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Tong J, Lu M, Wang R, An S, Wang J, Wang T, Xie C, Yu C. How Much Storage Precision Can Be Lost: Guidance for Near-Lossless Compression of Untargeted Metabolomics Mass Spectrometry Data. J Proteome Res 2024; 23:1702-1712. [PMID: 38640356 DOI: 10.1021/acs.jproteome.3c00851] [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] [Indexed: 04/21/2024]
Abstract
Several lossy compressors have achieved superior compression rates for mass spectrometry (MS) data at the cost of storage precision. Currently, the impacts of precision losses on MS data processing have not been thoroughly evaluated, which is critical for the future development of lossy compressors. We first evaluated different storage precision (32 bit and 64 bit) in lossless mzML files. We then applied 10 truncation transformations to generate precision-lossy files: five relative errors for intensities and five absolute errors for m/z values. MZmine3 and XCMS were used for feature detection and GNPS for compound annotation. Lastly, we compared Precision, Recall, F1 - score, and file sizes between lossy files and lossless files under different conditions. Overall, we revealed that the discrepancy between 32 and 64 bit precision was under 1%. We proposed an absolute m/z error of 10-4 and a relative intensity error of 2 × 10-2, adhering to a 5% error threshold (F1 - scores above 95%). For a stricter 1% error threshold (F1 - scores above 99%), an absolute m/z error of 2 × 10-5 and a relative intensity error of 2 × 10-3 were advised. This guidance aims to help researchers improve lossy compression algorithms and minimize the negative effects of precision losses on downstream data processing.
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Affiliation(s)
- Junjie Tong
- Central Hospital Affiliated to Shandong First Medical University, Jinan 250000, Shandong, China
- Key Laboratory of Tropical Medicinal Plant Chemistry of Ministry of Education, College of Chemistry and Chemical Engineering, Hainan Normal University, Haikou 571158, Hainan, China
| | - Miaoshan Lu
- Central Hospital Affiliated to Shandong First Medical University, Jinan 250000, Shandong, China
| | - Ruimin Wang
- Central Hospital Affiliated to Shandong First Medical University, Jinan 250000, Shandong, China
- Fudan University, Shanghai 200000, China
- Westlake University, Hangzhou 310024, Zhejiang, China
| | - Shaowei An
- Fudan University, Shanghai 200000, China
- Westlake University, Hangzhou 310024, Zhejiang, China
| | - Jinyin Wang
- Westlake University, Hangzhou 310024, Zhejiang, China
- Zhejiang University, Hangzhou 310009, Zhejiang, China
| | - Tong Wang
- Central Hospital Affiliated to Shandong First Medical University, Jinan 250000, Shandong, China
| | - Cong Xie
- Central Hospital Affiliated to Shandong First Medical University, Jinan 250000, Shandong, China
- Key Laboratory of Tropical Medicinal Plant Chemistry of Ministry of Education, College of Chemistry and Chemical Engineering, Hainan Normal University, Haikou 571158, Hainan, China
| | - Changbin Yu
- Central Hospital Affiliated to Shandong First Medical University, Jinan 250000, Shandong, China
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2
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Böttcher B, Kienast SD, Leufken J, Eggers C, Sharma P, Leufken CM, Morgner B, Drexler HCA, Schulz D, Allert S, Jacobsen ID, Vylkova S, Leidel SA, Brunke S. A highly conserved tRNA modification contributes to C. albicans filamentation and virulence. Microbiol Spectr 2024; 12:e0425522. [PMID: 38587411 PMCID: PMC11064501 DOI: 10.1128/spectrum.04255-22] [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: 10/18/2022] [Accepted: 01/18/2024] [Indexed: 04/09/2024] Open
Abstract
tRNA modifications play important roles in maintaining translation accuracy in all domains of life. Disruptions in the tRNA modification machinery, especially of the anticodon stem loop, can be lethal for many bacteria and lead to a broad range of phenotypes in baker's yeast. Very little is known about the function of tRNA modifications in host-pathogen interactions, where rapidly changing environments and stresses require fast adaptations. We found that two closely related fungal pathogens of humans, the highly pathogenic Candida albicans and its much less pathogenic sister species, Candida dubliniensis, differ in the function of a tRNA-modifying enzyme. This enzyme, Hma1, exhibits species-specific effects on the ability of the two fungi to grow in the hypha morphology, which is central to their virulence potential. We show that Hma1 has tRNA-threonylcarbamoyladenosine dehydratase activity, and its deletion alters ribosome occupancy, especially at 37°C-the body temperature of the human host. A C. albicans HMA1 deletion mutant also shows defects in adhesion to and invasion into human epithelial cells and shows reduced virulence in a fungal infection model. This links tRNA modifications to host-induced filamentation and virulence of one of the most important fungal pathogens of humans.IMPORTANCEFungal infections are on the rise worldwide, and their global burden on human life and health is frequently underestimated. Among them, the human commensal and opportunistic pathogen, Candida albicans, is one of the major causative agents of severe infections. Its virulence is closely linked to its ability to change morphologies from yeasts to hyphae. Here, this ability is linked-to our knowledge for the first time-to modifications of tRNA and translational efficiency. One tRNA-modifying enzyme, Hma1, plays a specific role in C. albicans and its ability to invade the host. This adds a so-far unknown layer of regulation to the fungal virulence program and offers new potential therapeutic targets to fight fungal infections.
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Affiliation(s)
- Bettina Böttcher
- Department of Microbial Pathogenicity Mechanisms, Leibniz Institute for Natural Product Research and Infection Biology – Hans Knoell Institute, Jena, Germany
- Septomics Research Center, Friedrich Schiller University and Leibniz Institute for Natural Product Research and Infection Biology – Hans Knoell Institute, Jena, Germany
| | - Sandra D. Kienast
- Max Planck Research Group for RNA Biology, Max Planck Institute for Molecular Biomedicine, Münster, Germany
- Research Group for Cellular RNA Biochemistry, Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern, Bern, Switzerland
| | - Johannes Leufken
- Max Planck Research Group for RNA Biology, Max Planck Institute for Molecular Biomedicine, Münster, Germany
- Research Group for Cellular RNA Biochemistry, Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern, Bern, Switzerland
| | - Cristian Eggers
- Max Planck Research Group for RNA Biology, Max Planck Institute for Molecular Biomedicine, Münster, Germany
- Research Group for Cellular RNA Biochemistry, Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern, Bern, Switzerland
| | - Puneet Sharma
- Max Planck Research Group for RNA Biology, Max Planck Institute for Molecular Biomedicine, Münster, Germany
- Research Group for Cellular RNA Biochemistry, Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern, Bern, Switzerland
| | - Christine M. Leufken
- Max Planck Research Group for RNA Biology, Max Planck Institute for Molecular Biomedicine, Münster, Germany
| | - Bianka Morgner
- Department of Microbial Pathogenicity Mechanisms, Leibniz Institute for Natural Product Research and Infection Biology – Hans Knoell Institute, Jena, Germany
| | - Hannes C. A. Drexler
- Bioanalytical Mass Spectrometry Unit, Max Planck Institute for Molecular Biomedicine, Münster, Germany
| | - Daniela Schulz
- Department of Microbial Pathogenicity Mechanisms, Leibniz Institute for Natural Product Research and Infection Biology – Hans Knoell Institute, Jena, Germany
| | - Stefanie Allert
- Department of Microbial Pathogenicity Mechanisms, Leibniz Institute for Natural Product Research and Infection Biology – Hans Knoell Institute, Jena, Germany
| | - Ilse D. Jacobsen
- Research Group Microbial Immunology, Leibniz Institute for Natural Product Research and Infection Biology – Hans Knoell Institute, Jena, Germany
- Institute of Microbiology, Friedrich Schiller University, Jena, Germany
| | - Slavena Vylkova
- Septomics Research Center, Friedrich Schiller University and Leibniz Institute for Natural Product Research and Infection Biology – Hans Knoell Institute, Jena, Germany
| | - Sebastian A. Leidel
- Max Planck Research Group for RNA Biology, Max Planck Institute for Molecular Biomedicine, Münster, Germany
- Research Group for Cellular RNA Biochemistry, Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern, Bern, Switzerland
| | - Sascha Brunke
- Department of Microbial Pathogenicity Mechanisms, Leibniz Institute for Natural Product Research and Infection Biology – Hans Knoell Institute, Jena, Germany
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3
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Wüthrich C, Zenobi R, Giannoukos S. Alternative electrolyte solutions for untargeted breath metabolomics using secondary-electrospray ionization high-resolution mass spectrometry. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2024; 38:e9714. [PMID: 38389333 DOI: 10.1002/rcm.9714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 01/15/2024] [Accepted: 01/16/2024] [Indexed: 02/24/2024]
Abstract
RATIONALE Secondary-electrospray ionization (SESI) coupled with high-resolution mass spectrometry is a powerful tool for the discovery of biomarkers in exhaled breath. A primary electrospray consisting of aqueous formic acid (FA) is currently used to charge the volatile organic compounds in breath. To investigate whether alternate electrospray compositions could enable different metabolite coverage and sensitivities, the electrospray dopants NaI and AgNO3 were tested. METHODS In a proof-of-principle manner, the exhaled breath of one subject was analyzed repeatedly with different electrospray solutions and with the help of a spectral stitching technique. Capillary diameter and position were optimized to achieve proper detection of exhaled breath. The detected features were then compared using formula annotation. Using an evaporation-based gas standard system, the signal response of the different solutions was probed. RESULTS Principal component analysis revealed a substantial difference in features detected with AgNO3 . With silver, more sulfur-containing features and more unsaturated hydrocarbon compounds were detected. Furthermore, more primary amines were potentially ionized, as indicated by van Krewelen diagrams. In total, twice as many features were unique to AgNO3 than for other electrospray dopants. Using gas standards at known concentrations, the high sensitivity of FA as a dopant was demonstrated but also indicated alternate sensitivities of the other electrospray solutions. CONCLUSIONS This work demonstrated the potential of AgNO3 as a complementary dopant for further biomarker discovery in SESI-based breath analysis.
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Affiliation(s)
- Cedric Wüthrich
- Department of Chemistry and Applied Biosciences, ETHZ, Zurich, Switzerland
| | - Renato Zenobi
- Department of Chemistry and Applied Biosciences, ETHZ, Zurich, Switzerland
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4
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Picciani M, Gabriel W, Giurcoiu VG, Shouman O, Hamood F, Lautenbacher L, Jensen CB, Müller J, Kalhor M, Soleymaniniya A, Kuster B, The M, Wilhelm M. Oktoberfest: Open-source spectral library generation and rescoring pipeline based on Prosit. Proteomics 2024; 24:e2300112. [PMID: 37672792 DOI: 10.1002/pmic.202300112] [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: 06/14/2023] [Revised: 08/17/2023] [Accepted: 08/18/2023] [Indexed: 09/08/2023]
Abstract
Machine learning (ML) and deep learning (DL) models for peptide property prediction such as Prosit have enabled the creation of high quality in silico reference libraries. These libraries are used in various applications, ranging from data-independent acquisition (DIA) data analysis to data-driven rescoring of search engine results. Here, we present Oktoberfest, an open source Python package of our spectral library generation and rescoring pipeline originally only available online via ProteomicsDB. Oktoberfest is largely search engine agnostic and provides access to online peptide property predictions, promoting the adoption of state-of-the-art ML/DL models in proteomics analysis pipelines. We demonstrate its ability to reproduce and even improve our results from previously published rescoring analyses on two distinct use cases. Oktoberfest is freely available on GitHub (https://github.com/wilhelm-lab/oktoberfest) and can easily be installed locally through the cross-platform PyPI Python package.
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Affiliation(s)
- Mario Picciani
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Wassim Gabriel
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Victor-George Giurcoiu
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Omar Shouman
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Firas Hamood
- Chair of Proteomics and Bioanalytics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Ludwig Lautenbacher
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Cecilia Bang Jensen
- Chair of Proteomics and Bioanalytics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Julian Müller
- Chair of Proteomics and Bioanalytics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Mostafa Kalhor
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Armin Soleymaniniya
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Bernhard Kuster
- Chair of Proteomics and Bioanalytics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Matthew The
- Chair of Proteomics and Bioanalytics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Mathias Wilhelm
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
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5
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Galgonek J, Vondrášek J. The IDSM mass spectrometry extension: searching mass spectra using SPARQL. Bioinformatics 2024; 40:btae174. [PMID: 38561173 PMCID: PMC11034985 DOI: 10.1093/bioinformatics/btae174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 02/24/2024] [Accepted: 03/28/2024] [Indexed: 04/04/2024] Open
Abstract
SUMMARY The Integrated Database of Small Molecules (IDSM) integrates data from small-molecule datasets, making them accessible through the SPARQL query language. Its unique feature is the ability to search for compounds through SPARQL based on their molecular structure. We extended IDSM to enable mass spectra databases to be integrated and searched for based on mass spectrum similarity. As sources of mass spectra, we employed the MassBank of North America database and the In Silico Spectral Database of natural products. AVAILABILITY AND IMPLEMENTATION The extension is an integral part of IDSM, which is available at https://idsm.elixir-czech.cz. The manual and usage examples are available at https://idsm.elixir-czech.cz/docs/ms. The source codes of all IDSM parts are available under open-source licences at https://github.com/idsm-src.
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Affiliation(s)
- Jakub Galgonek
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo náměstí 2, Prague 160 00, Czech Republic
| | - Jiří Vondrášek
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo náměstí 2, Prague 160 00, Czech Republic
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6
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Reder GK, Bjurström EY, Brunnsåker D, Kronström F, Lasin P, Tiukova I, Savolainen OI, Dodds JN, May JC, Wikswo JP, McLean JA, King RD. AutonoMS: Automated Ion Mobility Metabolomic Fingerprinting. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024; 35:542-550. [PMID: 38310603 PMCID: PMC10921458 DOI: 10.1021/jasms.3c00396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 01/11/2024] [Accepted: 01/17/2024] [Indexed: 02/06/2024]
Abstract
Automation is dramatically changing the nature of laboratory life science. Robotic lab hardware that can perform manual operations with greater speed, endurance, and reproducibility opens an avenue for faster scientific discovery with less time spent on laborious repetitive tasks. A major bottleneck remains in integrating cutting-edge laboratory equipment into automated workflows, notably specialized analytical equipment, which is designed for human usage. Here we present AutonoMS, a platform for automatically running, processing, and analyzing high-throughput mass spectrometry experiments. AutonoMS is currently written around an ion mobility mass spectrometry (IM-MS) platform and can be adapted to additional analytical instruments and data processing flows. AutonoMS enables automated software agent-controlled end-to-end measurement and analysis runs from experimental specification files that can be produced by human users or upstream software processes. We demonstrate the use and abilities of AutonoMS in a high-throughput flow-injection ion mobility configuration with 5 s sample analysis time, processing robotically prepared chemical standards and cultured yeast samples in targeted and untargeted metabolomics applications. The platform exhibited consistency, reliability, and ease of use while eliminating the need for human intervention in the process of sample injection, data processing, and analysis. The platform paves the way toward a more fully automated mass spectrometry analysis and ultimately closed-loop laboratory workflows involving automated experimentation and analysis coupled to AI-driven experimentation utilizing cutting-edge analytical instrumentation. AutonoMS documentation is available at https://autonoms.readthedocs.io.
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Affiliation(s)
- Gabriel K. Reder
- Department
of Computer Science and Engineering, Chalmers
University of Technology, Gothenburg 412 96, Sweden
- Department
of Applied Physics, SciLifeLab, KTH Royal
Institute of Technology, Solna 171 21, Sweden
| | - Erik Y. Bjurström
- Department
of Life Sciences, Chalmers University of
Technology, Gothenburg 412 96, Sweden
| | - Daniel Brunnsåker
- Department
of Computer Science and Engineering, Chalmers
University of Technology, Gothenburg 412 96, Sweden
| | - Filip Kronström
- Department
of Computer Science and Engineering, Chalmers
University of Technology, Gothenburg 412 96, Sweden
| | - Praphapan Lasin
- Department
of Life Sciences, Chalmers University of
Technology, Gothenburg 412 96, Sweden
| | - Ievgeniia Tiukova
- Department
of Life Sciences, Chalmers University of
Technology, Gothenburg 412 96, Sweden
| | - Otto I. Savolainen
- Department
of Life Sciences, Chalmers University of
Technology, Gothenburg 412 96, Sweden
- Institute
of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio 702 11, Finland
| | - James N. Dodds
- Chemistry
Department, The University of North Carolina
at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Jody C. May
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Center
for Innovative Technology, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - John P. Wikswo
- Vanderbilt
Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department
of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department
of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department
of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee 37240, United States
| | - John A. McLean
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Center
for Innovative Technology, Vanderbilt University, Nashville, Tennessee 37235, United States
- Vanderbilt
Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Ross D. King
- Department
of Computer Science and Engineering, Chalmers
University of Technology, Gothenburg 412 96, Sweden
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, U.K.
- The Alan
Turing Institute, London NW1 2DB, U.K.
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7
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Mitchell JM, Chi Y, Thapa M, Pang Z, Xia J, Li S. Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.13.580048. [PMID: 38405981 PMCID: PMC10888883 DOI: 10.1101/2024.02.13.580048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
To standardize metabolomics data analysis and facilitate future computational developments, it is essential is have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.
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Affiliation(s)
- Joshua M. Mitchell
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA
| | - Yuanye Chi
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA
| | - Maheshwor Thapa
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA
| | - Zhiqiang Pang
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Jianguo Xia
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Shuzhao Li
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA
- University of Connecticut School of Medicine, Farmington, CT 06032, USA
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8
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Zulfiqar M, Crusoe MR, König-Ries B, Steinbeck C, Peters K, Gadelha L. Implementation of FAIR Practices in Computational Metabolomics Workflows-A Case Study. Metabolites 2024; 14:118. [PMID: 38393009 PMCID: PMC10891576 DOI: 10.3390/metabo14020118] [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: 12/21/2023] [Revised: 01/30/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024] Open
Abstract
Scientific workflows facilitate the automation of data analysis tasks by integrating various software and tools executed in a particular order. To enable transparency and reusability in workflows, it is essential to implement the FAIR principles. Here, we describe our experiences implementing the FAIR principles for metabolomics workflows using the Metabolome Annotation Workflow (MAW) as a case study. MAW is specified using the Common Workflow Language (CWL), allowing for the subsequent execution of the workflow on different workflow engines. MAW is registered using a CWL description on WorkflowHub. During the submission process on WorkflowHub, a CWL description is used for packaging MAW using the Workflow RO-Crate profile, which includes metadata in Bioschemas. Researchers can use this narrative discussion as a guideline to commence using FAIR practices for their bioinformatics or cheminformatics workflows while incorporating necessary amendments specific to their research area.
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Affiliation(s)
- Mahnoor Zulfiqar
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, 07743 Jena, Germany;
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, 07743 Jena, Germany;
| | - Michael R. Crusoe
- ELIXIR (The European Life-Sciences Infrastructure for Biological Information) Germany, Institute of Bio- and Geosciences (IBG-5)—Computational Metagenomics, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany;
| | - Birgitta König-Ries
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, 07743 Jena, Germany;
- Institute for Informatics, Friedrich Schiller University Jena, 07743 Jena, Germany
- iDiv—German Centre for Integrative Biodiversity Research, Halle-Jena-Leipzig, 04103 Leipzig, Germany;
| | - Christoph Steinbeck
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, 07743 Jena, Germany;
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, 07743 Jena, Germany;
| | - Kristian Peters
- iDiv—German Centre for Integrative Biodiversity Research, Halle-Jena-Leipzig, 04103 Leipzig, Germany;
- Geobotany and Botanical Gardens, Martin-Luther University of Halle-Wittenberg, 06108 Halle, Germany
- Leibniz Institute of Plant Biochemistry, 06120 Halle, Germany
| | - Luiz Gadelha
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, 07743 Jena, Germany;
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, 07743 Jena, Germany;
- Institute for Informatics, Friedrich Schiller University Jena, 07743 Jena, Germany
- German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
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9
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Walzer M, Jeong K, Tabb DL, Vizcaíno JA. TopDownApp: An open and modular platform for analysis and visualisation of top-down proteomics data. Proteomics 2024; 24:e2200403. [PMID: 37787899 DOI: 10.1002/pmic.202200403] [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: 06/02/2023] [Revised: 09/13/2023] [Accepted: 09/13/2023] [Indexed: 10/04/2023]
Abstract
Although Top-down (TD) proteomics techniques, aimed at the analysis of intact proteins and proteoforms, are becoming increasingly popular, efforts are needed at different levels to generalise their adoption. In this context, there are numerous improvements that are possible in the area of open science practices, including a greater application of the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. These include, for example, increased data sharing practices and readily available open data standards. Additionally, the field would benefit from the development of open data analysis workflows that can enable data reuse of public datasets, something that is increasingly common in other proteomics fields.
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Affiliation(s)
- Mathias Walzer
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK
| | - Kyowon Jeong
- Applied Bioinformatics, Computer Science Department, University of Tübingen, Tübingen, Germany
| | - David L Tabb
- Institut Pasteur, Université Paris Cité, CNRS UAR 2024, Mass Spectrometry for Biology Unit, Paris, France
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK
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10
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Ara T, Kodama Y, Tokimatsu T, Fukuda A, Kosuge T, Mashima J, Tanizawa Y, Tanjo T, Ogasawara O, Fujisawa T, Nakamura Y, Arita M. DDBJ update in 2023: the MetaboBank for metabolomics data and associated metadata. Nucleic Acids Res 2024; 52:D67-D71. [PMID: 37971299 PMCID: PMC10767850 DOI: 10.1093/nar/gkad1046] [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: 10/12/2023] [Revised: 10/21/2023] [Accepted: 10/27/2023] [Indexed: 11/19/2023] Open
Abstract
The Bioinformation and DNA Data Bank of Japan (DDBJ) Center (https://www.ddbj.nig.ac.jp) provides database archives that cover a wide range of fields in life sciences. As a founding member of the International Nucleotide Sequence Database Collaboration (INSDC), DDBJ accepts and distributes nucleotide sequence data as well as their study and sample information along with the National Center for Biotechnology Information in the United States and the European Bioinformatics Institute (EBI). Besides INSDC databases, the DDBJ Center provides databases for functional genomics (GEA: Genomic Expression Archive), metabolomics (MetaboBank) and human genetic and phenotypic data (JGA: Japanese Genotype-phenotype Archive). These database systems have been built on the National Institute of Genetics (NIG) supercomputer, which is also open for domestic life science researchers to analyze large-scale sequence data. This paper reports recent updates on the archival databases and the services of the DDBJ Center, highlighting the newly redesigned MetaboBank. MetaboBank uses BioProject and BioSample in its metadata description making it suitable for multi-omics large studies. Its collaboration with MetaboLights at EBI brings synergy in locating and reusing public data.
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Affiliation(s)
- Takeshi Ara
- Bioinformation and DDBJ Center, National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan
| | - Yuichi Kodama
- Bioinformation and DDBJ Center, National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan
| | - Toshiaki Tokimatsu
- Bioinformation and DDBJ Center, National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan
| | - Asami Fukuda
- Bioinformation and DDBJ Center, National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan
| | - Takehide Kosuge
- Bioinformation and DDBJ Center, National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan
| | - Jun Mashima
- Bioinformation and DDBJ Center, National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan
| | - Yasuhiro Tanizawa
- Bioinformation and DDBJ Center, National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan
| | - Tomoya Tanjo
- Bioinformation and DDBJ Center, National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan
| | - Osamu Ogasawara
- Bioinformation and DDBJ Center, National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan
| | - Takatomo Fujisawa
- Bioinformation and DDBJ Center, National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan
| | - Yasukazu Nakamura
- Bioinformation and DDBJ Center, National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan
| | - Masanori Arita
- Bioinformation and DDBJ Center, National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan
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11
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van Wijk KJ, Leppert T, Sun Z, Kearly A, Li M, Mendoza L, Guzchenko I, Debley E, Sauermann G, Routray P, Malhotra S, Nelson A, Sun Q, Deutsch EW. Detection of the Arabidopsis Proteome and Its Post-translational Modifications and the Nature of the Unobserved (Dark) Proteome in PeptideAtlas. J Proteome Res 2024; 23:185-214. [PMID: 38104260 DOI: 10.1021/acs.jproteome.3c00536] [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] [Indexed: 12/19/2023]
Abstract
This study describes a new release of the Arabidopsis thaliana PeptideAtlas proteomics resource (build 2023-10) providing protein sequence coverage, matched mass spectrometry (MS) spectra, selected post-translational modifications (PTMs), and metadata. 70 million MS/MS spectra were matched to the Araport11 annotation, identifying ∼0.6 million unique peptides and 18,267 proteins at the highest confidence level and 3396 lower confidence proteins, together representing 78.6% of the predicted proteome. Additional identified proteins not predicted in Araport11 should be considered for the next Arabidopsis genome annotation. This release identified 5198 phosphorylated proteins, 668 ubiquitinated proteins, 3050 N-terminally acetylated proteins, and 864 lysine-acetylated proteins and mapped their PTM sites. MS support was lacking for 21.4% (5896 proteins) of the predicted Araport11 proteome: the "dark" proteome. This dark proteome is highly enriched for E3 ligases, transcription factors, and for certain (e.g., CLE, IDA, PSY) but not other (e.g., THIONIN, CAP) signaling peptides families. A machine learning model trained on RNA expression data and protein properties predicts the probability that proteins will be detected. The model aids in discovery of proteins with short half-life (e.g., SIG1,3 and ERF-VII TFs) and for developing strategies to identify the missing proteins. PeptideAtlas is linked to TAIR, tracks in JBrowse, and several other community proteomics resources.
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Affiliation(s)
- Klaas J van Wijk
- Section of Plant Biology, School of Integrative Plant Sciences (SIPS), Cornell University, Ithaca, New York 14853, United States
| | - Tami Leppert
- Institute for Systems Biology (ISB), Seattle, Washington 98109, United States
| | - Zhi Sun
- Institute for Systems Biology (ISB), Seattle, Washington 98109, United States
| | - Alyssa Kearly
- Boyce Thompson Institute, Ithaca, New York 14853, United States
| | - Margaret Li
- Institute for Systems Biology (ISB), Seattle, Washington 98109, United States
| | - Luis Mendoza
- Institute for Systems Biology (ISB), Seattle, Washington 98109, United States
| | - Isabell Guzchenko
- Section of Plant Biology, School of Integrative Plant Sciences (SIPS), Cornell University, Ithaca, New York 14853, United States
| | - Erica Debley
- Section of Plant Biology, School of Integrative Plant Sciences (SIPS), Cornell University, Ithaca, New York 14853, United States
| | - Georgia Sauermann
- Section of Plant Biology, School of Integrative Plant Sciences (SIPS), Cornell University, Ithaca, New York 14853, United States
| | - Pratyush Routray
- Section of Plant Biology, School of Integrative Plant Sciences (SIPS), Cornell University, Ithaca, New York 14853, United States
| | - Sagunya Malhotra
- Institute for Systems Biology (ISB), Seattle, Washington 98109, United States
| | - Andrew Nelson
- Boyce Thompson Institute, Ithaca, New York 14853, United States
| | - Qi Sun
- Computational Biology Service Unit, Cornell University, Ithaca, New York 14853, United States
| | - Eric W Deutsch
- Institute for Systems Biology (ISB), Seattle, Washington 98109, United States
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12
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Gabriel W, Picciani M, The M, Wilhelm M. Deep Learning-Assisted Analysis of Immunopeptidomics Data. Methods Mol Biol 2024; 2758:457-483. [PMID: 38549030 DOI: 10.1007/978-1-0716-3646-6_25] [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] [Indexed: 04/02/2024]
Abstract
Liquid chromatography-coupled mass spectrometry (LC-MS/MS) is the primary method to obtain direct evidence for the presentation of disease- or patient-specific human leukocyte antigen (HLA). However, compared to the analysis of tryptic peptides in proteomics, the analysis of HLA peptides still poses computational and statistical challenges. Recently, fragment ion intensity-based matching scores assessing the similarity between predicted and observed spectra were shown to substantially increase the number of confidently identified peptides, particularly in use cases where non-tryptic peptides are analyzed. In this chapter, we describe in detail three procedures on how to benefit from state-of-the-art deep learning models to analyze and validate single spectra, single measurements, and multiple measurements in mass spectrometry-based immunopeptidomics. For this, we explain how to use the Universal Spectrum Explorer (USE), online Oktoberfest, and offline Oktoberfest. For intensity-based scoring, Oktoberfest uses fragment ion intensity and retention time predictions from the deep learning framework Prosit, a deep neural network trained on a very large number of synthetic peptides and tandem mass spectra generated within the ProteomeTools project. The examples shown highlight how deep learning-assisted analysis can increase the number of identified HLA peptides, facilitate the discovery of confidently identified neo-epitopes, or provide assistance in the assessment of the presence of cryptic peptides, such as spliced peptides.
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Affiliation(s)
- Wassim Gabriel
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Mario Picciani
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Matthew The
- Chair of Proteomics and Bioanalytics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Mathias Wilhelm
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany.
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13
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Zweigle J, Bugsel B, Fabregat-Palau J, Zwiener C. PFΔScreen - an open-source tool for automated PFAS feature prioritization in non-target HRMS data. Anal Bioanal Chem 2024; 416:349-362. [PMID: 38030884 PMCID: PMC10761406 DOI: 10.1007/s00216-023-05070-2] [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: 09/26/2023] [Revised: 11/17/2023] [Accepted: 11/21/2023] [Indexed: 12/01/2023]
Abstract
Per- and polyfluoroalkyl substances (PFAS) are a huge group of anthropogenic chemicals with unique properties that are used in countless products and applications. Due to the high stability of their C-F bonds, PFAS or their transformation products (TPs) are persistent in the environment, leading to ubiquitous detection in various samples worldwide. Since PFAS are industrial chemicals, the availability of authentic PFAS reference standards is limited, making non-target screening (NTS) approaches based on high-resolution mass spectrometry (HRMS) necessary for a more comprehensive characterization. NTS usually is a time-consuming process, since only a small fraction of the detected chemicals can be identified. Therefore, efficient prioritization of relevant HRMS signals is one of the most crucial steps. We developed PFΔScreen, a Python-based open-source tool with a simple graphical user interface (GUI) to perform efficient feature prioritization using several PFAS-specific techniques such as the highly promising MD/C-m/C approach, Kendrick mass defect analysis, diagnostic fragments (MS2), fragment mass differences (MS2), and suspect screening. Feature detection from vendor-independent MS raw data (mzML, data-dependent acquisition) is performed via pyOpenMS (or custom feature lists) with subsequent calculations for prioritization and identification of PFAS in both HPLC- and GC-HRMS data. The PFΔScreen workflow is presented on four PFAS-contaminated agricultural soil samples from south-western Germany. Over 15 classes of PFAS (more than 80 single compounds with several isomers) could be identified, including four novel classes, potentially TPs of the precursors fluorotelomer mercapto alkyl phosphates (FTMAPs). PFΔScreen can be used within the Python environment and is easily automatically installable and executable on Windows. Its source code is freely available on GitHub ( https://github.com/JonZwe/PFAScreen ).
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Affiliation(s)
- Jonathan Zweigle
- Environmental Analytical Chemistry, Department of Geosciences, University of Tübingen, Schnarrenbergstraße 94-96, 72076, Tübingen, Germany.
| | - Boris Bugsel
- Environmental Analytical Chemistry, Department of Geosciences, University of Tübingen, Schnarrenbergstraße 94-96, 72076, Tübingen, Germany
| | - Joel Fabregat-Palau
- Hydrogeochemistry, Department of Geosciences, University of Tübingen, Schnarrenbergstraße 94-96, 72076, Tübingen, Germany
| | - Christian Zwiener
- Environmental Analytical Chemistry, Department of Geosciences, University of Tübingen, Schnarrenbergstraße 94-96, 72076, Tübingen, Germany.
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14
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Wüthrich C, Giannoukos S, Zenobi R. Elucidating the Role of Ion Suppression in Secondary Electrospray Ionization. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:2498-2507. [PMID: 37843816 PMCID: PMC10623576 DOI: 10.1021/jasms.3c00219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 09/11/2023] [Accepted: 09/29/2023] [Indexed: 10/17/2023]
Abstract
Ion suppression is a known matrix effect in electrospray ionization (ESI), ambient pressure chemical ionization (APCI), and desorption electrospray ionization (DESI), but its characterization in secondary electrospray ionization (SESI) is lacking. A thorough understanding of this effect is crucial for quantitative applications of SESI, such as breath analysis. In this study, gas standards were generated by using an evaporation-based system to assess the susceptibility and suppression potential of acetone, deuterated acetone, deuterated acetic acid, and pyridine. Gas-phase effects were found to dominate ion suppression, with pyridine exhibiting the most significant suppressive effect, which is potentially linked to its gas-phase basicity. The impact of increased acetone levels on the volatiles from exhaled breath condensate was also examined. In humid conditions, a noticeable decrease in intensity of approximately 30% was observed for several features at an acetone concentration of 1 ppm. Considering that this concentration is expected for breath analysis, it becomes crucial to account for this effect when SESI is utilized to quantitatively determine specific compounds.
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Affiliation(s)
- Cedric Wüthrich
- Department
of Chemistry and Applied Biosciences, ETH
Zürich, 8093 Zürich, Switzerland
| | - Stamatios Giannoukos
- Department
of Chemistry and Applied Biosciences, ETH
Zürich, 8093 Zürich, Switzerland
| | - Renato Zenobi
- Department
of Chemistry and Applied Biosciences, ETH
Zürich, 8093 Zürich, Switzerland
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15
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Mandal K, Wicaksono G, Yu C, Adams JJ, Hoopmann MR, Temple WC, Izgutdina A, Escobar BP, Gorelik M, Ihling CH, Nix MA, Naik A, Xie WH, Hübner J, Rollins LA, Reid SM, Ramos E, Kasap C, Steri V, Serrano JAC, Salangsang F, Phojanakong P, McMillan M, Gavallos V, Leavitt AD, Logan AC, Rooney CM, Eyquem J, Sinz A, Huang BJ, Stieglitz E, Smith CC, Moritz RL, Sidhu SS, Huang L, Wiita AP. Structural surfaceomics reveals an AML-specific conformation of integrin β 2 as a CAR T cellular therapy target. NATURE CANCER 2023; 4:1592-1609. [PMID: 37904046 PMCID: PMC10663162 DOI: 10.1038/s43018-023-00652-6] [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: 08/19/2022] [Accepted: 09/12/2023] [Indexed: 11/01/2023]
Abstract
Safely expanding indications for cellular therapies has been challenging given a lack of highly cancer-specific surface markers. Here we explore the hypothesis that tumor cells express cancer-specific surface protein conformations that are invisible to standard target discovery pipelines evaluating gene or protein expression, and these conformations can be identified and immunotherapeutically targeted. We term this strategy integrating cross-linking mass spectrometry with glycoprotein surface capture 'structural surfaceomics'. As a proof of principle, we apply this technology to acute myeloid leukemia (AML), a hematologic malignancy with dismal outcomes and no known optimal immunotherapy target. We identify the activated conformation of integrin β2 as a structurally defined, widely expressed AML-specific target. We develop and characterize recombinant antibodies to this protein conformation and show that chimeric antigen receptor T cells eliminate AML cells and patient-derived xenografts without notable toxicity toward normal hematopoietic cells. Our findings validate an AML conformation-specific target antigen and demonstrate a tool kit for applying these strategies more broadly.
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Affiliation(s)
- Kamal Mandal
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Gianina Wicaksono
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Clinton Yu
- Department of Physiology and Biophysics, University of California Irvine, Irvine, CA, USA
| | - Jarrett J Adams
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- School of Pharmacy, University of Waterloo, Kitchener, Ontario, Canada
| | | | - William C Temple
- Department of Pediatrics, Division of Hematology/Oncology, University of California San Francisco, San Francisco, CA, USA
- Department of Pediatrics, Division of Allergy, Immunology, and Bone Marrow Transplantation, University of California San Francisco, San Francisco, CA, USA
| | - Adila Izgutdina
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Bonell Patiño Escobar
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Maryna Gorelik
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Christian H Ihling
- Department of Pharmaceutical Chemistry and Bioanalytics, Institute of Pharmacy, Martin-Luther University Halle-Wittenberg, Halle, Germany
| | - Matthew A Nix
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Akul Naik
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA
| | - William H Xie
- UCSF/Gladstone Institute for Genomic Immunology, San Francisco, CA, USA
| | - Juwita Hübner
- Department of Pediatrics, Division of Hematology/Oncology, University of California San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - Lisa A Rollins
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston Methodist Hospital-Texas Children's Hospital, Houston, TX, USA
| | - Sandy M Reid
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston Methodist Hospital-Texas Children's Hospital, Houston, TX, USA
| | - Emilio Ramos
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Corynn Kasap
- Department of Medicine, Division of Hematology/Oncology, University of California San Francisco, San Francisco, CA, USA
| | - Veronica Steri
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - Juan Antonio Camara Serrano
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - Fernando Salangsang
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - Paul Phojanakong
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - Melanie McMillan
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Victor Gavallos
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Andrew D Leavitt
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Aaron C Logan
- Department of Medicine, Division of Hematology/Oncology, University of California San Francisco, San Francisco, CA, USA
| | - Cliona M Rooney
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston Methodist Hospital-Texas Children's Hospital, Houston, TX, USA
| | - Justin Eyquem
- UCSF/Gladstone Institute for Genomic Immunology, San Francisco, CA, USA
- Department of Medicine, Division of Hematology/Oncology, University of California San Francisco, San Francisco, CA, USA
| | - Andrea Sinz
- Department of Pharmaceutical Chemistry and Bioanalytics, Institute of Pharmacy, Martin-Luther University Halle-Wittenberg, Halle, Germany
| | - Benjamin J Huang
- Department of Pediatrics, Division of Hematology/Oncology, University of California San Francisco, San Francisco, CA, USA
| | - Elliot Stieglitz
- Department of Pediatrics, Division of Hematology/Oncology, University of California San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - Catherine C Smith
- Department of Medicine, Division of Hematology/Oncology, University of California San Francisco, San Francisco, CA, USA
| | | | - Sachdev S Sidhu
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- School of Pharmacy, University of Waterloo, Kitchener, Ontario, Canada
| | - Lan Huang
- Department of Physiology and Biophysics, University of California Irvine, Irvine, CA, USA
| | - Arun P Wiita
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA.
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA.
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.
- Chan Zuckerberg Biohub San Francisco, San Francisco, CA, USA.
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16
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Deberneh HM, Sadygov RG. Flexible Quality Control for Protein Turnover Rates Using d2ome. Int J Mol Sci 2023; 24:15553. [PMID: 37958536 PMCID: PMC10649227 DOI: 10.3390/ijms242115553] [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: 09/19/2023] [Revised: 10/20/2023] [Accepted: 10/22/2023] [Indexed: 11/15/2023] Open
Abstract
Bioinformatics tools are used to estimate in vivo protein turnover rates from the LC-MS data of heavy water labeled samples in high throughput. The quantification includes peak detection and integration in the LC-MS domain of complex input data of the mammalian proteome, which requires the integration of results from different experiments. The existing software tools for the estimation of turnover rate use predefined, built-in, stringent filtering criteria to select well-fitted peptides and determine turnover rates for proteins. The flexible control of filtering and quality measures will help to reduce the effects of fluctuations and interferences to the signals from target peptides while retaining an adequate number of peptides. This work describes an approach for flexible error control and filtering measures implemented in the computational tool d2ome for automating protein turnover rates. The error control measures (based on spectral properties and signal features) reduced the standard deviation and tightened the confidence intervals of the estimated turnover rates.
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Affiliation(s)
- Henock M. Deberneh
- Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, TX 77555-1068, USA
| | - Rovshan G. Sadygov
- Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, TX 77555-1068, USA
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17
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Kusebauch U, Lorenzetti APR, Campbell DS, Pan M, Shteynberg D, Kapil C, Midha MK, López García de Lomana A, Baliga NS, Moritz RL. A comprehensive spectral assay library to quantify the Halobacterium salinarum NRC-1 proteome by DIA/SWATH-MS. Sci Data 2023; 10:697. [PMID: 37833331 PMCID: PMC10575869 DOI: 10.1038/s41597-023-02590-5] [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: 06/23/2023] [Accepted: 09/21/2023] [Indexed: 10/15/2023] Open
Abstract
Data-Independent Acquisition (DIA) is a mass spectrometry-based method to reliably identify and reproducibly quantify large fractions of a target proteome. The peptide-centric data analysis strategy employed in DIA requires a priori generated spectral assay libraries. Such assay libraries allow to extract quantitative data in a targeted approach and have been generated for human, mouse, zebrafish, E. coli and few other organisms. However, a spectral assay library for the extreme halophilic archaeon Halobacterium salinarum NRC-1, a model organism that contributed to several notable discoveries, is not publicly available yet. Here, we report a comprehensive spectral assay library to measure 2,563 of 2,646 annotated H. salinarum NRC-1 proteins. We demonstrate the utility of this library by measuring global protein abundances over time under standard growth conditions. The H. salinarum NRC-1 library includes 21,074 distinct peptides representing 97% of the predicted proteome and provides a new, valuable resource to confidently measure and quantify any protein of this archaeon. Data and spectral assay libraries are available via ProteomeXchange (PXD042770, PXD042774) and SWATHAtlas (SAL00312-SAL00319).
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Affiliation(s)
- Ulrike Kusebauch
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | | | - David S Campbell
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | - Min Pan
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | - David Shteynberg
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | - Charu Kapil
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | - Mukul K Midha
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | - Adrián López García de Lomana
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
- Center for Systems Biology, University of Iceland, Reykjavik, Iceland
| | - Nitin S Baliga
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
- Departments of Biology and Microbiology, University of Washington, Seattle, WA, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
- Lawrence Berkeley National Lab, Berkeley, CA, USA
| | - Robert L Moritz
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA.
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18
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Wallmann G, Leduc A, Slavov N. Data-Driven Optimization of DIA Mass Spectrometry by DO-MS. J Proteome Res 2023; 22:3149-3158. [PMID: 37695820 PMCID: PMC10591957 DOI: 10.1021/acs.jproteome.3c00177] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Indexed: 09/13/2023]
Abstract
Mass spectrometry (MS) enables specific and accurate quantification of proteins with ever-increasing throughput and sensitivity. Maximizing this potential of MS requires optimizing data acquisition parameters and performing efficient quality control for large datasets. To facilitate these objectives for data-independent acquisition (DIA), we developed a second version of our framework for data-driven optimization of MS methods (DO-MS). The DO-MS app v2.0 (do-ms.slavovlab.net) allows one to optimize and evaluate results from both label-free and multiplexed DIA (plexDIA) and supports optimizations particularly relevant to single-cell proteomics. We demonstrate multiple use cases, including optimization of duty cycle methods, peptide separation, number of survey scans per duty cycle, and quality control of single-cell plexDIA data. DO-MS allows for interactive data display and generation of extensive reports, including publication of quality figures that can be easily shared. The source code is available at github.com/SlavovLab/DO-MS.
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Affiliation(s)
- Georg Wallmann
- Departments
of Bioengineering, Biology, Chemistry and Chemical Biology, Single
Cell Proteomics Center, Northeastern University, Boston, Massachusetts 02115, United States
| | - Andrew Leduc
- Departments
of Bioengineering, Biology, Chemistry and Chemical Biology, Single
Cell Proteomics Center, Northeastern University, Boston, Massachusetts 02115, United States
| | - Nikolai Slavov
- Departments
of Bioengineering, Biology, Chemistry and Chemical Biology, Single
Cell Proteomics Center, Northeastern University, Boston, Massachusetts 02115, United States
- Parallel
Squared Technology Institute, Watertown, Massachusetts 02472, United States
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19
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Huang Q, Szklarczyk D, Wang M, Simonovic M, von Mering C. PaxDb 5.0: Curated Protein Quantification Data Suggests Adaptive Proteome Changes in Yeasts. Mol Cell Proteomics 2023; 22:100640. [PMID: 37659604 PMCID: PMC10551891 DOI: 10.1016/j.mcpro.2023.100640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 08/25/2023] [Accepted: 08/30/2023] [Indexed: 09/04/2023] Open
Abstract
The "Protein Abundances Across Organisms" database (PaxDb) is an integrative metaresource dedicated to protein abundance levels, in tissue-specific or whole-organism proteomes. PaxDb focuses on computing best-estimate abundances for proteins in normal/healthy contexts and expresses abundance values for each protein in "parts per million" in relation to all other protein molecules in the cell. The uniform data reprocessing, quality scoring, and integrated orthology relations have made PaxDb one of the preferred tools for comparisons between individual datasets, tissues, or organisms. In describing the latest version 5.0 of PaxDb, we particularly emphasize the data integration from various types of raw data and how we expanded the number of organisms and tissue groups as well as the proteome coverage. The current collection of PaxDb includes 831 original datasets from 170 species, including 22 Archaea, 81 Bacteria, and 67 Eukaryota. Apart from detailing the data update, we also present a comparative analysis of the human proteome subset of PaxDb against the two most widely used human proteome data resources: Human Protein Atlas and Genotype-Tissue Expression. Lastly, through our protein abundance data, we reveal an evolutionary trend in the usage of sulfur-containing amino acids in the proteomes of Fungi.
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Affiliation(s)
- Qingyao Huang
- Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
| | - Damian Szklarczyk
- Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
| | - Mingcong Wang
- Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
| | - Milan Simonovic
- Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
| | - Christian von Mering
- Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland.
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20
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Das S, Helmus R, Dong Y, Beijer S, Praetorius A, Parsons JR, Jansen B. Organic contaminants in bio-based fertilizer treated soil: Target and suspect screening approaches. CHEMOSPHERE 2023; 337:139261. [PMID: 37379984 DOI: 10.1016/j.chemosphere.2023.139261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 06/11/2023] [Accepted: 06/16/2023] [Indexed: 06/30/2023]
Abstract
Using bio-based fertilizer (BBF) in agricultural soil can reduce the dependency on chemical fertilizer and increase sustainability by recycling nutrient-rich side-streams. However, organic contaminants in BBFs may lead to residues in the treated soil. This study assessed the presence of organic contaminants in BBF treated soils, which is essential for evaluating sustainability/risks of BBF use. Soil samples from two field studies amended with 15 BBFs from various sources (agricultural, poultry, veterinary, and sludge) were analyzed. A combination of QuEChERS-based extraction, liquid chromatography quadrupole time of flight mass spectrometry-based (LC-QTOF-MS) quantitative analysis, and an advanced, automated data interpretation workflow was optimized to extract and analyze organic contaminants in BBF-treated agricultural soil. The comprehensive screening of organic contaminants was performed using target analysis and suspect screening. Of the 35 target contaminants, only three contaminants were detected in the BBF-treated soil with concentrations ranging from 0.4 ng g-1 to 28.7 ng g-1; out of these three detected contaminants, two were also present in the control soil sample. Suspect screening using patRoon (an R-based open-source software platform) workflows and the NORMAN Priority List resulted in tentative identification of 20 compounds (at level 2 and level 3 confidence level), primarily pharmaceuticals and industrial chemicals, with only one overlapping compound in two experimental sites. The contamination profiles of the soil treated with BBFs sourced from veterinary and sludge were similar, with common pharmaceutical features identified. The suspect screening results suggest that the contaminants found in BBF-treated soil might come from alternative sources other than BBFs.
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Affiliation(s)
- Supta Das
- Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, Netherlands.
| | - Rick Helmus
- Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, Netherlands
| | - Yan Dong
- Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, Netherlands
| | - Steven Beijer
- Van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, Netherlands
| | - Antonia Praetorius
- Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, Netherlands
| | - John R Parsons
- Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, Netherlands
| | - Boris Jansen
- Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, Netherlands
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21
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Lu M, Tong J, Fang W, Wang J, An S, Wang R, Jiang H, Yu C. Column storage enables edge computation of biological big data on 5G networks. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:17197-17219. [PMID: 37920052 DOI: 10.3934/mbe.2023766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
With the continuous improvement of biological detection technology, the scale of biological data is also increasing, which overloads the central-computing server. The use of edge computing in 5G networks can provide higher processing performance for large biological data analysis, reduce bandwidth consumption and improve data security. Appropriate data compression and reading strategy becomes the key technology to implement edge computing. We introduce the column storage strategy into mass spectrum data so that part of the analysis scenario can be completed by edge computing. Data produced by mass spectrometry is a typical biological big data based. A blood sample analysed by mass spectrometry can produce a 10 gigabytes digital file. By introducing the column storage strategy and combining the related prior knowledge of mass spectrometry, the structure of the mass spectrum data is reorganized, and the result file is effectively compressed. Data can be processed immediately near the scientific instrument, reducing the bandwidth requirements and the pressure of the central server. Here, we present Aird-Slice, a mass spectrum data format using the column storage strategy. Aird-Slice reduces volume by 48% compared to vendor files and speeds up the critical computational step of ion chromatography extraction by an average of 116 times over the test dataset. Aird-Slice provides the ability to analyze biological data using an edge computing architecture on 5G networks.
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Affiliation(s)
- Miaoshan Lu
- Zhejiang University, Hangzhou 310009, Zhejiang Province, China
- School of Engineering, Westlake University, Hangzhou, China
- Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, China
- Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China
| | - Junjie Tong
- Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China
| | - Weidong Fang
- Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing, Guilin University of Electronic Technology, Guilin 541004, China
| | - Jinyin Wang
- Zhejiang University, Hangzhou 310009, Zhejiang Province, China
| | | | | | - Hengxuan Jiang
- Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China
| | - Changbin Yu
- Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China
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22
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Wallmann G, Leduc A, Slavov N. Data-Driven Optimization of DIA Mass Spectrometry by DO-MS. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.02.526809. [PMID: 36778474 PMCID: PMC9915643 DOI: 10.1101/2023.02.02.526809] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Mass spectrometry (MS) enables specific and accurate quantification of proteins with ever increasing throughput and sensitivity. Maximizing this potential of MS requires optimizing data acquisition parameters and performing efficient quality control for large datasets. To facilitate these objectives for data independent acquisition (DIA), we developed a second version of our framework for data-driven optimization of mass spectrometry methods (DO-MS). The DO-MS app v2.0 ( do-ms.slavovlab.net ) allows to optimize and evaluate results from both label free and multiplexed DIA (plexDIA) and supports optimizations particularly relevant for single-cell proteomics. We demonstrate multiple use cases, including optimization of duty cycle methods, peptide separation, number of survey scans per duty cycle, and quality control of single-cell plexDIA data. DO-MS allows for interactive data display and generation of extensive reports, including publication quality figures, that can be easily shared. The source code is available at: github.com/SlavovLab/DO-MS .
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Affiliation(s)
- Georg Wallmann
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, Northeastern University, Boston, MA 02115, USA
| | - Andrew Leduc
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, Northeastern University, Boston, MA 02115, USA
| | - Nikolai Slavov
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, Northeastern University, Boston, MA 02115, USA
- Parallel Squared Technology Institute, Watertown, MA 02472, USA
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23
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Park J, Wilkins C, Avtonomov D, Hong J, Back S, Kim H, Shulman N, MacLean BX, Lee SW, Kim S. Targeted proteomics data interpretation with DeepMRM. CELL REPORTS METHODS 2023; 3:100521. [PMID: 37533638 PMCID: PMC10391571 DOI: 10.1016/j.crmeth.2023.100521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 04/18/2023] [Accepted: 06/15/2023] [Indexed: 08/04/2023]
Abstract
Targeted proteomics is widely utilized in clinical proteomics; however, researchers often devote substantial time to manual data interpretation, which hinders the transferability, reproducibility, and scalability of this approach. We introduce DeepMRM, a software package based on deep learning algorithms for object detection developed to minimize manual intervention in targeted proteomics data analysis. DeepMRM was evaluated on internal and public datasets, demonstrating superior accuracy compared with the community standard tool Skyline. To promote widespread adoption, we have incorporated a stand-alone graphical user interface for DeepMRM and integrated its algorithm into the Skyline software package as an external tool.
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Affiliation(s)
| | | | | | - Jiwon Hong
- Department of Chemistry, Center for Proteogenomic Research, Korea University, Seoul 02841, Republic of Korea
| | - Seunghoon Back
- Department of Chemistry, Center for Proteogenomic Research, Korea University, Seoul 02841, Republic of Korea
| | - Hokeun Kim
- Department of Chemistry, Center for Proteogenomic Research, Korea University, Seoul 02841, Republic of Korea
| | - Nicholas Shulman
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Brendan X. MacLean
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Sang-Won Lee
- Department of Chemistry, Center for Proteogenomic Research, Korea University, Seoul 02841, Republic of Korea
| | - Sangtae Kim
- Bertis Bioscience, Inc., San Diego, CA 92121, USA
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24
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Majeed HA, Bos TS, Voeten RLC, Kranenburg RF, van Asten AC, Somsen GW, Kohler I. Trapped ion mobility mass spectrometry of new psychoactive substances: Isomer-specific identification of ring-substituted cathinones. Anal Chim Acta 2023; 1264:341276. [PMID: 37230720 DOI: 10.1016/j.aca.2023.341276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 04/18/2023] [Accepted: 04/23/2023] [Indexed: 05/27/2023]
Abstract
New psychoactive substances (NPS) are synthetic derivatives of illicit drugs designed to mimic their psychoactive effects. NPS are typically not controlled under drug acts or their legal status depends on their molecular structure. Discriminating isomeric forms of NPS is therefore crucial for forensic laboratories. In this study, a trapped ion mobility spectrometry time-of-flight mass spectrometry (TIMS-TOFMS) approach was developed for the identification of ring-positional isomers of synthetic cathinones, a class of compounds representing two-third of all NPS seized in Europe in 2020. The optimized workflow features narrow ion-trapping regions, mobility calibration by internal reference, and a dedicated data-analysis tool, allowing for accurate relative ion-mobility assessment and high-confidence isomer identification. Ortho-, meta- and para-isomers of methylmethcathinone (MMC) and bicyclic ring isomers of methylone were assigned based on their specific ion mobilities within 5 min, including sample preparation and data analysis. The resolution of two distinct protomers per cathinone isomer added to the confidence in identification. The developed approach was successfully applied to the unambiguous assignment of MMC isomers in confiscated street samples. These findings demonstrate the potential of TIMS-TOFMS for forensic case work requiring fast and highly-confident assignment cathinone-drug isomers in confiscated samples.
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Affiliation(s)
- Hany A Majeed
- Division of Bioanalytical Chemistry, Amsterdam Institute of Molecular and Life Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, the Netherlands; Centre for Analytical Sciences Amsterdam (CASA), 1098 XH, Amsterdam, the Netherlands
| | - Tijmen S Bos
- Division of Bioanalytical Chemistry, Amsterdam Institute of Molecular and Life Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, the Netherlands; Centre for Analytical Sciences Amsterdam (CASA), 1098 XH, Amsterdam, the Netherlands
| | - Robert L C Voeten
- Division of Bioanalytical Chemistry, Amsterdam Institute of Molecular and Life Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, the Netherlands; Centre for Analytical Sciences Amsterdam (CASA), 1098 XH, Amsterdam, the Netherlands
| | - Ruben F Kranenburg
- Centre for Analytical Sciences Amsterdam (CASA), 1098 XH, Amsterdam, the Netherlands; Forensic Laboratory, Unit Amsterdam, Dutch National Police, Kabelweg 25, 1014 BA, Amsterdam, the Netherlands; Van't Hoff Institute for Molecular Sciences, University of Amsterdam, P.O. Box 94157, 1090 GD, Amsterdam, the Netherlands
| | - Arian C van Asten
- Centre for Analytical Sciences Amsterdam (CASA), 1098 XH, Amsterdam, the Netherlands; Van't Hoff Institute for Molecular Sciences, University of Amsterdam, P.O. Box 94157, 1090 GD, Amsterdam, the Netherlands; Co van Ledden Hulsebosch Center (CLHC), Amsterdam Center for Forensic Science and Medicine, P.O. Box 94157, 1090 GD, Amsterdam, the Netherlands
| | - Govert W Somsen
- Division of Bioanalytical Chemistry, Amsterdam Institute of Molecular and Life Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, the Netherlands; Centre for Analytical Sciences Amsterdam (CASA), 1098 XH, Amsterdam, the Netherlands
| | - Isabelle Kohler
- Division of Bioanalytical Chemistry, Amsterdam Institute of Molecular and Life Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, the Netherlands; Centre for Analytical Sciences Amsterdam (CASA), 1098 XH, Amsterdam, the Netherlands; Co van Ledden Hulsebosch Center (CLHC), Amsterdam Center for Forensic Science and Medicine, P.O. Box 94157, 1090 GD, Amsterdam, the Netherlands.
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25
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Ross D, Bilbao A, Lee JY, Zheng X. mzapy: An Open-Source Python Library Enabling Efficient Extraction and Processing of Ion Mobility Spectrometry-Mass Spectrometry Data in the MZA File Format. Anal Chem 2023. [PMID: 37307589 DOI: 10.1021/acs.analchem.3c01653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Analysis of ion mobility spectrometry (IMS) data has been challenging and limited the full utility of these measurements. Unlike liquid chromatography-mass spectrometry, where a plethora of tools with well-established algorithms exist, the incorporation of the additional IMS dimension requires upgrading existing computational pipelines and developing new algorithms to fully exploit the advantages of the technology. We have recently reported MZA, a new and simple mass spectrometry data structure based on the broadly supported HDF5 format and created to facilitate software development. While this format is inherently supportive of application development, the availability of core libraries in popular programming languages with standard mass spectrometry utilities will facilitate fast software development and broader adoption of the format. To this end, we present a Python package, mzapy, for efficient extraction and processing of mass spectrometry data in the MZA format, especially for complex data containing ion mobility spectrometry dimension. In addition to raw data extraction, mzapy contains supporting utilities enabling tasks including calibration, signal processing, peak finding, and generating plots. Being implemented in pure Python and having minimal and largely standardized dependencies makes mzapy uniquely suited to application development in the multiomics domain. The mzapy package is free and open-source, includes comprehensive documentation, and is structured to support future extension to meet the evolving needs of the MS community. The software source code is freely available at https://github.com/PNNL-m-q/mzapy.
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Affiliation(s)
- Dylan Ross
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Aivett Bilbao
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Joon-Yong Lee
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Xueyun Zheng
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
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26
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Tabb DL, Jeong K, Druart K, Gant MS, Brown KA, Nicora C, Zhou M, Couvillion S, Nakayasu E, Williams JE, Peterson HK, McGuire MK, McGuire MA, Metz TO, Chamot-Rooke J. Comparing Top-Down Proteoform Identification: Deconvolution, PrSM Overlap, and PTM Detection. J Proteome Res 2023. [PMID: 37235544 DOI: 10.1021/acs.jproteome.2c00673] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Generating top-down tandem mass spectra (MS/MS) from complex mixtures of proteoforms benefits from improvements in fractionation, separation, fragmentation, and mass analysis. The algorithms to match MS/MS to sequences have undergone a parallel evolution, with both spectral alignment and match-counting approaches producing high-quality proteoform-spectrum matches (PrSMs). This study assesses state-of-the-art algorithms for top-down identification (ProSight PD, TopPIC, MSPathFinderT, and pTop) in their yield of PrSMs while controlling false discovery rate. We evaluated deconvolution engines (ThermoFisher Xtract, Bruker AutoMSn, Matrix Science Mascot Distiller, TopFD, and FLASHDeconv) in both ThermoFisher Orbitrap-class and Bruker maXis Q-TOF data (PXD033208) to produce consistent precursor charges and mass determinations. Finally, we sought post-translational modifications (PTMs) in proteoforms from bovine milk (PXD031744) and human ovarian tissue. Contemporary identification workflows produce excellent PrSM yields, although approximately half of all identified proteoforms from these four pipelines were specific to only one workflow. Deconvolution algorithms disagree on precursor masses and charges, contributing to identification variability. Detection of PTMs is inconsistent among algorithms. In bovine milk, 18% of PrSMs produced by pTop and TopMG were singly phosphorylated, but this percentage fell to 1% for one algorithm. Applying multiple search engines produces more comprehensive assessments of experiments. Top-down algorithms would benefit from greater interoperability.
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Affiliation(s)
- David L Tabb
- Université Paris Cité, Institut Pasteur, CNRS UAR 2024, Mass Spectrometry for Biology Unit, Paris 75015, France
| | - Kyowon Jeong
- Applied Bioinformatics, Computer Science Department, University of Tübingen, Tübingen 72076, Germany
| | - Karen Druart
- Université Paris Cité, Institut Pasteur, CNRS UAR 2024, Mass Spectrometry for Biology Unit, Paris 75015, France
| | - Megan S Gant
- Université Paris Cité, Institut Pasteur, CNRS UAR 2024, Mass Spectrometry for Biology Unit, Paris 75015, France
| | - Kyle A Brown
- School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin 53705, United States
| | - Carrie Nicora
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Mowei Zhou
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Sneha Couvillion
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Ernesto Nakayasu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Janet E Williams
- Department of Animal, Veterinary, and Food Sciences, University of Idaho, Moscow, Idaho 83844, United States
| | - Haley K Peterson
- Department of Animal, Veterinary, and Food Sciences, University of Idaho, Moscow, Idaho 83844, United States
| | - Michelle K McGuire
- Margaret Ritchie School of Family and Consumer Sciences, University of Idaho, Moscow, Idaho 83844, United States
| | - Mark A McGuire
- Department of Animal, Veterinary, and Food Sciences, University of Idaho, Moscow, Idaho 83844, United States
| | - Thomas O Metz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Julia Chamot-Rooke
- Université Paris Cité, Institut Pasteur, CNRS UAR 2024, Mass Spectrometry for Biology Unit, Paris 75015, France
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27
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Vangeenderhuysen P, Van Arnhem J, Pomian B, De Graeve M, De Commer L, Falony G, Raes J, Zhernakova A, Fu J, Hemeryck LY, Vanhaecke L. Dual UHPLC-HRMS Metabolomics and Lipidomics and Automated Data Processing Workflow for Comprehensive High-Throughput Gut Phenotyping. Anal Chem 2023. [PMID: 37220321 DOI: 10.1021/acs.analchem.2c05371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
In recent years, feces has surfaced as the matrix of choice for investigating the gut microbiome-health axis because of its non-invasive sampling and the unique reflection it offers of an individual's lifestyle. In cohort studies where the number of samples required is large, but availability is scarce, a clear need exists for high-throughput analyses. Such analyses should combine a wide physicochemical range of molecules with a minimal amount of sample and resources and downstream data processing workflows that are as automated and time efficient as possible. We present a dual fecal extraction and ultra high performance liquid chromatography-high resolution-quadrupole-orbitrap-mass spectrometry (UHPLC-HR-Q-Orbitrap-MS)-based workflow that enables widely targeted and untargeted metabolome and lipidome analysis. A total of 836 in-house standards were analyzed, of which 360 metabolites and 132 lipids were consequently detected in feces. Their targeted profiling was validated successfully with respect to repeatability (78% CV < 20%), reproducibility (82% CV < 20%), and linearity (81% R2 > 0.9), while also enabling holistic untargeted fingerprinting (15,319 features, CV < 30%). To automate targeted processing, we optimized an R-based targeted peak extraction (TaPEx) algorithm relying on a database comprising retention time and mass-to-charge ratio (360 metabolites and 132 lipids), with batch-specific quality control curation. The latter was benchmarked toward vendor-specific targeted and untargeted software and our isotopologue parameter optimization/XCMS-based untargeted pipeline in LifeLines Deep cohort samples (n = 97). TaPEx clearly outperformed the untargeted approaches (81.3 vs 56.7-66.0% compounds detected). Finally, our novel dual fecal metabolomics-lipidomics-TaPEx method was successfully applied to Flemish Gut Flora Project cohort (n = 292) samples, leading to a sample-to-result time reduction of 60%.
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Affiliation(s)
- P Vangeenderhuysen
- Laboratory of Integrative Metabolomics (LIMET), Department of Translational Physiology, Infectiology and Public Health, Faculty of Veterinary Medicine, Ghent University, Salisburylaan 133, 9820 Merelbeke, Belgium
| | - J Van Arnhem
- Laboratory of Integrative Metabolomics (LIMET), Department of Translational Physiology, Infectiology and Public Health, Faculty of Veterinary Medicine, Ghent University, Salisburylaan 133, 9820 Merelbeke, Belgium
| | - B Pomian
- Laboratory of Integrative Metabolomics (LIMET), Department of Translational Physiology, Infectiology and Public Health, Faculty of Veterinary Medicine, Ghent University, Salisburylaan 133, 9820 Merelbeke, Belgium
| | - M De Graeve
- Laboratory of Integrative Metabolomics (LIMET), Department of Translational Physiology, Infectiology and Public Health, Faculty of Veterinary Medicine, Ghent University, Salisburylaan 133, 9820 Merelbeke, Belgium
| | - L De Commer
- Department of Microbiology and Immunology, Rega Institute, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
- VIB, Center for Microbiology, Gaston Geenslaan 1, 3001 Leuven, Belgium
| | - G Falony
- Department of Microbiology and Immunology, Rega Institute, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
- VIB, Center for Microbiology, Gaston Geenslaan 1, 3001 Leuven, Belgium
| | - J Raes
- Department of Microbiology and Immunology, Rega Institute, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
- VIB, Center for Microbiology, Gaston Geenslaan 1, 3001 Leuven, Belgium
| | - A Zhernakova
- Department of Genetics, University of Groningen, Antonius Deusinglaan 1, 9700 AB Groningen, The Netherlands
| | - J Fu
- Department of Genetics, University of Groningen, Antonius Deusinglaan 1, 9700 AB Groningen, The Netherlands
- Department of Pediatrics, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - L Y Hemeryck
- Laboratory of Integrative Metabolomics (LIMET), Department of Translational Physiology, Infectiology and Public Health, Faculty of Veterinary Medicine, Ghent University, Salisburylaan 133, 9820 Merelbeke, Belgium
| | - L Vanhaecke
- Laboratory of Integrative Metabolomics (LIMET), Department of Translational Physiology, Infectiology and Public Health, Faculty of Veterinary Medicine, Ghent University, Salisburylaan 133, 9820 Merelbeke, Belgium
- Institute for Global Food Security, School of Biological Sciences, Queen's University, University Road, BT7 1NN Belfast, Northern Ireland, U.K
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28
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van Wijk KJ, Leppert T, Sun Z, Deutsch EW. Does the Ubiquitination Degradation Pathway Really Reach inside of the Chloroplast? A Re-Evaluation of Mass Spectrometry-Based Assignments of Ubiquitination. J Proteome Res 2023. [PMID: 37092802 DOI: 10.1021/acs.jproteome.3c00178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
A recent paper in Science Advances by Sun et al. claims that intra-chloroplast proteins in the model plant Arabidopsis can be polyubiquitinated and then extracted into the cytosol for subsequent degradation by the proteasome. Most of this conclusion hinges on several sets of mass spectrometry (MS) data. If the proposed results and conclusion are true, this would be a major change in the proteolysis/proteostasis field, breaking the long-standing dogma that there are no polyubiquitination mechanisms within chloroplast organelles (nor in mitochondria). Given its importance, we reanalyzed their raw MS data using both open and closed sequence database searches and encountered many issues not only with the results but also discrepancies between stated methods (e.g., use of alkylating agent iodoacetamide (IAA)) and observed mass modifications. Although there is likely enrichment of ubiquitination signatures in a subset of the data (probably from ubiquitination in the cytosol), we show that runaway alkylation with IAA caused extensive artifactual modifications of N termini and lysines to the point that a large fraction of the desired ubiquitination signatures is indistinguishable from artifactual acetamide signatures, and thus, no intra-chloroplast polyubiquitination conclusions can be drawn from these data. We provide recommendations on how to avoid such perils in future work.
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Affiliation(s)
- Klaas J van Wijk
- Section of Plant Biology, School of Integrative Plant Sciences (SIPS), Cornell University, Ithaca, New York 14853, United States
| | - Tami Leppert
- Institute for Systems Biology (ISB), Seattle, Washington 98109, United States
| | - Zhi Sun
- Institute for Systems Biology (ISB), Seattle, Washington 98109, United States
| | - Eric W Deutsch
- Institute for Systems Biology (ISB), Seattle, Washington 98109, United States
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29
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Feraud M, O'Brien JW, Samanipour S, Dewapriya P, van Herwerden D, Kaserzon S, Wood I, Rauert C, Thomas KV. InSpectra - A platform for identifying emerging chemical threats. JOURNAL OF HAZARDOUS MATERIALS 2023; 455:131486. [PMID: 37172382 DOI: 10.1016/j.jhazmat.2023.131486] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 04/20/2023] [Accepted: 04/23/2023] [Indexed: 05/14/2023]
Abstract
Non-target analysis (NTA) employing high-resolution mass spectrometry (HRMS) coupled with liquid chromatography is increasingly being used to identify chemicals of biological relevance. HRMS datasets are large and complex making the identification of potentially relevant chemicals extremely challenging. As they are recorded in vendor-specific formats, interpreting them is often reliant on vendor-specific software that may not accommodate advancements in data processing. Here we present InSpectra, a vendor independent automated platform for the systematic detection of newly identified emerging chemical threats. InSpectra is web-based, open-source/access and modular providing highly flexible and extensible NTA and suspect screening workflows. As a cloud-based platform, InSpectra exploits parallel computing and big data archiving capabilities with a focus for sharing and community curation of HRMS data. InSpectra offers a reproducible and transparent approach for the identification, tracking and prioritisation of emerging chemical threats.
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Affiliation(s)
- Mathieu Feraud
- Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Australia
| | - Jake W O'Brien
- Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Australia; Van 't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Netherlands.
| | - Saer Samanipour
- Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Australia; Van 't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Netherlands; UvA Data Science Center, University of Amsterdam, Netherlands.
| | - Pradeep Dewapriya
- Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Australia
| | - Denice van Herwerden
- Van 't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Netherlands
| | - Sarit Kaserzon
- Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Australia
| | - Ian Wood
- School of Mathematics and Physics, The University of Queensland, Australia
| | - Cassandra Rauert
- Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Australia
| | - Kevin V Thomas
- Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Australia
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30
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Cabrera ER, Laganowsky A, Clowers BH. FTflow: An Open-Source Python GUI for FT-IM-MS Experiments. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:790-793. [PMID: 36854177 PMCID: PMC10370402 DOI: 10.1021/jasms.2c00352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
As part of a larger effort to aid in seamless integration of Fourier-based multiplexed ion mobility with a range mass analyzers, we have developed an all-in-one graphical user interface tool for FT-IM-MS data analysis that runs directly within a web browser. This tool, FTflow, accepts mzML files and displays necessary information such as mass spectra and extracted ion chromatograms in order to reconstruct arrival time distributions. It also extracts the corresponding mobility-related information (e.g., Ko and CCS) for each of the target ion populations. Furthermore, input fields for experimental conditions are clearly laid out for users and ease-of-use. With flexibility in mind, the processing scripts and GUI interface are written entirely in Python and allows users the option to modify source code to fit their specific needs. While the intention for this tool is to be a starting point for exploratory analysis of FT-IM-MS data, it has the capability to be adapted for use in more automated data processing pipelines through direct access of core processing routines.
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Affiliation(s)
- Elvin R. Cabrera
- Department of Chemistry, Washington State University, Pullman, WA 99164, USA
| | - Arthur Laganowsky
- Department of Chemistry, Texas A&M University, College Station, TX 77843, USA
| | - Brian H. Clowers
- Department of Chemistry, Washington State University, Pullman, WA 99164, USA
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31
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Novák J, Schug KA, Havlíček V. Quantitation of small molecules from liquid chromatography-mass spectrometric accurate mass datasets using CycloBranch. EUROPEAN JOURNAL OF MASS SPECTROMETRY (CHICHESTER, ENGLAND) 2023; 29:102-110. [PMID: 37000628 DOI: 10.1177/14690667231164766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Gaussian and exponentially modified Gaussian functions were incorporated into integrating algorithms used by an open-source, cross-platform tool called CycloBranch. The quantitation is demonstrated on bacterial pyoverdines separated by fine isotope features. Using our algorithm, we can separate the m/z values 694.25802 and 694.26731 (a 0.009 Da difference), where the former belongs to the most intense peak of pyoverdine D (PvdD), and the latter to the second most intense peak of pyoverdine E (PvdE) in the respective isotopic clusters of [M + Fe-H]2+ ions. The areas under chromatographic curves of standards were analyzed for the limit of detection (LOD), limit of quantitation (LOQ), and regression coefficient calculations. The quantitative module returned a LOD and LOQ of 1.4 and 4.3 ng/mL, respectively, for both PvdD and PvdE in human urine. If present and detected in mass spectra, the intensities of user-defined [M + H]+, [M + Na]+, [M + K]+, [M + Fe-H]2+, or other ion types, can be accumulated and used for quantitation. The quantitation result is returned by CycloBranch in seconds or minutes, contrary to an hours-long manual approach, prone to user-born errors originating from necessary copying among various software environments. Native Bruker, Waters, Thermo, txt, mgf, mzML, and mzXML data formats are supported in CycloBranch, which is freely available at https://ms.biomed.cas.cz/cyclobranch.
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Affiliation(s)
- Jiří Novák
- Institute of Microbiology, 48311Czech Academy of Sciences, Prague, Czech Republic
- Faculty of Information Technology, Czech Technical University in Prague, Prague, Czech Republic
| | - Kevin A Schug
- Department of Chemistry and Biochemistry, The University of Texas Arlington, Arlington, TX, USA
| | - Vladimír Havlíček
- Institute of Microbiology, 48311Czech Academy of Sciences, Prague, Czech Republic
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32
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Parker EJ, Billane KC, Austen N, Cotton A, George RM, Hopkins D, Lake JA, Pitman JK, Prout JN, Walker HJ, Williams A, Cameron DD. Untangling the Complexities of Processing and Analysis for Untargeted LC-MS Data Using Open-Source Tools. Metabolites 2023; 13:metabo13040463. [PMID: 37110122 PMCID: PMC10142740 DOI: 10.3390/metabo13040463] [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: 02/01/2023] [Revised: 03/16/2023] [Accepted: 03/20/2023] [Indexed: 04/29/2023] Open
Abstract
Untargeted metabolomics is a powerful tool for measuring and understanding complex biological chemistries. However, employment, bioinformatics and downstream analysis of mass spectrometry (MS) data can be daunting for inexperienced users. Numerous open-source and free-to-use data processing and analysis tools exist for various untargeted MS approaches, including liquid chromatography (LC), but choosing the 'correct' pipeline isn't straight-forward. This tutorial, in conjunction with a user-friendly online guide presents a workflow for connecting these tools to process, analyse and annotate various untargeted MS datasets. The workflow is intended to guide exploratory analysis in order to inform decision-making regarding costly and time-consuming downstream targeted MS approaches. We provide practical advice concerning experimental design, organisation of data and downstream analysis, and offer details on sharing and storing valuable MS data for posterity. The workflow is editable and modular, allowing flexibility for updated/changing methodologies and increased clarity and detail as user participation becomes more common. Hence, the authors welcome contributions and improvements to the workflow via the online repository. We believe that this workflow will streamline and condense complex mass-spectrometry approaches into easier, more manageable, analyses thereby generating opportunities for researchers previously discouraged by inaccessible and overly complicated software.
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Affiliation(s)
| | - Kathryn C Billane
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
| | - Nichola Austen
- Department of Biology, University of Oxford, Oxford OX1 3RB, UK
| | - Anne Cotton
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
| | - Rachel M George
- biOMICS Mass Spectrometry Centre, University of Sheffield, Sheffield S10 2TN, UK
| | - David Hopkins
- Department of Earth and Environmental Sciences, University of Manchester, Manchester M13 9PL, UK
| | - Janice A Lake
- Department of Earth and Environmental Sciences, University of Manchester, Manchester M13 9PL, UK
| | - James K Pitman
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
| | - James N Prout
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
| | - Heather J Walker
- biOMICS Mass Spectrometry Centre, University of Sheffield, Sheffield S10 2TN, UK
| | - Alex Williams
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
| | - Duncan D Cameron
- Department of Earth and Environmental Sciences, University of Manchester, Manchester M13 9PL, UK
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33
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Decoding Metabolic Reprogramming in Plants under Pathogen Attacks, a Comprehensive Review of Emerging Metabolomics Technologies to Maximize Their Applications. Metabolites 2023; 13:metabo13030424. [PMID: 36984864 PMCID: PMC10055942 DOI: 10.3390/metabo13030424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 03/01/2023] [Accepted: 03/09/2023] [Indexed: 03/15/2023] Open
Abstract
In their environment, plants interact with a multitude of living organisms and have to cope with a large variety of aggressions of biotic or abiotic origin. What has been known for several decades is that the extraordinary variety of chemical compounds the plants are capable of synthesizing may be estimated in the range of hundreds of thousands, but only a fraction has been fully characterized to be implicated in defense responses. Despite the vast importance of these metabolites for plants and also for human health, our knowledge about their biosynthetic pathways and functions is still fragmentary. Recent progress has been made particularly for the phenylpropanoids and oxylipids metabolism, which is more emphasized in this review. With an increasing interest in monitoring plant metabolic reprogramming, the development of advanced analysis methods should now follow. This review capitalizes on the advanced technologies used in metabolome mapping in planta, including different metabolomics approaches, imaging, flux analysis, and interpretation using bioinformatics tools. Advantages and limitations with regards to the application of each technique towards monitoring which metabolite class or type are highlighted, with special emphasis on the necessary future developments to better mirror such intricate metabolic interactions in planta.
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34
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Gatto L, Aebersold R, Cox J, Demichev V, Derks J, Emmott E, Franks AM, Ivanov AR, Kelly RT, Khoury L, Leduc A, MacCoss MJ, Nemes P, Perlman DH, Petelski AA, Rose CM, Schoof EM, Van Eyk J, Vanderaa C, Yates JR, Slavov N. Initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments. Nat Methods 2023; 20:375-386. [PMID: 36864200 PMCID: PMC10130941 DOI: 10.1038/s41592-023-01785-3] [Citation(s) in RCA: 39] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 01/24/2023] [Indexed: 03/04/2023]
Abstract
Analyzing proteins from single cells by tandem mass spectrometry (MS) has recently become technically feasible. While such analysis has the potential to accurately quantify thousands of proteins across thousands of single cells, the accuracy and reproducibility of the results may be undermined by numerous factors affecting experimental design, sample preparation, data acquisition and data analysis. We expect that broadly accepted community guidelines and standardized metrics will enhance rigor, data quality and alignment between laboratories. Here we propose best practices, quality controls and data-reporting recommendations to assist in the broad adoption of reliable quantitative workflows for single-cell proteomics. Resources and discussion forums are available at https://single-cell.net/guidelines .
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Affiliation(s)
- Laurent Gatto
- Computational Biology and Bioinformatics Unit, de Duve Institute, Université Catholique de Louvain, Brussels, Belgium
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Juergen Cox
- Max Planck Institute of Biochemistry, Martinsried, Germany
| | | | - Jason Derks
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single-Cell Proteomics Center and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Edward Emmott
- Centre for Proteome Research, Department of Biochemistry and Systems Biology, University of Liverpool, Liverpool, UK
| | - Alexander M Franks
- Department of Statistics and Applied Probability, University of California Santa Barbara, Santa Barbara, CA, USA
| | - Alexander R Ivanov
- Department of Chemistry and Chemical Biology, Barnett Institute of Chemical and Biological Analysis, Northeastern University, Boston, MA, USA
| | - Ryan T Kelly
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA
| | - Luke Khoury
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single-Cell Proteomics Center and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Andrew Leduc
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single-Cell Proteomics Center and Barnett Institute, Northeastern University, Boston, MA, USA
| | | | - Peter Nemes
- Department of Chemistry and Biochemistry, University of Maryland, College Park, MD, USA
| | - David H Perlman
- Merck Exploratory Science Center, Merck Sharp & Dohme Corp., Cambridge, MA, USA
| | - Aleksandra A Petelski
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single-Cell Proteomics Center and Barnett Institute, Northeastern University, Boston, MA, USA
- Parallel Squared Technology Institute, Watertown, MA, USA
| | - Christopher M Rose
- Department of Microchemistry, Proteomics and Lipidomics, Genentech Inc., South San Francisco, CA, USA
| | - Erwin M Schoof
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark
| | | | - Christophe Vanderaa
- Computational Biology and Bioinformatics Unit, de Duve Institute, Université Catholique de Louvain, Brussels, Belgium
| | - John R Yates
- Departments of Molecular Medicine and Neurobiology, the Scripps Research Institute, La Jolla, CA, USA
| | - Nikolai Slavov
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single-Cell Proteomics Center and Barnett Institute, Northeastern University, Boston, MA, USA.
- Parallel Squared Technology Institute, Watertown, MA, USA.
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35
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Deutsch EW, Mendoza L, Shteynberg DD, Hoopmann MR, Sun Z, Eng JK, Moritz RL. Trans-Proteomic Pipeline: Robust Mass Spectrometry-Based Proteomics Data Analysis Suite. J Proteome Res 2023; 22:615-624. [PMID: 36648445 PMCID: PMC10166710 DOI: 10.1021/acs.jproteome.2c00624] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
The Trans-Proteomic Pipeline (TPP) mass spectrometry data analysis suite has been in continual development and refinement since its first tools, PeptideProphet and ProteinProphet, were published 20 years ago. The current release provides a large complement of tools for spectrum processing, spectrum searching, search validation, abundance computation, protein inference, and more. Many of the tools include machine-learning modeling to extract the most information from data sets and build robust statistical models to compute the probabilities that derived information is correct. Here we present the latest information on the many TPP tools, and how TPP can be deployed on various platforms from personal Windows laptops to Linux clusters and expansive cloud computing environments. We describe tutorials on how to use TPP in a variety of ways and describe synergistic projects that leverage TPP. We conclude with plans for continued development of TPP.
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Affiliation(s)
- Eric W Deutsch
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Luis Mendoza
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | | | | | - Zhi Sun
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Jimmy K Eng
- Proteomics Resource, University of Washington, Seattle, Washington 98195, United States
| | - Robert L Moritz
- Institute for Systems Biology, Seattle, Washington 98109, United States
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36
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Gabriels R, Declercq A, Bouwmeester R, Degroeve S, Martens L. psm_utils: A High-Level Python API for Parsing and Handling Peptide-Spectrum Matches and Proteomics Search Results. J Proteome Res 2023; 22:557-560. [PMID: 36508242 DOI: 10.1021/acs.jproteome.2c00609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
A plethora of proteomics search engine output file formats are in circulation. This lack of standardized output files greatly complicates generic downstream processing of peptide-spectrum matches (PSMs) and PSM files. While standards exist to solve this problem, these are far from universally supported by search engines. Moreover, software libraries are available to read a selection of PSM file formats, but a package to parse PSM files into a unified data structure has been missing. Here, we present psm_utils, a Python package to read and write various PSM file formats and to handle peptidoforms, PSMs, and PSM lists in a unified and user-friendly Python-, command line-, and web-interface. psm_utils was developed with pragmatism and maintainability in mind, adhering to community standards and relying on existing packages where possible. The Python API and command line interface greatly facilitate handling various PSM file formats. Moreover, a user-friendly web application was built using psm_utils that allows anyone to interconvert PSM files and retrieve basic PSM statistics. psm_utils is freely available under the permissive Apache2 license at https://github.com/compomics/psm_utils.
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Affiliation(s)
- Ralf Gabriels
- VIB-UGent Center for Medical Biotechnology, VIB, 9052 Ghent, Belgium.,Department of Biomolecular Medicine, Ghent University, 9000 Ghent, Belgium
| | - Arthur Declercq
- VIB-UGent Center for Medical Biotechnology, VIB, 9052 Ghent, Belgium.,Department of Biomolecular Medicine, Ghent University, 9000 Ghent, Belgium
| | - Robbin Bouwmeester
- VIB-UGent Center for Medical Biotechnology, VIB, 9052 Ghent, Belgium.,Department of Biomolecular Medicine, Ghent University, 9000 Ghent, Belgium
| | - Sven Degroeve
- VIB-UGent Center for Medical Biotechnology, VIB, 9052 Ghent, Belgium.,Department of Biomolecular Medicine, Ghent University, 9000 Ghent, Belgium
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, 9052 Ghent, Belgium.,Department of Biomolecular Medicine, Ghent University, 9000 Ghent, Belgium
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37
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Lin A, Deatherage Kaiser BL, Hutchison JR, Bilmes JA, Noble WS. MS1Connect: a mass spectrometry run similarity measure. Bioinformatics 2023; 39:7005198. [PMID: 36702456 PMCID: PMC9913042 DOI: 10.1093/bioinformatics/btad058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 01/05/2023] [Accepted: 01/24/2023] [Indexed: 01/28/2023] Open
Abstract
MOTIVATION Interpretation of newly acquired mass spectrometry data can be improved by identifying, from an online repository, previous mass spectrometry runs that resemble the new data. However, this retrieval task requires computing the similarity between an arbitrary pair of mass spectrometry runs. This is particularly challenging for runs acquired using different experimental protocols. RESULTS We propose a method, MS1Connect, that calculates the similarity between a pair of runs by examining only the intact peptide (MS1) scans, and we show evidence that the MS1Connect score is accurate. Specifically, we show that MS1Connect outperforms several baseline methods on the task of predicting the species from which a given proteomics sample originated. In addition, we show that MS1Connect scores are highly correlated with similarities computed from fragment (MS2) scans, even though these data are not used by MS1Connect. AVAILABILITY AND IMPLEMENTATION The MS1Connect software is available at https://github.com/bmx8177/MS1Connect. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Andy Lin
- Chemical and Biological Signatures, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | | | - Janine R Hutchison
- Chemical and Biological Signatures, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Jeffrey A Bilmes
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA 98195, USA
| | - William Stafford Noble
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.,Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA
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38
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Deutsch EW, Vizcaíno JA, Jones AR, Binz PA, Lam H, Klein J, Bittremieux W, Perez-Riverol Y, Tabb DL, Walzer M, Ricard-Blum S, Hermjakob H, Neumann S, Mak TD, Kawano S, Mendoza L, Van Den Bossche T, Gabriels R, Bandeira N, Carver J, Pullman B, Sun Z, Hoffmann N, Shofstahl J, Zhu Y, Licata L, Quaglia F, Tosatto SCE, Orchard SE. Proteomics Standards Initiative at Twenty Years: Current Activities and Future Work. J Proteome Res 2023; 22:287-301. [PMID: 36626722 PMCID: PMC9903322 DOI: 10.1021/acs.jproteome.2c00637] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Indexed: 01/11/2023]
Abstract
The Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI) has been successfully developing guidelines, data formats, and controlled vocabularies (CVs) for the proteomics community and other fields supported by mass spectrometry since its inception 20 years ago. Here we describe the general operation of the PSI, including its leadership, working groups, yearly workshops, and the document process by which proposals are thoroughly and publicly reviewed in order to be ratified as PSI standards. We briefly describe the current state of the many existing PSI standards, some of which remain the same as when originally developed, some of which have undergone subsequent revisions, and some of which have become obsolete. Then the set of proposals currently being developed are described, with an open call to the community for participation in the forging of the next generation of standards. Finally, we describe some synergies and collaborations with other organizations and look to the future in how the PSI will continue to promote the open sharing of data and thus accelerate the progress of the field of proteomics.
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Affiliation(s)
- Eric W. Deutsch
- Institute
for Systems Biology, Seattle, Washington 98109, United States
| | - Juan Antonio Vizcaíno
- European
Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Andrew R. Jones
- Institute
of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Pierre-Alain Binz
- Clinical
Chemistry Service, Lausanne University Hospital, 1011 976 Lausanne, Switzerland
| | - Henry Lam
- Department
of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong 999077, P. R. China.
| | - Joshua Klein
- Program for
Bioinformatics, Boston University, Boston, Massachusetts 02215, United States
| | - Wout Bittremieux
- Skaggs
School
of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California 92093, United States
- Department
of Computer Science, University of Antwerp, 2020 Antwerpen, Belgium
| | - Yasset Perez-Riverol
- European
Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - David L. Tabb
- SA MRC
Centre for TB Research, DST/NRF Centre of Excellence for Biomedical
TB Research, Division of Molecular Biology and Human Genetics, Faculty
of Medicine and Health Sciences, Stellenbosch
University, Cape Town 7602, South Africa
| | - Mathias Walzer
- European
Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Sylvie Ricard-Blum
- Univ.
Lyon, Université Lyon 1, ICBMS, UMR 5246, 69622 Villeurbanne, France
| | - Henning Hermjakob
- European
Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Steffen Neumann
- Bioinformatics
and Scientific Data, Leibniz Institute of
Plant Biochemistry, 06120 Halle, Germany
- German
Centre for Integrative Biodiversity Research (iDiv), 04103 Halle-Jena-Leipzig, Germany
| | - Tytus D. Mak
- Mass Spectrometry
Data Center, National Institute of Standards
and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United
States
| | - Shin Kawano
- Database
Center for Life Science, Joint Support Center for Data Science Research, Research Organization of Information and Systems, Chiba 277-0871, Japan
- Faculty
of Contemporary Society, Toyama University
of International Studies, Toyama 930-1292, Japan
- School
of Frontier Engineering, Kitasato University, Sagamihara 252-0373, Japan
| | - Luis Mendoza
- Institute
for Systems Biology, Seattle, Washington 98109, United States
| | - Tim Van Den Bossche
- VIB-UGent
Center for Medical Biotechnology, VIB, 9052 Ghent, Belgium
- Department
of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, 9052 Ghent, Belgium
| | - Ralf Gabriels
- VIB-UGent
Center for Medical Biotechnology, VIB, 9052 Ghent, Belgium
- Department
of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, 9052 Ghent, Belgium
| | - Nuno Bandeira
- Skaggs
School
of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California 92093, United States
- Center
for Computational Mass Spectrometry, Department of Computer Science
and Engineering, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego 92093-0404, United States
| | - Jeremy Carver
- Center
for Computational Mass Spectrometry, Department of Computer Science
and Engineering, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego 92093-0404, United States
| | - Benjamin Pullman
- Center
for Computational Mass Spectrometry, Department of Computer Science
and Engineering, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego 92093-0404, United States
| | - Zhi Sun
- Institute
for Systems Biology, Seattle, Washington 98109, United States
| | - Nils Hoffmann
- Institute
for Bio- and Geosciences (IBG-5), Forschungszentrum
Jülich GmbH, 52428 Jülich, Germany
| | - Jim Shofstahl
- Thermo
Fisher Scientific, 355 River Oaks Parkway, San Jose, California 95134, United States
| | - Yunping Zhu
- National
Center for Protein Sciences (Beijing), Beijing
Institute of Lifeomics, #38, Life Science Park, Changping District, Beijing 102206, China
| | - Luana Licata
- Fondazione
Human Technopole, 20157 Milan, Italy
- Department
of Biology, University of Rome Tor Vergata, 00133 Rome, Italy
| | - Federica Quaglia
- Institute
of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council (CNR-IBIOM), 70126 Bari, Italy
- Department
of Biomedical Sciences, University of Padova, 35131 Padova, Italy
| | | | - Sandra E. Orchard
- European
Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
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39
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Wüthrich C, Fan Z, Vergères G, Wahl F, Zenobi R, Giannoukos S. Analysis of volatile short-chain fatty acids in the gas phase using secondary electrospray ionization coupled to mass spectrometry. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:553-561. [PMID: 36606412 DOI: 10.1039/d2ay01778d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Quantification of metabolites present within exhaled breath is a major challenge for on-line breath analysis. It is also important for gauging the analytical performance, accuracy, reproducibility, reliability, and stability of the measuring technology. Short-chain fatty acids (SCFAs) are of high interest for nutrition and health. Their quantification enables a deep mechanistic understanding of a wide range of biological processes and metabolic pathways, while their high volatility makes them an attractive target for breath analysis. This article reports, for the first time, the development and testing of a modular, dynamic vapor generator for the qualitative and quantitative analysis of volatile SCFAs in the gaseous phase using a secondary electrospray ionization (SESI) source coupled to a high-resolution mass spectrometer. Representative compounds tested included acetic acid, propionic acid, butyric acid, pentanoic acid and hexanoic acid. Gas-phase experiments were performed both in dry and humid (95% relative humidity) conditions from ppt to low ppb concentrations. The results obtained exhibited excellent linearity within the examined concentration range, low limits of detection and quantification down to the lower ppt area. Mixture effects were also investigated and are presented.
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Affiliation(s)
- Cedric Wüthrich
- Department of Chemistry and Applied Biosciences, ETHZ, Zurich, Switzerland.
| | - Zhiyuan Fan
- Department of Chemistry and Applied Biosciences, ETHZ, Zurich, Switzerland.
| | - Guy Vergères
- Food Microbial Systems Research Division, Agroscope, Bern, Switzerland
| | - Fabian Wahl
- Food Microbial Systems Research Division, Agroscope, Bern, Switzerland
| | - Renato Zenobi
- Department of Chemistry and Applied Biosciences, ETHZ, Zurich, Switzerland.
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40
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Bow AJ, Rifkin RE, Priester C, Christopher CJ, Grzeskowiak RM, Hecht S, Adair SH, Mulon PY, Castro HF, Campagna SR, Anderson DE. Temporal metabolic profiling of bone healing in a caprine tibia segmental defect model. Front Vet Sci 2023; 9:1023650. [PMID: 36733424 PMCID: PMC9886884 DOI: 10.3389/fvets.2022.1023650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Accepted: 12/30/2022] [Indexed: 01/18/2023] Open
Abstract
Bone tissue engineering is an emerging field of regenerative medicine, with a wide array of biomaterial technologies and therapeutics employed. However, it is difficult to objectively compare these various treatments during various stages of tissue response. Metabolomics is rapidly emerging as a powerful analytical tool to establish broad-spectrum metabolic signatures for a target biological system. Developing an effective biomarker panel for bone repair from small molecule data would provide an objective metric to readily assess the efficacy of novel therapeutics in relation to natural healing mechanisms. In this study we utilized a large segmental bone defect in goats to reflect trauma resulting in substantial volumetric bone loss. Characterization of the native repair capacity was then conducted over a period of 12 months through the combination of standard (radiography, computed tomography, histology, biomechanics) data and ultra-high-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) metabolic profiling. Standard metrics demonstrated that samples formed soft callus structures that later mineralized. Small molecule profiles showed distinct temporal patterns associated with the bone tissue repair process. Specifically, increased lactate and amino acid levels at early time points indicated an environment conducive to osteoblast differentiation and extracellular matrix formation. Citrate and pyruvate abundances increased at later time points indicating increasing mineral content within the defect region. Taurine, shikimate, and pantothenate distribution profiles appeared to represent a shift toward a more homeostatic remodeling environment with the differentiation and activity of osteoclasts offsetting the earlier deposition phases of bone repair. The generation of a comprehensive metabolic reference portfolio offers a potent mechanism for examining novel biomaterials and can serve as guide for the development of new targeted therapeutics to improve the rate, magnitude, and quality of bone regeneration.
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Affiliation(s)
- Austin J. Bow
- Department of Large Animal Clinical Sciences, University of Tennessee College of Veterinary Medicine, Knoxville, TN, United States,*Correspondence: Austin J. Bow ✉
| | - Rebecca E. Rifkin
- Department of Large Animal Clinical Sciences, University of Tennessee College of Veterinary Medicine, Knoxville, TN, United States
| | - Caitlin Priester
- Department of Animal Science, University of Tennessee, Knoxville, Knoxville, TN, United States
| | | | - Remigiusz M. Grzeskowiak
- Department of Large Animal Clinical Sciences, University of Tennessee College of Veterinary Medicine, Knoxville, TN, United States
| | - Silke Hecht
- Department of Small Animal Clinical Sciences, University of Tennessee College of Veterinary Medicine, Knoxville, TN, United States
| | - Steve H. Adair
- Department of Large Animal Clinical Sciences, University of Tennessee College of Veterinary Medicine, Knoxville, TN, United States
| | - Pierre-Yves Mulon
- Department of Large Animal Clinical Sciences, University of Tennessee College of Veterinary Medicine, Knoxville, TN, United States
| | - Hector F. Castro
- Department of Chemistry, University of Tennessee, Knoxville, Knoxville, TN, United States,Biological and Small Molecule Mass Spectrometry Core and the Department of Chemistry, University of Tennessee, Knoxville, Knoxville, TN, United States
| | - Shawn R. Campagna
- Department of Chemistry, University of Tennessee, Knoxville, Knoxville, TN, United States,Biological and Small Molecule Mass Spectrometry Core and the Department of Chemistry, University of Tennessee, Knoxville, Knoxville, TN, United States
| | - David E. Anderson
- University of Tennessee College of Veterinary Medicine, Knoxville, TN, United States,David E. Anderson ✉
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41
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Deutsch EW, Bandeira N, Perez-Riverol Y, Sharma V, Carver J, Mendoza L, Kundu DJ, Wang S, Bandla C, Kamatchinathan S, Hewapathirana S, Pullman B, Wertz J, Sun Z, Kawano S, Okuda S, Watanabe Y, MacLean B, MacCoss M, Zhu Y, Ishihama Y, Vizcaíno J. The ProteomeXchange consortium at 10 years: 2023 update. Nucleic Acids Res 2023; 51:D1539-D1548. [PMID: 36370099 PMCID: PMC9825490 DOI: 10.1093/nar/gkac1040] [Citation(s) in RCA: 129] [Impact Index Per Article: 129.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/20/2022] [Accepted: 10/23/2022] [Indexed: 11/13/2022] Open
Abstract
Mass spectrometry (MS) is by far the most used experimental approach in high-throughput proteomics. The ProteomeXchange (PX) consortium of proteomics resources (http://www.proteomexchange.org) was originally set up to standardize data submission and dissemination of public MS proteomics data. It is now 10 years since the initial data workflow was implemented. In this manuscript, we describe the main developments in PX since the previous update manuscript in Nucleic Acids Research was published in 2020. The six members of the Consortium are PRIDE, PeptideAtlas (including PASSEL), MassIVE, jPOST, iProX and Panorama Public. We report the current data submission statistics, showcasing that the number of datasets submitted to PX resources has continued to increase every year. As of June 2022, more than 34 233 datasets had been submitted to PX resources, and from those, 20 062 (58.6%) just in the last three years. We also report the development of the Universal Spectrum Identifiers and the improvements in capturing the experimental metadata annotations. In parallel, we highlight that data re-use activities of public datasets continue to increase, enabling connections between PX resources and other popular bioinformatics resources, novel research and also new data resources. Finally, we summarise the current state-of-the-art in data management practices for sensitive human (clinical) proteomics data.
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Affiliation(s)
| | - Nuno Bandeira
- Center for Computational Mass Spectrometry, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Dept. Computer Science and Engineering, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
| | - Yasset Perez-Riverol
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | | | - Jeremy J Carver
- Center for Computational Mass Spectrometry, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Dept. Computer Science and Engineering, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
| | - Luis Mendoza
- Institute for Systems Biology, Seattle WA 98109, USA
| | - Deepti J Kundu
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Shengbo Wang
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Chakradhar Bandla
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Selvakumar Kamatchinathan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Suresh Hewapathirana
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Benjamin S Pullman
- Center for Computational Mass Spectrometry, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Dept. Computer Science and Engineering, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
| | - Julie Wertz
- Center for Computational Mass Spectrometry, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Dept. Computer Science and Engineering, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
| | - Zhi Sun
- Institute for Systems Biology, Seattle WA 98109, USA
| | - Shin Kawano
- Faculty of Contemporary Society, Toyama University of International Studies, Toyama 930-1292, Japan
- Database Center for Life Science (DBCLS), Joint Support-Center for Data Science Research, Research Organization of Information and Systems, Chiba 277-0871, Japan
- School of Frontier Engineering, Kitasato University, Sagamihara 252-0373, Japan
| | - Shujiro Okuda
- Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8510, Japan
| | - Yu Watanabe
- Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8510, Japan
| | | | | | - Yunping Zhu
- Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Lifeomics, Beijing 102206, China
| | - Yasushi Ishihama
- Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto 606-8501, Japan
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
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42
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Wishart DS, Rout M, Lee BL, Berjanskii M, LeVatte M, Lipfert M. Practical Aspects of NMR-Based Metabolomics. Handb Exp Pharmacol 2023; 277:1-41. [PMID: 36271165 DOI: 10.1007/164_2022_613] [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] [Indexed: 06/16/2023]
Abstract
While NMR-based metabolomics is only about 20 years old, NMR has been a key part of metabolic and metabolism studies for >40 years. Historically, metabolic researchers used NMR because of its high level of reproducibility, superb instrument stability, facile sample preparation protocols, inherently quantitative character, non-destructive nature, and amenability to automation. In this chapter, we provide a short history of NMR-based metabolomics. We then provide a detailed description of some of the practical aspects of performing NMR-based metabolomics studies including sample preparation, pulse sequence selection, and spectral acquisition and processing. The two different approaches to metabolomics data analysis, targeted vs. untargeted, are briefly outlined. We also describe several software packages to help users process NMR spectra obtained via these two different approaches. We then give several examples of useful or interesting applications of NMR-based metabolomics, ranging from applications to drug toxicology, to identifying inborn errors of metabolism to analyzing the contents of biofluids from dairy cattle. Throughout this chapter, we will highlight the strengths and limitations of NMR-based metabolomics. Additionally, we will conclude with descriptions of recent advances in NMR hardware, methodology, and software and speculate about where NMR-based metabolomics is going in the next 5-10 years.
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Affiliation(s)
- David S Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada.
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada.
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB, Canada.
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada.
| | - Manoj Rout
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Brian L Lee
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Mark Berjanskii
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Marcia LeVatte
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Matthias Lipfert
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
- Reference Standard Management & NMR QC, Lonza Group AG, Visp, Switzerland
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43
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Peralbo-Molina Á, Solà-Santos P, Perera-Lluna A, Chicano-Gálvez E. Data Processing and Analysis in Mass Spectrometry-Based Metabolomics. Methods Mol Biol 2023; 2571:207-239. [PMID: 36152164 DOI: 10.1007/978-1-0716-2699-3_20] [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] [Indexed: 06/16/2023]
Abstract
Metabolomics is the latest of the omics sciences. It attempts to measure and characterize metabolites-small chemical compounds <1500 Da-on cells, tissue, or biofluids, which are usually products of biological reactions. As metabolic reactions are closer to the phenotype, metabolomics has emerged as an attractive science for various areas of research, including personalized medicine. However, due to the complexity of data obtained and the absence of curated databases for metabolite identification, data processing is the major bottleneck in this area since most technicians lack the required bioinformatics expertise to process datasets in a reliable and fast manner. The aim of this chapter is to describe the available tools for data processing that makes an inexperienced researcher capable of obtaining reliable results without having to undergo through huge parametrization steps.
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Affiliation(s)
- Ángela Peralbo-Molina
- IMIBIC Mass Spectrometry and Molecular Imaging Unit, Maimonides, Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba (UCO), Córdoba, Spain.
| | - Pol Solà-Santos
- B2SLab, Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Barcelona, Spain
- Networking Biomedical Research Centre in the Subject Area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
| | - Alexandre Perera-Lluna
- B2SLab, Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Barcelona, Spain
- Networking Biomedical Research Centre in the Subject Area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
| | - Eduardo Chicano-Gálvez
- IMIBIC Mass Spectrometry and Molecular Imaging Unit, Maimonides, Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba (UCO), Córdoba, Spain
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44
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Software Tool for Visualization and Validation of Protein Turnover Rates Using Heavy Water Metabolic Labeling and LC-MS. Int J Mol Sci 2022; 23:ijms232314620. [PMID: 36498948 PMCID: PMC9740640 DOI: 10.3390/ijms232314620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/16/2022] [Accepted: 11/18/2022] [Indexed: 11/24/2022] Open
Abstract
Metabolic stable isotope labeling followed by liquid chromatography coupled with mass spectrometry (LC-MS) is a powerful tool for in vivo protein turnover studies of individual proteins on a large scale and with high throughput. Turnover rates of thousands of proteins from dozens of time course experiments are determined by data processing tools, which are essential components of the workflows for automated extraction of turnover rates. The development of sophisticated algorithms for estimating protein turnover has been emphasized. However, the visualization and annotation of the time series data are no less important. The visualization tools help to validate the quality of the model fits, their goodness-of-fit characteristics, mass spectral features of peptides, and consistency of peptide identifications, among others. Here, we describe a graphical user interface (GUI) to visualize the results from the protein turnover analysis tool, d2ome, which determines protein turnover rates from metabolic D2O labeling followed by LC-MS. We emphasize the specific features of the time series data and their visualization in the GUI. The time series data visualized by the GUI can be saved in JPEG format for storage and further dissemination.
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45
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Zhang D, Lin Q, Xia T, Zhao J, Zhang W, Ouyang Z, Xia Y. LipidOA: A Machine-Learning and Prior-Knowledge-Based Tool for Structural Annotation of Glycerophospholipids. Anal Chem 2022; 94:16759-16767. [DOI: 10.1021/acs.analchem.2c03505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Affiliation(s)
- Donghui Zhang
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing100084, China
- MOE Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology, Department of Chemistry, Tsinghua University, Beijing10084, China
| | - Qiaohong Lin
- MOE Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology, Department of Chemistry, Tsinghua University, Beijing10084, China
| | - Tian Xia
- MOE Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology, Department of Chemistry, Tsinghua University, Beijing10084, China
| | - Jing Zhao
- MOE Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology, Department of Chemistry, Tsinghua University, Beijing10084, China
| | - Wenpeng Zhang
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing100084, China
| | - Zheng Ouyang
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing100084, China
| | - Yu Xia
- MOE Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology, Department of Chemistry, Tsinghua University, Beijing10084, China
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46
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Bittremieux W, Wang M, Dorrestein PC. The critical role that spectral libraries play in capturing the metabolomics community knowledge. Metabolomics 2022; 18:94. [PMID: 36409434 DOI: 10.1007/s11306-022-01947-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 10/19/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND Spectral library searching is currently the most common approach for compound annotation in untargeted metabolomics. Spectral libraries applicable to liquid chromatography mass spectrometry have grown in size over the past decade to include hundreds of thousands to millions of mass spectra and tens of thousands of compounds, forming an essential knowledge base for the interpretation of metabolomics experiments. AIM OF REVIEW We describe existing spectral library resources, highlight different strategies for compiling spectral libraries, and discuss quality considerations that should be taken into account when interpreting spectral library searching results. Finally, we describe how spectral libraries are empowering the next generation of machine learning tools in computational metabolomics, and discuss several opportunities for using increasingly accessible large spectral libraries. KEY SCIENTIFIC CONCEPTS OF REVIEW This review focuses on the current state of spectral libraries for untargeted LC-MS/MS based metabolomics. We show how the number of entries in publicly accessible spectral libraries has increased more than 60-fold in the past eight years to aid molecular interpretation and we discuss how the role of spectral libraries in untargeted metabolomics will evolve in the near future.
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Affiliation(s)
- Wout Bittremieux
- Collaborative Mass Spectrometry Innovation Center, 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
| | - Mingxun Wang
- Department of Computer Science, University of California Riverside, Riverside, CA, 92507, USA
| | - Pieter C Dorrestein
- Collaborative Mass Spectrometry Innovation Center, 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.
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47
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Gassaway BM, Li J, Rad R, Mintseris J, Mohler K, Levy T, Aguiar M, Beausoleil SA, Paulo JA, Rinehart J, Huttlin EL, Gygi SP. A multi-purpose, regenerable, proteome-scale, human phosphoserine resource for phosphoproteomics. Nat Methods 2022; 19:1371-1375. [PMID: 36280721 PMCID: PMC9847208 DOI: 10.1038/s41592-022-01638-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 09/06/2022] [Indexed: 01/21/2023]
Abstract
Mass-spectrometry-based phosphoproteomics has become indispensable for understanding cellular signaling in complex biological systems. Despite the central role of protein phosphorylation, the field still lacks inexpensive, regenerable, and diverse phosphopeptides with ground-truth phosphorylation positions. Here, we present Iterative Synthetically Phosphorylated Isomers (iSPI), a proteome-scale library of human-derived phosphoserine-containing phosphopeptides that is inexpensive, regenerable, and diverse, with precisely known positions of phosphorylation. We demonstrate possible uses of iSPI, including use as a phosphopeptide standard, a tool to evaluate and optimize phosphorylation-site localization algorithms, and a benchmark to compare performance across data analysis pipelines. We also present AScorePro, an updated version of the AScore algorithm specifically optimized for phosphorylation-site localization in higher energy fragmentation spectra, and the FLR viewer, a web tool for phosphorylation-site localization, to enable community use of the iSPI resource. iSPI and its associated data constitute a useful, multi-purpose resource for the phosphoproteomics community.
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Affiliation(s)
- Brandon M. Gassaway
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA.,These authors contributed equally: Brandon M. Gassaway, Jiaming Li
| | - Jiaming Li
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA.,These authors contributed equally: Brandon M. Gassaway, Jiaming Li
| | - Ramin Rad
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA
| | - Julian Mintseris
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA
| | - Kyle Mohler
- Department of Cellular and Molecular Physiology and Systems Biology Institute, Yale Medical School, New Haven, CT, USA
| | - Tyler Levy
- Cell Signaling Technology, Danvers, MA, USA
| | | | | | - Joao A. Paulo
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA
| | - Jesse Rinehart
- Department of Cellular and Molecular Physiology and Systems Biology Institute, Yale Medical School, New Haven, CT, USA
| | | | - Steven P. Gygi
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA.,Correspondence and requests for materials should be addressed to Steven P. Gygi.
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48
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Chang D, Klein J, Hackett WE, Nalehua MR, Wan XF, Zaia J. Improving Statistical Certainty of Glycosylation Similarity between Influenza A Virus Variants Using Data-Independent Acquisition Mass Spectrometry. Mol Cell Proteomics 2022; 21:100412. [PMID: 36103992 PMCID: PMC9593740 DOI: 10.1016/j.mcpro.2022.100412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 08/22/2022] [Accepted: 09/08/2022] [Indexed: 01/18/2023] Open
Abstract
Amino acid sequences of immunodominant domains of hemagglutinin (HA) on the surface of influenza A virus (IAV) evolve rapidly, producing viral variants. HA mediates receptor recognition, binding and cell entry, and serves as the target for IAV vaccines. Glycosylation, a post-translational modification that places large branched polysaccharide molecules on proteins, can modulate the function of HA and shield antigenic regions allowing for viral evasion from immune responses. Our previous work showed that subtle changes in the HA protein sequence can have a measurable change in glycosylation. Thus, being able to quantitatively measure glycosylation changes in variants is critical for understanding how HA function may change throughout viral evolution. Moreover, understanding quantitatively how the choice of viral expression systems affects glycosylation can help in the process of vaccine design and manufacture. Although IAV vaccines are most commonly expressed in chicken eggs, cell-based vaccines have many advantages, and the adoption of more cell-based vaccines would be an important step in mitigating seasonal influenza and protecting against future pandemics. Here, we have investigated the use of data-independent acquisition (DIA) mass spectrometry for quantitative glycoproteomics. We found that DIA improved the sensitivity of glycopeptide detection for four variants of A/Switzerland/9715293/2013 (H3N2): WT and mutant, each expressed in embryonated chicken eggs and Madin-Darby canine kidney cells. We used the Tanimoto similarity metric to quantify changes in glycosylation between WT and mutant and between egg-expressed and cell-expressed virus. Our DIA site-specific glycosylation similarity comparison of WT and mutant expressed in eggs confirmed our previous analysis while achieving greater depth of coverage. We found that sequence variations and changing viral expression systems affected distinct glycosylation sites of HA. Our methods can be applied to track glycosylation changes in circulating IAV variants to bolster genomic surveillance already being done, for a more complete understanding of IAV evolution.
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Affiliation(s)
- Deborah Chang
- Department of Biochemistry, Center for Biomedical Mass Spectrometry, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Joshua Klein
- Boston University Bioinformatics Program, Boston University, Boston, Massachusetts, USA
| | - William E Hackett
- Boston University Bioinformatics Program, Boston University, Boston, Massachusetts, USA
| | - Mary Rachel Nalehua
- Boston University Bioinformatics Program, Boston University, Boston, Massachusetts, USA
| | - Xiu-Feng Wan
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, Missouri, USA; Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, Missouri, USA; Department of Electrical Engineering & Computer Science, College of Engineering, University of Missouri, Columbia, Missouri, USA; Bond Life Sciences Center, University of Missouri, Columbia, Missouri, USA
| | - Joseph Zaia
- Department of Biochemistry, Center for Biomedical Mass Spectrometry, Boston University School of Medicine, Boston, Massachusetts, USA; Boston University Bioinformatics Program, Boston University, Boston, Massachusetts, USA.
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49
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Jones AR, Deutsch EW, Vizcaíno JA. Is DIA proteomics data FAIR? Current data sharing practices, available bioinformatics infrastructure and recommendations for the future. Proteomics 2022; 23:e2200014. [PMID: 36074795 PMCID: PMC10155627 DOI: 10.1002/pmic.202200014] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/27/2022] [Accepted: 08/29/2022] [Indexed: 11/06/2022]
Abstract
Data independent acquisition (DIA) proteomics techniques have matured enormously in recent years, thanks to multiple technical developments in e.g. instrumentation and data analysis approaches. However, there are many improvements that are still possible for DIA data in the area of the FAIR (Findability, Accessibility, Interoperability and Reusability) data principles. These include more tailored data sharing practices and open data standards, since public databases and data standards for proteomics were mostly designed with DDA data in mind. Here we first describe the current state of the art in the context of FAIR data for proteomics in general, and for DIA approaches in particular. For improving the current situation for DIA data, we make the following recommendations for the future: (i) development of an open data standard for spectral libraries; (ii) make mandatory the availability of the spectral libraries used in DIA experiments in ProteomeXchange resources; (iii) improve the support for DIA data in the data standards developed by the Proteomics Standards Initiative; and (iv) improve the support for DIA datasets in ProteomeXchange resources, including more tailored metadata requirements. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Andrew R Jones
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 3BX, UK
| | - Eric W Deutsch
- Institute for Systems Biology, Seattle, Washington, 98109, USA
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, UK
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50
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An Open-Source Pipeline for Processing Direct Infusion Mass Spectrometry Data of the Human Plasma Metabolome. Metabolites 2022; 12:metabo12080768. [PMID: 36005640 PMCID: PMC9415960 DOI: 10.3390/metabo12080768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 07/25/2022] [Accepted: 08/18/2022] [Indexed: 11/30/2022] Open
Abstract
Direct infusion mass spectrometry (DIMS) is growing in popularity as an effective method for the screening of biological samples in clinical metabolomics. Being quick to execute, DIMS generally requires special skills when interpreting the results of measurements. By inspecting the similarities between two-dimensional electrospray ionization with quadrupole time-of-flight (ESI-QTOF) and matrix-assisted laser desorption/ionization (MALDI) mass spectra, the pipeline for processing QTOF mass spectra using open-source packages (MALDIquant, MSnbase and MetaboAnalystR) was tested. Previously, all algorithmic workflows have relied on the application of software either provided by a vendor or privately developed by enthusiasts. Here, we computationally examined two ways of interpreting the DIMS results of human blood metabolomic profiling. The studied spectra were acquired using ESI-QTOF maXis Impact II (Bruker Daltonics, Billerica, MA, USA), then pre-processed using COMPASS/DataAnalysis commercial software and mapped onto the metabolites using in-lab-developed MatLab scripts. Alternatively, in this work we used the open-source packages MALDIquant, for spectrum pre-processing, and MetaboAnalystR, for data interpretation, instead of the low-availability commercial and home-made tools. Using a set of 100 plasma samples (20 from volunteers with normal body mass index and 80 from patients at different stages of obesity), we observed a high degree of concordance in annotated metabolic pathways between the proprietary DataAnalysis/MatLab pipeline and our freely available solution.
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