1
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Szabo D, Falconer TM, Fisher CM, Heise T, Phillips AL, Vas G, Williams AJ, Kruve A. Online and Offline Prioritization of Chemicals of Interest in Suspect Screening and Non-targeted Screening with High-Resolution Mass Spectrometry. Anal Chem 2024; 96:3707-3716. [PMID: 38380899 PMCID: PMC10918621 DOI: 10.1021/acs.analchem.3c05705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 01/30/2024] [Accepted: 02/07/2024] [Indexed: 02/22/2024]
Abstract
Recent advances in high-resolution mass spectrometry (HRMS) have enabled the detection of thousands of chemicals from a single sample, while computational methods have improved the identification and quantification of these chemicals in the absence of reference standards typically required in targeted analysis. However, to determine the presence of chemicals of interest that may pose an overall impact on ecological and human health, prioritization strategies must be used to effectively and efficiently highlight chemicals for further investigation. Prioritization can be based on a chemical's physicochemical properties, structure, exposure, and toxicity, in addition to its regulatory status. This Perspective aims to provide a framework for the strategies used for chemical prioritization that can be implemented to facilitate high-quality research and communication of results. These strategies are categorized as either "online" or "offline" prioritization techniques. Online prioritization techniques trigger the isolation and fragmentation of ions from the low-energy mass spectra in real time, with user-defined parameters. Offline prioritization techniques, in contrast, highlight chemicals of interest after the data has been acquired; detected features can be filtered and ranked based on the relative abundance or the predicted structure, toxicity, and concentration imputed from the tandem mass spectrum (MS2). Here we provide an overview of these prioritization techniques and how they have been successfully implemented and reported in the literature to find chemicals of elevated risk to human and ecological environments. A complete list of software and tools is available from https://nontargetedanalysis.org/.
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Affiliation(s)
- Drew Szabo
- Department
of Materials and Environmental Chemistry, Stockholm University, Stockholm 106 91, Sweden
| | - Travis M. Falconer
- Forensic
Chemistry Center, Office of Regulatory Science, Office of Regulatory
Affairs, US Food and Drug Administration, Cincinnati, Ohio 45237, United States
| | - Christine M. Fisher
- Center
for Food Safety and Applied Nutrition, US Food and Drug Administration, College Park, Maryland 20740, United States
| | - Ted Heise
- MED
Institute Inc, West Lafayette, Indiana 47906, United States
| | - Allison L. Phillips
- Center
for Public Health and Environmental Assessment, US Environmental Protection Agency, Corvallis, Oregon 97333, United States
| | - Gyorgy Vas
- VasAnalytical, Flemington, New Jersey 08822, United States
- Intertek
Pharmaceutical Services, Whitehouse, New Jersey 08888, United States
| | - Antony J. Williams
- Center
for Computational Toxicology and Exposure, Office of Research and
Development, US Environmental Protection
Agency, Durham, North Carolina 27711, United States
| | - Anneli Kruve
- Department
of Materials and Environmental Chemistry, Stockholm University, Stockholm 106 91, Sweden
- Department
of Environmental Science, Stockholm University, Stockholm 106 91, Sweden
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2
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Vosough M, Salemi A, Rockel S, Schmidt TC. Enhanced efficiency of MS/MS all-ion fragmentation for non-targeted analysis of trace contaminants in surface water using multivariate curve resolution and data fusion. Anal Bioanal Chem 2024; 416:1165-1177. [PMID: 38206346 PMCID: PMC10850027 DOI: 10.1007/s00216-023-05102-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 11/18/2023] [Accepted: 11/30/2023] [Indexed: 01/12/2024]
Abstract
Data-independent acquisition-all-ion fragmentation (DIA-AIF) mode of mass spectrometry can facilitate wide-scope non-target analysis of contaminants in surface water due to comprehensive spectral identification. However, because of the complexity of the resulting MS2 AIF spectra, identifying unknown pollutants remains a significant challenge, with a significant bottleneck in translating non-targeted chemical signatures into environmental impacts. The present study proposes to process fused MS1 and MS2 data sets obtained from LC-HRMS/MS measurements in non-targeted AIF workflows on surface water samples using multivariate curve resolution-alternating least squares (MCR-ALS). This enables straightforward assignment between precursor ions obtained from resolved MS1 spectra and their corresponding MS2 spectra. The method was evaluated for two sets of tap water and surface water contaminated with 14 target chemicals as a proof of concept. The data set of surface water samples consisting of 3506 MS1 and 2170 MS2 AIF mass spectral features was reduced to 81 components via a fused MS1-MS2 MCR model that describes at least 98.8% of the data. Each component summarizes the distinct chromatographic elution of components together with their corresponding MS1 and MS2 spectra. MS2 spectral similarity of more than 82% was obtained for most target chemicals. This highlights the potential of this method for unraveling the composition of MS/MS complex data in a water environment. Ultimately, the developed approach was applied to the retrospective non-target analysis of an independent set of surface water samples.
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Affiliation(s)
- Maryam Vosough
- Instrumental Analytical Chemistry and Centre for Water and Environmental Research (ZWU), University of Duisburg-Essen, Universitätsstr. 5, Essen, 45141, Germany.
- Department of Clean Technologies, Chemistry and Chemical Engineering Research Center of Iran, P.O. Box 14335-186, Tehran, Iran.
| | - Amir Salemi
- Instrumental Analytical Chemistry and Centre for Water and Environmental Research (ZWU), University of Duisburg-Essen, Universitätsstr. 5, Essen, 45141, Germany
| | - Sarah Rockel
- Instrumental Analytical Chemistry and Centre for Water and Environmental Research (ZWU), University of Duisburg-Essen, Universitätsstr. 5, Essen, 45141, Germany
| | - Torsten C Schmidt
- Instrumental Analytical Chemistry and Centre for Water and Environmental Research (ZWU), University of Duisburg-Essen, Universitätsstr. 5, Essen, 45141, Germany
- IWW Water Centre, Moritzstr. 26, Mülheim an der Ruhr, 45476, Germany
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3
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Stincone P, Pakkir Shah AK, Schmid R, Graves LG, Lambidis SP, Torres RR, Xia SN, Minda V, Aron AT, Wang M, Hughes CC, Petras D. Evaluation of Data-Dependent MS/MS Acquisition Parameters for Non-Targeted Metabolomics and Molecular Networking of Environmental Samples: Focus on the Q Exactive Platform. Anal Chem 2023; 95:12673-12682. [PMID: 37578818 PMCID: PMC10469366 DOI: 10.1021/acs.analchem.3c01202] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Accepted: 07/19/2023] [Indexed: 08/15/2023]
Abstract
Non-targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a widely used tool for metabolomics analysis, enabling the detection and annotation of small molecules in complex environmental samples. Data-dependent acquisition (DDA) of product ion spectra is thereby currently one of the most frequently applied data acquisition strategies. The optimization of DDA parameters is central to ensuring high spectral quality, coverage, and number of compound annotations. Here, we evaluated the influence of 10 central DDA settings of the Q Exactive mass spectrometer on natural organic matter samples from ocean, river, and soil environments. After data analysis with classical and feature-based molecular networking using MZmine and GNPS, we compared the total number of network nodes, multivariate clustering, and spectrum quality-related metrics such as annotation and singleton rates, MS/MS placement, and coverage. Our results show that automatic gain control, microscans, mass resolving power, and dynamic exclusion are the most critical parameters, whereas collision energy, TopN, and isolation width had moderate and apex trigger, monoisotopic selection, and isotopic exclusion minor effects. The insights into the data acquisition ergonomics of the Q Exactive platform presented here can guide new users and provide them with initial method parameters, some of which may also be transferable to other sample types and MS platforms.
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Affiliation(s)
- Paolo Stincone
- Cluster
of Excellence-Controlling Microbes to Fight Infection, University of Tübingen, Tübingen 72076, Germany
| | - Abzer K. Pakkir Shah
- Cluster
of Excellence-Controlling Microbes to Fight Infection, University of Tübingen, Tübingen 72076, Germany
| | - Robin Schmid
- Institute
of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo nám. 2, 160 00 Praha 6, Czech Republic
| | - Lana G. Graves
- Faculty
of Mathematics and Natural Sciences, Environmental Systems Analysis, University of Tübingen, Tübingen 72076, Germany
- Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin 12587, Germany
| | - Stilianos P. Lambidis
- Cluster
of Excellence-Controlling Microbes to Fight Infection, University of Tübingen, Tübingen 72076, Germany
| | - Ralph R. Torres
- University
of California San Diego, Scripps Institution of Oceanography, La Jolla, California 92093, United States
| | - Shu-Ning Xia
- Cluster
of Excellence-Controlling Microbes to Fight Infection, University of Tübingen, Tübingen 72076, Germany
| | - Vidit Minda
- Department
of Chemistry and Biochemistry, University
of Denver, Denver, Colorado 80210, United States
- Department
of Pharmacology and Pharmaceutical Sciences, University of Missouri−Kansas City, Kansas City, Missouri 64108, United States
| | - Allegra T. Aron
- Department
of Chemistry and Biochemistry, University
of Denver, Denver, Colorado 80210, United States
| | - Mingxun Wang
- Department
of Computer Science, University of California
Riverside, Riverside, California 92507, United States
| | - Chambers C. Hughes
- Cluster
of Excellence-Controlling Microbes to Fight Infection, University of Tübingen, Tübingen 72076, Germany
- Department
of Microbial Bioactive Compounds, Interfaculty Institute for Microbiology
and Infection Medicine, University of Tübingen, Tübingen 72076, Germany
- German
Center for Infection Research, Partner Site
Tübingen, Tübingen 72076, Germany
| | - Daniel Petras
- Cluster
of Excellence-Controlling Microbes to Fight Infection, University of Tübingen, Tübingen 72076, Germany
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4
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Defossez E, Bourquin J, von Reuss S, Rasmann S, Glauser G. Eight key rules for successful data-dependent acquisition in mass spectrometry-based metabolomics. MASS SPECTROMETRY REVIEWS 2023; 42:131-143. [PMID: 34145627 PMCID: PMC10078780 DOI: 10.1002/mas.21715] [Citation(s) in RCA: 41] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 05/28/2021] [Accepted: 06/04/2021] [Indexed: 05/10/2023]
Abstract
In recent years, metabolomics has emerged as a pivotal approach for the holistic analysis of metabolites in biological systems. The rapid progress in analytical equipment, coupled to the rise of powerful data processing tools, now provides unprecedented opportunities to deepen our understanding of the relationships between biochemical processes and physiological or phenotypic conditions in living organisms. However, to obtain unbiased data coverage of hundreds or thousands of metabolites remains a challenging task. Among the panel of available analytical methods, targeted and untargeted mass spectrometry approaches are among the most commonly used. While targeted metabolomics usually relies on multiple-reaction monitoring acquisition, untargeted metabolomics use either data-independent acquisition (DIA) or data-dependent acquisition (DDA) methods. Unlike DIA, DDA offers the possibility to get real, selective MS/MS spectra and thus to improve metabolite assignment when performing untargeted metabolomics. Yet, DDA settings are more complex to establish than DIA settings, and as a result, DDA is more prone to errors in method development and application. Here, we present a tutorial which provides guidelines on how to optimize the technical parameters essential for proper DDA experiments in metabolomics applications. This tutorial is organized as a series of rules describing the impact of the different parameters on data acquisition and data quality. It is primarily intended to metabolomics users and mass spectrometrists that wish to acquire both theoretical background and practical tips for developing effective DDA methods.
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Affiliation(s)
- Emmanuel Defossez
- Laboratory of Functional Ecology, Institute of BiologyUniversity of NeuchâtelNeuchâtelSwitzerland
| | | | - Stephan von Reuss
- Laboratory of Bioanalytical Chemistry, Institute of ChemistryUniversity of NeuchâtelNeuchâtelSwitzerland
- Neuchâtel Platform of Analytical ChemistryUniversity of NeuchâtelNeuchâtelSwitzerland
| | - Sergio Rasmann
- Laboratory of Functional Ecology, Institute of BiologyUniversity of NeuchâtelNeuchâtelSwitzerland
| | - Gaétan Glauser
- Neuchâtel Platform of Analytical ChemistryUniversity of NeuchâtelNeuchâtelSwitzerland
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5
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Mass Spectrometric Methods for Non-Targeted Screening of Metabolites: A Future Perspective for the Identification of Unknown Compounds in Plant Extracts. SEPARATIONS 2022. [DOI: 10.3390/separations9120415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Phyto products are widely used in natural products, such as medicines, cosmetics or as so-called “superfoods”. However, the exact metabolite composition of these products is still unknown, due to the time-consuming process of metabolite identification. Non-target screening by LC-HRMS/MS could be a technique to overcome these problems with its capacity to identify compounds based on their retention time, accurate mass and fragmentation pattern. In particular, the use of computational tools, such as deconvolution algorithms, retention time prediction, in silico fragmentation and sophisticated search algorithms, for comparison of spectra similarity with mass spectral databases facilitate researchers to conduct a more exhaustive profiling of metabolic contents. This review aims to provide an overview of various techniques and tools for non-target screening of phyto samples using LC-HRMS/MS.
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6
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Stereoselective effects of chiral epoxiconazole on the metabolomic and lipidomic profiling of leek. Food Chem 2022; 405:134962. [DOI: 10.1016/j.foodchem.2022.134962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Revised: 11/05/2022] [Accepted: 11/12/2022] [Indexed: 11/18/2022]
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7
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Guo J, Yu H, Xing S, Huan T. Addressing big data challenges in mass spectrometry-based metabolomics. Chem Commun (Camb) 2022; 58:9979-9990. [PMID: 35997016 DOI: 10.1039/d2cc03598g] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Advancements in computer science and software engineering have greatly facilitated mass spectrometry (MS)-based untargeted metabolomics. Nowadays, gigabytes of metabolomics data are routinely generated from MS platforms, containing condensed structural and quantitative information from thousands of metabolites. Manual data processing is almost impossible due to the large data size. Therefore, in the "omics" era, we are faced with new challenges, the big data challenges of how to accurately and efficiently process the raw data, extract the biological information, and visualize the results from the gigantic amount of collected data. Although important, proposing solutions to address these big data challenges requires broad interdisciplinary knowledge, which can be challenging for many metabolomics practitioners. Our laboratory in the Department of Chemistry at the University of British Columbia is committed to combining analytical chemistry, computer science, and statistics to develop bioinformatics tools that address these big data challenges. In this Feature Article, we elaborate on the major big data challenges in metabolomics, including data acquisition, feature extraction, quantitative measurements, statistical analysis, and metabolite annotation. We also introduce our recently developed bioinformatics solutions for these challenges. Notably, all of the bioinformatics tools and source codes are freely available on GitHub (https://www.github.com/HuanLab), along with revised and regularly updated content.
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Affiliation(s)
- Jian Guo
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, BC Canada, V6T 1Z1, Canada.
| | - Huaxu Yu
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, BC Canada, V6T 1Z1, Canada.
| | - Shipei Xing
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, BC Canada, V6T 1Z1, Canada.
| | - Tao Huan
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, BC Canada, V6T 1Z1, Canada.
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8
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Optimized Identification of Triacylglycerols in Milk by HPLC-HRMS. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-022-02270-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
AbstractThis work has developed an optimized workflow for the targeted analysis of triacylglycerols (TAGs) in milk by liquid chromatography coupled with a Q-Exactive Orbitrap mass spectrometer. First, the effects of resolution (17,500; 35,000; 70,000; 140,000) and automatic gain control target (AGC, from 2×104, 2×105, 1×106, and 3×106) have been optimized with the goal to minimize the injection time, maximize the number of scans, and minimize the mass error. Then, the flow rate of the liquid chromatography system was also optimized by maximizing the number of theoretical plates. The resulting optimized parameters consisted of a flow rate of 200 μL/min, mass resolution of 35,000, and AGC target of 2×105. Such optimal conditions were applied for targeted TAG analysis of milk fat extracts. Up to 14 target triglycerides in milk fat were identified performing a data-dependent HPLC-HRMS-MS2 experiment (t-SIM-ddMS2). The findings reported here can be helpful for MS-based lipidomic workflows and targeted milk lipid analysis.
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9
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HRPIF data mining based on data-dependent/independent acquisition for Rhei Radix et Rhizoma metabolite screening in rats. J Chromatogr B Analyt Technol Biomed Life Sci 2022; 1190:123095. [PMID: 35032891 DOI: 10.1016/j.jchromb.2021.123095] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 11/19/2021] [Accepted: 12/30/2021] [Indexed: 11/20/2022]
Abstract
In traditional Chinese medicine (TCM), components with identical nuclei often share structural similarity, indicating the possibility of similar second-level mass spectrometry (MS/MS) fragments. High-resolution product-ion filter (HRPIF) technique can be utilized to identify metabolites, with similar fragments, in vivo. In principle, this technique applies to TCM; however, its application has been restricted due to the limitations of traditional MS/MS data acquisition. Therefore, a novel analysis strategy, based on data-dependent acquisition (DDA) and data-independent acquisition (DIA) datasets, has been developed for the determination of template product ions and efficient non-targeted identification of TCM-related components in vivo by HRPIF and background subtraction (BS). This DDA-DIA combination strategy, taking Rhei Radix et Rhizoma as a test case, identified 71 anthraquinone prototype components in vitro (36 of which were discovered for the first time), and 45 related components in vivo, confirming glucuronidation and sulfation as the main reactions. The developed strategy could rapidly identify TCM-related components in vivo with high sensitivity, indicating the immense importance of this novel HRPIF data mining technology in TCM analysis.
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10
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Li L, Wang Y, Li Y, Zhu H, Feng B. A pseudotargeted method based on sequential window acquisition of all theoretical spectra mass spectrometry acquisition and its application in quality assessment of traditional Chinese medicine preparation-Yuanhu Zhitong tablet. J Sep Sci 2021; 45:650-658. [PMID: 34794207 DOI: 10.1002/jssc.202100611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 11/08/2021] [Accepted: 11/13/2021] [Indexed: 11/07/2022]
Abstract
Quality control plays a key role in the application of Chinese materia medica, especially in the preparation of traditional Chinese medicine. A pseudotargeted analysis method using an ultra-high-performance liquid chromatography-quadrupole-time-of-flight-mass spectrometry that was operated in the sequential window acquisition of all theoretical spectra mode was proposed to explore the chemical markers of traditional Chinese medicine preparation. Full-scan-based untargeted analysis was applied to extract the target ions. After data preprocessing, 302 target ions were extracted and used for the subsequent sequential window acquisition of all theoretical spectra analyses. The established sequential window acquisition of all theoretical spectra-based pseudotargeted approaches exhibited good repeatability and a wide linear range. The established method was successfully applied to discover analytical markers for the Yuanhu Zhitong tablet. After multivariate statistical analysis, 94 potential markers were identified. Ten markers were annotated by matching accurate m/z and product ion information obtained from previous reports. It is clearly indicated that the pseudotargeted analysis could make a great contribution to the quality assessment of traditional Chinese medicine preparation as a newly emerging technique.
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Affiliation(s)
- Lele Li
- School of Pharmacy, Jilin Medical University, Jilin, P. R. China
| | - Yang Wang
- Jilin Ginseng Academy, Changchun University of Chinese Medicine, Changchun, P. R. China
| | - Yuxuan Li
- School of Pharmacy, Jilin Medical University, Jilin, P. R. China
| | - Heyun Zhu
- School of Pharmacy, Jilin Medical University, Jilin, P. R. China
| | - Bo Feng
- School of Pharmacy, Jilin Medical University, Jilin, P. R. China
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11
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Guo J, Shen S, Xing S, Yu H, Huan T. ISFrag: De Novo Recognition of In-Source Fragments for Liquid Chromatography-Mass Spectrometry Data. Anal Chem 2021; 93:10243-10250. [PMID: 34270210 DOI: 10.1021/acs.analchem.1c01644] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
In-source fragmentation (ISF) is a naturally occurring phenomenon during electrospray ionization (ESI) in liquid chromatography-mass spectrometry (LC-MS) analysis. ISF leads to false metabolite annotation in untargeted metabolomics, prompting misinterpretation of the underlying biological mechanisms. Conventional metabolomic data cleaning mainly focuses on the annotation of adducts and isotopes, and the recognition of ISF features is mainly based on common neutral losses and the LC coelution pattern. In this work, we recognized three increasingly important patterns of ISF features, including (1) coeluting with their precursor ions, (2) being in the tandem MS (MS2) spectra of their precursor ions, and (3) sharing similar MS2 fragmentation patterns with their precursor ions. Based on these patterns, we developed an R package, ISFrag, to comprehensively recognize all possible ISF features from LC-MS data generated from full-scan, data-dependent acquisition, and data-independent acquisition modes without the assistance of common neutral loss information or MS2 spectral library. Tested using metabolite standards, we achieved a 100% correct recognition of level 1 ISF features and over 80% correct recognition for level 2 ISF features. Further application of ISFrag on untargeted metabolomics data allows us to identify ISF features that can potentially cause false metabolite annotation at an omics-scale. With the help of ISFrag, we performed a systematic investigation of how ISF features are influenced by different MS parameters, including capillary voltage, end plate offset, ion energy, and "collision energy". Our results show that while increasing energies can increase the number of real metabolic features and ISF features, the percentage of ISF features might not necessarily increase. Finally, using ISFrag, we created an ISF pathway to visualize the relationships between multiple ISF features that belong to the same precursor ion. ISFrag is freely available on GitHub (https://github.com/HuanLab/ISFrag).
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Affiliation(s)
- Jian Guo
- Department of Chemistry, Faculty of Science, University of British Columbia, 2036 Main Mall, Vancouver, V6T 1Z1 British Columbia Canada
| | - Sam Shen
- Department of Chemistry, Faculty of Science, University of British Columbia, 2036 Main Mall, Vancouver, V6T 1Z1 British Columbia Canada
| | - Shipei Xing
- Department of Chemistry, Faculty of Science, University of British Columbia, 2036 Main Mall, Vancouver, V6T 1Z1 British Columbia Canada
| | - Huaxu Yu
- Department of Chemistry, Faculty of Science, University of British Columbia, 2036 Main Mall, Vancouver, V6T 1Z1 British Columbia Canada
| | - Tao Huan
- Department of Chemistry, Faculty of Science, University of British Columbia, 2036 Main Mall, Vancouver, V6T 1Z1 British Columbia Canada
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12
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Zuo Z, Cao L, Nothia LF, Mohimani H. MS2Planner: improved fragmentation spectra coverage in untargeted mass spectrometry by iterative optimized data acquisition. Bioinformatics 2021; 37:i231-i236. [PMID: 34252948 PMCID: PMC8336448 DOI: 10.1093/bioinformatics/btab279] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
Motivation Untargeted mass spectrometry experiments enable the profiling of metabolites in complex biological samples. The collected fragmentation spectra are the metabolite’s fingerprints that are used for molecule identification and discovery. Two main mass spectrometry strategies exist for the collection of fragmentation spectra: data-dependent acquisition (DDA) and data-independent acquisition (DIA). In the DIA strategy, all the metabolites ions in predefined mass-to-charge ratio ranges are co-isolated and co-fragmented, resulting in multiplexed fragmentation spectra that are challenging to annotate. In contrast, in the DDA strategy, fragmentation spectra are dynamically and specifically collected for the most abundant ions observed, causing redundancy and sub-optimal fragmentation spectra collection. Yet, DDA results in less multiplexed fragmentation spectra that can be readily annotated. Results We introduce the MS2Planner workflow, an Iterative Optimized Data Acquisition strategy that optimizes the number of high-quality fragmentation spectra over multiple experimental acquisitions using topological sorting. Our results showed that MS2Planner increases the annotation rate by 38.6% and is 62.5% more sensitive and 9.4% more specific compared to DDA. Availability and implementation MS2Planner code is available at https://github.com/mohimanilab/MS2Planner. The generation of the inclusion list from MS2Planner was performed with python scripts available at https://github.com/lfnothias/IODA_MS. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zeyuan Zuo
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Liu Cao
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Louis-Félix Nothia
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA
- To whom correspondence should be addressed. or
| | - Hosein Mohimani
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- To whom correspondence should be addressed. or
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13
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Zhu H, Wu X, Huo J, Hou J, Long H, Zhang Z, Wang B, Tian M, Chen K, Guo D, Lei M, Wu W. A five-dimensional data collection strategy for multicomponent discovery and characterization in Traditional Chinese Medicine: Gastrodia Rhizoma as a case study. J Chromatogr A 2021; 1653:462405. [PMID: 34332318 DOI: 10.1016/j.chroma.2021.462405] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 07/07/2021] [Accepted: 07/08/2021] [Indexed: 01/01/2023]
Abstract
Establishing the identity of bioactive compounds to control the quality of Traditional Chinese Medicines is made more challenging by the complexity of the metabolite matrix, the existence of isomers, and the range of compound concentration and polarity observed between individual samples of the same plant in a multicomponent preparation. In addition, LC-MS analysis has limited capability for the separation and analysis of potentially important trace compounds and isomers, which hinders the comprehensive metabolite characterization of functional foods and Traditional Natural Medicine. To facilitate and improve the chemical composition characterization and enhance metabolite discernment, a comprehensive strategy was developed which integrates ion mobility mass spectrometry (IMS) with offline two-dimensional liquid chromatography based on hydrophilic interaction chromatography (HILIC) and conventional reversed phase (RP) C18 chromatography. Through application of the HILIC × RP offline 2D-LC approach, trace compounds were enriched and separated promoting a more efficient and detailed analysis of the matrix complexity. Comprehensive non-targeted multidimensional data (Rt1D, Rt2D, MS, CCS and MS/MS) and data-independent-acquisition (DIA) mass data of the metabolites in complex food and drug samples were obtained in the IMS-DIA-MS/MS mode on a Waters-SYNAPT G2-Si mass spectrometer with an ESI source. Through the application of high-efficiency neutral loss (NLs) and diagnostic product ions (DPIs) filter strategies, information from DIA mass data permitted the rapid detection and identification of compounds. The identification coverage of metabolites with low-quality MS/MS data was also improved. In the absence of analytical standards, Collision Cross Section (CCS) prediction and matching strategies based on theoretical chemical structures provided a method to distingish isomers. To demonstrate the efficacy of the technique this comprehensive strategy was applied to the compound characterization of Gastrodia Rhizoma (GR). Characterization of 272 compounds was achieved, including 146 unreported compounds. The results affirm that this comprehensive five-dimensional data collection strategy has the capacity to support the in-depth study of the high level of chemical diversity in Traditional Chinese Medicines.
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Affiliation(s)
- Haodong Zhu
- School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, PR China; Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Laboratory for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Xingdong Wu
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Laboratory for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Jiangyan Huo
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Laboratory for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Jinjun Hou
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Laboratory for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Huali Long
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Laboratory for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Zijia Zhang
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Laboratory for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Bing Wang
- School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, PR China; Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Laboratory for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China.
| | - Menghua Tian
- Zhaotong Tianma Research Institute, Zhaotong, Yunnan 657000, PR China
| | - Kaixian Chen
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Laboratory for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - De'an Guo
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Laboratory for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Min Lei
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Laboratory for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China.
| | - Wanying Wu
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Laboratory for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China.
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14
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Ultra-high-performance liquid chromatography high-resolution mass spectrometry variants for metabolomics research. Nat Methods 2021; 18:733-746. [PMID: 33972782 DOI: 10.1038/s41592-021-01116-4] [Citation(s) in RCA: 129] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Accepted: 03/12/2021] [Indexed: 02/03/2023]
Abstract
Ultra-high-performance liquid chromatography high-resolution mass spectrometry (UHPLC-HRMS) variants currently represent the best tools to tackle the challenges of complexity and lack of comprehensive coverage of the metabolome. UHPLC offers flexible and efficient separation coupled with high-sensitivity detection via HRMS, allowing for the detection and identification of a broad range of metabolites. Here we discuss current common strategies for UHPLC-HRMS-based metabolomics, with a focus on expanding metabolome coverage.
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15
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Davies V, Wandy J, Weidt S, van der Hooft JJJ, Miller A, Daly R, Rogers S. Rapid Development of Improved Data-Dependent Acquisition Strategies. Anal Chem 2021; 93:5676-5683. [PMID: 33784814 PMCID: PMC8047769 DOI: 10.1021/acs.analchem.0c03895] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Tandem mass spectrometry (LC-MS/MS) is widely used to identify unknown ions in untargeted metabolomics. Data-dependent acquisition (DDA) chooses which ions to fragment based upon intensities observed in MS1 survey scans and typically only fragments a small subset of the ions present. Despite this inefficiency, relatively little work has addressed the development of new DDA methods, partly due to the high overhead associated with running the many extracts necessary to optimize approaches in busy MS facilities. In this work, we first provide theoretical results that show how much improvement is possible over current DDA strategies. We then describe an in silico framework for fast and cost-efficient development of new DDA strategies using a previously developed virtual metabolomics mass spectrometer (ViMMS). Additional functionality is added to ViMMS to allow methods to be used both in simulation and on real samples via an Instrument Application Programming Interface (IAPI). We demonstrate this framework through the development and optimization of two new DDA methods that introduce new advanced ion prioritization strategies. Upon application of these developed methods to two complex metabolite mixtures, our results show that they are able to fragment more unique ions than standard DDA strategies.
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Affiliation(s)
- Vinny Davies
- School of Computing Science, University of Glasgow, Glasgow G12 8QQ, United Kingdom
| | - Joe Wandy
- Glasgow Polyomics, University of Glasgow, Glasgow G12 8QQ, United Kingdom
| | - Stefan Weidt
- Glasgow Polyomics, University of Glasgow, Glasgow G12 8QQ, United Kingdom
| | - Justin J J van der Hooft
- Bioinformatics Group, Department of Plant Sciences, Wageningen University, 6780 PB Wageningen, The Netherlands
| | - Alice Miller
- School of Computing Science, University of Glasgow, Glasgow G12 8QQ, United Kingdom
| | - Rónán Daly
- Glasgow Polyomics, University of Glasgow, Glasgow G12 8QQ, United Kingdom
| | - Simon Rogers
- School of Computing Science, University of Glasgow, Glasgow G12 8QQ, United Kingdom
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16
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Cho K, Schwaiger-Haber M, Naser FJ, Stancliffe E, Sindelar M, Patti GJ. Targeting unique biological signals on the fly to improve MS/MS coverage and identification efficiency in metabolomics. Anal Chim Acta 2021; 1149:338210. [PMID: 33551064 PMCID: PMC8189644 DOI: 10.1016/j.aca.2021.338210] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 12/16/2020] [Accepted: 01/05/2021] [Indexed: 12/22/2022]
Abstract
When using liquid chromatography/mass spectrometry (LC/MS) to perform untargeted metabolomics, it is common to detect thousands of features from a biological extract. Although it is impractical to collect non-chimeric MS/MS data for each in a single chromatographic run, this is generally unnecessary because most features do not correspond to unique metabolites of biological relevance. Here we show that relatively simple data-processing strategies that can be applied on the fly during acquisition of data with an Orbitrap ID-X, such as blank subtraction and well-established adduct or isotope calculations, decrease the number of features to target for MS/MS analysis by up to an order of magnitude for various types of biological matrices. We demonstrate that annotating these non-biological contaminants and redundancies in real time during data acquisition enables comprehensive MS/MS data to be acquired on each remaining feature at a single collision energy. To ensure that an appropriate collision energy is applied, we introduce a method using a series of hidden ion-trap scans in an Orbitrap ID-X to find an optimal value for each feature that can then be applied in a subsequent high-resolution Orbitrap scan. Data from 100 metabolite standards indicate that this real-time optimization of collision energies leads to more informative MS/MS patterns compared to using a single fixed collision energy alone. As a benchmark to evaluate the overall workflow, we manually annotated unique biological features by independently subjecting E. coli samples to a credentialing analysis. While credentialing led to a more rigorous reduction in feature number, on-the-fly annotation with blank subtraction on an Orbitrap ID-X did not inappropriately discard unique biological metabolites. Taken together, our results reveal that optimal fragmentation data can be obtained in a single LC/MS/MS run for >90% of the unique biological metabolites in a sample when features are annotated during acquisition and collision energies are selected by using parallel mass spectrometry detection.
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Affiliation(s)
- Kevin Cho
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA; Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Michaela Schwaiger-Haber
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA; Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Fuad J Naser
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA; Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Ethan Stancliffe
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA; Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Miriam Sindelar
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA; Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Gary J Patti
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA; Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA.
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17
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Data processing strategies for non-targeted analysis of foods using liquid chromatography/high-resolution mass spectrometry. Trends Analyt Chem 2021. [DOI: 10.1016/j.trac.2021.116188] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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18
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Guo J, Shen S, Xing S, Huan T. DaDIA: Hybridizing Data-Dependent and Data-Independent Acquisition Modes for Generating High-Quality Metabolomic Data. Anal Chem 2021; 93:2669-2677. [DOI: 10.1021/acs.analchem.0c05022] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Jian Guo
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver V6T 1Z1, British Columbia, Canada
| | - Sam Shen
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver V6T 1Z1, British Columbia, Canada
| | - Shipei Xing
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver V6T 1Z1, British Columbia, Canada
| | - Tao Huan
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver V6T 1Z1, British Columbia, Canada
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19
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Xu L, Xu Z, Strashnov I, Liao X. Use of information dependent acquisition mass spectra and sequential window acquisition of all theoretical fragment-ion mass spectra for fruit juices metabolomics and authentication. Metabolomics 2020; 16:81. [PMID: 32638130 DOI: 10.1007/s11306-020-01701-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 07/01/2020] [Indexed: 02/04/2023]
Abstract
INTRODUCTION LC-MS based untargeted metabolomics are the main untargeted methods used for juice metabolomics to solve the authentication problem faced in fruit juice industry. OBJECTIVES To evaluate the performances of different untargeted metabolomics methods on fruit juices metabolomics and authentication, orange and apple fruit juices were selected for this study. METHODS IDA-MS and SWATH-MS based on UHPLC-QTOF were used for the metabolomics and authenticity determination of apple and orange juices, including the lab-made samples of oranges (Citrus sinensis Osb.) from Jiangxi Province, apples (Malus domestica Borkh) from Shandong Province, and different brands of commercial orange and apple juice samples from markets. RESULTS IDA-MS and SWATH-MS could both acquire numerous MS1 features and MS2 information of juice components, while SWATH-MS excels at the acquisition rate of MS2. Distinctive separation between authentic orange juice and not authentic orange juice could be seen from principal component analysis and hierarchical clustering analysis based on both IDA-MS and SWATH-MS. After analysis of variance, fold change analysis and orthogonal projection to latent structures discriminant mode, 53 and 46 potential markers were defined by IDA-MS and SWATH-MS (with 77.4% and 100% MS2 acquisition rate) separately. Subsequently, these potential markers were putatively annotated using general chemical databases with 6 more annotated by SWATH-MS. Furthermore, 7 of the potential markers, l-asparagine, umbelliferone, glucosamine, phlorin, epicatechin, phytosphingosine and chlorogenic acid, were identified with standards. For the consideration of model simplicity, two determined makers (umbelliferone and chlorogenic acid) were selected to construct the DD-SIMCA model in commercial samples because of their good correlation with apple adulteration proportion, and the sensitivity and specificity of the model were 100% and 95%. CONCLUSION SWATH-MS excels at the MS2 acquisition of juice components and potential markers. This study provides an overall performance comparison between IDA-MS and SWATH-MS, and guidance for the method selection on fruit juice metabolomics and juice authenticity determination. Two of the potential markers determined, umbelliferone and chlorogenic acid, could be used as apple juice indicators in orange juice.
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Affiliation(s)
- Lei Xu
- Institute of Quality Standard & Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Key Laboratory of Agro-Food Safety and Quality, Ministry of Agriculture and Rural Affairs, Beijing, 100081, China
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing Key Laboratory for Food Nonthermal Processing, Key Lab of Fruit and Vegetable Processing, Ministry of Agriculture and Rural Affairs, Beijing, 100083, China
| | - Zhenzhen Xu
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing Key Laboratory for Food Nonthermal Processing, Key Lab of Fruit and Vegetable Processing, Ministry of Agriculture and Rural Affairs, Beijing, 100083, China.
| | - Ilya Strashnov
- School of Chemistry, University of Manchester, Brunswick Street, Manchester, UK
| | - Xiaojun Liao
- Institute of Quality Standard & Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Key Laboratory of Agro-Food Safety and Quality, Ministry of Agriculture and Rural Affairs, Beijing, 100081, China
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20
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Xu L, Xu Z, Wang X, Wang B, Liao X. The application of pseudotargeted metabolomics method for fruit juices discrimination. Food Chem 2020; 316:126278. [DOI: 10.1016/j.foodchem.2020.126278] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 01/18/2020] [Accepted: 01/20/2020] [Indexed: 02/07/2023]
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21
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Wandy J, Davies V, J J van der Hooft J, Weidt S, Daly R, Rogers S. In Silico Optimization of Mass Spectrometry Fragmentation Strategies in Metabolomics. Metabolites 2019; 9:E219. [PMID: 31600991 PMCID: PMC6836109 DOI: 10.3390/metabo9100219] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 09/27/2019] [Accepted: 10/02/2019] [Indexed: 12/20/2022] Open
Abstract
Liquid chromatography (LC) coupled to tandem mass spectrometry (MS/MS) is widely used in identifying small molecules in untargeted metabolomics. Various strategies exist to acquire MS/MS fragmentation spectra; however, the development of new acquisition strategies is hampered by the lack of simulators that let researchers prototype, compare, and optimize strategies before validations on real machines. We introduce Virtual Metabolomics Mass Spectrometer (ViMMS), a metabolomics LC-MS/MS simulator framework that allows for scan-level control of the MS2 acquisition process in silico. ViMMS can generate new LC-MS/MS data based on empirical data or virtually re-run a previous LC-MS/MS analysis using pre-existing data to allow the testing of different fragmentation strategies. To demonstrate its utility, we show how ViMMS can be used to optimize N for Top-N data-dependent acquisition (DDA) acquisition, giving results comparable to modifying N on the mass spectrometer. We expect that ViMMS will save method development time by allowing for offline evaluation of novel fragmentation strategies and optimization of the fragmentation strategy for a particular experiment.
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Affiliation(s)
- Joe Wandy
- Glasgow Polyomics, University of Glasgow, Glasgow G61 1BD, UK.
| | - Vinny Davies
- School of Computing Science, University of Glasgow, Glasgow G12 8RZ, UK.
| | - Justin J J van der Hooft
- Bioinformatics Group, Department of Plant Sciences, Wageningen University, 6780 PB Wageningen, The Netherlands.
| | - Stefan Weidt
- Glasgow Polyomics, University of Glasgow, Glasgow G61 1BD, UK.
| | - Rónán Daly
- Glasgow Polyomics, University of Glasgow, Glasgow G61 1BD, UK.
| | - Simon Rogers
- School of Computing Science, University of Glasgow, Glasgow G12 8RZ, UK.
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22
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Hutchins PD, Russell JD, Coon JJ. Accelerating Lipidomic Method Development through in Silico Simulation. Anal Chem 2019; 91:9698-9706. [PMID: 31298839 DOI: 10.1021/acs.analchem.9b01234] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Judicious selection of mass spectrometry (MS) acquisition parameters is essential for effectively profiling the broad diversity and dynamic range of biomolecules. Typically, acquisition parameters are individually optimized to maximally characterize analytes from each new sample matrix. This time-consuming process often ignores the synergistic relationship between MS method parameters, producing suboptimal results. Here we detail the creation of an algorithm which accurately simulates LC-MS/MS lipidomic data acquisition performance for a benchtop quadrupole-Orbitrap MS system. By coupling this simulation tool with a genetic algorithm for constrained parameter optimization, we demonstrate the efficient identification of LC-MS/MS method parameter sets individually suited for specific sample matrices. Finally, we utilize the in silico simulation to examine how continued developments in MS acquisition speed and sensitivity will further increase the power of MS lipidomics as a vital tool for impactful biochemical analysis.
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Affiliation(s)
- Paul D Hutchins
- Department of Chemistry , University of Wisconsin-Madison , Madison , Wisconsin 53706 , United States.,Genome Center of Wisconsin , Madison , Wisconsin 53706 , United States
| | - Jason D Russell
- Morgridge Institute for Research , Madison , Wisconsin 53715 , United States.,Genome Center of Wisconsin , Madison , Wisconsin 53706 , United States
| | - Joshua J Coon
- Department of Chemistry , University of Wisconsin-Madison , Madison , Wisconsin 53706 , United States.,Morgridge Institute for Research , Madison , Wisconsin 53715 , United States.,Genome Center of Wisconsin , Madison , Wisconsin 53706 , United States.,Department of Biomolecular Chemistry , University of Wisconsin-Madison , Madison , Wisconsin 53706 , United States
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23
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Hohrenk LL, Vosough M, Schmidt TC. Implementation of Chemometric Tools To Improve Data Mining and Prioritization in LC-HRMS for Nontarget Screening of Organic Micropollutants in Complex Water Matrixes. Anal Chem 2019; 91:9213-9220. [DOI: 10.1021/acs.analchem.9b01984] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Lotta L. Hohrenk
- Instrumental Analytical Chemistry and Centre for Water and Environmental Research (ZWU) University of Duisburg-Essen, Universitätsstrasse 5, Essen 45141, Germany
| | - Maryam Vosough
- Department of Clean Technologies, Chemistry and Chemical Engineering Research Center of Iran,
P.O. Box 14335-186, Tehran 1496813151, Iran
| | - Torsten C. Schmidt
- Instrumental Analytical Chemistry and Centre for Water and Environmental Research (ZWU) University of Duisburg-Essen, Universitätsstrasse 5, Essen 45141, Germany
- IWW Water Centre, Moritzstr. 26, Mülheim an der Ruhr 45476, Germany
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24
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Monge ME, Dodds JN, Baker ES, Edison AS, Fernández FM. Challenges in Identifying the Dark Molecules of Life. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2019; 12:177-199. [PMID: 30883183 PMCID: PMC6716371 DOI: 10.1146/annurev-anchem-061318-114959] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Metabolomics is the study of the metabolome, the collection of small molecules in living organisms, cells, tissues, and biofluids. Technological advances in mass spectrometry, liquid- and gas-phase separations, nuclear magnetic resonance spectroscopy, and big data analytics have now made it possible to study metabolism at an omics or systems level. The significance of this burgeoning scientific field cannot be overstated: It impacts disciplines ranging from biomedicine to plant science. Despite these advances, the central bottleneck in metabolomics remains the identification of key metabolites that play a class-discriminant role. Because metabolites do not follow a molecular alphabet as proteins and nucleic acids do, their identification is much more time consuming, with a high failure rate. In this review, we critically discuss the state-of-the-art in metabolite identification with specific applications in metabolomics and how technologies such as mass spectrometry, ion mobility, chromatography, and nuclear magnetic resonance currently contribute to this challenging task.
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Affiliation(s)
- María Eugenia Monge
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), C1425FQD, Ciudad de Buenos Aires, Argentina
| | - James N Dodds
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - Erin S Baker
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - Arthur S Edison
- Department of Genetics, Department of Biochemistry and Molecular Biology, and Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia 30602, USA
| | - Facundo M Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology and Petit Institute for Biochemistry and Bioscience, Atlanta, Georgia 30332, USA;
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25
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Wolfender JL, Nuzillard JM, van der Hooft JJJ, Renault JH, Bertrand S. Accelerating Metabolite Identification in Natural Product Research: Toward an Ideal Combination of Liquid Chromatography–High-Resolution Tandem Mass Spectrometry and NMR Profiling, in Silico Databases, and Chemometrics. Anal Chem 2018; 91:704-742. [DOI: 10.1021/acs.analchem.8b05112] [Citation(s) in RCA: 113] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Jean-Luc Wolfender
- School of Pharmaceutical Sciences, EPGL, University of Geneva, University of Lausanne, CMU, 1 Rue Michel Servet, 1211 Geneva 4, Switzerland
| | - Jean-Marc Nuzillard
- Institut de Chimie Moléculaire de Reims, UMR CNRS 7312, Université de Reims Champagne Ardenne, 51687 Reims Cedex 2, France
| | | | - Jean-Hugues Renault
- Institut de Chimie Moléculaire de Reims, UMR CNRS 7312, Université de Reims Champagne Ardenne, 51687 Reims Cedex 2, France
| | - Samuel Bertrand
- Groupe Mer, Molécules, Santé-EA 2160, UFR des Sciences Pharmaceutiques et Biologiques, Université de Nantes, 44035 Nantes, France
- ThalassOMICS Metabolomics Facility, Plateforme Corsaire, Biogenouest, 44035 Nantes, France
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26
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Seitzer PM, Searle BC. Incorporating In-Source Fragment Information Improves Metabolite Identification Accuracy in Untargeted LC–MS Data Sets. J Proteome Res 2018; 18:791-796. [DOI: 10.1021/acs.jproteome.8b00601] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Phillip M. Seitzer
- Proteome Software, 1340 Southwest Bertha Boulevard Suite 10, Portland, Oregon 97219, United States
| | - Brian C. Searle
- Proteome Software, 1340 Southwest Bertha Boulevard Suite 10, Portland, Oregon 97219, United States
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
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