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Kambhampati S, Hubbard AH, Koley S, Gomez JD, Marsolais F, Evans BS, Young JD, Allen DK. SIMPEL: using stable isotopes to elucidate dynamics of context specific metabolism. Commun Biol 2024; 7:172. [PMID: 38347116 PMCID: PMC10861564 DOI: 10.1038/s42003-024-05844-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 01/23/2024] [Indexed: 02/15/2024] Open
Abstract
The capacity to leverage high resolution mass spectrometry (HRMS) with transient isotope labeling experiments is an untapped opportunity to derive insights on context-specific metabolism, that is difficult to assess quantitatively. Tools are needed to comprehensively mine isotopologue information in an automated, high-throughput way without errors. We describe a tool, Stable Isotope-assisted Metabolomics for Pathway Elucidation (SIMPEL), to simplify analysis and interpretation of isotope-enriched HRMS datasets. The efficacy of SIMPEL is demonstrated through examples of central carbon and lipid metabolism. In the first description, a dual-isotope labeling experiment is paired with SIMPEL and isotopically nonstationary metabolic flux analysis (INST-MFA) to resolve fluxes in central metabolism that would be otherwise challenging to quantify. In the second example, SIMPEL was paired with HRMS-based lipidomics data to describe lipid metabolism based on a single labeling experiment. Available as an R package, SIMPEL extends metabolomics analyses to include isotopologue signatures necessary to quantify metabolic flux.
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Affiliation(s)
- Shrikaar Kambhampati
- Donald Danforth Plant Science Center, St. Louis, MO, 63132, USA.
- Jack H. Skirball Center for Chemical Biology and Proteomics, The Salk Institute for Biological Studies, La Jolla, CA, 92037, USA.
| | - Allen H Hubbard
- Donald Danforth Plant Science Center, St. Louis, MO, 63132, USA
| | - Somnath Koley
- Donald Danforth Plant Science Center, St. Louis, MO, 63132, USA
| | - Javier D Gomez
- Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN, 37235, USA
| | - Frédéric Marsolais
- London Research and Development Center, London, ON, N5V 4T3, Canada
- Department of Biology, University of Western Ontario, London, ON, N6A 5B7, Canada
| | - Bradley S Evans
- Donald Danforth Plant Science Center, St. Louis, MO, 63132, USA
| | - Jamey D Young
- Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN, 37235, USA
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37235, USA
| | - Doug K Allen
- Donald Danforth Plant Science Center, St. Louis, MO, 63132, USA.
- Agricultural Research Service, US Department of Agriculture, St. Louis, MO, 63132, USA.
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2
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Martin MR, Bittremieux W, Hassoun S. Molecular structure discovery for untargeted metabolomics using biotransformation rules and global molecular networking. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.04.578795. [PMID: 38370723 PMCID: PMC10871291 DOI: 10.1101/2024.02.04.578795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Although untargeted mass spectrometry-based metabolomics is crucial for understanding life's molecular underpinnings, its effectiveness is hampered by low annotation rates of the generated tandem mass spectra. To address this issue, we introduce a novel data-driven approach, Biotransformation-based Annotation Method (BAM), that leverages molecular structural similarities inherent in biochemical reactions. BAM operates by applying biotransformation rules to known 'anchor' molecules, which exhibit high spectral similarity to unknown spectra, thereby hypothesizing and ranking potential structures for the corresponding 'suspect' molecule. BAM's effectiveness is demonstrated by its success in annotating suspect spectra in a global molecular network comprising hundreds of millions of spectra. BAM was able to assign correct molecular structures to 24.2 % of examined anchor-suspect cases, thereby demonstrating remarkable advancement in metabolite annotation.
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3
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Sheng Y, Wang J, Liu S, Jiang Y. IMN4NPD: An Integrated Molecular Networking Workflow for Natural Product Dereplication. Anal Chem 2024. [PMID: 38324659 DOI: 10.1021/acs.analchem.3c04746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Molecular networking has emerged as a standard approach for natural product (NP) discovery. However, the current pipeline based on molecular networks tends to prioritize larger clusters comprising multiple nodes. To address this issue, we present the integrated molecular networking workflow for NP dereplication (IMN4NPD). This approach not only expedites the rapid dereplication of extensive clusters within the molecular network but also places specific emphasis on self-looped or pairs of nodes, which are often overlooked by the current methods. By amalgamating the outputs from various computational tools, we efficiently dereplicate compounds falling into specific categories and provide annotations for both large cluster nodes and self-looped or pair of nodes within the molecular network. Furthermore, we have incorporated several fundamentally distinct similarity algorithms, namely, Spec2Vec and MS2DeepScore, for constructing the t-SNE network. Through comparison with modified cosine similarity, we have observed that integrating additional diverse spectral similarity measures, the resulting t-SNE network enhanced the ability to dereplicate NPs. Demonstrating the use case of an ethanol extract of Plumula nelumbinis, we illustrate that an integration of multiple computational solutions with IMN4NPD aids the dereplication, especially self-looped nodes, and in the discovery of novel compounds in NPs.
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Affiliation(s)
- Yanghao Sheng
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, China
- Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Jue Wang
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Shao Liu
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, China
- Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Yueping Jiang
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, China
- Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- College of Pharmacy, Changsha Medical University, Changsha 410219, Hunan, China
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4
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Wu G, Wu T, Chen Y, He X, Liu P, Wang D, Geng J, Zhang XX. A comprehensive insight into the transformation pathways and products of fluoxetine and venlafaxine in wastewater based on molecular networking nontarget screening. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 907:167727. [PMID: 37864996 DOI: 10.1016/j.scitotenv.2023.167727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 10/04/2023] [Accepted: 10/08/2023] [Indexed: 10/23/2023]
Abstract
Fluoxetine (FLX) and venlafaxine (VEN) are widely used antidepressant pharmaceuticals and were frequently detected in wastewater. Despite incomplete mineralization during biological wastewater treatment processes has been revealed, little is known about their transformation products (TPs) formed in the biological systems. To fill this gap, batch reactors and molecular networking nontarget screening were employed to identify the TPs and explore the transformation pathways of FLX and VEN in wastewater. On the basis, the concentrations of the TPs in wastewater treatment plants (WWTPs) were determined and their toxicity was predicted. The removal rate constants per unit of biomass of FLX and VEN were up to 0.3192 and 0.1644 L/(gMLSS*d) in batch experiments, respectively. Subsequently, 11 TPs of VEN and 11 TPs of FLX were tentatively identified, among which 9 TPs of FLX and 5 TPs of VEN were newly reported in this study. The proposed transformation pathways provided new insights into the transformation reactions including dehydrogenation, N-formylation and hydroxylation for FLX, and formylation, epoxidation and methylation for VEN. Particularly, N-succinylation and demethylation were the dominant transformation pathways for FLX and VEN during transformation processes. The results of sampling campaigns revealed that the accumulated concentration of TPs were higher than the concentrations of VEN in effluent of WWTPs. In silico prediction results suggested that certain TPs have higher toxicity, persistence and biodegradability than their corresponding parent compounds of FLX and VEN. In addition, VEN-TP264(a) showed higher ecological risks than VEN. This study revealed the transformation processes and fate of FLX and VEN in wastewater, indicating that greater concerns should be exerted on the toxicity detection and control of the TPs of FLX and VEN in the treated wastewater.
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Affiliation(s)
- Gang Wu
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, Jiangsu, China
| | - Tianshu Wu
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, Jiangsu, China
| | - Yiran Chen
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, Jiangsu, China; School of Environment, Hohai University, Nanjing 211100, Jiangsu, China
| | - Xiwei He
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, Jiangsu, China
| | - Peng Liu
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, Jiangsu, China
| | - Depeng Wang
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, Jiangsu, China
| | - Jinju Geng
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, Jiangsu, China; Key Laboratory of the Three Gorges Reservoir Region's Eco-Environment, Ministry of Education, Chongqing University, Chongqing 400044, China
| | - Xu-Xiang Zhang
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, Jiangsu, China.
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5
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Xu Y, Li J, Mao H, You W, Chen J, Xu H, Wu J, Gong Y, Guo L, Liu T, Li W, Xu B, Xie J. Structural annotation, semi-quantification and toxicity prediction of pyrrolizidine alkaloids from functional food: In silico and molecular networking strategy. Food Chem Toxicol 2023; 176:113738. [PMID: 37003509 DOI: 10.1016/j.fct.2023.113738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 03/12/2023] [Accepted: 03/19/2023] [Indexed: 04/03/2023]
Abstract
Many traditional Chinese herbs contain pyrrolizidine alkaloids (PAs), which have been reported to be toxic to livestock and humans. However, the lack of PAs standards makes it difficult to effectively conduct a risk assessment in the varied components of traditional Chinese medicine. It is necessary to propose a suitable strategy to obtain the representative occurrence data of PAs in complex systems. A comprehensive approach for annotating the structures, concentration, and mutagenicity of PAs in three Chinese herbs has been proposed in this article. First, feature-based molecular networking (FBMN) combined with network annotation propagation (NAP) on the Global Natural Products Social Molecular Networking web platform speeds up the process of annotating PAs found in Chinese herbs. Second, a semi-quantitative prediction model based on the quantitative structures and ionization intensity relationship (QSIIR) is used to forecast the amounts of PAs in complex substrates. Finally, the T.E.S.T. was used to provide predictions regarding the mutagenicity of annotated PAs. The goal of this study was to develop a strategy for combining the results of several computer models for PA screening to conduct a comprehensive analysis of PAs, which is a crucial step in risk assessment of unknown PAs in traditional Chinese herbal preparations.
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Affiliation(s)
- Yaping Xu
- State Key Laboratory of Toxicology and Medical Countermeasures, Laboratory of Toxicant Analysis, Institute of Pharmacology and Toxicology, Academy of Military Medical Sciences, Beijing, China
| | - Jie Li
- State Key Laboratory of Toxicology and Medical Countermeasures, Laboratory of Toxicant Analysis, Institute of Pharmacology and Toxicology, Academy of Military Medical Sciences, Beijing, China
| | - Huajian Mao
- Scientific Research Support Center, Academy of Military Medical Sciences, Beijing, China
| | - Wei You
- State Key Laboratory of Toxicology and Medical Countermeasures, Laboratory of Toxicant Analysis, Institute of Pharmacology and Toxicology, Academy of Military Medical Sciences, Beijing, China
| | - Jia Chen
- State Key Laboratory of Toxicology and Medical Countermeasures, Laboratory of Toxicant Analysis, Institute of Pharmacology and Toxicology, Academy of Military Medical Sciences, Beijing, China
| | - Hua Xu
- State Key Laboratory of Toxicology and Medical Countermeasures, Laboratory of Toxicant Analysis, Institute of Pharmacology and Toxicology, Academy of Military Medical Sciences, Beijing, China
| | - Jianfeng Wu
- State Key Laboratory of Toxicology and Medical Countermeasures, Laboratory of Toxicant Analysis, Institute of Pharmacology and Toxicology, Academy of Military Medical Sciences, Beijing, China
| | - Ying Gong
- State Key Laboratory of Toxicology and Medical Countermeasures, Laboratory of Toxicant Analysis, Institute of Pharmacology and Toxicology, Academy of Military Medical Sciences, Beijing, China
| | - Lei Guo
- State Key Laboratory of Toxicology and Medical Countermeasures, Laboratory of Toxicant Analysis, Institute of Pharmacology and Toxicology, Academy of Military Medical Sciences, Beijing, China
| | - Tao Liu
- Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Wuju Li
- Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Bin Xu
- State Key Laboratory of Toxicology and Medical Countermeasures, Laboratory of Toxicant Analysis, Institute of Pharmacology and Toxicology, Academy of Military Medical Sciences, Beijing, China.
| | - Jianwei Xie
- State Key Laboratory of Toxicology and Medical Countermeasures, Laboratory of Toxicant Analysis, Institute of Pharmacology and Toxicology, Academy of Military Medical Sciences, Beijing, China.
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6
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Cai Y, Zhou Z, Zhu ZJ. Advanced analytical and informatic strategies for metabolite annotation in untargeted metabolomics. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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7
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Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking. Nat Commun 2022; 13:6656. [PMID: 36333358 PMCID: PMC9636193 DOI: 10.1038/s41467-022-34537-6] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 10/27/2022] [Indexed: 11/06/2022] Open
Abstract
Liquid chromatography - mass spectrometry (LC-MS) based untargeted metabolomics allows to measure both known and unknown metabolites in the metabolome. However, unknown metabolite annotation is a major challenge in untargeted metabolomics. Here, we develop an approach, namely, knowledge-guided multi-layer network (KGMN), to enable global metabolite annotation from knowns to unknowns in untargeted metabolomics. The KGMN approach integrates three-layer networks, including knowledge-based metabolic reaction network, knowledge-guided MS/MS similarity network, and global peak correlation network. To demonstrate the principle, we apply KGMN in an in vitro enzymatic reaction system and different biological samples, with ~100-300 putative unknowns annotated in each data set. Among them, >80% unknown metabolites are corroborated with in silico MS/MS tools. Finally, we validate 5 metabolites that are absent in common MS/MS libraries through repository mining and synthesis of chemical standards. Together, the KGMN approach enables efficient unknown annotations, and substantially advances the discovery of recurrent unknown metabolites for common biological samples from model organisms, towards deciphering dark matter in untargeted metabolomics.
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8
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Boiko DA, Kozlov KS, Burykina JV, Ilyushenkova VV, Ananikov VP. Fully Automated Unconstrained Analysis of High-Resolution Mass Spectrometry Data with Machine Learning. J Am Chem Soc 2022; 144:14590-14606. [PMID: 35939718 DOI: 10.1021/jacs.2c03631] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Mass spectrometry (MS) is a convenient, highly sensitive, and reliable method for the analysis of complex mixtures, which is vital for materials science, life sciences fields such as metabolomics and proteomics, and mechanistic research in chemistry. Although it is one of the most powerful methods for individual compound detection, complete signal assignment in complex mixtures is still a great challenge. The unconstrained formula-generating algorithm, covering the entire spectra and revealing components, is a "dream tool" for researchers. We present the framework for efficient MS data interpretation, describing a novel approach for detailed analysis based on deisotoping performed by gradient-boosted decision trees and a neural network that generates molecular formulas from the fine isotopic structure, approaching the long-standing inverse spectral problem. The methods were successfully tested on three examples: fragment ion analysis in protein sequencing for proteomics, analysis of the natural samples for life sciences, and study of the cross-coupling catalytic system for chemistry.
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Affiliation(s)
- Daniil A Boiko
- Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky Prospekt 47, Moscow 119991, Russia
| | - Konstantin S Kozlov
- Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky Prospekt 47, Moscow 119991, Russia
| | - Julia V Burykina
- Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky Prospekt 47, Moscow 119991, Russia
| | - Valentina V Ilyushenkova
- Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky Prospekt 47, Moscow 119991, Russia
| | - Valentine P Ananikov
- Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky Prospekt 47, Moscow 119991, Russia
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9
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Amara A, Frainay C, Jourdan F, Naake T, Neumann S, Novoa-del-Toro EM, Salek RM, Salzer L, Scharfenberg S, Witting M. Networks and Graphs Discovery in Metabolomics Data Analysis and Interpretation. Front Mol Biosci 2022; 9:841373. [PMID: 35350714 PMCID: PMC8957799 DOI: 10.3389/fmolb.2022.841373] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 02/18/2022] [Indexed: 01/19/2023] Open
Abstract
Both targeted and untargeted mass spectrometry-based metabolomics approaches are used to understand the metabolic processes taking place in various organisms, from prokaryotes, plants, fungi to animals and humans. Untargeted approaches allow to detect as many metabolites as possible at once, identify unexpected metabolic changes, and characterize novel metabolites in biological samples. However, the identification of metabolites and the biological interpretation of such large and complex datasets remain challenging. One approach to address these challenges is considering that metabolites are connected through informative relationships. Such relationships can be formalized as networks, where the nodes correspond to the metabolites or features (when there is no or only partial identification), and edges connect nodes if the corresponding metabolites are related. Several networks can be built from a single dataset (or a list of metabolites), where each network represents different relationships, such as statistical (correlated metabolites), biochemical (known or putative substrates and products of reactions), or chemical (structural similarities, ontological relations). Once these networks are built, they can subsequently be mined using algorithms from network (or graph) theory to gain insights into metabolism. For instance, we can connect metabolites based on prior knowledge on enzymatic reactions, then provide suggestions for potential metabolite identifications, or detect clusters of co-regulated metabolites. In this review, we first aim at settling a nomenclature and formalism to avoid confusion when referring to different networks used in the field of metabolomics. Then, we present the state of the art of network-based methods for mass spectrometry-based metabolomics data analysis, as well as future developments expected in this area. We cover the use of networks applications using biochemical reactions, mass spectrometry features, chemical structural similarities, and correlations between metabolites. We also describe the application of knowledge networks such as metabolic reaction networks. Finally, we discuss the possibility of combining different networks to analyze and interpret them simultaneously.
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Affiliation(s)
- Adam Amara
- Section of Nutrition and Metabolism, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - Clément Frainay
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Fabien Jourdan
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
- MetaboHUB-Metatoul, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Thomas Naake
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
| | - Steffen Neumann
- Bioinformatics and Scientific Data, Leibniz Institute of Plant Biochemistry, Halle (Saale), Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
| | - Elva María Novoa-del-Toro
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | | | - Liesa Salzer
- Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, Neuherberg, Germany
| | - Sarah Scharfenberg
- Bioinformatics and Scientific Data, Leibniz Institute of Plant Biochemistry, Halle (Saale), Germany
| | - Michael Witting
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, Neuherberg, Germany
- Chair of Analytical Food Chemistry, TUM School of Life Sciences, Freising, Germany
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10
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Yu JS, Nothias LF, Wang M, Kim DH, Dorrestein PC, Kang KB, Yoo HH. Tandem Mass Spectrometry Molecular Networking as a Powerful and Efficient Tool for Drug Metabolism Studies. Anal Chem 2022; 94:1456-1464. [DOI: 10.1021/acs.analchem.1c04925] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Jun Sang Yu
- Institute of Pharmaceutical Science and Technology and College of Pharmacy, Hanyang University, Ansan 15588, Republic of Korea
| | - Louis-Félix Nothias
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California 92093, United States
| | - Mingxun Wang
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California 92093, United States
| | - Dong Hyun Kim
- Department of Pharmacology, Inje University College of Medicine, Busan 47392, Republic of Korea
| | - Pieter C. Dorrestein
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California 92093, United States
| | - Kyo Bin Kang
- Research Institute of Pharmaceutical Sciences, College of Pharmacy, Sookmyung Women’s University, Seoul 04310, Republic of Korea
| | - Hye Hyun Yoo
- Institute of Pharmaceutical Science and Technology and College of Pharmacy, Hanyang University, Ansan 15588, Republic of Korea
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11
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Giera M, Yanes O, Siuzdak G. Metabolite discovery: Biochemistry's scientific driver. Cell Metab 2022; 34:21-34. [PMID: 34986335 PMCID: PMC10131248 DOI: 10.1016/j.cmet.2021.11.005] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 07/26/2021] [Accepted: 11/09/2021] [Indexed: 01/19/2023]
Abstract
Metabolite identification represents a major challenge, and opportunity, for biochemistry. The collective characterization and quantification of metabolites in living organisms, with its many successes, represents a major biochemical knowledgebase and the foundation of metabolism's rebirth in the 21st century; yet, characterizing newly observed metabolites has been an enduring obstacle. Crystallography and NMR spectroscopy have been of extraordinary importance, although their applicability in resolving metabolism's fine structure has been restricted by their intrinsic requirement of sufficient and sufficiently pure materials. Mass spectrometry has been a key technology, especially when coupled with high-performance separation technologies and emerging informatic and database solutions. Even more so, the collective of artificial intelligence technologies are rapidly evolving to help solve the metabolite characterization conundrum. This perspective describes this challenge, how it was historically addressed, and how metabolomics is evolving to address it today and in the future.
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Affiliation(s)
- Martin Giera
- Leiden University Medical Center, Center for Proteomics and Metabolomics, Albinusdreef 2, Leiden 2333 ZA, the Netherlands
| | - Oscar Yanes
- Universitat Rovira i Virgili, Department of Electronic Engineering, IISPV, Tarragona, Spain; CIBER on Diabetes and Associated Metabolic Diseases (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain.
| | - Gary Siuzdak
- Scripps Center for Metabolomics, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA.
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12
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Tian Z, Liu F, Li D, Fernie AR, Chen W. Strategies for structure elucidation of small molecules based on LC–MS/MS data from complex biological samples. Comput Struct Biotechnol J 2022; 20:5085-5097. [PMID: 36187931 PMCID: PMC9489805 DOI: 10.1016/j.csbj.2022.09.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 09/03/2022] [Accepted: 09/03/2022] [Indexed: 11/06/2022] Open
Abstract
LC–MS/MS is a major analytical platform for metabolomics, which has become a recent hotspot in the research fields of life and environmental sciences. By contrast, structure elucidation of small molecules based on LC–MS/MS data remains a major challenge in the chemical and biological interpretation of untargeted metabolomics datasets. In recent years, several strategies for structure elucidation using LC–MS/MS data from complex biological samples have been proposed, these strategies can be simply categorized into two types, one based on structure annotation of mass spectra and for the other on retention time prediction. These strategies have helped many scientists conduct research in metabolite-related fields and are indispensable for the development of future tools. Here, we summarized the characteristics of the current tools and strategies for structure elucidation of small molecules based on LC–MS/MS data, and further discussed the directions and perspectives to improve the power of the tools or strategies for structure elucidation.
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13
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Xu R, Lee J, Chen L, Zhu J. Enhanced detection and annotation of small molecules in metabolomics using molecular-network-oriented parameter optimization. Mol Omics 2021; 17:665-676. [PMID: 34355227 DOI: 10.1039/d1mo00005e] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Metabolomics, especially the large-scale untargeted metabolomics, generates massive amounts of data on a regular basis, which often needs to be filtered, screened, analyzed and annotated via a variety of approaches. Data-dependent-acquisition (DDA) mode including inclusion and exclusion rules for tandem mass spectrometry (MS) is routinely used to perform such analyses. While the parameters of data acquisition are important in these processes, there is a lack of systematic studies on these parameters that can be used in data collection to generate metabolic features for molecular-network (MN) analysis on the Global Natural Products Social Molecular Networking (GNPS) platform. To explore the key parameters that impact the formation and quality of MNs, several data-acquisition parameters for metabolomic studies were proposed in this study. The influences of MS1 resolution, normalized collision energy (NCE), intensity threshold, and exclusion time on GNPS analyses were demonstrated. Moreover, an optimization workflow dedicated to Thermo Scientific QE Hybrid Orbitrap instruments is described, and a comparison of phytochemical contents from two forms of black raspberry extract was performed based on the GNPS MN results. Overall, we expect this study to provide additional thoughts on developing a natural-product-analysis workflow using the GNPS network, and to shed some light on future analyses that utilize similar instrumental setups.
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Affiliation(s)
- Rui Xu
- Human Nutrition Program, The Ohio State University, Columbus, Ohio 43210, USA.
| | - Jisun Lee
- Human Nutrition Program, The Ohio State University, Columbus, Ohio 43210, USA.
| | - Li Chen
- Human Nutrition Program, The Ohio State University, Columbus, Ohio 43210, USA.
| | - Jiangjiang Zhu
- Human Nutrition Program, The Ohio State University, Columbus, Ohio 43210, USA. and James Comprehensive Cancer Center, The Ohio State University, 400 W 12th Ave, Columbus, Ohio 43210, USA
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14
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Metabolite Profiling of Christia vespertilionis Leaf Metabolome via Molecular Network Approach. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11083526] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Christia vespertilionis (L.f.) Bakh. f. is an ornamental plant with unique butterfly-shaped leaves, hence its vernacular name “butterfly wing” or “rerama” in Malay. In Malaysia, the green-leafed variety of this plant has gained popularity in recent years due to testimonial reports by local consumers of its medicinal uses, which include treatment for cancer. Despite these popular uses, there is very limited information on the phytochemistry of the leaf of this plant, presenting a significant gap in the cheminformatics of the plant species. Herein, we report a substantially detailed phytochemical profile of the leaf metabolome of the green-leafed variety of C. vespertilionis, obtained by deploying an untargeted tandem mass spectrometry-based molecular networking approach. The detailed inspection of the molecular network map generated for the leaf metabolome enabled the putative identification of 60 metabolites, comprising 13 phenolic acids, 20 flavonoids, 2 benzyltetrahydroisoquinoline-type alkaloids, 4 hydroxyjasmonic acid derivatives, 2 phenethyl derivatives, 3 monoacylglycerols, 4 fatty acid amides, 2 chlorophyll derivatives, 4 carotenoids, 2 organic acids, 1 nucleoside, and 3 amino acids. Flavonoids are the major class of metabolites that characterize the plant leaves. Employing a mass-targeted isolation approach, two new derivatives of apigenin-6-C-β-glucoside, the major constituents of the plant leaf, were successfully purified and spectroscopically characterized as apigenin-6-C-β-glucoside 4′-O-α-apiofuranoside (28) and apigenin-6-C-β-[(4″,6″-O-dimalonyl)-glucoside] 4′-O-α-apiofuranoside (47). This work provides further information on the chemical space of the plant leaf, which is a prerequisite to further research towards its valorization as a potential phytopharmaceutical product.
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15
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Krettler CA, Thallinger GG. A map of mass spectrometry-based in silico fragmentation prediction and compound identification in metabolomics. Brief Bioinform 2021; 22:6184408. [PMID: 33758925 DOI: 10.1093/bib/bbab073] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 01/29/2021] [Accepted: 02/12/2021] [Indexed: 12/27/2022] Open
Abstract
Metabolomics, the comprehensive study of the metabolome, and lipidomics-the large-scale study of pathways and networks of cellular lipids-are major driving forces in enabling personalized medicine. Complicated and error-prone data analysis still remains a bottleneck, however, especially for identifying novel metabolites. Comparing experimental mass spectra to curated databases containing reference spectra has been the gold standard for identification of compounds, but constructing such databases is a costly and time-demanding task. Many software applications try to circumvent this process by utilizing cutting-edge advances in computational methods-including quantum chemistry and machine learning-and simulate mass spectra by performing theoretical, so called in silico fragmentations of compounds. Other solutions concentrate directly on experimental spectra and try to identify structural properties by investigating reoccurring patterns and the relationships between them. The considerable progress made in the field allows recent approaches to provide valuable clues to expedite annotation of experimental mass spectra. This review sheds light on individual strengths and weaknesses of these tools, and attempts to evaluate them-especially in view of lipidomics, when considering complex mixtures found in biological samples as well as mass spectrometer inter-instrument variability.
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Affiliation(s)
- Christoph A Krettler
- Institute of Biomedical Informatics, Graz University of Technology, Stremayrgasse 16/I, 8010, Graz, Austria.,Omics Center Graz, BioTechMed-Graz, Stiftingtalstrasse 24, 8010, Graz, Austria
| | - Gerhard G Thallinger
- Institute of Biomedical Informatics, Graz University of Technology, Stremayrgasse 16/I, 8010, Graz, Austria.,Omics Center Graz, BioTechMed-Graz, Stiftingtalstrasse 24, 8010, Graz, Austria
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16
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Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships. PLoS Comput Biol 2021; 17:e1008724. [PMID: 33591968 PMCID: PMC7909622 DOI: 10.1371/journal.pcbi.1008724] [Citation(s) in RCA: 74] [Impact Index Per Article: 24.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 02/26/2021] [Accepted: 01/19/2021] [Indexed: 12/18/2022] Open
Abstract
Spectral similarity is used as a proxy for structural similarity in many tandem mass spectrometry (MS/MS) based metabolomics analyses such as library matching and molecular networking. Although weaknesses in the relationship between spectral similarity scores and the true structural similarities have been described, little development of alternative scores has been undertaken. Here, we introduce Spec2Vec, a novel spectral similarity score inspired by a natural language processing algorithm—Word2Vec. Spec2Vec learns fragmental relationships within a large set of spectral data to derive abstract spectral embeddings that can be used to assess spectral similarities. Using data derived from GNPS MS/MS libraries including spectra for nearly 13,000 unique molecules, we show how Spec2Vec scores correlate better with structural similarity than cosine-based scores. We demonstrate the advantages of Spec2Vec in library matching and molecular networking. Spec2Vec is computationally more scalable allowing structural analogue searches in large databases within seconds. Most metabolomics analyses rely upon matching observed fragmentation mass spectra to library spectra for structural annotation or compare spectra with each other through network analysis. As a key part of such processes, scoring functions are used to assess the similarity between pairs of fragment spectra. No studies have so far proposed scores fundamentally different to the popular cosine-based similarity score, despite the fact that its limitations are well understood. We propose a novel spectral similarity score known as Spec2Vec which adapts algorithms from natural language processing to learn relationships between peaks from co-occurrences across large spectra datasets. We find that similarities computed with Spec2Vec i) correlate better to structural similarity than cosine-based scores, ii) subsequently gives better performance in library matching tasks, and iii) is computationally more scalable than cosine-based scores. Given the central place of similarity scoring in key metabolomics analysis tasks such as library matching and spectral networking, we expect Spec2Vec to make a broad impact in all fields that rely upon untargeted metabolomics.
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17
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Desmet S, Saeys Y, Verstaen K, Dauwe R, Kim H, Niculaes C, Fukushima A, Goeminne G, Vanholme R, Ralph J, Boerjan W, Morreel K. Maize specialized metabolome networks reveal organ-preferential mixed glycosides. Comput Struct Biotechnol J 2021; 19:1127-1144. [PMID: 33680356 PMCID: PMC7890092 DOI: 10.1016/j.csbj.2021.01.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 01/06/2021] [Accepted: 01/07/2021] [Indexed: 12/13/2022] Open
Abstract
Despite the scientific and economic importance of maize, little is known about its specialized metabolism. Here, five maize organs were profiled using different reversed-phase liquid chromatography-mass spectrometry methods. The resulting spectral metadata, combined with candidate substrate-product pair (CSPP) networks, allowed the structural characterization of 427 of the 5,420 profiled compounds, including phenylpropanoids, flavonoids, benzoxazinoids, and auxin-related compounds, among others. Only 75 of the 427 compounds were already described in maize. Analysis of the CSPP networks showed that phenylpropanoids are present in all organs, whereas other metabolic classes are rather organ-enriched. Frequently occurring CSPP mass differences often corresponded with glycosyl- and acyltransferase reactions. The interplay of glycosylations and acylations yields a wide variety of mixed glycosides, bearing substructures corresponding to the different biochemical classes. For example, in the tassel, many phenylpropanoid and flavonoid-bearing glycosides also contain auxin-derived moieties. The characterized compounds and mass differences are an important step forward in metabolic pathway discovery and systems biology research. The spectral metadata of the 5,420 compounds is publicly available (DynLib spectral database, https://bioit3.irc.ugent.be/dynlib/).
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Affiliation(s)
- Sandrien Desmet
- Department of Plant Biotechnology and Bioinformatics, Ghent University, B-9052 Gent, Belgium.,Center for Plant Systems Biology, VIB, B-9052 Gent, Belgium
| | - Yvan Saeys
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, B-9052 Gent, Belgium.,Data Mining and Modelling for Biomedicine, Center for Inflammation Research, VIB, B-9052 Gent, Belgium
| | - Kevin Verstaen
- Data Mining and Modelling for Biomedicine, Center for Inflammation Research, VIB, B-9052 Gent, Belgium
| | - Rebecca Dauwe
- Unité de Recherche BIOPI EA3900, Université de Picardie Jules Verne, 80000 Amiens, France
| | - Hoon Kim
- Department of Biochemistry and the U.S. Department of Energy Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, University of Wisconsin, Madison, WI 53726, United States
| | - Claudiu Niculaes
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany
| | - Atsushi Fukushima
- RIKEN Center for Sustainable Resource Science, Yokohama, Kanagawa 230-0045, Japan
| | - Geert Goeminne
- Department of Plant Biotechnology and Bioinformatics, Ghent University, B-9052 Gent, Belgium.,VIB Metabolomics Core Ghent, VIB, B-9052 Gent, Belgium
| | - Ruben Vanholme
- Department of Plant Biotechnology and Bioinformatics, Ghent University, B-9052 Gent, Belgium.,Center for Plant Systems Biology, VIB, B-9052 Gent, Belgium
| | - John Ralph
- Department of Biochemistry and the U.S. Department of Energy Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, University of Wisconsin, Madison, WI 53726, United States
| | - Wout Boerjan
- Department of Plant Biotechnology and Bioinformatics, Ghent University, B-9052 Gent, Belgium.,Center for Plant Systems Biology, VIB, B-9052 Gent, Belgium
| | - Kris Morreel
- Department of Plant Biotechnology and Bioinformatics, Ghent University, B-9052 Gent, Belgium.,Center for Plant Systems Biology, VIB, B-9052 Gent, Belgium
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18
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Dueñas ME, Lee YJ. Single-Cell Metabolomics by Mass Spectrometry Imaging. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1280:69-82. [PMID: 33791975 DOI: 10.1007/978-3-030-51652-9_5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Multicellular organisms achieve their complex living activities through the highly organized metabolic interplay of individual cells and tissues. This complexity has driven the need to spatially resolve metabolomics down to the cellular and subcellular level. Recent technological advances have enabled mass spectrometry imaging (MSI), especially matrix-assisted laser desorption/ionization (MALDI), to become a powerful tool for the visualization of molecular species down to subcellular spatial resolution. In the present chapter, we summarize recent advances in the field of MALDI-MSI, with respect to single-cell level resolution metabolomics directly on tissue. In more detail, we focus on advancements in instrumentation for MSI at single-cell resolution, and the applications towards metabolomic scale imaging. Finally, we discuss new computational tools to aid in metabolite identification, future perspective, and the overall direction that the field of single-cell metabolomics directly on tissue may take in the years to come.
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Affiliation(s)
- Maria Emilia Dueñas
- Department of Chemistry, Iowa State University, Ames, IA, USA.
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK.
| | - Young Jin Lee
- Department of Chemistry, Iowa State University, Ames, IA, USA
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19
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Orešič M, McGlinchey A, Wheelock CE, Hyötyläinen T. Metabolic Signatures of the Exposome-Quantifying the Impact of Exposure to Environmental Chemicals on Human Health. Metabolites 2020; 10:metabo10110454. [PMID: 33182712 PMCID: PMC7698239 DOI: 10.3390/metabo10110454] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 11/08/2020] [Accepted: 11/09/2020] [Indexed: 02/06/2023] Open
Abstract
Human health and well-being are intricately linked to environmental quality. Environmental exposures can have lifelong consequences. In particular, exposures during the vulnerable fetal or early development period can affect structure, physiology and metabolism, causing potential adverse, often permanent, health effects at any point in life. External exposures, such as the “chemical exposome” (exposures to environmental chemicals), affect the host’s metabolism and immune system, which, in turn, mediate the risk of various diseases. Linking such exposures to adverse outcomes, via intermediate phenotypes such as the metabolome, is one of the central themes of exposome research. Much progress has been made in this line of research, including addressing some key challenges such as analytical coverage of the exposome and metabolome, as well as the integration of heterogeneous, multi-omics data. There is strong evidence that chemical exposures have a marked impact on the metabolome, associating with specific disease risks. Herein, we review recent progress in the field of exposome research as related to human health as well as selected metabolic and autoimmune diseases, with specific emphasis on the impacts of chemical exposures on the host metabolome.
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Affiliation(s)
- Matej Orešič
- School of Medical Sciences, Örebro University, SE-701 82 Örebro, Sweden; (M.O.); (A.M.)
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
| | - Aidan McGlinchey
- School of Medical Sciences, Örebro University, SE-701 82 Örebro, Sweden; (M.O.); (A.M.)
| | - Craig E. Wheelock
- Division of Physiological Chemistry II, Department of Medical Biochemistry and Biophysics, Karolinska Institute, SE-171 77 Stockholm, Sweden;
| | - Tuulia Hyötyläinen
- MTM Research Centre, School of Science and Technology, Örebro University, SE-701 82 Örebro, Sweden
- Correspondence:
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20
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Xing S, Hu Y, Yin Z, Liu M, Tang X, Fang M, Huan T. Retrieving and Utilizing Hypothetical Neutral Losses from Tandem Mass Spectra for Spectral Similarity Analysis and Unknown Metabolite Annotation. Anal Chem 2020; 92:14476-14483. [DOI: 10.1021/acs.analchem.0c02521] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Shipei Xing
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1 BC, Canada
| | - Yan Hu
- Department of Computer Sciences, University of British Columbia, 2366 Main Mall, Vancouver, V6T 1Z1 BC, Canada
| | - Zixuan Yin
- Fortinet, 4190 Still Creek Dr, Burnaby, V5C 6C6 BC, Canada
| | - Min Liu
- School of Civil and Environmental Engineering, Nanyang Technological University, 639798, Singapore
| | - Xiaoyu Tang
- Institute of Chemical Biology, Shenzhen Bay Laboratory, Shenzhen 518132, China
| | - Mingliang Fang
- School of Civil and Environmental Engineering, Nanyang Technological University, 639798, Singapore
| | - Tao Huan
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1 BC, Canada
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21
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Senan O, Aguilar-Mogas A, Navarro M, Capellades J, Noon L, Burks D, Yanes O, Guimerà R, Sales-Pardo M. CliqueMS: a computational tool for annotating in-source metabolite ions from LC-MS untargeted metabolomics data based on a coelution similarity network. Bioinformatics 2020; 35:4089-4097. [PMID: 30903689 PMCID: PMC6792096 DOI: 10.1093/bioinformatics/btz207] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Revised: 01/30/2019] [Accepted: 03/21/2019] [Indexed: 11/26/2022] Open
Abstract
Motivation The analysis of biological samples in untargeted metabolomic studies using LC-MS yields tens of thousands of ion signals. Annotating these features is of the utmost importance for answering questions as fundamental as, e.g. how many metabolites are there in a given sample. Results Here, we introduce CliqueMS, a new algorithm for annotating in-source LC-MS1 data. CliqueMS is based on the similarity between coelution profiles and therefore, as opposed to most methods, allows for the annotation of a single spectrum. Furthermore, CliqueMS improves upon the state of the art in several dimensions: (i) it uses a more discriminatory feature similarity metric; (ii) it treats the similarities between features in a transparent way by means of a simple generative model; (iii) it uses a well-grounded maximum likelihood inference approach to group features; (iv) it uses empirical adduct frequencies to identify the parental mass and (v) it deals more flexibly with the identification of the parental mass by proposing and ranking alternative annotations. We validate our approach with simple mixtures of standards and with real complex biological samples. CliqueMS reduces the thousands of features typically obtained in complex samples to hundreds of metabolites, and it is able to correctly annotate more metabolites and adducts from a single spectrum than available tools. Availability and implementation https://CRAN.R-project.org/package=cliqueMS and https://github.com/osenan/cliqueMS. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Oriol Senan
- Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona, Spain
| | - Antoni Aguilar-Mogas
- Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona, Spain
| | - Miriam Navarro
- Department of Electronic Engineering, Metabolomics Platform, IISPV, Universitat Rovira i Virgili, Tarragona, Spain.,CIBER of Diabetes and Associated Metabolic Diseases (CIBERDEM), Madrid, Spain
| | - Jordi Capellades
- Department of Electronic Engineering, Metabolomics Platform, IISPV, Universitat Rovira i Virgili, Tarragona, Spain.,CIBER of Diabetes and Associated Metabolic Diseases (CIBERDEM), Madrid, Spain
| | - Luke Noon
- CIBER of Diabetes and Associated Metabolic Diseases (CIBERDEM), Madrid, Spain.,Centro de Investigación Príncipe Felipe, Valencia, Spain
| | - Deborah Burks
- CIBER of Diabetes and Associated Metabolic Diseases (CIBERDEM), Madrid, Spain.,Centro de Investigación Príncipe Felipe, Valencia, Spain
| | - Oscar Yanes
- Department of Electronic Engineering, Metabolomics Platform, IISPV, Universitat Rovira i Virgili, Tarragona, Spain.,CIBER of Diabetes and Associated Metabolic Diseases (CIBERDEM), Madrid, Spain
| | - Roger Guimerà
- Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona, Spain.,ICREA, Barcelona, Spain
| | - Marta Sales-Pardo
- Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona, Spain
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22
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MESSAR: Automated recommendation of metabolite substructures from tandem mass spectra. PLoS One 2020; 15:e0226770. [PMID: 31945070 PMCID: PMC6964822 DOI: 10.1371/journal.pone.0226770] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Accepted: 12/03/2019] [Indexed: 11/19/2022] Open
Abstract
Despite the increasing importance of non-targeted metabolomics to answer various life science questions, extracting biochemically relevant information from metabolomics spectral data is still an incompletely solved problem. Most computational tools to identify tandem mass spectra focus on a limited set of molecules of interest. However, such tools are typically constrained by the availability of reference spectra or molecular databases, limiting their applicability of generating structural hypotheses for unknown metabolites. In contrast, recent advances in the field illustrate the possibility to expose the underlying biochemistry without relying on metabolite identification, in particular via substructure prediction. We describe an automated method for substructure recommendation motivated by association rule mining. Our framework captures potential relationships between spectral features and substructures learned from public spectral libraries. These associations are used to recommend substructures for any unknown mass spectrum. Our method does not require any predefined metabolite candidates, and therefore it can be used for the hypothesis generation or partial identification of unknown unknowns. The method is called MESSAR (MEtabolite SubStructure Auto-Recommender) and is implemented in a free online web service available at messar.biodatamining.be.
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23
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Zheng SJ, Zheng J, Xiong CF, Xiao HM, Liu SJ, Feng YQ. Hydrogen–Deuterium Scrambling Based on Chemical Isotope Labeling Coupled with LC–MS: Application to Amine Metabolite Identification in Untargeted Metabolomics. Anal Chem 2020; 92:2043-2051. [DOI: 10.1021/acs.analchem.9b04512] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- Shu-Jian Zheng
- Frontier Science Center for Immunology and Metabolism, Department of Chemistry, Wuhan University, Wuhan 430072, People’s Republic of China
| | - Jie Zheng
- Frontier Science Center for Immunology and Metabolism, Department of Chemistry, Wuhan University, Wuhan 430072, People’s Republic of China
| | - Cai-Feng Xiong
- Frontier Science Center for Immunology and Metabolism, Department of Chemistry, Wuhan University, Wuhan 430072, People’s Republic of China
| | - Hua-Ming Xiao
- Frontier Science Center for Immunology and Metabolism, Department of Chemistry, Wuhan University, Wuhan 430072, People’s Republic of China
| | - Shi-Jie Liu
- Frontier Science Center for Immunology and Metabolism, Department of Chemistry, Wuhan University, Wuhan 430072, People’s Republic of China
| | - Yu-Qi Feng
- Frontier Science Center for Immunology and Metabolism, Department of Chemistry, Wuhan University, Wuhan 430072, People’s Republic of China
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24
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Ivanisevic J, Want EJ. From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data. Metabolites 2019; 9:metabo9120308. [PMID: 31861212 PMCID: PMC6950334 DOI: 10.3390/metabo9120308] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 12/09/2019] [Accepted: 12/12/2019] [Indexed: 12/31/2022] Open
Abstract
Untargeted metabolomics (including lipidomics) is a holistic approach to biomarker discovery and mechanistic insights into disease onset and progression, and response to intervention. Each step of the analytical and statistical pipeline is crucial for the generation of high-quality, robust data. Metabolite identification remains the bottleneck in these studies; therefore, confidence in the data produced is paramount in order to maximize the biological output. Here, we outline the key steps of the metabolomics workflow and provide details on important parameters and considerations. Studies should be designed carefully to ensure appropriate statistical power and adequate controls. Subsequent sample handling and preparation should avoid the introduction of bias, which can significantly affect downstream data interpretation. It is not possible to cover the entire metabolome with a single platform; therefore, the analytical platform should reflect the biological sample under investigation and the question(s) under consideration. The large, complex datasets produced need to be pre-processed in order to extract meaningful information. Finally, the most time-consuming steps are metabolite identification, as well as metabolic pathway and network analysis. Here we discuss some widely used tools and the pitfalls of each step of the workflow, with the ultimate aim of guiding the reader towards the most efficient pipeline for their metabolomics studies.
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Affiliation(s)
- Julijana Ivanisevic
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Rue du Bugnon 19, 1005 Lausanne, Switzerland
- Correspondence: (J.I.); (E.J.W.)
| | - Elizabeth J. Want
- Section of Biomolecular Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK
- Correspondence: (J.I.); (E.J.W.)
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25
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González-Riano C, Dudzik D, Garcia A, Gil-de-la-Fuente A, Gradillas A, Godzien J, López-Gonzálvez Á, Rey-Stolle F, Rojo D, Ruperez FJ, Saiz J, Barbas C. Recent Developments along the Analytical Process for Metabolomics Workflows. Anal Chem 2019; 92:203-226. [PMID: 31625723 DOI: 10.1021/acs.analchem.9b04553] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Carolina González-Riano
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain
| | - Danuta Dudzik
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain.,Department of Biopharmaceutics and Pharmacodynamics, Faculty of Pharmacy , Medical University of Gdańsk , 80-210 Gdańsk , Poland
| | - Antonia Garcia
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain
| | - Alberto Gil-de-la-Fuente
- Department of Information Technology, Escuela Politécnica Superior , Universidad San Pablo-CEU , 28003 Madrid , Spain
| | - Ana Gradillas
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain
| | - Joanna Godzien
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain.,Clinical Research Centre , Medical University of Bialystok , 15-089 Bialystok , Poland
| | - Ángeles López-Gonzálvez
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain
| | - Fernanda Rey-Stolle
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain
| | - David Rojo
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain
| | - Francisco J Ruperez
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain
| | - Jorge Saiz
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain
| | - Coral Barbas
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain
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26
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Moumbock AFA, Ntie-Kang F, Akone SH, Li J, Gao M, Telukunta KK, Günther S. An overview of tools, software, and methods for natural product fragment and mass spectral analysis. PHYSICAL SCIENCES REVIEWS 2019. [DOI: 10.1515/psr-2018-0126] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Abstract
One major challenge in natural product (NP) discovery is the determination of the chemical structure of unknown metabolites using automated software tools from either GC–mass spectrometry (MS) or liquid chromatography–MS/MS data only. This chapter reviews the existing spectral libraries and predictive computational tools used in MS-based untargeted metabolomics, which is currently a hot topic in NP structure elucidation. We begin by focusing on spectral databases and the general workflow of MS annotation. We then describe software and tools used in MS, particularly those used to predict fragmentation patterns, mass spectral classifiers, and tools for fragmentation trees analysis. We then round up the chapter by looking at more advanced approaches implemented in tools for competitive fragmentation modeling and quantum chemical approaches.
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27
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Jang I, Lee JU, Lee JM, Kim BH, Moon B, Hong J, Oh HB. LC–MS/MS Software for Screening Unknown Erectile Dysfunction Drugs and Analogues: Artificial Neural Network Classification, Peak-Count Scoring, Simple Similarity Search, and Hybrid Similarity Search Algorithms. Anal Chem 2019; 91:9119-9128. [DOI: 10.1021/acs.analchem.9b01643] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Inae Jang
- Department of Chemistry, Sogang University, Seoul 04107, Republic of Korea
| | - Jae-ung Lee
- Department of Chemistry, Sogang University, Seoul 04107, Republic of Korea
| | - Jung-min Lee
- Department of Chemistry, Sogang University, Seoul 04107, Republic of Korea
| | - Beom Hee Kim
- College of Pharmacy, Kyunghee University, Seoul 02447, Republic of Korea
| | - Bongjin Moon
- Department of Chemistry, Sogang University, Seoul 04107, Republic of Korea
| | - Jongki Hong
- College of Pharmacy, Kyunghee University, Seoul 02447, Republic of Korea
| | - Han Bin Oh
- Department of Chemistry, Sogang University, Seoul 04107, Republic of Korea
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Ji H, Xu Y, Lu H, Zhang Z. Deep MS/MS-Aided Structural-Similarity Scoring for Unknown Metabolite Identification. Anal Chem 2019; 91:5629-5637. [DOI: 10.1021/acs.analchem.8b05405] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Hongchao Ji
- College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, People’s Republic of China
| | - Yamei Xu
- College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, People’s Republic of China
| | - Hongmei Lu
- College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, People’s Republic of China
| | - Zhimin Zhang
- College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, People’s Republic of China
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Frainay C, Schymanski EL, Neumann S, Merlet B, Salek RM, Jourdan F, Yanes O. Mind the Gap: Mapping Mass Spectral Databases in Genome-Scale Metabolic Networks Reveals Poorly Covered Areas. Metabolites 2018; 8:E51. [PMID: 30223552 PMCID: PMC6161000 DOI: 10.3390/metabo8030051] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Revised: 09/06/2018] [Accepted: 09/07/2018] [Indexed: 11/23/2022] Open
Abstract
The use of mass spectrometry-based metabolomics to study human, plant and microbial biochemistry and their interactions with the environment largely depends on the ability to annotate metabolite structures by matching mass spectral features of the measured metabolites to curated spectra of reference standards. While reference databases for metabolomics now provide information for hundreds of thousands of compounds, barely 5% of these known small molecules have experimental data from pure standards. Remarkably, it is still unknown how well existing mass spectral libraries cover the biochemical landscape of prokaryotic and eukaryotic organisms. To address this issue, we have investigated the coverage of 38 genome-scale metabolic networks by public and commercial mass spectral databases, and found that on average only 40% of nodes in metabolic networks could be mapped by mass spectral information from standards. Next, we deciphered computationally which parts of the human metabolic network are poorly covered by mass spectral libraries, revealing gaps in the eicosanoids, vitamins and bile acid metabolism. Finally, our network topology analysis based on the betweenness centrality of metabolites revealed the top 20 most important metabolites that, if added to MS databases, may facilitate human metabolome characterization in the future.
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Affiliation(s)
- Clément Frainay
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRA, ENVT, INP-Purpan, UPS, 31555 Toulouse, France.
| | - Emma L Schymanski
- Eawag: Swiss Federal Institute for Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland.
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg.
| | - Steffen Neumann
- Leibniz Institute of Plant Biochemistry, Department of Stress and Developmental Biology, Weinberg 3, 06120 Halle, Germany.
- German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig Deutscher Platz 5e, 04103 Leipzig, Germany.
| | - Benjamin Merlet
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRA, ENVT, INP-Purpan, UPS, 31555 Toulouse, France.
| | - Reza M Salek
- The International Agency for Research on Cancer (IARC), 150 Cours Albert Thomas, 69372 Lyon CEDEX 08, France.
| | - Fabien Jourdan
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRA, ENVT, INP-Purpan, UPS, 31555 Toulouse, France.
| | - Oscar Yanes
- Metabolomics Platform, IISPV, Department of Electronic Engineering, Universitat Rovira i Virgili, Avinguda Paisos Catalans 26, 43007 Tarragona, Spain.
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Monforte de Lemos 3-5, 28029 Madrid, Spain.
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30
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Fu Y, Zhang Y, Zhou Z, Lu X, Lin X, Zhao C, Xu G. Screening and Determination of Potential Risk Substances Based on Liquid Chromatography–High-Resolution Mass Spectrometry. Anal Chem 2018; 90:8454-8461. [DOI: 10.1021/acs.analchem.8b01153] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Yanqing Fu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yanhui Zhang
- School of Computer Science & Technology, Dalian University of Technology, Dalian 116023, China
| | - Zhihui Zhou
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xin Lu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaohui Lin
- School of Computer Science & Technology, Dalian University of Technology, Dalian 116023, China
| | - Chunxia Zhao
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Guowang Xu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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Blaženović I, Kind T, Ji J, Fiehn O. Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics. Metabolites 2018; 8:E31. [PMID: 29748461 PMCID: PMC6027441 DOI: 10.3390/metabo8020031] [Citation(s) in RCA: 373] [Impact Index Per Article: 62.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2018] [Revised: 04/26/2018] [Accepted: 05/06/2018] [Indexed: 01/17/2023] Open
Abstract
The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included.
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Affiliation(s)
- Ivana Blaženović
- NIH West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, CA 95616, USA.
| | - Tobias Kind
- NIH West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, CA 95616, USA.
| | - Jian Ji
- State Key Laboratory of Food Science and Technology, School of Food Science of Jiangnan University, School of Food Science Synergetic Innovation Center of Food Safety and Nutrition, Wuxi 214122, China.
| | - Oliver Fiehn
- NIH West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, CA 95616, USA.
- Department of Biochemistry, Faculty of Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
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32
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Peters K, Worrich A, Weinhold A, Alka O, Balcke G, Birkemeyer C, Bruelheide H, Calf OW, Dietz S, Dührkop K, Gaquerel E, Heinig U, Kücklich M, Macel M, Müller C, Poeschl Y, Pohnert G, Ristok C, Rodríguez VM, Ruttkies C, Schuman M, Schweiger R, Shahaf N, Steinbeck C, Tortosa M, Treutler H, Ueberschaar N, Velasco P, Weiß BM, Widdig A, Neumann S, Dam NMV. Current Challenges in Plant Eco-Metabolomics. Int J Mol Sci 2018; 19:E1385. [PMID: 29734799 PMCID: PMC5983679 DOI: 10.3390/ijms19051385] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Revised: 04/24/2018] [Accepted: 04/25/2018] [Indexed: 12/22/2022] Open
Abstract
The relatively new research discipline of Eco-Metabolomics is the application of metabolomics techniques to ecology with the aim to characterise biochemical interactions of organisms across different spatial and temporal scales. Metabolomics is an untargeted biochemical approach to measure many thousands of metabolites in different species, including plants and animals. Changes in metabolite concentrations can provide mechanistic evidence for biochemical processes that are relevant at ecological scales. These include physiological, phenotypic and morphological responses of plants and communities to environmental changes and also interactions with other organisms. Traditionally, research in biochemistry and ecology comes from two different directions and is performed at distinct spatiotemporal scales. Biochemical studies most often focus on intrinsic processes in individuals at physiological and cellular scales. Generally, they take a bottom-up approach scaling up cellular processes from spatiotemporally fine to coarser scales. Ecological studies usually focus on extrinsic processes acting upon organisms at population and community scales and typically study top-down and bottom-up processes in combination. Eco-Metabolomics is a transdisciplinary research discipline that links biochemistry and ecology and connects the distinct spatiotemporal scales. In this review, we focus on approaches to study chemical and biochemical interactions of plants at various ecological levels, mainly plant⁻organismal interactions, and discuss related examples from other domains. We present recent developments and highlight advancements in Eco-Metabolomics over the last decade from various angles. We further address the five key challenges: (1) complex experimental designs and large variation of metabolite profiles; (2) feature extraction; (3) metabolite identification; (4) statistical analyses; and (5) bioinformatics software tools and workflows. The presented solutions to these challenges will advance connecting the distinct spatiotemporal scales and bridging biochemistry and ecology.
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Affiliation(s)
- Kristian Peters
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany.
| | - Anja Worrich
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
- Institute of Biodiversity, Friedrich Schiller University Jena, Dornburger-Str. 159, 07743 Jena, Germany.
- UFZ-Helmholtz-Centre for Environmental Research, Department Environmental Microbiology, Permoserstraße 15, 04318 Leipzig, Germany.
| | - Alexander Weinhold
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
- Institute of Biodiversity, Friedrich Schiller University Jena, Dornburger-Str. 159, 07743 Jena, Germany.
| | - Oliver Alka
- Applied Bioinformatics Group, Center for Bioinformatics, University of Tübingen, Sand 14, 72076 Tübingen, Germany.
| | - Gerd Balcke
- Leibniz Institute of Plant Biochemistry, Cell and Metabolic Biology, Weinberg 3, 06120 Halle (Saale), Germany.
| | - Claudia Birkemeyer
- Institute of Analytical Chemistry, University of Leipzig, Linnéstr. 3, 04103 Leipzig, Germany.
| | - Helge Bruelheide
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
- Institute of Biology/Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg, Am Kirchtor 1, 06108 Halle (Saale), Germany.
| | - Onno W Calf
- Molecular Interaction Ecology, Institute for Water and Wetland Research (IWWR), Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands.
| | - Sophie Dietz
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany.
| | - Kai Dührkop
- Department of Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany.
| | - Emmanuel Gaquerel
- Centre for Organismal Studies, Heidelberg University, Im Neuenheimer Feld 360, 69120 Heidelberg, Germany.
| | - Uwe Heinig
- Weizmann Institute of Science, Faculty of Biochemistry, Department of Plant Sciences, 234 Herzl St., P.O. Box 26, Rehovot 7610001, Israel.
| | - Marlen Kücklich
- Institute of Biology, University of Leipzig, Talstraße 33, 04109 Leipzig, Germany.
| | - Mirka Macel
- Molecular Interaction Ecology, Institute for Water and Wetland Research (IWWR), Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands.
| | - Caroline Müller
- Chemical Ecology, Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany.
| | - Yvonne Poeschl
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
- Institute of Informatics, Martin Luther University Halle-Wittenberg, Von-Seckendorff-Platz 1, 06120 Halle (Saale), Germany.
| | - Georg Pohnert
- Institute of Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessingstr. 8, 07743 Jena, Germany.
| | - Christian Ristok
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
| | - Victor Manuel Rodríguez
- Group of Genetics, Breeding and Biochemistry of Brassica, Misión Biológica de Galicia (CSIC), Apartado 28, 36080 Pontevedra, Spain.
| | - Christoph Ruttkies
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany.
| | - Meredith Schuman
- Department of Molecular Ecology, Max Planck Institute for Chemical Ecology, Hans-Knöll-Straße 8, 07745 Jena, Germany.
| | - Rabea Schweiger
- Chemical Ecology, Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany.
| | - Nir Shahaf
- Weizmann Institute of Science, Faculty of Biochemistry, Department of Plant Sciences, 234 Herzl St., P.O. Box 26, Rehovot 7610001, Israel.
| | - Christoph Steinbeck
- Institute of Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessingstr. 8, 07743 Jena, Germany.
| | - Maria Tortosa
- Group of Genetics, Breeding and Biochemistry of Brassica, Misión Biológica de Galicia (CSIC), Apartado 28, 36080 Pontevedra, Spain.
| | - Hendrik Treutler
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany.
| | - Nico Ueberschaar
- Institute of Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessingstr. 8, 07743 Jena, Germany.
| | - Pablo Velasco
- Group of Genetics, Breeding and Biochemistry of Brassica, Misión Biológica de Galicia (CSIC), Apartado 28, 36080 Pontevedra, Spain.
| | - Brigitte M Weiß
- Institute of Biology, University of Leipzig, Talstraße 33, 04109 Leipzig, Germany.
| | - Anja Widdig
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
- Institute of Biology, University of Leipzig, Talstraße 33, 04109 Leipzig, Germany.
- Research Group of Primate Kin Selection, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103 Leipzig, Germany.
| | - Steffen Neumann
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany.
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
| | - Nicole M van Dam
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
- Institute of Biodiversity, Friedrich Schiller University Jena, Dornburger-Str. 159, 07743 Jena, Germany.
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Misra BB. New tools and resources in metabolomics: 2016-2017. Electrophoresis 2018; 39:909-923. [PMID: 29292835 DOI: 10.1002/elps.201700441] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2017] [Revised: 12/17/2017] [Accepted: 12/18/2017] [Indexed: 01/07/2023]
Abstract
Rapid advances in mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based platforms for metabolomics have led to an upsurge of data every single year. Newer high-throughput platforms, hyphenated technologies, miniaturization, and tool kits in data acquisition efforts in metabolomics have led to additional challenges in metabolomics data pre-processing, analysis, interpretation, and integration. Thanks to the informatics, statistics, and computational community, new resources continue to develop for metabolomics researchers. The purpose of this review is to provide a summary of the metabolomics tools, software, and databases that were developed or improved during 2016-2017, thus, enabling readers, developers, and researchers access to a succinct but thorough list of resources for further improvisation, implementation, and application in due course of time.
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Affiliation(s)
- Biswapriya B Misra
- Department of Internal Medicine, Section of Molecular Medicine, Medical Center Boulevard, Winston-Salem, NC, USA
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Domingo-Almenara X, Montenegro-Burke JR, Benton HP, Siuzdak G. Annotation: A Computational Solution for Streamlining Metabolomics Analysis. Anal Chem 2018; 90:480-489. [PMID: 29039932 PMCID: PMC5750104 DOI: 10.1021/acs.analchem.7b03929] [Citation(s) in RCA: 105] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Metabolite identification is still considered an imposing bottleneck in liquid chromatography mass spectrometry (LC/MS) untargeted metabolomics. The identification workflow usually begins with detecting relevant LC/MS peaks via peak-picking algorithms and retrieving putative identities based on accurate mass searching. However, accurate mass search alone provides poor evidence for metabolite identification. For this reason, computational annotation is used to reveal the underlying metabolites monoisotopic masses, improving putative identification in addition to confirmation with tandem mass spectrometry. This review examines LC/MS data from a computational and analytical perspective, focusing on the occurrence of neutral losses and in-source fragments, to understand the challenges in computational annotation methodologies. Herein, we examine the state-of-the-art strategies for computational annotation including: (i) peak grouping or full scan (MS1) pseudo-spectra extraction, i.e., clustering all mass spectral signals stemming from each metabolite; (ii) annotation using ion adduction and mass distance among ion peaks; (iii) incorporation of biological knowledge such as biotransformations or pathways; (iv) tandem MS data; and (v) metabolite retention time calibration, usually achieved by prediction from molecular descriptors. Advantages and pitfalls of each of these strategies are discussed, as well as expected future trends in computational annotation.
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Affiliation(s)
- Xavier Domingo-Almenara
- Scripps Center for Metabolomics, The Scripps Research Institute , 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | - J Rafael Montenegro-Burke
- Scripps Center for Metabolomics, The Scripps Research Institute , 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | - H Paul Benton
- Scripps Center for Metabolomics, The Scripps Research Institute , 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | - Gary Siuzdak
- Scripps Center for Metabolomics, The Scripps Research Institute , 10550 North Torrey Pines Road, La Jolla, California 92037, United States
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Godzien J, Gil de la Fuente A, Otero A, Barbas C. Metabolite Annotation and Identification. COMPREHENSIVE ANALYTICAL CHEMISTRY 2018. [DOI: 10.1016/bs.coac.2018.07.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Fenaille F, Barbier Saint-Hilaire P, Rousseau K, Junot C. Data acquisition workflows in liquid chromatography coupled to high resolution mass spectrometry-based metabolomics: Where do we stand? J Chromatogr A 2017; 1526:1-12. [PMID: 29074071 DOI: 10.1016/j.chroma.2017.10.043] [Citation(s) in RCA: 85] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Revised: 10/15/2017] [Accepted: 10/16/2017] [Indexed: 01/08/2023]
Abstract
Typical mass spectrometry (MS) based untargeted metabolomics protocols are tedious as well as time- and sample-consuming. In particular, they often rely on "full-scan-only" analyses using liquid chromatography (LC) coupled to high resolution mass spectrometry (HRMS) from which metabolites of interest are first highlighted, and then tentatively identified by using targeted MS/MS experiments. However, this situation is evolving with the emergence of integrated HRMS based-data acquisition protocols able to perform multi-event acquisitions. Most of these protocols, referring to as data dependent and data independent acquisition (DDA and DIA, respectively), have been initially developed for proteomic applications and have recently demonstrated their applicability to biomedical studies. In this context, the aim of this article is to take stock of the progress made in the field of DDA- and DIA-based protocols, and evaluate their ability to change conventional metabolomic and lipidomic data acquisition workflows, through a review of HRMS instrumentation, DDA and DIA workflows, and also associated informatics tools.
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Affiliation(s)
- François Fenaille
- Service de Pharmacologie et Immuno-Analyse (SPI), Laboratoire d'Etude du Métabolisme des Médicaments, CEA, INRA, Université Paris Saclay, MetaboHUB, F-91191 Gif-sur-Yvette, France
| | - Pierre Barbier Saint-Hilaire
- Service de Pharmacologie et Immuno-Analyse (SPI), Laboratoire d'Etude du Métabolisme des Médicaments, CEA, INRA, Université Paris Saclay, MetaboHUB, F-91191 Gif-sur-Yvette, France
| | - Kathleen Rousseau
- Service de Pharmacologie et Immuno-Analyse (SPI), Laboratoire d'Etude du Métabolisme des Médicaments, CEA, INRA, Université Paris Saclay, MetaboHUB, F-91191 Gif-sur-Yvette, France
| | - Christophe Junot
- Service de Pharmacologie et Immuno-Analyse (SPI), CEA, INRA, Université Paris Saclay, MetaboHUB, F-91191 Gif-sur-Yvette, France.
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37
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Naz S, Gallart-Ayala H, Reinke SN, Mathon C, Blankley R, Chaleckis R, Wheelock CE. Development of a Liquid Chromatography-High Resolution Mass Spectrometry Metabolomics Method with High Specificity for Metabolite Identification Using All Ion Fragmentation Acquisition. Anal Chem 2017. [PMID: 28641411 DOI: 10.1021/acs.analchem.7b00925] [Citation(s) in RCA: 88] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
High-resolution mass spectrometry (HRMS)-based metabolomics approaches have made significant advances. However, metabolite identification is still a major challenge with significant bottleneck in translating metabolomics data into biological context. In the current study, a liquid chromatography (LC)-HRMS metabolomics method was developed using an all ion fragmentation (AIF) acquisition approach. To increase the specificity in metabolite annotation, four criteria were considered: (i) accurate mass (AM), (ii) retention time (RT), (iii) MS/MS spectrum, and (iv) product/precursor ion intensity ratios. We constructed an in-house mass spectral library of 408 metabolites containing AMRT and MS/MS spectra information at four collision energies. The percent relative standard deviations between ion ratios of a metabolite in an analytical standard vs sample matrix were used as an additional metric for establishing metabolite identity. A data processing method for targeted metabolite screening was then created, merging m/z, RT, MS/MS, and ion ratio information for each of the 413 metabolites. In the data processing method, the precursor ion and product ion were considered as the quantifier and qualifier ion, respectively. We also included a scheme to distinguish coeluting isobaric compounds by selecting a specific product ion as the quantifier ion instead of the precursor ion. An advantage of the current AIF approach is the concurrent collection of full scan data, enabling identification of metabolites not included in the database. Our data acquisition strategy enables a simultaneous mixture of database-dependent targeted and nontargeted metabolomics in combination with improved accuracy in metabolite identification, increasing the quality of the biological information acquired in a metabolomics experiment.
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Affiliation(s)
- Shama Naz
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet , Stockholm SE 17177, Sweden
| | - Hector Gallart-Ayala
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet , Stockholm SE 17177, Sweden
| | - Stacey N Reinke
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet , Stockholm SE 17177, Sweden
| | - Caroline Mathon
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet , Stockholm SE 17177, Sweden
| | | | - Romanas Chaleckis
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet , Stockholm SE 17177, Sweden.,Gunma University Initiative for Advanced Research (GIAR), Gunma University , Gunma, Japan
| | - Craig E Wheelock
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet , Stockholm SE 17177, Sweden.,Gunma University Initiative for Advanced Research (GIAR), Gunma University , Gunma, Japan
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38
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Hansen RL, Lee YJ. High-Spatial Resolution Mass Spectrometry Imaging: Toward Single Cell Metabolomics in Plant Tissues. CHEM REC 2017; 18:65-77. [PMID: 28685965 DOI: 10.1002/tcr.201700027] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2017] [Indexed: 12/27/2022]
Abstract
Mass spectrometry imaging (MSI) is a powerful tool that has advanced our understanding of complex biological processes by enabling unprecedented details of metabolic biology to be uncovered. Through the use of high-spatial resolution MSI, metabolite localizations can be obtained with high precision. Here we describe our recent progress to enhance the spatial resolution of matrix-assisted laser desorption/ionization (MALDI) MSI from ∼50 μm with the commercial configuration to ∼5 μm. Additionally, we describe our efforts to develop a 'multiplex MSI' data acquisition method to allow more chemical information to be obtained on a single tissue in a single instrument run, and the development of new matrices to improve the ionization efficiency for a variety of small molecule metabolites. In combination, these contributions, along with the efforts of others, will bring MSI experiments closer to achieving metabolomic scale.
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Affiliation(s)
- Rebecca L Hansen
- Department of Chemistry, Iowa State University, 35 A Roy J Carver Co-Lab, 1111 WOI Road Ames, IA 50011, United States of America
| | - Young Jin Lee
- Department of Chemistry, Iowa State University, 35 A Roy J Carver Co-Lab, 1111 WOI Road Ames, IA 50011, United States of America
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39
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Aksenov AA, da Silva R, Knight R, Lopes NP, Dorrestein PC. Global chemical analysis of biology by mass spectrometry. Nat Rev Chem 2017. [DOI: 10.1038/s41570-017-0054] [Citation(s) in RCA: 104] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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