Medicine: Current Challenges and Perspectives. Front Mol Biosci 2021; 8:698337. [PMID: 34616770 PMCID: PMC8488110 DOI: 10.3389/fmolb.2021.698337] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 08/30/2021] [Indexed: 12/12/2022] Open
Abstract
Personalized medicine is probably the most promising area being developed in modern medicine. This approach attempts to optimize the therapies and the patient care based on the individual patient characteristics. Its success highly depends on the way the characterization of the disease and its evolution, the patient’s classification, its follow-up and the treatment could be optimized. Thus, personalized medicine must combine innovative tools to measure, integrate and model data. Towards this goal, clinical metabolomics appears as ideally suited to obtain relevant information. Indeed, the metabolomics signature brings crucial insight to stratify patients according to their responses to a pathology and/or a treatment, to provide prognostic and diagnostic biomarkers, and to improve therapeutic outcomes. However, the translation of metabolomics from laboratory studies to clinical practice remains a subsequent challenge. Nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are the two key platforms for the measurement of the metabolome. NMR has several advantages and features that are essential in clinical metabolomics. Indeed, NMR spectroscopy is inherently very robust, reproducible, unbiased, quantitative, informative at the structural molecular level, requires little sample preparation and reduced data processing. NMR is also well adapted to the measurement of large cohorts, to multi-sites and to longitudinal studies. This review focus on the potential of NMR in the context of clinical metabolomics and personalized medicine. Starting with the current status of NMR-based metabolomics at the clinical level and highlighting its strengths, weaknesses and challenges, this article also explores how, far from the initial “opposition” or “competition”, NMR and MS have been integrated and have demonstrated a great complementarity, in terms of sample classification and biomarker identification. Finally, a perspective discussion provides insight into the current methodological developments that could significantly raise NMR as a more resolutive, sensitive and accessible tool for clinical applications and point-of-care diagnosis. Thanks to these advances, NMR has a strong potential to join the other analytical tools currently used in clinical settings.
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Affiliation(s)
| | | | - Pascal de Tullio
- Metabolomics Group, Center for Interdisciplinary Research of Medicine (CIRM), Department of Pharmacy, Université de Liège, Liège, Belgique
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10
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Li X, Cai S, He Z, Reilly J, Zeng Z, Strang N, Shu X. Metabolomics in Retinal Diseases: An Update. BIOLOGY 2021; 10:944. [PMID: 34681043 PMCID: PMC8533136 DOI: 10.3390/biology10100944] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 09/16/2021] [Accepted: 09/18/2021] [Indexed: 12/17/2022]
Abstract
Retinal diseases are a leading cause of visual loss and blindness, affecting a significant proportion of the population worldwide and having a detrimental impact on quality of life, with consequent economic burden. The retina is highly metabolically active, and a number of retinal diseases are associated with metabolic dysfunction. To better understand the pathogenesis underlying such retinopathies, new technology has been developed to elucidate the mechanism behind retinal diseases. Metabolomics is a relatively new "omics" technology, which has developed subsequent to genomics, transcriptomics, and proteomics. This new technology can provide qualitative and quantitative information about low-molecular-weight metabolites (M.W. < 1500 Da) in a given biological system, which shed light on the physiological or pathological state of a cell or tissue sample at a particular time point. In this article we provide an extensive review of the application of metabolomics to retinal diseases, with focus on age-related macular degeneration (AMD), diabetic retinopathy (DR), retinopathy of prematurity (ROP), glaucoma, and retinitis pigmentosa (RP).
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Affiliation(s)
- Xing Li
- School of Basic Medical Sciences, Shaoyang University, Shaoyang 422000, China; (X.L.); (Z.H.)
| | - Shichang Cai
- Department of Human Anatomy, School of Medicine, Hunan University of Medicine, Huaihua 418000, China;
| | - Zhiming He
- School of Basic Medical Sciences, Shaoyang University, Shaoyang 422000, China; (X.L.); (Z.H.)
| | - James Reilly
- Department of Biological and Biomedical Sciences, Glasgow Caledonian University, Glasgow G4 0BA, UK;
| | - Zhihong Zeng
- College of Biological and Environmental Engineering, Changsha University, Changsha 410022, China;
| | - Niall Strang
- Department of Vision Science, Glasgow Caledonian University, Glasgow G4 0BA, UK;
| | - Xinhua Shu
- School of Basic Medical Sciences, Shaoyang University, Shaoyang 422000, China; (X.L.); (Z.H.)
- Department of Biological and Biomedical Sciences, Glasgow Caledonian University, Glasgow G4 0BA, UK;
- Department of Vision Science, Glasgow Caledonian University, Glasgow G4 0BA, UK;
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11
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Ouzia S, Royer AL, Pezzolato M, Benedetto A, Biasibetti E, Guitton Y, Le Bizec B, Bozetta E, Dervilly G. Nandrolone and estradiol biomarkers identification in bovine urine applying a liquid chromatography high-resolution mass spectrometry metabolomics approach. Drug Test Anal 2021; 14:879-886. [PMID: 34242491 DOI: 10.1002/dta.3126] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 06/14/2021] [Accepted: 07/07/2021] [Indexed: 11/08/2022]
Abstract
With the aim of specifically investigating patterns associated with three steroid treatments (17β-nandrolone, 17β-estradiol, and 17β-nandrolone + 17β-estradiol) in bovine, an reversed phase liquid chromatography (RPLC)-electrospray ionization (ESI)(+/-)-high-resolution mass spectrometry (HRMS) study was conducted to characterize the urinary profiles of involved animals. Although specific fingerprints with strong differences could be highlighted between urinary metabolite profiles within urine samples collected on control and treated animals, it appeared further that significant discriminations could also be observed between steroid treatments, evidencing thus specific patterns and candidate biomarkers associated to each treatment. An MS-2 structural elucidation step enabled level-1 identification of two biomarkers mainly involved in energy pathways, in relation to skeletal muscle functioning. These results make it possible to envisage a global strategy for the detection of anabolic practices involving steroids, while at the same time providing clues as to the compounds used, which would facilitate the confirmation stage to follow.
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Affiliation(s)
| | | | - Marzia Pezzolato
- Centro di Referenza Nazionale Indagini Biologiche Anabolizzanti Animali - CIBA, Experimental Zooprophylactic Institute of Piedmont, Liguria and Valle d'Aosta, Torino, Italy
| | - Alessandro Benedetto
- Centro di Referenza Nazionale Indagini Biologiche Anabolizzanti Animali - CIBA, Experimental Zooprophylactic Institute of Piedmont, Liguria and Valle d'Aosta, Torino, Italy
| | - Elena Biasibetti
- Centro di Referenza Nazionale Indagini Biologiche Anabolizzanti Animali - CIBA, Experimental Zooprophylactic Institute of Piedmont, Liguria and Valle d'Aosta, Torino, Italy
| | | | | | - Elena Bozetta
- Centro di Referenza Nazionale Indagini Biologiche Anabolizzanti Animali - CIBA, Experimental Zooprophylactic Institute of Piedmont, Liguria and Valle d'Aosta, Torino, Italy
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12
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Marchand J, Guitton Y, Martineau E, Royer AL, Balgoma D, Le Bizec B, Giraudeau P, Dervilly G. Extending the Lipidome Coverage by Combining Different Mass Spectrometric Platforms: An Innovative Strategy to Answer Chemical Food Safety Issues. Foods 2021; 10:foods10061218. [PMID: 34071212 PMCID: PMC8230090 DOI: 10.3390/foods10061218] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 05/20/2021] [Accepted: 05/22/2021] [Indexed: 01/30/2023] Open
Abstract
From a general public health perspective, a strategy combining non-targeted and targeted lipidomics MS-based approaches is proposed to identify disrupted patterns in serum lipidome upon growth promoter treatment in pigs. Evaluating the relative contributions of the platforms involved, the study aims at investigating the potential of innovative analytical approaches to highlight potential chemical food safety threats. Serum samples collected during an animal experiment involving control and treated pigs, whose food had been supplemented with ractopamine, were extracted and characterised using three MS strategies: Non-targeted RP LC-HRMS; the targeted Lipidyzer™ platform (differential ion mobility associated with shotgun lipidomics) and a homemade LC-HRMS triglyceride platform. The strategy enabled highlighting specific lipid profile patterns involving various lipid classes, mainly in relation to cholesterol esters, sphingomyelins, lactosylceramide, phosphatidylcholines and triglycerides. Thanks to the combination of non-targeted and targeted MS approaches, various compartments of the pig serum lipidome could be explored, including commonly characterised lipids (Lipidyzer™), triglyceride isomers (Triglyceride platform) and unique lipid features (non-targeted LC-HRMS). Thanks to their respective characteristics, the complementarity of the three tools could be demonstrated for public health purposes, with enhanced coverage, level of characterization and applicability.
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Affiliation(s)
- Jérémy Marchand
- LABERCA, Oniris, INRAE, 44307 Nantes, France; (J.M.); (Y.G.); (A.-L.R.); (D.B.); (B.L.B.)
- CEISAM UMR 6230, Université de Nantes, CNRS, 44000 Nantes, France;
| | - Yann Guitton
- LABERCA, Oniris, INRAE, 44307 Nantes, France; (J.M.); (Y.G.); (A.-L.R.); (D.B.); (B.L.B.)
| | - Estelle Martineau
- CEISAM UMR 6230, Université de Nantes, CNRS, 44000 Nantes, France;
- SpectroMaîtrise, CAPACITES SAS, 26 Bd Vincent Gâche, 44200 Nantes, France
| | - Anne-Lise Royer
- LABERCA, Oniris, INRAE, 44307 Nantes, France; (J.M.); (Y.G.); (A.-L.R.); (D.B.); (B.L.B.)
| | - David Balgoma
- LABERCA, Oniris, INRAE, 44307 Nantes, France; (J.M.); (Y.G.); (A.-L.R.); (D.B.); (B.L.B.)
| | - Bruno Le Bizec
- LABERCA, Oniris, INRAE, 44307 Nantes, France; (J.M.); (Y.G.); (A.-L.R.); (D.B.); (B.L.B.)
| | - Patrick Giraudeau
- CEISAM UMR 6230, Université de Nantes, CNRS, 44000 Nantes, France;
- Correspondence: (P.G.); (G.D.); Tel.: +33-251125709 (P.G.); +33-240687880 (G.D.)
| | - Gaud Dervilly
- LABERCA, Oniris, INRAE, 44307 Nantes, France; (J.M.); (Y.G.); (A.-L.R.); (D.B.); (B.L.B.)
- Correspondence: (P.G.); (G.D.); Tel.: +33-251125709 (P.G.); +33-240687880 (G.D.)
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Kupče Ē, Frydman L, Webb AG, Yong JRJ, Claridge TDW. Parallel nuclear magnetic resonance spectroscopy. ACTA ACUST UNITED AC 2021. [DOI: 10.1038/s43586-021-00024-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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Joseph D, Sukumaran S, Chandra K, Pudakalakatti SM, Dubey A, Singh A, Atreya HS. Rapid nuclear magnetic resonance data acquisition with improved resolution and sensitivity for high-throughput metabolomic analysis. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2021; 59:300-314. [PMID: 33030750 DOI: 10.1002/mrc.5106] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 09/18/2020] [Accepted: 10/04/2020] [Indexed: 06/11/2023]
Abstract
Nuclear magnetic resonance (NMR)-based metabolomics has witnessed rapid advancements in recent years with the continuous development of new methods to enhance the sensitivity, resolution, and speed of data acquisition. Some of the approaches were earlier used for peptide and protein resonance assignments and have now been adapted to metabolomics. At the same time, new NMR methods involving novel data acquisition techniques, suited particularly for high-throughput analysis in metabolomics, have been developed. In this review, we focus on the different sampling strategies or data acquisition methods that have been developed in our laboratory and other groups to acquire NMR spectra rapidly with high sensitivity and resolution for metabolomics. In particular, we focus on the use of multiple receivers, phase modulation NMR spectroscopy, and fast-pulsing methods for identification and assignments of metabolites.
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Affiliation(s)
- David Joseph
- NMR Research Centre, Indian Institute of Science, Bangalore, 560012, India
| | - Sujeesh Sukumaran
- NMR Research Centre, Indian Institute of Science, Bangalore, 560012, India
| | - Kousik Chandra
- NMR Research Centre, Indian Institute of Science, Bangalore, 560012, India
| | | | - Abhinav Dubey
- NMR Research Centre, Indian Institute of Science, Bangalore, 560012, India
| | - Amrinder Singh
- NMR Research Centre, Indian Institute of Science, Bangalore, 560012, India
| | - Hanudatta S Atreya
- NMR Research Centre, Indian Institute of Science, Bangalore, 560012, India
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15
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Johnson H, Puppa M, van der Merwe M, Tipirneni-Sajja A. CRAFT for NMR lipidomics: Targeting lipid metabolism in leucine-supplemented tumor-bearing mice. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2021; 59:138-146. [PMID: 32876975 DOI: 10.1002/mrc.5092] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 08/19/2020] [Accepted: 08/26/2020] [Indexed: 06/11/2023]
Abstract
Lipid profiling by 1 H-NMR has gained increasing utility in many fields because of its intrinsically quantitative, nondestructive nature and the ability to differentiate small molecules based on their spectral location. Most nuclear magnetic resonance (NMR) techniques for metabolite quantification use frequency domain analysis that involves many user-dependent steps such as phase and baseline correction and quantification by either manual integration or peak fitting. Recently, Bayesian analysis of time-domain NMR data has been shown to reduce operator bias and increase automation in NMR spectroscopy. In this study, we demonstrate the use of CRAFT (complete reduction to amplitude-frequency table), a Bayesian-based approach to automate processing in NMR-based lipidomics using lipid standards and tissue samples of healthy and tumor-bearing mice supplemented with leucine. Complex mixtures of lipid standards were prepared and examined using CRAFT to validate it against conventional Fourier transform (FT)-NMR and derive a fingerprint to be used for analyzing lipid profiles of serum and liver samples. CRAFT and FT-NMR were comparable in accuracy, with CRAFT achieving higher correlation in quantifying several lipid species. Analysis of the serum lipidome of tumor-bearing mice revealed hyperlipidemia and no signs of hepatic triglyceride accumulation compared with that of the healthy group demonstrating that the tumor-bearing mice were in a state of precachexia. Leucine-supplementation was associated with minimal changes in the lipid profile in both tissues. In conclusion, our study demonstrates that the CRAFT method can accurately identify and quantify lipids in complex lipid mixtures and murine tissue samples and, hence, will increase automation and reproducibility in NMR-based lipidomics.
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Affiliation(s)
- Hayden Johnson
- Department of Biomedical Engineering, The University of Memphis, Memphis, TN, USA
| | - Melissa Puppa
- College of Health Sciences, The University of Memphis, Memphis, TN, USA
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16
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Alves MA, Lamichhane S, Dickens A, McGlinchey A, Ribeiro HC, Sen P, Wei F, Hyötyläinen T, Orešič M. Systems biology approaches to study lipidomes in health and disease. Biochim Biophys Acta Mol Cell Biol Lipids 2020; 1866:158857. [PMID: 33278596 DOI: 10.1016/j.bbalip.2020.158857] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 11/13/2020] [Accepted: 11/27/2020] [Indexed: 12/15/2022]
Abstract
Lipids have many important biological roles, such as energy storage sources, structural components of plasma membranes and as intermediates in metabolic and signaling pathways. Lipid metabolism is under tight homeostatic control, exhibiting spatial and dynamic complexity at multiple levels. Consequently, lipid-related disturbances play important roles in the pathogenesis of most of the common diseases. Lipidomics, defined as the study of lipidomes in biological systems, has emerged as a rapidly-growing field. Due to the chemical and functional diversity of lipids, the application of a systems biology approach is essential if one is to address lipid functionality at different physiological levels. In parallel with analytical advances to measure lipids in biological matrices, the field of computational lipidomics has been rapidly advancing, enabling modeling of lipidomes in their pathway, spatial and dynamic contexts. This review focuses on recent progress in systems biology approaches to study lipids in health and disease, with specific emphasis on methodological advances and biomedical applications.
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Affiliation(s)
- Marina Amaral Alves
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku 20520, Finland
| | - Santosh Lamichhane
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku 20520, Finland
| | - Alex Dickens
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku 20520, Finland
| | - Aidan McGlinchey
- School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden
| | | | - Partho Sen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku 20520, Finland; School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden
| | - Fang Wei
- Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan, PR China
| | | | - Matej Orešič
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku 20520, Finland; School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden.
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17
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Letertre MPM, Dervilly G, Giraudeau P. Combined Nuclear Magnetic Resonance Spectroscopy and Mass Spectrometry Approaches for Metabolomics. Anal Chem 2020; 93:500-518. [PMID: 33155816 DOI: 10.1021/acs.analchem.0c04371] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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18
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Martineau E, Dumez JN, Giraudeau P. Fast quantitative 2D NMR for metabolomics and lipidomics: A tutorial. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2020; 58:390-403. [PMID: 32239573 DOI: 10.1002/mrc.4899] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 05/17/2019] [Accepted: 05/28/2019] [Indexed: 06/11/2023]
Abstract
Nuclear magnetic resonance (NMR) is a well-known analytical technique for the analysis of complex mixtures. Its quantitative capability makes it ideally suited to metabolomics or lipidomics studies involving large sample collections of complex biological samples. To overcome the ubiquitous limitation of spectral overcrowding when recording 1D NMR spectra on such samples, the acquisition of 2D NMR spectra allows a better separation between overlapped resonances while yielding accurate quantitative data when appropriate analytical protocols are implemented. Moreover, the experiment duration can be considerably reduced by applying fast acquisition methods. Here, we describe the general workflow to acquire fast quantitative 2D NMR spectra in the "omics" context. It is illustrated on three representative and complementary experiments: UF COSY, ZF-TOCSY with nonuniform sampling, and HSQC with nonuniform sampling. After giving some details and recommendations on how to apply this protocol, its implementation in the case of targeted and untargeted metabolomics/lipidomics studies is described.
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Affiliation(s)
- Estelle Martineau
- CEISAM, CNRS UMR 6230, Université de Nantes, Nantes, France
- SpectroMaitrise, CAPACITES SAS, Nantes, France
| | | | - Patrick Giraudeau
- CEISAM, CNRS UMR 6230, Université de Nantes, Nantes, France
- Institut Universitaire de France, Paris Cedex 5, France
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Narduzzi L, Dervilly G, Marchand A, Audran M, Le Bizec B, Buisson C. Applying metabolomics to detect growth hormone administration in athletes: Proof of concept. Drug Test Anal 2020; 12:887-899. [DOI: 10.1002/dta.2798] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 03/29/2020] [Accepted: 03/29/2020] [Indexed: 02/06/2023]
Affiliation(s)
- Luca Narduzzi
- Laboratoire d’Etude des Résidus et Contaminants dans les Aliments (LABERCA), Oniris INRAE Nantes F‐44307 France
| | - Gaud Dervilly
- Laboratoire d’Etude des Résidus et Contaminants dans les Aliments (LABERCA), Oniris INRAE Nantes F‐44307 France
| | - Alexandre Marchand
- Département des analyses Agence Française de Lutte contre le Dopage (AFLD) Châtenay‐Malabry France
| | - Michel Audran
- Département des analyses Agence Française de Lutte contre le Dopage (AFLD) Châtenay‐Malabry France
| | - Bruno Le Bizec
- Laboratoire d’Etude des Résidus et Contaminants dans les Aliments (LABERCA), Oniris INRAE Nantes F‐44307 France
| | - Corinne Buisson
- Département des analyses Agence Française de Lutte contre le Dopage (AFLD) Châtenay‐Malabry France
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Narduzzi L, Dervilly G, Audran M, Le Bizec B, Buisson C. A role for metabolomics in the antidoping toolbox? Drug Test Anal 2020; 12:677-690. [DOI: 10.1002/dta.2788] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 01/30/2020] [Accepted: 03/05/2020] [Indexed: 12/12/2022]
Affiliation(s)
- Luca Narduzzi
- Laboratoire d’Etude des Résidus et Contaminants dans les Aliments (LABERCA)Oniris, INRAE Nantes France
| | - Gaud Dervilly
- Laboratoire d’Etude des Résidus et Contaminants dans les Aliments (LABERCA)Oniris, INRAE Nantes France
| | - Michel Audran
- Département des analysesAgence Française de Lutte contre le Dopage (AFLD) Châtenay‐Malabry France
| | - Bruno Le Bizec
- Laboratoire d’Etude des Résidus et Contaminants dans les Aliments (LABERCA)Oniris, INRAE Nantes France
| | - Corinne Buisson
- Département des analysesAgence Française de Lutte contre le Dopage (AFLD) Châtenay‐Malabry France
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Féraud B, Martineau E, Leenders J, Govaerts B, de Tullio P, Giraudeau P. Combining rapid 2D NMR experiments with novel pre-processing workflows and MIC quality measures for metabolomics. Metabolomics 2020; 16:42. [PMID: 32189152 DOI: 10.1007/s11306-020-01662-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Accepted: 03/10/2020] [Indexed: 11/28/2022]
Abstract
INTRODUCTION The use of 2D NMR data sources (COSY in this paper) allows to reach general metabolomics results which are at least as good as the results obtained with 1D NMR data, and this with a less advanced and less complex level of pre-processing. But a major issue still exists and can largely slow down a generalized use of 2D data sources in metabolomics: the experiment duration. OBJECTIVE The goal of this paper is to overcome the experiment duration issue in our recently published MIC strategy by considering faster 2D COSY acquisition techniques: a conventional COSY with a reduced number of transients and the use of the Non-Uniform Sampling (NUS) method. These faster alternatives are all submitted to novel 2D pre-processing workflows and to Metabolomic Informative Content analyses. Eventually, results are compared to those obtained with conventional COSY spectra. METHODS To pre-process the 2D data sources, the Global Peak List (GPL) workflow and the Vectorization workflow are used. To compare this data sources and to detect the more informative one(s), MIC (Metabolomic Informative Content) indexes are used, based on clustering and inertia measures of quality. RESULTS Results are discussed according to a multi-factor experimental design (which is unsupervised and based on human urine samples). Descriptive PCA results and MIC indexes are shown, leading to the direct and objective comparison of the different data sets. CONCLUSION In conclusion, it is demonstrated that conventional COSY spectra recorded with only one transient per increment and COSY spectra recorded with 50% of non-uniform sampling provide very similar MIC results as the initial COSY recorded with four transients, but in a much shorter time. Consequently, using techniques like the reduction of the number of transients or NUS can really open the door to a potential high-throughput use of 2D COSY spectra in metabolomics.
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Affiliation(s)
- Baptiste Féraud
- Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA), Université Catholique de Louvain (UCL), Voie du Roman Pays 20, bte L1.04.01, 1348, Louvain-la-Neuve, Belgium.
- Machine Learning Group, Université Catholique de Louvain (UCL), 1348, Louvain-la-Neuve, Belgium.
| | - Estelle Martineau
- Université de Nantes, CNRS, CEISAM UMR 6230, 44000, Nantes, France
- Spectromaîtrise, CAPACITES SAS, 26 Bd Vincent Gâche, 44200, Nantes, France
| | - Justine Leenders
- Metabolomics Group, Center for Interdisciplinary Research on Medicines (CIRM), Université de Liège (ULG), Liège, Belgium
| | - Bernadette Govaerts
- Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA), Université Catholique de Louvain (UCL), Voie du Roman Pays 20, bte L1.04.01, 1348, Louvain-la-Neuve, Belgium
| | - Pascal de Tullio
- Metabolomics Group, Center for Interdisciplinary Research on Medicines (CIRM), Université de Liège (ULG), Liège, Belgium
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22
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Hajjar G, Merchak N, Daniel C, Rizk T, Akoka S, Bejjani J. Improved lipid mixtures profiling by 1H NMR using reference lineshape adjustment and deconvolution techniques. Talanta 2020; 208:120475. [DOI: 10.1016/j.talanta.2019.120475] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 09/19/2019] [Accepted: 10/13/2019] [Indexed: 10/25/2022]
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23
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Giraudeau P. NMR-based metabolomics and fluxomics: developments and future prospects. Analyst 2020; 145:2457-2472. [DOI: 10.1039/d0an00142b] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Recent NMR developments are acting as game changers for metabolomics and fluxomics – a critical and perspective review.
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Wishart DS. NMR metabolomics: A look ahead. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2019; 306:155-161. [PMID: 31377153 DOI: 10.1016/j.jmr.2019.07.013] [Citation(s) in RCA: 108] [Impact Index Per Article: 21.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 04/13/2019] [Accepted: 07/08/2019] [Indexed: 05/24/2023]
Abstract
NMR has been used to perform metabolic studies, metabolic profiling and metabolomics in biofluids and tissues for more than 40 years. This close connection between metabolic measurements and NMR has flourished because of NMR's many unique strengths for characterizing the chemical composition of complex mixtures. However, a number of other technologies, including mass spectrometry, have appeared in the past few years that are encroaching on NMR's dominance in metabolomics and metabolic studies. In this brief review, some of the current strengths and existing limitations of NMR-based metabolomics are highlighted. Additionally, a number of recent advances in NMR hardware, methodology and software are also described and these advancements are used to speculate about where NMR-based metabolomics is going, what needs to be done to make it more popular and how it will evolve in the next 5-10 years.
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Affiliation(s)
- David S Wishart
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada; Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E8, Canada.
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25
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Féraud B, Leenders J, Martineau E, Giraudeau P, Govaerts B, de Tullio P. Two data pre-processing workflows to facilitate the discovery of biomarkers by 2D NMR metabolomics. Metabolomics 2019; 15:63. [PMID: 30993405 DOI: 10.1007/s11306-019-1524-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Accepted: 04/09/2019] [Indexed: 12/21/2022]
Abstract
INTRODUCTION The pre-processing of analytical data in metabolomics must be considered as a whole to allow the construction of a global and unique object for any further simultaneous data analysis or multivariate statistical modelling. For 1D 1H-NMR metabolomics experiments, best practices for data pre-processing are well defined, but not yet for 2D experiments (for instance COSY in this paper). OBJECTIVE By considering the added value of a second dimension, the objective is to propose two workflows dedicated to 2D NMR data handling and preparation (the Global Peak List and Vectorization approaches) and to compare them (with respect to each other and with 1D standards). This will allow to detect which methodology is the best in terms of amount of metabolomic content and to explore the advantages of the selected workflow in distinguishing among treatment groups and identifying relevant biomarkers. Therefore, this paper explores both the necessity of novel 2D pre-processing workflows, the evaluation of their quality and the evaluation of their performance in the subsequent determination of accurate (2D) biomarkers. METHODS To select the more informative data source, MIC (Metabolomic Informative Content) indexes are used, based on clustering and inertia measures of quality. Then, to highlight biomarkers or critical spectral zones, the PLS-DA model is used, along with more advanced sparse algorithms (sPLS and L-sOPLS). RESULTS Results are discussed according to two different experimental designs (one which is unsupervised and based on human urine samples, and the other which is controlled and based on spiked serum media). MIC indexes are shown, leading to the choice of the more relevant workflow to use thereafter. Finally, biomarkers are provided for each case and the predictive power of each candidate model is assessed with cross-validated measures of RMSEP. CONCLUSION In conclusion, it is shown that no solution can be universally the best in every case, but that 2D experiments allow to clearly find relevant cross peak biomarkers even with a poor initial separability between groups. The MIC measures linked with the candidate workflows (2D GPL, 2D vectorization, 1D, and with specific parameters) lead to visualize which data set must be used as a priority to more easily find biomarkers. The diversity of data sources, mainly 1D versus 2D, may often lead to complementary or confirmatory results.
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Affiliation(s)
- Baptiste Féraud
- Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA), Université Catholique de Louvain (UCL), Voie du Roman Pays 20, bte L1.04.01, 1348, Louvain-la-Neuve, Belgium.
- Machine Learning Group, Université Catholique de Louvain (UCL), Louvain-la-Neuve, Belgium.
| | - Justine Leenders
- Center for Interdisciplinary Research on Medicines (CIRM), Metabolomics group, Université de Liège (ULg), Liege, Belgium
| | - Estelle Martineau
- EBSI Team, Chimie et Interdisciplinarité, Synthèse, Analyse, Modélisation (CEISAM), CNRS, UMR 6230, Université de Nantes, Nantes, France
- Spectromaîtrise, CAPACITES SAS, 26 Bd Vincent Gâche, 44200, Nantes, France
| | - Patrick Giraudeau
- EBSI Team, Chimie et Interdisciplinarité, Synthèse, Analyse, Modélisation (CEISAM), CNRS, UMR 6230, Université de Nantes, Nantes, France
- Institut Universitaire de France, 1 rue Descartes, 75005, Paris Cedex 5, France
| | - Bernadette Govaerts
- Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA), Université Catholique de Louvain (UCL), Voie du Roman Pays 20, bte L1.04.01, 1348, Louvain-la-Neuve, Belgium
| | - Pascal de Tullio
- Center for Interdisciplinary Research on Medicines (CIRM), Metabolomics group, Université de Liège (ULg), Liege, Belgium
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Abstract
Nuclear magnetic resonance (NMR) spectroscopy exhibits a great potential for the quantitative analysis of complex biological samples such as those encountered in metabolomics. To overcome the ubiquitous problem of overlapping peaks in 1D NMR spectra of complex mixtures, acquisition of 2D NMR spectra allows a better separation between overlapped resonances while yielding accurate quantitative data when appropriate analytical protocols are implemented. The experiment duration can be made compatible with high-throughput studies on large sample collections by relying on fast acquisition methods. Here, we describe the general metabolomics workflow to acquire fast quantitative 2D NMR data with a focus on targeted or untargeted analyses.
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Khoury S, Canlet C, Lacroix MZ, Berdeaux O, Jouhet J, Bertrand-Michel J. Quantification of Lipids: Model, Reality, and Compromise. Biomolecules 2018; 8:E174. [PMID: 30558107 PMCID: PMC6316828 DOI: 10.3390/biom8040174] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2018] [Revised: 11/30/2018] [Accepted: 12/04/2018] [Indexed: 12/30/2022] Open
Abstract
Lipids are key molecules in various biological processes, thus their quantification is a crucial point in a lot of studies and should be taken into account in lipidomics development. This family is complex and presents a very large diversity of structures, so analyzing and quantifying all this diversity is a real challenge. In this review, the different techniques to analyze lipids will be presented: from nuclear magnetic resonance (NMR) to mass spectrometry (with and without chromatography) including universal detectors. First of all, the state of the art of quantification, with the definitions of terms and protocol standardization, will be presented with quantitative lipidomics in mind, and then technical considerations and limitations of analytical chemistry's tools, such as NMR, mass spectrometry and universal detectors, will be discussed, particularly in terms of absolute quantification.
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Affiliation(s)
- Spiro Khoury
- Centre des Sciences du Goût et de l'Alimentation, AgroSup Dijon, CNRS, INRA, Université Bourgogne Franche-Comté, 9E Boulevard Jeanne d'Arc, F-21000 Dijon, France.
- French LipidomYstes Network, 31000 Toulouse, France.
| | - Cécile Canlet
- Toxalim, Research Centre in Food Toxicology, Université de Toulouse, INRA, ENVT, INP-Purpan, UPS, F-31027 Toulouse, France.
- Axiom Platform, MetaToul-MetaboHUB, National Infrastructure for Metabolomics and Fluxomics, F-31027 Toulouse, France.
| | - Marlène Z Lacroix
- INTHERES, Université de Toulouse, INRA, ENVT, 31432 Toulouse, France.
| | - Olivier Berdeaux
- Centre des Sciences du Goût et de l'Alimentation, AgroSup Dijon, CNRS, INRA, Université Bourgogne Franche-Comté, 9E Boulevard Jeanne d'Arc, F-21000 Dijon, France.
- French LipidomYstes Network, 31000 Toulouse, France.
| | - Juliette Jouhet
- French LipidomYstes Network, 31000 Toulouse, France.
- Laboratoire de Physiologie Cellulaire et Végétale, Université Grenoble Alpes, CNRS, INRA, CEA, 38000 Grenoble, France.
| | - Justine Bertrand-Michel
- French LipidomYstes Network, 31000 Toulouse, France.
- MetaToul-Lipidomic Core Facility, MetaboHUB, I2MC U1048, Inserm, 31432 Toulouse, France.
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