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Zhang N, Chen Q, Zhang P, Zhou K, Liu Y, Wang H, Duan S, Xie Y, Yu W, Kong Z, Ren L, Hou W, Yang J, Gong X, Dong L, Fang X, Shi L, Yu Y, Zheng Y. Quartet metabolite reference materials for inter-laboratory proficiency test and data integration of metabolomics profiling. Genome Biol 2024; 25:34. [PMID: 38268000 PMCID: PMC10809448 DOI: 10.1186/s13059-024-03168-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 01/09/2024] [Indexed: 01/26/2024] Open
Abstract
BACKGROUND Various laboratory-developed metabolomic methods lead to big challenges in inter-laboratory comparability and effective integration of diverse datasets. RESULTS As part of the Quartet Project, we establish a publicly available suite of four metabolite reference materials derived from B lymphoblastoid cell lines from a family of parents and monozygotic twin daughters. We generate comprehensive LC-MS-based metabolomic data from the Quartet reference materials using targeted and untargeted strategies in different laboratories. The Quartet multi-sample-based signal-to-noise ratio enables objective assessment of the reliability of intra-batch and cross-batch metabolomics profiling in detecting intrinsic biological differences among the four groups of samples. Significant variations in the reliability of the metabolomics profiling are identified across laboratories. Importantly, ratio-based metabolomics profiling, by scaling the absolute values of a study sample relative to those of a common reference sample, enables cross-laboratory quantitative data integration. Thus, we construct the ratio-based high-confidence reference datasets between two reference samples, providing "ground truth" for inter-laboratory accuracy assessment, which enables objective evaluation of quantitative metabolomics profiling using various instruments and protocols. CONCLUSIONS Our study provides the community with rich resources and best practices for inter-laboratory proficiency tests and data integration, ensuring reliability of large-scale and longitudinal metabolomic studies.
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Affiliation(s)
- Naixin Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qiaochu Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Peipei Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Kejun Zhou
- Human Metabolomics Institute, Inc., Shenzhen, Guangdong, China
| | - Yaqing Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Haiyan Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Shumeng Duan
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yongming Xie
- Shanghai Applied Protein Technology Co. Ltd, Shanghai, China
| | - Wenxiang Yu
- Novogene Bioinformatics Institute, Beijing, China
| | - Ziqing Kong
- Calibra Diagnostics, Hangzhou, Zhejiang, China
| | - Luyao Ren
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Wanwan Hou
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jingcheng Yang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
- Greater Bay Area Institute of Precision Medicine, Guangzhou, Guangdong, China
| | | | | | - Xiang Fang
- National Institute of Metrology, Beijing, China
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
- International Human Phenome Institute, Shanghai, China
| | - Ying Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China.
| | - Yuanting Zheng
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China.
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Jeppesen MJ, Powers R. Multiplatform untargeted metabolomics. Magn Reson Chem 2023; 61:628-653. [PMID: 37005774 PMCID: PMC10948111 DOI: 10.1002/mrc.5350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 03/29/2023] [Accepted: 03/30/2023] [Indexed: 06/19/2023]
Abstract
Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.
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Affiliation(s)
- Micah J. Jeppesen
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
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Ding J, Feng YQ. Mass spectrometry-based metabolomics for clinical study: Recent progresses and applications. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Lippa KA, Aristizabal-Henao JJ, Beger RD, Bowden JA, Broeckling C, Beecher C, Clay Davis W, Dunn WB, Flores R, Goodacre R, Gouveia GJ, Harms AC, Hartung T, Jones CM, Lewis MR, Ntai I, Percy AJ, Raftery D, Schock TB, Sun J, Theodoridis G, Tayyari F, Torta F, Ulmer CZ, Wilson I, Ubhi BK. Reference materials for MS-based untargeted metabolomics and lipidomics: a review by the metabolomics quality assurance and quality control consortium (mQACC). Metabolomics 2022; 18:24. [PMID: 35397018 PMCID: PMC8994740 DOI: 10.1007/s11306-021-01848-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 10/07/2021] [Indexed: 12/17/2022]
Abstract
INTRODUCTION The metabolomics quality assurance and quality control consortium (mQACC) is enabling the identification, development, prioritization, and promotion of suitable reference materials (RMs) to be used in quality assurance (QA) and quality control (QC) for untargeted metabolomics research. OBJECTIVES This review aims to highlight current RMs, and methodologies used within untargeted metabolomics and lipidomics communities to ensure standardization of results obtained from data analysis, interpretation and cross-study, and cross-laboratory comparisons. The essence of the aims is also applicable to other 'omics areas that generate high dimensional data. RESULTS The potential for game-changing biochemical discoveries through mass spectrometry-based (MS) untargeted metabolomics and lipidomics are predicated on the evolution of more confident qualitative (and eventually quantitative) results from research laboratories. RMs are thus critical QC tools to be able to assure standardization, comparability, repeatability and reproducibility for untargeted data analysis, interpretation, to compare data within and across studies and across multiple laboratories. Standard operating procedures (SOPs) that promote, describe and exemplify the use of RMs will also improve QC for the metabolomics and lipidomics communities. CONCLUSIONS The application of RMs described in this review may significantly improve data quality to support metabolomics and lipidomics research. The continued development and deployment of new RMs, together with interlaboratory studies and educational outreach and training, will further promote sound QA practices in the community.
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Affiliation(s)
- Katrice A Lippa
- Chemical Sciences Division, National Institute of Standards and Technology (NIST), Gaithersburg, MD, 20899, USA
| | - Juan J Aristizabal-Henao
- Department of Physiological Sciences, Center for Environmental and Human Toxicology, College of Veterinary Medicine, University of Florida, Gainesville, FL, 32610, USA
- BERG LLC, 500 Old Connecticut Path, Building B, 3rd Floor, Framingham, MA, 01710, USA
| | - Richard D Beger
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration (FDA), Jefferson, AR, 72079, USA
| | - John A Bowden
- Department of Physiological Sciences, Center for Environmental and Human Toxicology, College of Veterinary Medicine, University of Florida, Gainesville, FL, 32610, USA
| | - Corey Broeckling
- Analytical Resources Core: Bioanalysis and Omics Center, Colorado State University, Fort Collins, CO, 80523, USA
| | | | - W Clay Davis
- Chemical Sciences Division, National Institute of Standards and Technology (NIST), Charleston, SC, 29412, USA
| | - Warwick B Dunn
- School of Biosciences, Institute of Metabolism and Systems Research and Phenome Centre Birmingham, University of Birmingham, Birmingham, B15, 2TT, UK
| | - Roberto Flores
- Division of Program Coordination, Planning and Strategic Initiatives, Office of Nutrition Research, Office of the Director, National Institutes of Health (NIH), Bethesda, MD, 20892, USA
| | - Royston Goodacre
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, BioSciences Building, Crown St., Liverpool, L69 7ZB, UK
| | - Gonçalo J Gouveia
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, 30602, USA
| | - Amy C Harms
- Biomedical Metabolomics Facility Leiden, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Thomas Hartung
- Bloomberg School of Public Health, Environmental Health and Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Christina M Jones
- Chemical Sciences Division, National Institute of Standards and Technology (NIST), Gaithersburg, MD, 20899, USA
| | - Matthew R Lewis
- National Phenome Centre, Imperial College London, London, SW7 2AZ, UK
| | - Ioanna Ntai
- Thermo Fisher Scientific, San Jose, CA, 95134, USA
| | - Andrew J Percy
- Cambridge Isotope Laboratories, Inc., Tewksbury, MA, 01876, USA
| | - Dan Raftery
- Northwest Metabolomics Research Center, University of Washington, Seattle, WA, 98109, USA
| | - Tracey B Schock
- Chemical Sciences Division, National Institute of Standards and Technology (NIST), Charleston, SC, 29412, USA
| | - Jinchun Sun
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration (FDA), Jefferson, AR, 72079, USA
| | | | - Fariba Tayyari
- Department of Internal Medicine, University of Iowa, Iowa City, IA, 52242, USA
| | - Federico Torta
- Centre for Life Sciences, National University of Singapore, 28 Medical Drive, Singapore, 117456, Singapore
| | - Candice Z Ulmer
- Centers for Disease Control and Prevention (CDC), Atlanta, GA, 30341, USA
| | - Ian Wilson
- Computational & Systems Medicine, Imperial College, Exhibition Rd, London, SW7 2AZ, UK
| | - Baljit K Ubhi
- MOBILion Systems Inc., 4 Hillman Drive Suite 130, Chadds Ford, PA, 19317, USA.
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Stavarache C, Nicolescu A, Duduianu C, Ailiesei GL, Balan-Porcăraşu M, Cristea M, Macsim AM, Popa O, Stavarache C, Hîrtopeanu A, Barbeş L, Stan R, Iovu H, Deleanu C. A Real-Life Reproducibility Assessment for NMR Metabolomics. Diagnostics (Basel) 2022; 12:diagnostics12030559. [PMID: 35328113 PMCID: PMC8947115 DOI: 10.3390/diagnostics12030559] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 02/02/2022] [Accepted: 02/09/2022] [Indexed: 02/01/2023] Open
Abstract
Nuclear magnetic resonance (NMR) metabolomics is currently popular enough to attract both specialized and non-specialized NMR groups involving both analytical trained personnel and newcomers, including undergraduate students. Recent interlaboratory studies performed by established NMR metabolomics groups demonstrated high reproducibility of the state-of-the-art NMR equipment and SOPs. There is, however, no assessment of NMR reproducibility when mixing both analytical experts and newcomers. An interlaboratory assessment of NMR quantitation reproducibility was performed using two NMR instruments belonging to different laboratories and involving several operators with different backgrounds and metabolomics expertise for the purpose of assessing the limiting factors for data reproducibility in a multipurpose NMR environment. The variability induced by the operator, automatic pipettes, NMR tubes and NMR instruments was evaluated in order to assess the limiting factors for quantitation reproducibility. The results estimated the expected reproducibility data in a real-life multipurpose NMR laboratory to a maximum 4% variability, demonstrating that the current NMR equipment and SOPs may compensate some of the operator-induced variability.
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Affiliation(s)
- Cristina Stavarache
- “C.D. Nenitescu” Centre of Organic Chemistry, Romanian Academy, 060023 Bucharest, Romania; (C.S.); (C.D.); (O.P.); (C.S.); (A.H.)
- Advanced Polymer Materials Group, University Politehnica of Bucharest, 011061 Bucharest, Romania;
| | - Alina Nicolescu
- “C.D. Nenitescu” Centre of Organic Chemistry, Romanian Academy, 060023 Bucharest, Romania; (C.S.); (C.D.); (O.P.); (C.S.); (A.H.)
- “Petru Poni” Institute of Macromolecular Chemistry, Romanian Academy, 700487 Iasi, Romania; (G.L.A.); (M.B.-P.); (M.C.); (A.-M.M.)
- Correspondence: (A.N.); (L.B.); (C.D.)
| | - Cătălin Duduianu
- “C.D. Nenitescu” Centre of Organic Chemistry, Romanian Academy, 060023 Bucharest, Romania; (C.S.); (C.D.); (O.P.); (C.S.); (A.H.)
- Faculty of Applied Chemistry and Material Science, University Politehnica of Bucharest, 011061 Bucharest, Romania;
| | - Gabriela Liliana Ailiesei
- “Petru Poni” Institute of Macromolecular Chemistry, Romanian Academy, 700487 Iasi, Romania; (G.L.A.); (M.B.-P.); (M.C.); (A.-M.M.)
| | - Mihaela Balan-Porcăraşu
- “Petru Poni” Institute of Macromolecular Chemistry, Romanian Academy, 700487 Iasi, Romania; (G.L.A.); (M.B.-P.); (M.C.); (A.-M.M.)
| | - Mihaela Cristea
- “Petru Poni” Institute of Macromolecular Chemistry, Romanian Academy, 700487 Iasi, Romania; (G.L.A.); (M.B.-P.); (M.C.); (A.-M.M.)
| | - Ana-Maria Macsim
- “Petru Poni” Institute of Macromolecular Chemistry, Romanian Academy, 700487 Iasi, Romania; (G.L.A.); (M.B.-P.); (M.C.); (A.-M.M.)
| | - Oana Popa
- “C.D. Nenitescu” Centre of Organic Chemistry, Romanian Academy, 060023 Bucharest, Romania; (C.S.); (C.D.); (O.P.); (C.S.); (A.H.)
- Faculty of Applied Chemistry and Material Science, University Politehnica of Bucharest, 011061 Bucharest, Romania;
| | - Carmen Stavarache
- “C.D. Nenitescu” Centre of Organic Chemistry, Romanian Academy, 060023 Bucharest, Romania; (C.S.); (C.D.); (O.P.); (C.S.); (A.H.)
| | - Anca Hîrtopeanu
- “C.D. Nenitescu” Centre of Organic Chemistry, Romanian Academy, 060023 Bucharest, Romania; (C.S.); (C.D.); (O.P.); (C.S.); (A.H.)
| | - Lucica Barbeş
- Department of Chemistry and Chemical Engineering, “Ovidius” University of Constanta, 900527 Constanta, Romania
- Correspondence: (A.N.); (L.B.); (C.D.)
| | - Raluca Stan
- Faculty of Applied Chemistry and Material Science, University Politehnica of Bucharest, 011061 Bucharest, Romania;
| | - Horia Iovu
- Advanced Polymer Materials Group, University Politehnica of Bucharest, 011061 Bucharest, Romania;
| | - Calin Deleanu
- “C.D. Nenitescu” Centre of Organic Chemistry, Romanian Academy, 060023 Bucharest, Romania; (C.S.); (C.D.); (O.P.); (C.S.); (A.H.)
- “Petru Poni” Institute of Macromolecular Chemistry, Romanian Academy, 700487 Iasi, Romania; (G.L.A.); (M.B.-P.); (M.C.); (A.-M.M.)
- Correspondence: (A.N.); (L.B.); (C.D.)
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Castelli FA, Rosati G, Moguet C, Fuentes C, Marrugo-Ramírez J, Lefebvre T, Volland H, Merkoçi A, Simon S, Fenaille F, Junot C. Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests. Anal Bioanal Chem 2022; 414:759-789. [PMID: 34432105 PMCID: PMC8386160 DOI: 10.1007/s00216-021-03586-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 07/24/2021] [Accepted: 07/27/2021] [Indexed: 12/30/2022]
Abstract
Metabolomics refers to the large-scale detection, quantification, and analysis of small molecules (metabolites) in biological media. Although metabolomics, alone or combined with other omics data, has already demonstrated its relevance for patient stratification in the frame of research projects and clinical studies, much remains to be done to move this approach to the clinical practice. This is especially true in the perspective of being applied to personalized/precision medicine, which aims at stratifying patients according to their risk of developing diseases, and tailoring medical treatments of patients according to individual characteristics in order to improve their efficacy and limit their toxicity. In this review article, we discuss the main challenges linked to analytical chemistry that need to be addressed to foster the implementation of metabolomics in the clinics and the use of the data produced by this approach in personalized medicine. First of all, there are already well-known issues related to untargeted metabolomics workflows at the levels of data production (lack of standardization), metabolite identification (small proportion of annotated features and identified metabolites), and data processing (from automatic detection of features to multi-omic data integration) that hamper the inter-operability and reusability of metabolomics data. Furthermore, the outputs of metabolomics workflows are complex molecular signatures of few tens of metabolites, often with small abundance variations, and obtained with expensive laboratory equipment. It is thus necessary to simplify these molecular signatures so that they can be produced and used in the field. This last point, which is still poorly addressed by the metabolomics community, may be crucial in a near future with the increased availability of molecular signatures of medical relevance and the increased societal demand for participatory medicine.
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Affiliation(s)
- Florence Anne Castelli
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (MTS), Gif-sur-Yvette cedex, 91191, France
- MetaboHUB, Gif-sur-Yvette, France
| | - Giulio Rosati
- Institut Català de Nanociència i Nanotecnologia (ICN2), Edifici ICN2 Campus UAB, 08193 Bellaterra, Barcelona, Spain
| | - Christian Moguet
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (MTS), Gif-sur-Yvette cedex, 91191, France
| | - Celia Fuentes
- Institut Català de Nanociència i Nanotecnologia (ICN2), Edifici ICN2 Campus UAB, 08193 Bellaterra, Barcelona, Spain
| | - Jose Marrugo-Ramírez
- Institut Català de Nanociència i Nanotecnologia (ICN2), Edifici ICN2 Campus UAB, 08193 Bellaterra, Barcelona, Spain
| | - Thibaud Lefebvre
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (MTS), Gif-sur-Yvette cedex, 91191, France
- Centre de Recherche sur l'Inflammation/CRI, Université de Paris, Inserm, Paris, France
- CRMR Porphyrie, Hôpital Louis Mourier, AP-HP Nord - Université de Paris, Colombes, France
| | - Hervé Volland
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (MTS), Gif-sur-Yvette cedex, 91191, France
| | - Arben Merkoçi
- Institut Català de Nanociència i Nanotecnologia (ICN2), Edifici ICN2 Campus UAB, 08193 Bellaterra, Barcelona, Spain
| | - Stéphanie Simon
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (MTS), Gif-sur-Yvette cedex, 91191, France
| | - François Fenaille
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (MTS), Gif-sur-Yvette cedex, 91191, France
- MetaboHUB, Gif-sur-Yvette, France
| | - Christophe Junot
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (MTS), Gif-sur-Yvette cedex, 91191, France.
- MetaboHUB, Gif-sur-Yvette, France.
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Fu J, Zhang Y, Wang Y, Zhang H, Liu J, Tang J, Yang Q, Sun H, Qiu W, Ma Y, Li Z, Zheng M, Zhu F. Optimization of metabolomic data processing using NOREVA. Nat Protoc 2022; 17:129-51. [PMID: 34952956 DOI: 10.1038/s41596-021-00636-9] [Citation(s) in RCA: 97] [Impact Index Per Article: 48.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Accepted: 09/23/2021] [Indexed: 12/12/2022]
Abstract
A typical output of a metabolomic experiment is a peak table corresponding to the intensity of measured signals. Peak table processing, an essential procedure in metabolomics, is characterized by its study dependency and combinatorial diversity. While various methods and tools have been developed to facilitate metabolomic data processing, it is challenging to determine which processing workflow will give good performance for a specific metabolomic study. NOREVA, an out-of-the-box protocol, was therefore developed to meet this challenge. First, the peak table is subjected to many processing workflows that consist of three to five defined calculations in combinatorially determined sequences. Second, the results of each workflow are judged against objective performance criteria. Third, various benchmarks are analyzed to highlight the uniqueness of this newly developed protocol in (1) evaluating the processing performance based on multiple criteria, (2) optimizing data processing by scanning thousands of workflows, and (3) allowing data processing for time-course and multiclass metabolomics. This protocol is implemented in an R package for convenient accessibility and to protect users' data privacy. Preliminary experience in R language would facilitate the usage of this protocol, and the execution time may vary from several minutes to a couple of hours depending on the size of the analyzed data.
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Wasito H, Hermann G, Fitz V, Troyer C, Hann S, Koellensperger G. Yeast-based reference materials for quantitative metabolomics. Anal Bioanal Chem 2021; 414:4359-4368. [PMID: 34642781 PMCID: PMC9142427 DOI: 10.1007/s00216-021-03694-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 09/15/2021] [Accepted: 09/24/2021] [Indexed: 11/28/2022]
Abstract
We introduce a new concept of yeast-derived biological matrix reference material for metabolomics research relying on in vivo synthesis of a defined biomass, standardized extraction followed by absolute quantification with isotope dilution. The yeast Pichia pastoris was grown using full control- and online monitoring fed-batch fermentations followed by fast cold methanol quenching and boiling ethanol extraction. Dried extracts served for the quantification campaign. A metabolite panel of the evolutionarily conserved primary metabolome (amino acids, nucleotides, organic acids, and metabolites of the central carbon metabolism) was absolutely quantified by isotope dilution utilizing uniformly labeled 13C-yeast-based internal standards. The study involved two independent laboratories employing complementary mass spectrometry platforms, namely hydrophilic interaction liquid chromatography-high resolution mass spectrometry (HILIC-HRMS) and gas chromatography-tandem mass spectrometry (GC–MS/MS). Homogeneity, stability tests (on a panel of >70 metabolites over a period of 6 months), and excellent biological repeatability of independent fermentations over a period of 2 years showed the feasibility of producing biological reference materials on demand. The obtained control ranges proved to be fit for purpose as they were either superior or comparable to the established reference materials in the field.
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Affiliation(s)
- Hendri Wasito
- Institute of Analytical Chemistry, Department of Chemistry, University of Natural Resources and Life Sciences (BOKU) Vienna, Muthgasse 18, 1190, Vienna, Austria.,Department of Pharmacy, Faculty of Health Sciences, Jenderal Soedirman University, Dr. Soeparno Street, 53122, Purwokerto, Indonesia
| | - Gerrit Hermann
- Core Facility Mass Spectrometry, University of Natural Resources and Life Sciences (BOKU) Vienna, Muthgasse 11, 1190, Vienna, Austria.,ISOtopic Solutions, Waehringer Str. 38, 1090, Vienna, Austria
| | - Veronika Fitz
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Waehringer Str. 38, 1090, Vienna, Austria
| | - Christina Troyer
- Institute of Analytical Chemistry, Department of Chemistry, University of Natural Resources and Life Sciences (BOKU) Vienna, Muthgasse 18, 1190, Vienna, Austria
| | - Stephan Hann
- Institute of Analytical Chemistry, Department of Chemistry, University of Natural Resources and Life Sciences (BOKU) Vienna, Muthgasse 18, 1190, Vienna, Austria
| | - Gunda Koellensperger
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Waehringer Str. 38, 1090, Vienna, Austria. .,Vienna Metabolomics Center (VIME), University of Vienna, Althanstraße 14, 1090, Vienna, Austria.
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9
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Abstract
The urine metabolome constitutes a rich source of functional information reflecting physiological states that are influenced by distinct conditions and biological stresses, such as responses to drug treatments or disease manifestations. Although global liquid chromatography-mass spectrometry (MS) profiling provides the most comprehensive measurement of metabolites in complex biological samples, annotation remains a challenge, and computational approaches are necessary to translate the molecular composition into biological knowledge. Here, we investigated the use of tandem MS-based enhanced molecular networks (MolNetEnhancer) to improve the metabolite annotation of urine extracts. The samples (n = 10) were analyzed by hydrophilic interaction chromatography-quadrupole time-of-flight mass spectrometry in both electrospray ionization (ESI) modes. Consistent with other common data preprocessing software, the use of Progenesis QI led to the annotation of up to 20 metabolites based on MS2 library searches, showing a high fragmentation score (cosine similarity ≥ 0.7), that is, ∼2% of mass features containing MS2 spectra. Molecular networking based on library matching resulted in the annotation of up to 62 urinary compounds. Using a combination of unsupervised substructure discovery (MS2LDA), the in silico tool network annotation propagation (NAP), and ClassyFire chemical ontology, embedded in a multilayered molecular network by MolNetEnhancer, we were able to expand the chemical characterization to ∼50% of the data set. The integrative approach led to the annotation of 275 compounds at the metabolomics standards initiative (MSI) confidence level 2, as well as 459 and 578 urinary metabolites (MSI level 3) in both negative and positive ESI modes, respectively. The exhaustive MS2-based annotation outperformed similar studies applied to larger cohorts while offering the discovery of metabolites not identified by the MS2 library search. This is the first work that effectively integrates orthogonal annotation methods and MS2-based fragmentation studies to improve metabolite annotation in urine samples.
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Affiliation(s)
- Fausto Carnevale Neto
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington, 850 Republican Street, Seattle, Washington 98109, United States
| | - Daniel Raftery
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington, 850 Republican Street, Seattle, Washington 98109, United States
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, United States
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Rampler E, Abiead YE, Schoeny H, Rusz M, Hildebrand F, Fitz V, Koellensperger G. Recurrent Topics in Mass Spectrometry-Based Metabolomics and Lipidomics-Standardization, Coverage, and Throughput. Anal Chem 2021; 93:519-545. [PMID: 33249827 PMCID: PMC7807424 DOI: 10.1021/acs.analchem.0c04698] [Citation(s) in RCA: 74] [Impact Index Per Article: 24.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Evelyn Rampler
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
- Vienna Metabolomics Center (VIME), University of Vienna, Althanstraße 14, 1090 Vienna, Austria
- University of Vienna, Althanstraße 14, 1090 Vienna, Austria
| | - Yasin El Abiead
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Harald Schoeny
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Mate Rusz
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
- Institute of Inorganic
Chemistry, University of Vienna, Währinger Straße 42, 1090 Vienna, Austria
| | - Felina Hildebrand
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Veronika Fitz
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Gunda Koellensperger
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
- Vienna Metabolomics Center (VIME), University of Vienna, Althanstraße 14, 1090 Vienna, Austria
- University of Vienna, Althanstraße 14, 1090 Vienna, Austria
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