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Shahneh MRZ, Strobel M, Vitale GA, Geibel C, Abiead YE, Garg N, Wagner B, Forchhammer K, Aron A, Phelan VV, Petras D, Wang M. ModiFinder: Tandem Mass Spectral Alignment Enables Structural Modification Site Localization. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024. [PMID: 38830143 DOI: 10.1021/jasms.4c00061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
Untargeted tandem mass spectrometry (MS/MS) has become a high-throughput method to measure small molecules in complex samples. One key goal is the transformation of these MS/MS spectra into chemical structures. Computational techniques such as MS/MS library search have enabled the reidentification of known compounds. Analog library search and molecular networking extend this identification to unknown compounds. While there have been advancements in metrics for the similarity of MS/MS spectra of structurally similar compounds, there is still a lack of automated methods to provide site specific information about structural modifications. Here we introduce ModiFinder which leverages the alignment of peaks in MS/MS spectra between structurally related known and unknown small molecules. Specifically, ModiFinder focuses on shifted MS/MS fragment peaks in the MS/MS alignment. These shifted peaks putatively represent substructures of the known molecule that contain the site of the modification. ModiFinder synthesizes this information together and scores the likelihood for each atom in the known molecule to be the modification site. We demonstrate in this manuscript how ModiFinder can effectively localize modifications which extends the capabilities of MS/MS analog searching and molecular networking to accelerate the discovery of novel compounds.
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Affiliation(s)
- Mohammad Reza Zare Shahneh
- Department of Computer Science and Engineering, University of California Riverside, 900 University Ave., Riverside, California 92521, United States
| | - Michael Strobel
- Department of Computer Science and Engineering, University of California Riverside, 900 University Ave., Riverside, California 92521, United States
| | - Giovanni Andrea Vitale
- Interfaculty Institute of Microbiology and Infection Medicine, University of Tuebingen, Auf der Morgenstelle 24, Tuebingen 72076, Germany
| | - Christian Geibel
- Interfaculty Institute of Microbiology and Infection Medicine, University of Tuebingen, Auf der Morgenstelle 24, Tuebingen 72076, Germany
| | - Yasin El Abiead
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, 9500 Gilman Dr., San Diego, California 92093, United States
| | - Neha Garg
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta,, 950 Atlantic Drive, Atlanta, Georgia 30332, United States
| | - Berenike Wagner
- Interfaculty Institute of Microbiology and Infection Medicine, University of Tuebingen, Auf der Morgenstelle 28, Tuebingen 72076, Germany
| | - Karl Forchhammer
- Interfaculty Institute of Microbiology and Infection Medicine, University of Tuebingen, Auf der Morgenstelle 28, Tuebingen 72076, Germany
| | - Allegra Aron
- Department of Chemistry and Biochemistry, University of Denver, 2101 East Wesley Ave, Denver, Colorado 80210, United States
| | - Vanessa V Phelan
- Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado, Anschutz Medical Campus, 12850 E Montview Blvd, Aurora, Colorado 80045, United States
| | - Daniel Petras
- Department of Biochemistry, University of California Riverside, 900 University Ave., Riverside, California 92521, United States
| | - Mingxun Wang
- Department of Computer Science and Engineering, University of California Riverside, 900 University Ave., Riverside, California 92521, United States
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2
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Bui-Thi D, Liu Y, Lippens JL, Laukens K, De Vijlder T. TransExION: a transformer based explainable similarity metric for comparing IONS in tandem mass spectrometry. J Cheminform 2024; 16:61. [PMID: 38807166 PMCID: PMC11134763 DOI: 10.1186/s13321-024-00858-5] [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: 12/22/2023] [Accepted: 05/12/2024] [Indexed: 05/30/2024] Open
Abstract
Small molecule identification is a crucial task in analytical chemistry and life sciences. One of the most commonly used technologies to elucidate small molecule structures is mass spectrometry. Spectral library search of product ion spectra (MS/MS) is a popular strategy to identify or find structural analogues. This approach relies on the assumption that spectral similarity and structural similarity are correlated. However, popular spectral similarity measures, usually calculated based on identical fragment matches between the MS/MS spectra, do not always accurately reflect the structural similarity. In this study, we propose TransExION, a Transformer based Explainable similarity metric for IONS. TransExION detects related fragments between MS/MS spectra through their mass difference and uses these to estimate spectral similarity. These related fragments can be nearly identical, but can also share a substructure. TransExION also provides a post-hoc explanation of its estimation, which can be used to support scientists in evaluating the spectral library search results and thus in structure elucidation of unknown molecules. Our model has a Transformer based architecture and it is trained on the data derived from GNPS MS/MS libraries. The experimental results show that it improves existing spectral similarity measures in searching and interpreting structural analogues as well as in molecular networking. SCIENTIFIC CONTRIBUTION: We propose a transformer-based spectral similarity metrics that improves the comparison of small molecule tandem mass spectra. We provide a post hoc explanation that can serve as a good starting point for unknown spectra annotation based on database spectra.
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Affiliation(s)
- Danh Bui-Thi
- Computer Science Department, University of Antwerp, Middelheimlaan 1, 2020, Antwerp, Belgium
| | - Youzhong Liu
- Therapeutic Development and Supply, Janssen Pharmaceutica N.V., Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Jennifer L Lippens
- Therapeutic Development and Supply, Janssen Pharmaceutica N.V., Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Kris Laukens
- Computer Science Department, University of Antwerp, Middelheimlaan 1, 2020, Antwerp, Belgium
| | - Thomas De Vijlder
- Therapeutic Development and Supply, Janssen Pharmaceutica N.V., Turnhoutseweg 30, 2340, Beerse, Belgium.
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3
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Perez de Souza L, Fernie AR. Computational methods for processing and interpreting mass spectrometry-based metabolomics. Essays Biochem 2024; 68:5-13. [PMID: 37999335 PMCID: PMC11065554 DOI: 10.1042/ebc20230019] [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: 09/15/2023] [Revised: 11/10/2023] [Accepted: 11/15/2023] [Indexed: 11/25/2023]
Abstract
Metabolomics has emerged as an indispensable tool for exploring complex biological questions, providing the ability to investigate a substantial portion of the metabolome. However, the vast complexity and structural diversity intrinsic to metabolites imposes a great challenge for data analysis and interpretation. Liquid chromatography mass spectrometry (LC-MS) stands out as a versatile technique offering extensive metabolite coverage. In this mini-review, we address some of the hurdles posed by the complex nature of LC-MS data, providing a brief overview of computational tools designed to help tackling these challenges. Our focus centers on two major steps that are essential to most metabolomics investigations: the translation of raw data into quantifiable features, and the extraction of structural insights from mass spectra to facilitate metabolite identification. By exploring current computational solutions, we aim at providing a critical overview of the capabilities and constraints of mass spectrometry-based metabolomics, while introduce some of the most recent trends in data processing and analysis within the field.
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Affiliation(s)
- Leonardo Perez de Souza
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
| | - Alisdair R Fernie
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
- Center for Plant Systems Biology and Biotechnology, 4000 Plovdiv, Bulgaria
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4
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Ellin NR, Guo Y, Miranda-Quintana RA, Prentice BM. Extended similarity methods for efficient data mining in imaging mass spectrometry. DIGITAL DISCOVERY 2024; 3:805-817. [PMID: 38638647 PMCID: PMC11022984 DOI: 10.1039/d3dd00165b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 03/19/2024] [Indexed: 04/20/2024]
Abstract
Imaging mass spectrometry is a label-free imaging modality that allows for the spatial mapping of many compounds directly in tissues. In an imaging mass spectrometry experiment, a raster of the tissue surface produces a mass spectrum at each sampled x, y position, resulting in thousands of individual mass spectra, each comprising a pixel in the resulting ion images. However, efficient analysis of imaging mass spectrometry datasets can be challenging due to the hyperspectral characteristics of the data. Each spectrum contains several thousand unique compounds at discrete m/z values that result in unique ion images, which demands robust and efficient algorithms for searching, statistical analysis, and visualization. Some traditional post-processing techniques are fundamentally ill-equipped to dissect these types of data. For example, while principal component analysis (PCA) has long served as a useful tool for mining imaging mass spectrometry datasets to identify correlated analytes and biological regions of interest, the interpretation of the PCA scores and loadings can be non-trivial. The loadings often contain negative peaks in the PCA-derived pseudo-spectra, which are difficult to ascribe to underlying tissue biology. Herein, we have utilized extended similarity indices to streamline the interpretation of imaging mass spectrometry data. This novel workflow uses PCA as a pixel-selection method to parse out the most and least correlated pixels, which are then compared using the extended similarity indices. The extended similarity indices complement PCA by removing all non-physical artifacts and streamlining the interpretation of large volumes of imaging mass spectrometry spectra simultaneously. The linear complexity, O(N), of these indices suggests that large imaging mass spectrometry datasets can be analyzed in a 1 : 1 scale of time and space with respect to the size of the input data. The extended similarity indices algorithmic workflow is exemplified here by identifying discrete biological regions of mouse brain tissue.
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Affiliation(s)
- Nicholas R Ellin
- Department of Chemistry, University of Florida Gainesville FL 32611-7200 USA
| | - Yingchan Guo
- Department of Chemistry, University of Florida Gainesville FL 32611-7200 USA
| | - Ramón Alain Miranda-Quintana
- Department of Chemistry, University of Florida Gainesville FL 32611-7200 USA
- Quantum Theory Project, University of Florida Gainesville FL 32611-7200 USA
| | - Boone M Prentice
- Department of Chemistry, University of Florida Gainesville FL 32611-7200 USA
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5
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Mohanty I, Mannochio-Russo H, Schweer JV, El Abiead Y, Bittremieux W, Xing S, Schmid R, Zuffa S, Vasquez F, Muti VB, Zemlin J, Tovar-Herrera OE, Moraïs S, Desai D, Amin S, Koo I, Turck CW, Mizrahi I, Kris-Etherton PM, Petersen KS, Fleming JA, Huan T, Patterson AD, Siegel D, Hagey LR, Wang M, Aron AT, Dorrestein PC. The underappreciated diversity of bile acid modifications. Cell 2024; 187:1801-1818.e20. [PMID: 38471500 DOI: 10.1016/j.cell.2024.02.019] [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: 04/26/2023] [Revised: 11/30/2023] [Accepted: 02/15/2024] [Indexed: 03/14/2024]
Abstract
The repertoire of modifications to bile acids and related steroidal lipids by host and microbial metabolism remains incompletely characterized. To address this knowledge gap, we created a reusable resource of tandem mass spectrometry (MS/MS) spectra by filtering 1.2 billion publicly available MS/MS spectra for bile-acid-selective ion patterns. Thousands of modifications are distributed throughout animal and human bodies as well as microbial cultures. We employed this MS/MS library to identify polyamine bile amidates, prevalent in carnivores. They are present in humans, and their levels alter with a diet change from a Mediterranean to a typical American diet. This work highlights the existence of many more bile acid modifications than previously recognized and the value of leveraging public large-scale untargeted metabolomics data to discover metabolites. The availability of a modification-centric bile acid MS/MS library will inform future studies investigating bile acid roles in health and disease.
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Affiliation(s)
- Ipsita Mohanty
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Helena Mannochio-Russo
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Joshua V Schweer
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA; Department of Chemistry and Biochemistry, University of California, San Diego, San Diego, CA, USA
| | - Yasin El Abiead
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Wout Bittremieux
- Department of Computer Science, University of Antwerp, 2020 Antwerpen, Belgium
| | - Shipei Xing
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA; Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, Vancouver, BC, Canada
| | - Robin Schmid
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA; Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Simone Zuffa
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Felipe Vasquez
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Valentina B Muti
- Department of Computer Science and Engineering, University of California, Riverside, Riverside, CA, USA; Department of Chemistry and Biochemistry, University of Denver, Denver, CO 80210, USA
| | - Jasmine Zemlin
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA; Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA
| | - Omar E Tovar-Herrera
- Department of Life Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel; Goldman Sonnenfeldt School of Sustainability and Climate Change, Ben-Gurion University of the Negev, Be'er Sheva 84105, Israel
| | - Sarah Moraïs
- Department of Life Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel; Goldman Sonnenfeldt School of Sustainability and Climate Change, Ben-Gurion University of the Negev, Be'er Sheva 84105, Israel
| | - Dhimant Desai
- Department of Pharmacology, Penn State University College of Medicine, Hershey, PA, USA
| | - Shantu Amin
- Department of Pharmacology, Penn State University College of Medicine, Hershey, PA, USA
| | - Imhoi Koo
- Center for Molecular Toxicology and Carcinogenesis, Department of Veterinary and Biomedical Sciences, Pennsylvania State University, University Park, PA, USA
| | - Christoph W Turck
- Max Planck Institute of Psychiatry, Proteomics and Biomarkers, Kraepelinstrasse 2-10, Munich 80804, Germany; Key Laboratory of Animal Models and Human Disease Mechanisms of Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650201, China
| | - Itzhak Mizrahi
- Department of Life Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel; Goldman Sonnenfeldt School of Sustainability and Climate Change, Ben-Gurion University of the Negev, Be'er Sheva 84105, Israel
| | - Penny M Kris-Etherton
- Department of Nutritional Sciences, The Pennsylvania State University, University Park, PA, USA
| | - Kristina S Petersen
- Department of Nutritional Sciences, The Pennsylvania State University, University Park, PA, USA
| | - Jennifer A Fleming
- Department of Nutritional Sciences, The Pennsylvania State University, University Park, PA, USA
| | - Tao Huan
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, Vancouver, BC, Canada
| | - Andrew D Patterson
- Center for Molecular Toxicology and Carcinogenesis, Department of Veterinary and Biomedical Sciences, Pennsylvania State University, University Park, PA, USA
| | - Dionicio Siegel
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Lee R Hagey
- Department of Medicine, University of California, San Diego, San Diego, CA, USA
| | - Mingxun Wang
- Department of Computer Science and Engineering, University of California, Riverside, Riverside, CA, USA
| | - Allegra T Aron
- Department of Chemistry and Biochemistry, University of Denver, Denver, CO 80210, USA
| | - Pieter C Dorrestein
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA; Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA; Department of Pharmacology, University of California, San Diego, La Jolla, CA 92093, USA; Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA.
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6
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Engler Hart C, Kind T, Dorrestein PC, Healey D, Domingo-Fernández D. Weighting Low-Intensity MS/MS Ions and m/ z Frequency for Spectral Library Annotation. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024; 35:266-274. [PMID: 38271611 PMCID: PMC10854760 DOI: 10.1021/jasms.3c00353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 12/29/2023] [Accepted: 01/05/2024] [Indexed: 01/27/2024]
Abstract
Calculating spectral similarity is a fundamental step in MS/MS data analysis in untargeted metabolomics experiments, as it facilitates the identification of related spectra and the annotation of compounds. To improve matching accuracy when querying an experimental mass spectrum against a spectral library, previous approaches have proposed increasing peak intensities for high m/z ranges. These high m/z values tend to be smaller in magnitude, yet they offer more crucial information for identifying the chemical structure. Here, we evaluate the impact of using these weights for identifying structurally related compounds and mass spectral library searches. Additionally, we propose a weighting approach that (i) takes into account the frequency of the m/z values within a spectral library in order to assign higher importance to the most common peaks and (ii) increases the intensity of lower peaks, similar to previous approaches. To demonstrate our approach, we applied weighting preprocessing to modified cosine, entropy, and fidelity distance metrics and benchmarked it against previously reported weights. Our results demonstrate how weighting-based preprocessing can assist in annotating the structure of unknown spectra as well as identifying structurally similar compounds. Finally, we examined scenarios in which the utilization of weights resulted in diminished performance, pinpointing spectral features where the application of weights might be detrimental.
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Affiliation(s)
- Chloe Engler Hart
- Enveda Biosciences, 5700 Flatiron Parkway, Boulder, Colorado 80301, United States
| | - Tobias Kind
- Enveda Biosciences, 5700 Flatiron Parkway, Boulder, Colorado 80301, United States
| | - 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
| | - David Healey
- Enveda Biosciences, 5700 Flatiron Parkway, Boulder, Colorado 80301, United States
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7
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Bittremieux W, Avalon NE, Thomas SP, Kakhkhorov SA, Aksenov AA, Gomes PWP, Aceves CM, Caraballo-Rodríguez AM, Gauglitz JM, Gerwick WH, Huan T, Jarmusch AK, Kaddurah-Daouk RF, Kang KB, Kim HW, Kondić T, Mannochio-Russo H, Meehan MJ, Melnik AV, Nothias LF, O'Donovan C, Panitchpakdi M, Petras D, Schmid R, Schymanski EL, van der Hooft JJJ, Weldon KC, Yang H, Xing S, Zemlin J, Wang M, Dorrestein PC. Open access repository-scale propagated nearest neighbor suspect spectral library for untargeted metabolomics. Nat Commun 2023; 14:8488. [PMID: 38123557 PMCID: PMC10733301 DOI: 10.1038/s41467-023-44035-y] [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: 09/05/2023] [Accepted: 11/28/2023] [Indexed: 12/23/2023] Open
Abstract
Despite the increasing availability of tandem mass spectrometry (MS/MS) community spectral libraries for untargeted metabolomics over the past decade, the majority of acquired MS/MS spectra remain uninterpreted. To further aid in interpreting unannotated spectra, we created a nearest neighbor suspect spectral library, consisting of 87,916 annotated MS/MS spectra derived from hundreds of millions of MS/MS spectra originating from published untargeted metabolomics experiments. Entries in this library, or "suspects," were derived from unannotated spectra that could be linked in a molecular network to an annotated spectrum. Annotations were propagated to unknowns based on structural relationships to reference molecules using MS/MS-based spectrum alignment. We demonstrate the broad relevance of the nearest neighbor suspect spectral library through representative examples of propagation-based annotation of acylcarnitines, bacterial and plant natural products, and drug metabolism. Our results also highlight how the library can help to better understand an Alzheimer's brain phenotype. The nearest neighbor suspect spectral library is openly available for download or for data analysis through the GNPS platform to help investigators hypothesize candidate structures for unknown MS/MS spectra in untargeted metabolomics data.
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Affiliation(s)
- Wout Bittremieux
- Department of Computer Science, University of Antwerp, 2020, Antwerpen, Belgium.
| | - Nicole E Avalon
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, 92093, USA
| | - Sydney P Thomas
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
| | - Sarvar A Kakhkhorov
- Laboratory of Physical and Chemical Methods of Research, Center for Advanced Technologies, Tashkent, 100174, Uzbekistan
- Department of Food Science, Faculty of Science, University of Copenhagen, Rolighedsvej 26, 1958, Frederiksberg C, Denmark
| | - Alexander A Aksenov
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Chemistry, University of Connecticut, Storrs, CT, 06269, USA
- Arome Science inc., Farmington, CT, 06032, USA
| | - Paulo Wender P Gomes
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
| | - Christine M Aceves
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, 92037, USA
| | - Andrés Mauricio Caraballo-Rodríguez
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
| | - Julia M Gauglitz
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
| | - William H Gerwick
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, 92093, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
| | - Tao Huan
- Department of Chemistry, University of British Columbia, Vancouver, BC, V6T 1Z1, Canada
| | - Alan K Jarmusch
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
- Immunity, Inflammation, and Disease Laboratory, Division of Intramural Research, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, Durham, NC, 27709, USA
| | - Rima F Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, 27701, USA
- Department of Medicine, Duke University, Durham, NC, 27710, USA
- Duke Institute of Brain Sciences, Duke University, Durham, NC, 27710, USA
| | - Kyo Bin Kang
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Sookmyung Women's University, Seoul, 04310, Korea
| | - Hyun Woo Kim
- College of Pharmacy and Integrated Research Institute for Drug Development, Dongguk University, Goyang, 10326, Korea
| | - Todor Kondić
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4367, Belvaux, Luxembourg
| | - Helena Mannochio-Russo
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Biochemistry and Organic Chemistry, Institute of Chemistry, São Paulo State University, Araraquara, 14800-901, Brazil
| | - Michael J Meehan
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
| | - Alexey V Melnik
- Department of Chemistry, University of Connecticut, Storrs, CT, 06269, USA
- Arome Science inc., Farmington, CT, 06032, USA
| | - Louis-Felix Nothias
- Université Côte d'Azur, CNRS, ICN, Nice, France
- Interdisciplinary Institute for Artificial Intelligence (3iA) Côte d'Azur, Nice, France
| | - Claire O'Donovan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Morgan Panitchpakdi
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
| | - Daniel Petras
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
- Interfaculty Institute of Microbiology and Infection Medicine, University of Tuebingen, 72076, Tuebingen, Germany
- Department of Biochemistry, University of California Riverside, Riverside, CA, 92507, USA
| | - Robin Schmid
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
| | - Emma L Schymanski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4367, Belvaux, Luxembourg
| | - Justin J J van der Hooft
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
- Bioinformatics Group, Wageningen University & Research, 6708 PB, Wageningen, The Netherlands
| | - Kelly C Weldon
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
| | - Heejung Yang
- Laboratory of Natural Products Chemistry, College of Pharmacy, Kangwon National University, Chuncheon, 24341, Korea
| | - Shipei Xing
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Chemistry, University of British Columbia, Vancouver, BC, V6T 1Z1, Canada
| | - Jasmine Zemlin
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
| | - Mingxun Wang
- Department of Computer Science and Engineering, University of California Riverside, Riverside, CA, 92507, USA
| | - Pieter C Dorrestein
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA.
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA.
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8
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Jung YH, Kim JH. Feature-Based Molecular Networking Combined with Multivariate Analysis for the Characterization of Glutathione Adducts as a Smoking Gun of Bioactivation. Anal Chem 2023; 95:17450-17457. [PMID: 37976220 DOI: 10.1021/acs.analchem.3c01094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
Feature-based molecular networking (FBMN) is a powerful analytical tool for mass spectrometry (MS)-based untargeted metabolomics data analysis. FBMN plays an important role in drug metabolism studies, enabling the visualization of complex metabolomics data to achieve metabolite characterization. In this study, we propose a strategy for the characterization of glutathione (GSH) adducts formed via in vitro metabolic activation using FBMN assisted by multivariate analysis (MVA). Acetaminophen was used as a model substrate for method development, and the practical potential of the method was investigated by its application to 2-aminophenol (2-AP) and 2,4-dinitrochlorobenzene (DNCB). Two 2-AP GSH adducts and one DNCB GSH adduct were successfully characterized by forming networks with GSH even though the mass spectral information obtained for the parent compound was deficient. False positives were effectively filtered out by the variable influence on projection cutoff criteria obtained from orthogonal partial least-squares-discriminant analysis. The GSH adducts formed by enzymatic or nonenzymatic reactions were intuitively distinguished by the pie chart of FBMN results. In summary, our approach effectively characterizes GSH adducts, which serve as compelling evidence of bioactivation. It can be widely utilized to enhance risk assessment in the context of drug metabolism.
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Affiliation(s)
- Young-Heun Jung
- College of Pharmacy, Yeungnam University, Gyeongsan 38541, Republic of Korea
| | - Ju-Hyun Kim
- College of Pharmacy, Yeungnam University, Gyeongsan 38541, Republic of Korea
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9
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Ni X, Murray NB, Archer-Hartmann S, Pepi LE, Helm RF, Azadi P, Hong P. Toward Automatic Inference of Glycan Linkages Using MS n and Machine Learning─Proof of Concept Using Sialic Acid Linkages. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:2127-2135. [PMID: 37621000 PMCID: PMC10557947 DOI: 10.1021/jasms.3c00132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 08/10/2023] [Accepted: 08/15/2023] [Indexed: 08/26/2023]
Abstract
Glycosidic linkages in oligosaccharides play essential roles in determining their chemical properties and biological activities. MSn has been widely used to infer glycosidic linkages but requires a substantial amount of starting material, which limits its application. In addition, there is a lack of rigorous research on what MSn protocols are proper for characterizing glycosidic linkages. In this work, to deliver high-quality experimental data and analysis results, we propose a machine learning-based framework to establish appropriate MSn protocols and build effective data analysis methods. We demonstrate the proof-of-principle by applying our approach to elucidate sialic acid linkages (α2'-3' and α2'-6') in a set of sialyllactose standards and NIST sialic acid-containing N-glycans as well as identify several protocol configurations for producing high-quality experimental data. Our companion data analysis method achieves nearly 100% accuracy in classifying α2'-3' vs α2'-6' using MS5, MS4, MS3, or even MS2 spectra alone. The ability to determine glycosidic linkages using MS2 or MS3 is significant as it requires substantially less sample, enabling linkage analysis for quantity-limited natural glycans and synthesized materials, as well as shortens the overall experimental time. MS2 is also more amenable than MS3/4/5 to automation when coupled to direct infusion or LC-MS. Additionally, our method can predict the ratio of α2'-3' and α2'-6' in a mixture with 8.6% RMSE (root-mean-square error) across data sets using MS5 spectra. We anticipate that our framework will be generally applicable to analysis of other glycosidic linkages.
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Affiliation(s)
- Xinyi Ni
- Computer
Science, Brandeis University, Waltham, Massachusetts 02453, United States
| | - Nathan B. Murray
- Complex
Carbohydrate Research Center, University
of Georgia, Athens, Georgia 30602, United States
| | | | - Lauren E. Pepi
- Complex
Carbohydrate Research Center, University
of Georgia, Athens, Georgia 30602, United States
| | - Richard F. Helm
- Department
of Biochemistry, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Parastoo Azadi
- Complex
Carbohydrate Research Center, University
of Georgia, Athens, Georgia 30602, United States
| | - Pengyu Hong
- Computer
Science, Brandeis University, Waltham, Massachusetts 02453, United States
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10
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Miller A, York EM, Stopka SA, Martínez-François JR, Hossain MA, Baquer G, Regan MS, Agar NYR, Yellen G. Spatially resolved metabolomics and isotope tracing reveal dynamic metabolic responses of dentate granule neurons with acute stimulation. Nat Metab 2023; 5:1820-1835. [PMID: 37798473 PMCID: PMC10626993 DOI: 10.1038/s42255-023-00890-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 08/09/2023] [Indexed: 10/07/2023]
Abstract
Neuronal activity creates an intense energy demand that must be met by rapid metabolic responses. To investigate metabolic adaptations in the neuron-enriched dentate granule cell (DGC) layer within its native tissue environment, we employed murine acute hippocampal brain slices, coupled with fast metabolite preservation and followed by mass spectrometry (MS) imaging, to generate spatially resolved metabolomics and isotope-tracing data. Here we show that membrane depolarization induces broad metabolic changes, including increased glycolytic activity in DGCs. Increased glucose metabolism in response to stimulation is accompanied by mobilization of endogenous inosine into pentose phosphates via the action of purine nucleotide phosphorylase (PNP). The PNP reaction is an integral part of the neuronal response to stimulation, because inhibition of PNP leaves DGCs energetically impaired during recovery from strong activation. Performing MS imaging on brain slices bridges the gap between live-cell physiology and the deep chemical analysis enabled by MS.
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Affiliation(s)
- Anne Miller
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Center for Pathobiochemistry and Genetics, Medical University of Vienna, Vienna, Austria
| | - Elisa M York
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Sylwia A Stopka
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Md Amin Hossain
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Gerard Baquer
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael S Regan
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Nathalie Y R Agar
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Cancer Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
| | - Gary Yellen
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA.
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11
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Li Y, Fiehn O. Flash entropy search to query all mass spectral libraries in real time. Nat Methods 2023; 20:1475-1478. [PMID: 37735567 DOI: 10.1038/s41592-023-02012-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 08/15/2023] [Indexed: 09/23/2023]
Abstract
Public repositories of metabolomics mass spectra encompass more than 1 billion entries. With open search, dot product or entropy similarity, comparisons of a single tandem mass spectrometry spectrum take more than 8 h. Flash entropy search speeds up calculations more than 10,000 times to query 1 billion spectra in less than 2 s, without loss in accuracy. It benefits from using multiple threads and GPU calculations. This algorithm can fully exploit large spectral libraries with little memory overhead for any mass spectrometry laboratory.
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Affiliation(s)
- Yuanyue Li
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, CA, USA
| | - Oliver Fiehn
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, CA, USA.
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12
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Carvalho ARV, Reis JDE, Gomes PWP, Ferraz AC, Mardegan HA, Menegatto MBDS, Souza Lima RL, de Sarges MRV, Pamplona SDGSR, Jeunon Gontijo KS, de Magalhães JC, da Silva MN, Magalhães CLDB, Silva CYYE. Untargeted-based metabolomics analysis and in vitro/in silico antiviral activity of extracts from Phyllanthus brasiliensis (Aubl.) Poir. PHYTOCHEMICAL ANALYSIS : PCA 2023; 34:869-883. [PMID: 37403427 DOI: 10.1002/pca.3259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 06/05/2023] [Accepted: 06/14/2023] [Indexed: 07/06/2023]
Abstract
INTRODUCTION This study describes the molecular profile and the potential antiviral activity of extracts from Phyllanthus brasiliensis, a plant widely found in the Brazilian Amazon. The research aims to shed light on the potential use of this species as a natural antiviral agent. METHODS The extracts were analysed using liquid chromatography-mass spectrometry (LC-MS) system, a potent analytical technique to discover drug candidates. In the meantime, in vitro antiviral assays were performed against Mayaro, Oropouche, Chikungunya, and Zika viruses. In addition, the antiviral activity of annotated compounds was predicted by in silico methods. RESULTS Overall, 44 compounds were annotated in this study. The results revealed that P. brasiliensis has a high content of fatty acids, flavones, flavan-3-ols, and lignans. Furthermore, in vitro assays revealed potent antiviral activity against different arboviruses, especially lignan-rich extracts against Zika virus (ZIKV), as follows: methanolic extract from bark (MEB) [effective concentration for 50% of the cells (EC50 ) = 0.80 μg/mL, selectivity index (SI) = 377.59], methanolic extract from the leaf (MEL) (EC50 = 0.84 μg/mL, SI = 297.62), and hydroalcoholic extract from the leaf (HEL) (EC50 = 1.36 μg/mL, SI = 735.29). These results were supported by interesting in silico prediction, where tuberculatin (a lignan) showed a high antiviral activity score. CONCLUSIONS Phyllanthus brasiliensis extracts contain metabolites that could be a new kick-off point for the discovery of candidates for antiviral drug development, with lignans becoming a promising trend for further virology research.
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Affiliation(s)
- Alice Rhelly V Carvalho
- Laboratory of Liquid Chromatography, Institute of Exact and Natural Sciences, Federal University of Pará, Belém, Brazil
- Faculty of Pharmacy, Institute of Health Sciences, Federal University of Pará, Belém, Brazil
| | - José Diogo E Reis
- Laboratory of Liquid Chromatography, Institute of Exact and Natural Sciences, Federal University of Pará, Belém, Brazil
- Chemistry Post-Graduation Programme, Institute of Exact and Natural Sciences, Federal University of Pará, Belém, Brazil
| | - Paulo Wender P Gomes
- Collaborative Mass Spectrometry Innovation Centre, University of California San Diego, La Jolla, California, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, USA
| | - Ariane Coelho Ferraz
- Programa de Pós-Graduação em Ciências Biológicas, Núcleo de Pesquisas em Ciências Biológicas, Universidade Federal de Ouro Preto, Ouro Preto, Brazil
| | - Horrana A Mardegan
- Laboratory of Liquid Chromatography, Institute of Exact and Natural Sciences, Federal University of Pará, Belém, Brazil
- Pharmaceutical Sciences Post-Graduation Programme, Institute of Health Sciences, Federal University of Pará, Belém, Brazil
| | - Marília Bueno da Silva Menegatto
- Programa de Pós-Graduação em Ciências Biológicas, Núcleo de Pesquisas em Ciências Biológicas, Universidade Federal de Ouro Preto, Ouro Preto, Brazil
| | - Rafaela Lameira Souza Lima
- Programa de Pós-Graduação em Ciências Biológicas, Núcleo de Pesquisas em Ciências Biológicas, Universidade Federal de Ouro Preto, Ouro Preto, Brazil
| | - Maria Rosilda V de Sarges
- Laboratory of Liquid Chromatography, Institute of Exact and Natural Sciences, Federal University of Pará, Belém, Brazil
- Pharmaceutical Sciences Post-Graduation Programme, Institute of Health Sciences, Federal University of Pará, Belém, Brazil
| | - Sônia das G S R Pamplona
- Laboratory of Liquid Chromatography, Institute of Exact and Natural Sciences, Federal University of Pará, Belém, Brazil
- Chemistry Post-Graduation Programme, Institute of Exact and Natural Sciences, Federal University of Pará, Belém, Brazil
| | | | - José Carlos de Magalhães
- Programa de Pós-Graduação em Biotecnologia, Universidade Federal de São João del-Rei, São João del Rei, Brazil
| | - Milton N da Silva
- Laboratory of Liquid Chromatography, Institute of Exact and Natural Sciences, Federal University of Pará, Belém, Brazil
- Chemistry Post-Graduation Programme, Institute of Exact and Natural Sciences, Federal University of Pará, Belém, Brazil
| | - Cintia Lopes de Brito Magalhães
- Programa de Pós-Graduação em Ciências Biológicas, Núcleo de Pesquisas em Ciências Biológicas, Universidade Federal de Ouro Preto, Ouro Preto, Brazil
- Programa de Pós-Graduação em Biotecnologia, Universidade Federal de São João del-Rei, São João del Rei, Brazil
- Programa de Pós-Graduação em Biotecnologia, Núcleo de Pesquisas em Ciências Biológicas, Universidade Federal de Ouro Preto, Ouro Preto, Brazil
| | - Consuelo Yumiko Yoshioka E Silva
- Laboratory of Liquid Chromatography, Institute of Exact and Natural Sciences, Federal University of Pará, Belém, Brazil
- Faculty of Pharmacy, Institute of Health Sciences, Federal University of Pará, Belém, Brazil
- Pharmaceutical Sciences Post-Graduation Programme, Institute of Health Sciences, Federal University of Pará, Belém, Brazil
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13
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Moorthy AS, Erisman EP, Kearsley AJ, Liang Y, Sisco E, Wallace WE. On the challenge of unambiguous identification of fentanyl analogs: Exploring measurement diversity using standard reference mass spectral libraries. J Forensic Sci 2023; 68:1494-1503. [PMID: 37431311 PMCID: PMC10517722 DOI: 10.1111/1556-4029.15322] [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: 05/10/2023] [Revised: 06/13/2023] [Accepted: 06/22/2023] [Indexed: 07/12/2023]
Abstract
Fentanyl analogs are a class of designer drugs that are particularly challenging to unambiguously identify due to the mass spectral and retention time similarities of unique compounds. In this paper, we use agglomerative hierarchical clustering to explore the measurement diversity of fentanyl analogs and better understand the challenge of unambiguous identifications using analytical techniques traditionally available to drug chemists. We consider four measurements in particular: gas chromatography retention indices, electron ionization mass spectra, electrospray ionization tandem mass spectra, and direct analysis in real time mass spectra. Our analysis demonstrates how simultaneously considering data from multiple measurement techniques increases the observable measurement diversity of fentanyl analogs, which can reduce identification ambiguity. This paper further supports the use of multiple analytical techniques to identify fentanyl analogs (among other substances), as is recommended by the Scientific Working Group for the Analysis of Seized Drugs (SWGDRUG).
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Affiliation(s)
- Arun S Moorthy
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, Gaithersburg, Maryland, USA
| | - Edward P Erisman
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, Gaithersburg, Maryland, USA
| | - Anthony J Kearsley
- Mathematical Analysis and Modeling Group, Applied and Computational Mathematics Division, National Institute of Standards and Technology, Gaithersburg, Maryland, USA
| | - Yuxue Liang
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, Gaithersburg, Maryland, USA
| | - Edward Sisco
- Surface and Trace Chemical Analysis Group, Materials Measurement Science Division, National Institute of Standards and Technology, Gaithersburg, Maryland, USA
| | - William E Wallace
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, Gaithersburg, Maryland, USA
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14
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Ellin NR, Miranda-Quintana RA, Prentice BM. Extended Similarity Methods for Efficient Data Mining in Imaging Mass Spectrometry. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.27.550838. [PMID: 37546817 PMCID: PMC10402165 DOI: 10.1101/2023.07.27.550838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Imaging mass spectrometry is a label-free imaging modality that allows for the spatial mapping of many compounds directly in tissues. In an imaging mass spectrometry experiment, a raster of the tissue surface produces a mass spectrum at each sampled x , y position, resulting in thousands of individual mass spectra, each comprising a pixel in the resulting ion images. However, efficient analysis of imaging mass spectrometry datasets can be challenging due to the hyperspectral characteristics of the data. Each spectrum contains several thousand unique compounds at discrete m/z values that result in unique ion images, which demands robust and efficient algorithms for searching, statistical analysis, and visualization. Some traditional post-processing techniques are fundamentally ill-equipped to dissect these types of data. For example, while principal component analysis (PCA) has long served as a useful tool for mining imaging mass spectrometry datasets to identify correlated analytes and biological regions of interest, the interpretation of the PCA scores and loadings can be non-trivial. The loadings often containing negative peaks in the PCA-derived pseudo-spectra, which are difficult to ascribe to underlying tissue biology. Herein, we have utilized extended similarity indices to streamline the interpretation of imaging mass spectrometry data. This novel workflow uses PCA as a pixel-selection method to parse out the most and least correlated pixels, which are then compared using the extended similarity indices. The extended similarity indices complement PCA by removing all non-physical artifacts and streamlining the interpretation of large volumes of IMS spectra simultaneously. The linear complexity, O ( N ) , of these indices suggests that large imaging mass spectrometry datasets can be analyzed in a 1:1 scale of time and space with respect to the size of the input data. The extended similarity indices algorithmic workflow is exemplified here by identifying discrete biological regions of mouse brain tissue.
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Affiliation(s)
- Nicholas R Ellin
- Department of Chemistry, University of Florida, Gainesville, FL, 32611-7200; USA
| | - Ramón Alain Miranda-Quintana
- Department of Chemistry, University of Florida, Gainesville, FL, 32611-7200; USA
- Quantum Theory Project, University of Florida, Gainesville, FL, 32611-7200; USA
| | - Boone M Prentice
- Department of Chemistry, University of Florida, Gainesville, FL, 32611-7200; USA
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15
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Miller A, York E, Stopka S, Martínez-François J, Hossain MA, Baquer G, Regan M, Agar N, Yellen G. Spatially resolved metabolomics and isotope tracing reveal dynamic metabolic responses of dentate granule neurons with acute stimulation. RESEARCH SQUARE 2023:rs.3.rs-2276903. [PMID: 37546759 PMCID: PMC10402263 DOI: 10.21203/rs.3.rs-2276903/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Neuronal activity creates an intense energy demand that must be met by rapid metabolic responses. To investigate metabolic adaptations in the neuron-enriched dentate granule cell (DGC) layer within its native tissue environment, we employed murine acute hippocampal brain slices coupled with fast metabolite preservation, followed by mass spectrometry imaging (MALDI-MSI) to generate spatially resolved metabolomics and isotope tracing data. Here we show that membrane depolarization induces broad metabolic changes, including increased glycolytic activity in DGCs. Increased glucose metabolism in response to stimulation is accompanied by mobilization of endogenous inosine into pentose phosphates, via the action of purine nucleotide phosphorylase (PNP). The PNP reaction is an integral part of the neuronal response to stimulation, as inhibiting PNP leaves DGCs energetically impaired during recovery from strong activation. Performing MSI on brain slices bridges the gap between live cell physiology and the deep chemical analysis enabled by mass spectrometry.
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16
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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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