1
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Jiang Y, Rex DA, Schuster D, Neely BA, Rosano GL, Volkmar N, Momenzadeh A, Peters-Clarke TM, Egbert SB, Kreimer S, Doud EH, Crook OM, Yadav AK, Vanuopadath M, Hegeman AD, Mayta M, Duboff AG, Riley NM, Moritz RL, Meyer JG. Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry. ACS MEASUREMENT SCIENCE AU 2024; 4:338-417. [PMID: 39193565 PMCID: PMC11348894 DOI: 10.1021/acsmeasuresciau.3c00068] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 05/03/2024] [Accepted: 05/03/2024] [Indexed: 08/29/2024]
Abstract
Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.
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Affiliation(s)
- Yuming Jiang
- Department
of Computational Biomedicine, Cedars Sinai
Medical Center, Los Angeles, California 90048, United States
- Smidt Heart
Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Advanced
Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los
Angeles, California 90048, United States
| | - Devasahayam Arokia
Balaya Rex
- Center for
Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore 575018, India
| | - Dina Schuster
- Department
of Biology, Institute of Molecular Systems
Biology, ETH Zurich, Zurich 8093, Switzerland
- Department
of Biology, Institute of Molecular Biology
and Biophysics, ETH Zurich, Zurich 8093, Switzerland
- Laboratory
of Biomolecular Research, Division of Biology and Chemistry, Paul Scherrer Institute, Villigen 5232, Switzerland
| | - Benjamin A. Neely
- Chemical
Sciences Division, National Institute of
Standards and Technology, NIST, Charleston, South Carolina 29412, United States
| | - Germán L. Rosano
- Mass
Spectrometry
Unit, Institute of Molecular and Cellular
Biology of Rosario, Rosario, 2000 Argentina
| | - Norbert Volkmar
- Department
of Biology, Institute of Molecular Systems
Biology, ETH Zurich, Zurich 8093, Switzerland
| | - Amanda Momenzadeh
- Department
of Computational Biomedicine, Cedars Sinai
Medical Center, Los Angeles, California 90048, United States
- Smidt Heart
Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Advanced
Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los
Angeles, California 90048, United States
| | - Trenton M. Peters-Clarke
- Department
of Pharmaceutical Chemistry, University
of California—San Francisco, San Francisco, California, 94158, United States
| | - Susan B. Egbert
- Department
of Chemistry, University of Manitoba, Winnipeg, Manitoba, R3T 2N2 Canada
| | - Simion Kreimer
- Smidt Heart
Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Advanced
Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los
Angeles, California 90048, United States
| | - Emma H. Doud
- Center
for Proteome Analysis, Indiana University
School of Medicine, Indianapolis, Indiana, 46202-3082, United States
| | - Oliver M. Crook
- Oxford
Protein Informatics Group, Department of Statistics, University of Oxford, Oxford OX1 3LB, United
Kingdom
| | - Amit Kumar Yadav
- Translational
Health Science and Technology Institute, NCR Biotech Science Cluster 3rd Milestone Faridabad-Gurgaon
Expressway, Faridabad, Haryana 121001, India
| | | | - Adrian D. Hegeman
- Departments
of Horticultural Science and Plant and Microbial Biology, University of Minnesota, Twin Cities, Minnesota 55108, United States
| | - Martín
L. Mayta
- School
of Medicine and Health Sciences, Center for Health Sciences Research, Universidad Adventista del Plata, Libertador San Martin 3103, Argentina
- Molecular
Biology Department, School of Pharmacy and Biochemistry, Universidad Nacional de Rosario, Rosario 2000, Argentina
| | - Anna G. Duboff
- Department
of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Nicholas M. Riley
- Department
of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Robert L. Moritz
- Institute
for Systems biology, Seattle, Washington 98109, United States
| | - Jesse G. Meyer
- Department
of Computational Biomedicine, Cedars Sinai
Medical Center, Los Angeles, California 90048, United States
- Smidt Heart
Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Advanced
Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los
Angeles, California 90048, United States
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2
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Degnan DJ, Lewis LA, Bramer LM, McCue LA, Pesavento JJ, Zhou M, Bilbao A. IsoForma: An R Package for Quantifying and Visualizing Positional Isomers in Top-Down LC-MS/MS Data. J Proteome Res 2024; 23:3318-3321. [PMID: 38421884 DOI: 10.1021/acs.jproteome.3c00681] [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: 03/02/2024]
Abstract
Proteoforms, the different forms of a protein with sequence variations including post-translational modifications (PTMs), execute vital functions in biological systems, such as cell signaling and epigenetic regulation. Advances in top-down mass spectrometry (MS) technology have permitted the direct characterization of intact proteoforms and their exact number of modification sites, allowing for the relative quantification of positional isomers (PI). Protein positional isomers refer to a set of proteoforms with identical total mass and set of modifications, but varying PTM site combinations. The relative abundance of PI can be estimated by matching proteoform-specific fragment ions to top-down tandem MS (MS2) data to localize and quantify modifications. However, the current approaches heavily rely on manual annotation. Here, we present IsoForma, an open-source R package for the relative quantification of PI within a single tool. Benchmarking IsoForma's performance against two existing workflows produced comparable results and improvements in speed. Overall, IsoForma provides a streamlined process for quantifying PI, reduces the analysis time, and offers an essential framework for developing customized proteoform analysis workflows. The software is open source and available at https://github.com/EMSL-Computing/isoforma-lib.
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Affiliation(s)
- David J Degnan
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Logan A Lewis
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Lisa M Bramer
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Lee Ann McCue
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - James J Pesavento
- Saint Mary's College of California, Moraga, California 94575, United States
| | - Mowei Zhou
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Aivett Bilbao
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
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3
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Lerra L, Panatta M, Bär D, Zanini I, Tan JY, Pisano A, Mungo C, Baroux C, Panse VG, Marques AC, Santoro R. An RNA-dependent and phase-separated active subnuclear compartment safeguards repressive chromatin domains. Mol Cell 2024; 84:1667-1683.e10. [PMID: 38599210 PMCID: PMC11065421 DOI: 10.1016/j.molcel.2024.03.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 10/19/2023] [Accepted: 03/16/2024] [Indexed: 04/12/2024]
Abstract
The nucleus is composed of functionally distinct membraneless compartments that undergo phase separation (PS). However, whether different subnuclear compartments are connected remains elusive. We identified a type of nuclear body with PS features composed of BAZ2A that associates with active chromatin. BAZ2A bodies depend on RNA transcription and BAZ2A non-disordered RNA-binding TAM domain. Although BAZ2A and H3K27me3 occupancies anticorrelate in the linear genome, in the nuclear space, BAZ2A bodies contact H3K27me3 bodies. BAZ2A-body disruption promotes BAZ2A invasion into H3K27me3 domains, causing H3K27me3-body loss and gene upregulation. Weak BAZ2A-RNA interactions, such as with nascent transcripts, promote BAZ2A bodies, whereas the strong binder long non-coding RNA (lncRNA) Malat1 impairs them while mediating BAZ2A association to chromatin at nuclear speckles. In addition to unraveling a direct connection between nuclear active and repressive compartments through PS mechanisms, the results also showed that the strength of RNA-protein interactions regulates this process, contributing to nuclear organization and the regulation of chromatin and gene expression.
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Affiliation(s)
- Luigi Lerra
- Department of Molecular Mechanisms of Disease (DMMD), University of Zurich, Zurich 8057, Switzerland; RNA Biology Program, Life Science Zurich Graduate School, University of Zurich, Zurich 8057, Switzerland
| | - Martina Panatta
- Department of Molecular Mechanisms of Disease (DMMD), University of Zurich, Zurich 8057, Switzerland; RNA Biology Program, Life Science Zurich Graduate School, University of Zurich, Zurich 8057, Switzerland
| | - Dominik Bär
- Department of Molecular Mechanisms of Disease (DMMD), University of Zurich, Zurich 8057, Switzerland
| | - Isabella Zanini
- Department of Molecular Mechanisms of Disease (DMMD), University of Zurich, Zurich 8057, Switzerland
| | - Jennifer Yihong Tan
- Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland
| | - Agnese Pisano
- Institute of Medical Microbiology, University of Zurich, Zurich 8057, Switzerland
| | - Chiara Mungo
- Department of Molecular Mechanisms of Disease (DMMD), University of Zurich, Zurich 8057, Switzerland; Molecular Life Science Program, Life Science Zurich Graduate School, University of Zurich, Zurich 8057, Switzerland
| | - Célia Baroux
- Department of Plant and Microbial Biology and Zurich-Basel Plant Science Center, University of Zurich, Zurich 8057, Switzerland
| | - Vikram Govind Panse
- Institute of Medical Microbiology, University of Zurich, Zurich 8057, Switzerland
| | - Ana C Marques
- Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland
| | - Raffaella Santoro
- Department of Molecular Mechanisms of Disease (DMMD), University of Zurich, Zurich 8057, Switzerland.
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4
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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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5
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Tretter C, de Andrade Krätzig N, Pecoraro M, Lange S, Seifert P, von Frankenberg C, Untch J, Zuleger G, Wilhelm M, Zolg DP, Dreyer FS, Bräunlein E, Engleitner T, Uhrig S, Boxberg M, Steiger K, Slotta-Huspenina J, Ochsenreither S, von Bubnoff N, Bauer S, Boerries M, Jost PJ, Schenck K, Dresing I, Bassermann F, Friess H, Reim D, Grützmann K, Pfütze K, Klink B, Schröck E, Haller B, Kuster B, Mann M, Weichert W, Fröhling S, Rad R, Hiltensperger M, Krackhardt AM. Proteogenomic analysis reveals RNA as a source for tumor-agnostic neoantigen identification. Nat Commun 2023; 14:4632. [PMID: 37532709 PMCID: PMC10397250 DOI: 10.1038/s41467-023-39570-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 06/19/2023] [Indexed: 08/04/2023] Open
Abstract
Systemic pan-tumor analyses may reveal the significance of common features implicated in cancer immunogenicity and patient survival. Here, we provide a comprehensive multi-omics data set for 32 patients across 25 tumor types for proteogenomic-based discovery of neoantigens. By using an optimized computational approach, we discover a large number of tumor-specific and tumor-associated antigens. To create a pipeline for the identification of neoantigens in our cohort, we combine DNA and RNA sequencing with MS-based immunopeptidomics of tumor specimens, followed by the assessment of their immunogenicity and an in-depth validation process. We detect a broad variety of non-canonical HLA-binding peptides in the majority of patients demonstrating partially immunogenicity. Our validation process allows for the selection of 32 potential neoantigen candidates. The majority of neoantigen candidates originates from variants identified in the RNA data set, illustrating the relevance of RNA as a still understudied source of cancer antigens. This study underlines the importance of RNA-centered variant detection for the identification of shared biomarkers and potentially relevant neoantigen candidates.
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Affiliation(s)
- Celina Tretter
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
| | - Niklas de Andrade Krätzig
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IInd Medical Department, Munich, Germany
- Technical University of Munich, TUM School of Medicine, Center for Translational Cancer Research (TranslaTUM), Munich, Germany
- Technical University of Munich, TUM School of Medicine, Institute of Molecular Oncology and Functional Genomics, Munich, Germany
| | - Matteo Pecoraro
- Department of Proteomics and Signal Transduction, Max Plank Institute of Biochemistry, Munich, Germany
- Institute for Research in Biomedicine, Università della Svizzera italiana, Bellinzona, Switzerland
| | - Sebastian Lange
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IInd Medical Department, Munich, Germany
- Technical University of Munich, TUM School of Medicine, Center for Translational Cancer Research (TranslaTUM), Munich, Germany
- Technical University of Munich, TUM School of Medicine, Institute of Molecular Oncology and Functional Genomics, Munich, Germany
| | - Philipp Seifert
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
| | - Clara von Frankenberg
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
| | - Johannes Untch
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
| | - Gabriela Zuleger
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
| | - Mathias Wilhelm
- Technical University of Munich, TUM School of Life Sciences, Chair of Proteomics and Bioanalytics, Freising, Germany
- Technical University of Munich, TUM School of Life Sciences, Computational Mass Spectrometry, Freising, Germany
| | - Daniel P Zolg
- Technical University of Munich, TUM School of Life Sciences, Chair of Proteomics and Bioanalytics, Freising, Germany
| | - Florian S Dreyer
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
| | - Eva Bräunlein
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
| | - Thomas Engleitner
- Technical University of Munich, TUM School of Medicine, Center for Translational Cancer Research (TranslaTUM), Munich, Germany
- Technical University of Munich, TUM School of Medicine, Institute of Molecular Oncology and Functional Genomics, Munich, Germany
| | - Sebastian Uhrig
- German Cancer Consortium (DKTK), partner site Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Molecular Precision Oncology Program, NCT Heidelberg, Heidelberg, Germany
| | - Melanie Boxberg
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, Institute of Pathology, Munich, Germany
| | - Katja Steiger
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, Institute of Pathology, Munich, Germany
| | - Julia Slotta-Huspenina
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, Institute of Pathology, Munich, Germany
| | - Sebastian Ochsenreither
- German Cancer Consortium (DKTK), partner site Berlin and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Charité Comprehensive Cancer Center, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Department of Hematology, Oncology and Tumor Immunology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Nikolas von Bubnoff
- German Cancer Consortium (DKTK), partner site Freiburg and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute of Medical Bioinformatics and Systems Medicine (IBSM), Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Department of Hematology and Oncology, Medical Center, University of Schleswig Holstein, Campus Lübeck, Lübeck, Germany
| | - Sebastian Bauer
- German Cancer Consortium (DKTK), partner site Essen and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Medical Oncology and Sarcoma Center, West German Cancer Center, University Hospital Essen, Essen, Germany
| | - Melanie Boerries
- German Cancer Consortium (DKTK), partner site Freiburg and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute of Medical Bioinformatics and Systems Medicine (IBSM), Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Philipp J Jost
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
- Clinical Division of Oncology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
- University Comprehensive Cancer Center Graz, Medical University of Graz, Graz, Austria
| | - Kristina Schenck
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
| | - Iska Dresing
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
| | - Florian Bassermann
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
- Technical University of Munich, TUM School of Medicine, Center for Translational Cancer Research (TranslaTUM), Munich, Germany
| | - Helmut Friess
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, Department of Surgery, Munich, Germany
| | - Daniel Reim
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, Department of Surgery, Munich, Germany
| | - Konrad Grützmann
- German Cancer Consortium (DKTK), partner site Dresden and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Core Unit Molecular Tumor Diagnostics (CMTD), NCT Dresden, Dresden, Germany
- Institute for Medical Informatics and Biometry, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Katrin Pfütze
- German Cancer Consortium (DKTK), partner site Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Barbara Klink
- German Cancer Consortium (DKTK), partner site Dresden and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute for Clinical Genetics, University Hospital Carl Gustav Carus at the Technische Universität Dresden, Dresden, Germany
| | - Evelin Schröck
- German Cancer Consortium (DKTK), partner site Dresden and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute for Clinical Genetics, University Hospital Carl Gustav Carus at the Technische Universität Dresden, Dresden, Germany
- ERN GENTURIS, Hereditary Cancer Syndrome Center Dresden, Dresden, Germany
- National Center for Tumor Diseases Dresden (NCT/UCC), Dresden, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Bernhard Haller
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, Institute of AI and Informatics in Medicine, Munich, Germany
| | - Bernhard Kuster
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Life Sciences, Chair of Proteomics and Bioanalytics, Freising, Germany
- Technical University of Munich, TUM School of Life Sciences, Bavarian Biomolecular Mass Spectrometry Center (BayBioMS), Freising, Germany
| | - Matthias Mann
- Department of Proteomics and Signal Transduction, Max Plank Institute of Biochemistry, Munich, Germany
| | - Wilko Weichert
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, Institute of Pathology, Munich, Germany
| | - Stefan Fröhling
- German Cancer Consortium (DKTK), partner site Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Translational Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Roland Rad
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IInd Medical Department, Munich, Germany
- Technical University of Munich, TUM School of Medicine, Center for Translational Cancer Research (TranslaTUM), Munich, Germany
- Technical University of Munich, TUM School of Medicine, Institute of Molecular Oncology and Functional Genomics, Munich, Germany
| | - Michael Hiltensperger
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
| | - Angela M Krackhardt
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany.
- Technical University of Munich, TUM School of Medicine, Center for Translational Cancer Research (TranslaTUM), Munich, Germany.
- Malteser Krankenhaus St. Franziskus-Hospital, Flensburg, Germany.
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6
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Lauman R, Kim HJ, Pino LK, Scacchetti A, Xie Y, Robison F, Sidoli S, Bonasio R, Garcia BA. Expanding the Epitranscriptomic RNA Sequencing and Modification Mapping Mass Spectrometry Toolbox with Field Asymmetric Waveform Ion Mobility and Electrochemical Elution Liquid Chromatography. Anal Chem 2023; 95:5187-5195. [PMID: 36916610 PMCID: PMC10190205 DOI: 10.1021/acs.analchem.2c04114] [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] [Indexed: 03/15/2023]
Abstract
Post-transcriptional modifications of RNA strongly influence the RNA structure and function. Recent advances in RNA sequencing and mass spectrometry (MS) methods have identified over 140 of these modifications on a wide variety of RNA species. Most next-generation sequencing approaches can only map one RNA modification at a time, and while MS can assign multiple modifications simultaneously in an unbiased manner, MS cannot accurately catalog and assign RNA modifications in complex biological samples due to limitations in the fragment length and coverage depth. Thus, a facile method to identify novel RNA modifications while simultaneously locating them in the context of their RNA sequences is still lacking. We combined two orthogonal modes of RNA ion separation before MS identification: high-field asymmetric ion mobility separation (FAIMS) and electrochemically modulated liquid chromatography (EMLC). FAIMS RNA MS increases both coverage and throughput, while EMLC LC-MS orthogonally separates RNA molecules of different lengths and charges. The combination of the two methods offers a broadly applicable platform to improve the length and depth of MS-based RNA sequencing while providing contextual access to the analysis of RNA modifications.
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Affiliation(s)
- Richard Lauman
- Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, PA, USA
- Epigenetic Institute and Department of Cell and Developmental Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Hee Jong Kim
- Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, PA, USA
| | - Lindsay K. Pino
- Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, PA, USA
| | - Alessandro Scacchetti
- Epigenetic Institute and Department of Cell and Developmental Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Yixuan Xie
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA
| | - Faith Robison
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA
| | - Simone Sidoli
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Roberto Bonasio
- Epigenetic Institute and Department of Cell and Developmental Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Benjamin A. Garcia
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA
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7
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Panse C, Trachsel C, Türker C. Bridging data management platforms and visualization tools to enable ad-hoc and smart analytics in life sciences. J Integr Bioinform 2022; 19:jib-2022-0031. [PMID: 36073980 PMCID: PMC9800043 DOI: 10.1515/jib-2022-0031] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 07/29/2022] [Accepted: 08/11/2022] [Indexed: 01/09/2023] Open
Abstract
Core facilities have to offer technologies that best serve the needs of their users and provide them a competitive advantage in research. They have to set up and maintain instruments in the range of ten to a hundred, which produce large amounts of data and serve thousands of active projects and customers. Particular emphasis has to be given to the reproducibility of the results. More and more, the entire process from building the research hypothesis, conducting the experiments, doing the measurements, through the data explorations and analysis is solely driven by very few experts in various scientific fields. Still, the ability to perform the entire data exploration in real-time on a personal computer is often hampered by the heterogeneity of software, the data structure formats of the output, and the enormous data sizes. These impact the design and architecture of the implemented software stack. At the Functional Genomics Center Zurich (FGCZ), a joint state-of-the-art research and training facility of ETH Zurich and the University of Zurich, we have developed the B-Fabric system, which has served for more than a decade, an entire life sciences community with fundamental data science support. In this paper, we sketch how such a system can be used to glue together data (including metadata), computing infrastructures (clusters and clouds), and visualization software to support instant data exploration and visual analysis. We illustrate our in-daily life implemented approach using visualization applications of mass spectrometry data.
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Affiliation(s)
- Christian Panse
- Functional Genomics Center Zurich (FGCZ), University of Zurich/ETH Zurich, Winterthurerstrasse 190, CH-8057Zurich, Switzerland
| | - Christian Trachsel
- Functional Genomics Center Zurich (FGCZ), University of Zurich/ETH Zurich, Winterthurerstrasse 190, CH-8057Zurich, Switzerland
| | - Can Türker
- Functional Genomics Center Zurich (FGCZ), University of Zurich/ETH Zurich, Winterthurerstrasse 190, CH-8057Zurich, Switzerland
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8
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The addition of FAIMS increases targeted proteomics sensitivity from FFPE tumor biopsies. Sci Rep 2022; 12:13876. [PMID: 35974054 PMCID: PMC9381555 DOI: 10.1038/s41598-022-16358-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 07/08/2022] [Indexed: 12/02/2022] Open
Abstract
Mass spectrometry-based targeted proteomics allows objective protein quantitation of clinical biomarkers from a single section of formalin-fixed, paraffin-embedded (FFPE) tumor tissue biopsies. We combined high-field asymmetric waveform ion mobility spectrometry (FAIMS) and parallel reaction monitoring (PRM) to increase assay sensitivity. The modular nature of the FAIMS source allowed direct comparison of the performance of FAIMS-PRM to PRM. Limits of quantitation were determined by spiking synthetic peptides into a human spleen matrix. In addition, 20 clinical samples were analyzed using FAIMS-PRM and the quantitation of HER2 was compared with that obtained with the Ventana immunohistochemistry assay. FAIMS-PRM improved the overall signal-to-noise ratio over that from PRM and increased assay sensitivity in FFPE tissue analysis for four (HER2, EGFR, cMET, and KRAS) of five proteins of clinical interest. FAIMS-PRM enabled sensitive quantitation of basal HER2 expression in breast cancer samples classified as HER2 negative by immunohistochemistry. Furthermore, we determined the degree of FAIMS-dependent background reduction and showed that this correlated with an improved lower limit of quantitation with FAIMS. FAIMS-PRM is anticipated to benefit clinical trials in which multiple biomarker questions must be addressed and the availability of tumor biopsy samples is limited.
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9
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Hassan M, Juanola O, Keller I, Nanni P, Wolski W, Martínez-López S, Caparrós E, Francés R, Moghadamrad S. Paneth Cells Regulate Lymphangiogenesis under Control of Microbial Signals during Experimental Portal Hypertension. Biomedicines 2022; 10:biomedicines10071503. [PMID: 35884808 PMCID: PMC9313283 DOI: 10.3390/biomedicines10071503] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 06/15/2022] [Accepted: 06/22/2022] [Indexed: 11/16/2022] Open
Abstract
Intestinal microbiota can modulate portal hypertension through the regulation of the intestinal vasculature. We have recently demonstrated that bacterial antigens activate Paneth cells (PCs) to secrete products that regulate angiogenesis and portal hypertension. In the present work we hypothesized that Paneth cells regulate the development of lymphatic vessels under the control of intestinal microbiota during experimental portal hypertension. We used a mouse model of inducible PCs depletion (Math1Lox/LoxVilCreERT2) and performed partial portal vein ligation (PPVL) to induce portal hypertension. After 14 days, we performed mRNA sequencing and evaluated the expression of specific lymphangiogenic genes in small intestinal tissue. Intestinal and mesenteric lymphatic vessels proliferation was assessed by immunohistochemistry. Intestinal organoids with or without PCs were exposed to pathogen-associated molecular patterns, and conditioned media (CM) was used to stimulate human lymphatic endothelial cells (LECs). The lymphangiogenic activity of stimulated LECs was assessed by tube formation and wound healing assays. Secretome analysis of CM was performed using label-free proteomics quantification methods. Intestinal immune cell infiltration was evaluated by immunohistochemistry. We observed that the intestinal gene expression pattern was altered by the absence of PCs only in portal hypertensive mice. We found a decreased expression of specific lymphangiogenic genes in the absence of PCs during portal hypertension, resulting in a reduced proliferation of intestinal and mesenteric lymphatic vessels as compared to controls. In vitro analyses demonstrated that lymphatic tube formation and endothelial wound healing responses were reduced significantly in LECs treated with CM from organoids without PCs. Secretome analyses of CM revealed that PCs secrete proteins that are involved in lipid metabolism, cell growth and proliferation. Additionally, intestinal macrophages infiltrated the ileal mucosa and submucosa of mice with and without Paneth cells in response to portal hypertension. Our results suggest that intestinal microbiota signals stimulate Paneth cells to secrete factors that modulate the intestinal and mesenteric lymphatic vessels network during experimental portal hypertension.
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Affiliation(s)
- Mohsin Hassan
- Department of Hepatology & Gastroenterology, Charité Universitätsmedizin Berlin, 13353 Berlin, Germany;
- Department for Biomedical Research (DBMR), University of Bern, 3008 Bern, Switzerland
| | - Oriol Juanola
- Laboratories for Translational Research, Department of Gastroenterology and Hepatology, Ente Ospedaliero Cantonale, 6500 Bellinzona, Switzerland;
- Faculty of Biomedical Sciences, Università della Svizzera Italiana, 6900 Lugano, Switzerland
| | - Irene Keller
- Interfaculty Bioinformatics Unit, Swiss Institute of Bioinformatics, University of Bern, 3008 Bern, Switzerland;
| | - Paolo Nanni
- Functional Genomics Center Zurich, University/ETH Zurich, 8057 Zurich, Switzerland; (P.N.); (W.W.)
| | - Witold Wolski
- Functional Genomics Center Zurich, University/ETH Zurich, 8057 Zurich, Switzerland; (P.N.); (W.W.)
| | - Sebastián Martínez-López
- Hepatic and Intestinal Immunobiology Group, Departamento Medicina Clínica, Universidad Miguel Hernández, 03550 Alicante, Spain; (S.M.-L.); (E.C.); (R.F.)
- Instituto de Investigación Sanitaria ISABIAL, Hospital General Universitario, 03010 Alicante, Spain
| | - Esther Caparrós
- Hepatic and Intestinal Immunobiology Group, Departamento Medicina Clínica, Universidad Miguel Hernández, 03550 Alicante, Spain; (S.M.-L.); (E.C.); (R.F.)
- Instituto de Investigación Sanitaria ISABIAL, Hospital General Universitario, 03010 Alicante, Spain
| | - Rubén Francés
- Hepatic and Intestinal Immunobiology Group, Departamento Medicina Clínica, Universidad Miguel Hernández, 03550 Alicante, Spain; (S.M.-L.); (E.C.); (R.F.)
- Instituto de Investigación Sanitaria ISABIAL, Hospital General Universitario, 03010 Alicante, Spain
- Instituto de Investigación, Desarrollo e Innovación en Biotecnología Sanitaria de Elche (IDiBE), Universidad Miguel Hernández, 03207 Elche, Spain
- CIBERehd, Instituto Salud Carlos III, 28029 Madrid, Spain
| | - Sheida Moghadamrad
- Department for Biomedical Research (DBMR), University of Bern, 3008 Bern, Switzerland
- Laboratories for Translational Research, Department of Gastroenterology and Hepatology, Ente Ospedaliero Cantonale, 6500 Bellinzona, Switzerland;
- Faculty of Biomedical Sciences, Università della Svizzera Italiana, 6900 Lugano, Switzerland
- University Clinic of Visceral Surgery and Medicine, Inselspital, 3008 Bern, Switzerland
- Correspondence: ; Tel.: +41-58-666-7117
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10
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Smith TS, Andrejeva A, Christopher J, Crook OM, Elzek M, Lilley KS. Prior Signal Acquisition Software Versions for Orbitrap Underestimate Low Isobaric Mass Tag Intensities, Without Detriment to Differential Abundance Experiments. ACS MEASUREMENT SCIENCE AU 2022; 2:233-240. [PMID: 35726249 PMCID: PMC9204819 DOI: 10.1021/acsmeasuresciau.1c00053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/24/2022] [Accepted: 01/25/2022] [Indexed: 06/15/2023]
Abstract
Tandem mass tags (TMTs) enable simple and accurate quantitative proteomics for multiplexed samples by relative quantification of tag reporter ions. Orbitrap quantification of reporter ions has been associated with a characteristic notch region in intensity distribution, within which few reporter intensities are recorded. This has been resolved in version 3 of the instrument acquisition software Tune. However, 47% of Orbitrap Fusion, Lumos, or Eclipse submissions to PRIDE were generated using prior software versions. To quantify the impact of the notch on existing quantitative proteomics data, we generated a mixed species benchmark and acquired quantitative data using Tune versions 2 and 3. Intensities below the notch are predominantly underestimated with Tune version 2, leading to overestimation of the true differences in intensities between samples. However, when summarizing reporter ion intensities to higher-level features, such as peptides and proteins, few features are significantly affected. Targeted removal of spectra with reporter ion intensities below the notch is not beneficial for differential peptide or protein testing. Overall, we find that the systematic quantification bias associated with the notch is not detrimental for a typical proteomics experiment.
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Affiliation(s)
- Tom S. Smith
- MRC
Toxicology Unit, University of Cambridge, Cambridge CB2 1QR, U.K.
| | - Anna Andrejeva
- Department
of Biochemistry, University of Cambridge, Cambridge CB2 1QW, U.K.
| | - Josie Christopher
- Department
of Biochemistry, University of Cambridge, Cambridge CB2 1QW, U.K.
| | - Oliver M. Crook
- Department
of Statistics, University of Oxford, Oxford OX1 3LB, U.K.
| | - Mohamed Elzek
- MRC
Toxicology Unit, University of Cambridge, Cambridge CB2 1QR, U.K.
| | - Kathryn S. Lilley
- Department
of Biochemistry, University of Cambridge, Cambridge CB2 1QW, U.K.
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11
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Loroch S, Kopczynski D, Schneider AC, Schumbrutzki C, Feldmann I, Panagiotidis E, Reinders Y, Sakson R, Solari FA, Vening A, Swieringa F, Heemskerk JWM, Grandoch M, Dandekar T, Sickmann A. Toward Zero Variance in Proteomics Sample Preparation: Positive-Pressure FASP in 96-Well Format (PF96) Enables Highly Reproducible, Time- and Cost-Efficient Analysis of Sample Cohorts. J Proteome Res 2022; 21:1181-1188. [PMID: 35316605 PMCID: PMC8981309 DOI: 10.1021/acs.jproteome.1c00706] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
![]()
As novel liquid chromatography–mass
spectrometry (LC-MS)
technologies for proteomics offer a substantial increase in LC-MS
runs per day, robust and reproducible sample preparation emerges as
a new bottleneck for throughput. We introduce a novel strategy for
positive-pressure 96-well filter-aided sample preparation (PF96) on
a commercial positive-pressure solid-phase extraction device. PF96
allows for a five-fold increase in throughput in conjunction with
extraordinary reproducibility with Pearson product-moment correlations
on the protein level of r = 0.9993, as demonstrated
for mouse heart tissue lysate in 40 technical replicates. The targeted
quantification of 16 peptides in the presence of stable-isotope-labeled
reference peptides confirms that PF96 variance is barely assessable
against technical variation from nanoLC-MS instrumentation. We further
demonstrate that protein loads of 36–60 μg result in
optimal peptide recovery, but lower amounts ≥3 μg can
also be processed reproducibly. In summary, the reproducibility, simplicity,
and economy of time provide PF96 a promising future in biomedical
and clinical research.
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Affiliation(s)
- Stefan Loroch
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., 44139 Dortmund, Germany
| | - Dominik Kopczynski
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., 44139 Dortmund, Germany
| | - Adriana C Schneider
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., 44139 Dortmund, Germany.,Faculty of Biochemical and Chemical Engineering, Technical University of Dortmund, 44227 Dortmund, Germany
| | - Cornelia Schumbrutzki
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., 44139 Dortmund, Germany
| | - Ingo Feldmann
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., 44139 Dortmund, Germany
| | | | - Yvonne Reinders
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., 44139 Dortmund, Germany
| | - Roman Sakson
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., 44139 Dortmund, Germany
| | - Fiorella A Solari
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., 44139 Dortmund, Germany
| | - Alicia Vening
- Department of Biochemistry, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Frauke Swieringa
- Department of Biochemistry, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Johan W M Heemskerk
- Department of Biochemistry, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Maria Grandoch
- Institut für Pharmakologie und Klinische Pharmakologie, Universitätsklinikum der Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany
| | - Thomas Dandekar
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Albert Sickmann
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., 44139 Dortmund, Germany.,Medizinisches Proteom-Center, Ruhr-Universität Bochum, 44801 Bochum, Germany.,Department of Chemistry, College of Physical Sciences, University of Aberdeen, AB24 3FX Aberdeen, United Kingdom
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12
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Wendisch D, Dietrich O, Mari T, von Stillfried S, Ibarra IL, Mittermaier M, Mache C, Chua RL, Knoll R, Timm S, Brumhard S, Krammer T, Zauber H, Hiller AL, Pascual-Reguant A, Mothes R, Bülow RD, Schulze J, Leipold AM, Djudjaj S, Erhard F, Geffers R, Pott F, Kazmierski J, Radke J, Pergantis P, Baßler K, Conrad C, Aschenbrenner AC, Sawitzki B, Landthaler M, Wyler E, Horst D, Hippenstiel S, Hocke A, Heppner FL, Uhrig A, Garcia C, Machleidt F, Herold S, Elezkurtaj S, Thibeault C, Witzenrath M, Cochain C, Suttorp N, Drosten C, Goffinet C, Kurth F, Schultze JL, Radbruch H, Ochs M, Eils R, Müller-Redetzky H, Hauser AE, Luecken MD, Theis FJ, Conrad C, Wolff T, Boor P, Selbach M, Saliba AE, Sander LE. SARS-CoV-2 infection triggers profibrotic macrophage responses and lung fibrosis. Cell 2021; 184:6243-6261.e27. [PMID: 34914922 PMCID: PMC8626230 DOI: 10.1016/j.cell.2021.11.033] [Citation(s) in RCA: 268] [Impact Index Per Article: 89.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 07/28/2021] [Accepted: 11/23/2021] [Indexed: 12/15/2022]
Abstract
COVID-19-induced “acute respiratory distress syndrome” (ARDS) is associated with prolonged respiratory failure and high mortality, but the mechanistic basis of lung injury remains incompletely understood. Here, we analyze pulmonary immune responses and lung pathology in two cohorts of patients with COVID-19 ARDS using functional single-cell genomics, immunohistology, and electron microscopy. We describe an accumulation of CD163-expressing monocyte-derived macrophages that acquired a profibrotic transcriptional phenotype during COVID-19 ARDS. Gene set enrichment and computational data integration revealed a significant similarity between COVID-19-associated macrophages and profibrotic macrophage populations identified in idiopathic pulmonary fibrosis. COVID-19 ARDS was associated with clinical, radiographic, histopathological, and ultrastructural hallmarks of pulmonary fibrosis. Exposure of human monocytes to SARS-CoV-2, but not influenza A virus or viral RNA analogs, was sufficient to induce a similar profibrotic phenotype in vitro. In conclusion, we demonstrate that SARS-CoV-2 triggers profibrotic macrophage responses and pronounced fibroproliferative ARDS.
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Affiliation(s)
- Daniel Wendisch
- Charité - Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Oliver Dietrich
- Helmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz-Center for Infection Research (HZI), Würzburg, Germany
| | - Tommaso Mari
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Proteome Dynamics, Berlin, Germany
| | | | - Ignacio L Ibarra
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Mirja Mittermaier
- Charité - Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Charité, Universitätsmedizin Berlin, Berlin, Germany; Berlin Institute of Health (BIH), Berlin, Germany
| | - Christin Mache
- Unit 17 Influenza and other Respiratory Viruses, Robert Koch Institute, Berlin, Germany
| | - Robert Lorenz Chua
- Center for Digital Health, Berlin Institute of Health (BIH) and Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Rainer Knoll
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany; Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Germany
| | - Sara Timm
- Core Facility Electron Microscopy, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sophia Brumhard
- Charité - Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Tobias Krammer
- Helmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz-Center for Infection Research (HZI), Würzburg, Germany
| | - Henrik Zauber
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Proteome Dynamics, Berlin, Germany
| | - Anna Luisa Hiller
- Charité - Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Anna Pascual-Reguant
- Charité - Universitätsmedizin Berlin, Department of Rheumatology and Clinical Immunology, Charité; Deutsches Rheumaforschungszentrum, Immunodynamics, a Leibniz Institute, Berlin, Germany
| | - Ronja Mothes
- Deutsches Rheumaforschungszentrum, Immunodynamics, a Leibniz Institute, Berlin, Germany; Charité - Universitätsmedizin Berlin, Department of Neuropathology, Berlin, Germany
| | - Roman David Bülow
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany
| | - Jessica Schulze
- Unit 17 Influenza and other Respiratory Viruses, Robert Koch Institute, Berlin, Germany
| | - Alexander M Leipold
- Helmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz-Center for Infection Research (HZI), Würzburg, Germany
| | - Sonja Djudjaj
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany
| | - Florian Erhard
- Institute for Virology and Immunobiology, Julius-Maximilians-University Würzburg, Würzburg, Germany
| | - Robert Geffers
- Genome Analytics, Helmholtz-Center for Infection Research (HZI), Braunschweig, Germany
| | - Fabian Pott
- Berlin Institute of Health (BIH), Berlin, Germany; Charité - Universitätsmedizin Berlin, Institute of Virology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Julia Kazmierski
- Berlin Institute of Health (BIH), Berlin, Germany; Charité - Universitätsmedizin Berlin, Institute of Virology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Josefine Radke
- Berlin Institute of Health (BIH), Berlin, Germany; Charité - Universitätsmedizin Berlin, Department of Neuropathology, Berlin, Germany
| | - Panagiotis Pergantis
- Charité - Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Kevin Baßler
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany; Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Germany
| | - Claudia Conrad
- Charité - Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Anna C Aschenbrenner
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany; Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Germany; PRECISE Platform for Genomics and Epigenomics at Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), and University of Bonn, Bonn, Germany; Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Birgit Sawitzki
- Charité, Universitätsmedizin Berlin, Institute of Medical Immunology, Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Markus Landthaler
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Emanuel Wyler
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - David Horst
- Charité - Universitätsmedizin Berlin, Institute of Pathology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | | | - Stefan Hippenstiel
- Charité - Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Charité, Universitätsmedizin Berlin, Berlin, Germany; German Center for Lung Research (DZL), Germany
| | - Andreas Hocke
- Charité - Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Charité, Universitätsmedizin Berlin, Berlin, Germany; German Center for Lung Research (DZL), Germany
| | - Frank L Heppner
- Charité - Universitätsmedizin Berlin, Department of Neuropathology, Berlin, Germany; Cluster of Excellence, NeuroCure, Berlin, Germany; German Center for Neurodegenerative Diseases (DZNE) Berlin, Berlin, Germany
| | - Alexander Uhrig
- Charité - Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Carmen Garcia
- Charité - Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Felix Machleidt
- Charité - Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Susanne Herold
- German Center for Lung Research (DZL), Germany; Division of Infectious Diseases, Pulmonary and Critical Care Medicine, Department of Internal Medicine II, Universities of Giessen and Marburg Lung Center, Giessen, Germany; Institute of Lung Health (ILH), Giessen, Germany
| | - Sefer Elezkurtaj
- Charité - Universitätsmedizin Berlin, Institute of Pathology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Charlotte Thibeault
- Charité - Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Martin Witzenrath
- Charité - Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Charité, Universitätsmedizin Berlin, Berlin, Germany; German Center for Lung Research (DZL), Germany
| | - Clément Cochain
- Comprehensive Heart Failure Center Wuerzburg, University Hospital Würzburg, Germany; Institute of Experimental Biomedicine, University Hospital Würzburg, Würzburg, Germany
| | - Norbert Suttorp
- Charité - Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Charité, Universitätsmedizin Berlin, Berlin, Germany; German Center for Lung Research (DZL), Germany
| | - Christian Drosten
- Charité - Universitätsmedizin Berlin, Institute of Virology, Charité Universitätsmedizin Berlin, Berlin, Germany; German Center for Infection Research (DZIF), Braunschweig, Germany
| | - Christine Goffinet
- Berlin Institute of Health (BIH), Berlin, Germany; Charité - Universitätsmedizin Berlin, Institute of Virology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Florian Kurth
- Charité - Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Charité, Universitätsmedizin Berlin, Berlin, Germany; Department of Tropical Medicine, Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany; I. Department of Medicine, University Medical Center, Hamburg-Eppendorf, Hamburg, Germany
| | - Joachim L Schultze
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany; Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Germany; PRECISE Platform for Genomics and Epigenomics at Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), and University of Bonn, Bonn, Germany
| | - Helena Radbruch
- Charité - Universitätsmedizin Berlin, Department of Neuropathology, Berlin, Germany
| | - Matthias Ochs
- Core Facility Electron Microscopy, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; German Center for Lung Research (DZL), Germany; Institute of Functional Anatomy, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Roland Eils
- Center for Digital Health, Berlin Institute of Health (BIH) and Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Holger Müller-Redetzky
- Charité - Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Anja E Hauser
- Charité - Universitätsmedizin Berlin, Department of Rheumatology and Clinical Immunology, Charité; Deutsches Rheumaforschungszentrum, Immunodynamics, a Leibniz Institute, Berlin, Germany
| | - Malte D Luecken
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Department of Mathematics, Technische Universität München, Garching bei München, Germany
| | - Christian Conrad
- Center for Digital Health, Berlin Institute of Health (BIH) and Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Thorsten Wolff
- Unit 17 Influenza and other Respiratory Viruses, Robert Koch Institute, Berlin, Germany
| | - Peter Boor
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany
| | - Matthias Selbach
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Proteome Dynamics, Berlin, Germany; Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Antoine-Emmanuel Saliba
- Helmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz-Center for Infection Research (HZI), Würzburg, Germany.
| | - Leif Erik Sander
- Charité - Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Charité, Universitätsmedizin Berlin, Berlin, Germany; German Center for Lung Research (DZL), Germany.
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13
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Rueda-Mejia MP, Nägeli L, Lutz S, Hayes RD, Varadarajan AR, Grigoriev IV, Ahrens CH, Freimoser FM. Genome, transcriptome and secretome analyses of the antagonistic, yeast-like fungus Aureobasidium pullulans to identify potential biocontrol genes. MICROBIAL CELL 2021; 8:184-202. [PMID: 34395586 PMCID: PMC8329847 DOI: 10.15698/mic2021.08.757] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 05/21/2021] [Accepted: 05/26/2021] [Indexed: 12/27/2022]
Abstract
Aureobasidium pullulans is an extremotolerant, cosmopolitan yeast-like fungus that successfully colonises vastly different ecological niches. The species is widely used in biotechnology and successfully applied as a commercial biocontrol agent against postharvest diseases and fireblight. However, the exact mechanisms that are responsible for its antagonistic activity against diverse plant pathogens are not known at the molecular level. Thus, it is difficult to optimise and improve the biocontrol applications of this species. As a foundation for elucidating biocontrol mechanisms, we have de novo assembled a high-quality reference genome of a strongly antagonistic A. pullulans strain, performed dual RNA-seq experiments, and analysed proteins secreted during the interaction with the plant pathogen Fusarium oxysporum. Based on the genome annotation, potential biocontrol genes were predicted to encode secreted hydrolases or to be part of secondary metabolite clusters (e.g., NRPS-like, NRPS, T1PKS, terpene, and β-lactone clusters). Transcriptome and secretome analyses defined a subset of 79 A. pullulans genes (among the 10,925 annotated genes) that were transcriptionally upregulated or exclusively detected at the protein level during the competition with F. oxysporum. These potential biocontrol genes comprised predicted secreted hydrolases such as glycosylases, esterases, and proteases, as well as genes encoding enzymes, which are predicted to be involved in the synthesis of secondary metabolites. This study highlights the value of a sequential approach starting with genome mining and consecutive transcriptome and secretome analyses in order to identify a limited number of potential target genes for detailed, functional analyses.
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Affiliation(s)
- Maria Paula Rueda-Mejia
- Agroscope, Research Division Plant Protection, Müller-Thurgau-Strasse 29, 8820 Wädenswil, Switzerland
| | - Lukas Nägeli
- Agroscope, Research Division Plant Protection, Müller-Thurgau-Strasse 29, 8820 Wädenswil, Switzerland
| | - Stefanie Lutz
- Agroscope, Competence Division Method Development and Analytics, Müller-Thurgau-Strasse 29, 8820, Wädenswil, Switzerland
| | - Richard D Hayes
- U.S. Department of Energy Joint Genome Institute (JGI), Lawrence Berkeley National Laboratory, 1 Cyclotron Rd., Berkeley, California 94720, USA
| | - Adithi R Varadarajan
- Agroscope, Competence Division Method Development and Analytics, Müller-Thurgau-Strasse 29, 8820, Wädenswil, Switzerland
| | - Igor V Grigoriev
- U.S. Department of Energy Joint Genome Institute (JGI), Lawrence Berkeley National Laboratory, 1 Cyclotron Rd., Berkeley, California 94720, USA.,Department of Plant and Microbial Biology, University of California Berkeley, Koshland Hall, Berkeley, CA, USA
| | - Christian H Ahrens
- Agroscope, Competence Division Method Development and Analytics, Müller-Thurgau-Strasse 29, 8820, Wädenswil, Switzerland.,SIB, Swiss Institute of Bioinformatics, Wädenswil, Switzerland
| | - Florian M Freimoser
- Agroscope, Research Division Plant Protection, Müller-Thurgau-Strasse 29, 8820 Wädenswil, Switzerland
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14
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Kockmann T, Panse C. The rawrr R Package: Direct Access to Orbitrap Data and Beyond. J Proteome Res 2021; 20:2028-2034. [PMID: 33686856 DOI: 10.1021/acs.jproteome.0c00866] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The Bioconductor project (Nat. Methods 2015, 12 (2), 115-121) has shown that the R statistical environment is a highly valuable tool for genomics data analysis, but with respect to proteomics, we are still missing low-level infrastructure to enable performant and robust analysis workflows in R. Fundamentally important are libraries that provide raw data access. Our R package rawDiag (J. Proteome Res. 2018, 17 (8), 2908-2914) has provided the proof-of-principle how access to mass spectrometry raw files can be realized by wrapping a vendor-provided advanced programming interface (API) for the purpose of metadata analysis and visualization. Our novel package rawrr now provides complete, OS-independent access to all spectral data logged in Thermo Fisher Scientific raw files. In this technical note, we present implementation details and describe the main functionalities provided by the rawrr package. In addition, we report two use cases inspired by real-world research tasks that demonstrate the application of the package. The raw data used for demonstration purposes was deposited as MassIVE data set MSV000086542. Availability: https://github.com/fgcz/rawrr.
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Affiliation(s)
- Tobias Kockmann
- Functional Genomics Center Zurich, ETH Zurich/University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
| | - Christian Panse
- Functional Genomics Center Zurich, ETH Zurich/University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland.,Swiss Institute of Bioinformatics, Quartier Sorge - Batiment Amphipole, 1015 Lausanne, Switzerland
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15
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Degnan DJ, Bramer LM, White AM, Zhou M, Bilbao A, McCue LA. PSpecteR: A User-Friendly and Interactive Application for Visualizing Top-Down and Bottom-Up Proteomics Data in R. J Proteome Res 2021; 20:2014-2020. [PMID: 33661636 DOI: 10.1021/acs.jproteome.0c00857] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Visual examination of mass spectrometry data is necessary to assess data quality and to facilitate data exploration. Graphics provide the means to evaluate spectral properties, test alternative peptide/protein sequence matches, prepare annotated spectra for publication, and fine-tune parameters during wet lab procedures. Visual inspection of LC-MS data is constrained by proteomics visualization software designed for particular workflows or vendor-specific tools without open-source code. We built PSpecteR, an open-source and interactive R Shiny web application for visualization of LC-MS data, with support for several steps of proteomics data processing, including reading various mass spectrometry files, running open-source database search engines, labeling spectra with fragmentation patterns, testing post-translational modifications, plotting where identified fragments map to reference sequences, and visualizing algorithmic output and metadata. All figures, tables, and spectra are exportable within one easy-to-use graphical user interface. Our current software provides a flexible and modern R framework to support fast implementation of additional features. The open-source code is readily available (https://github.com/EMSL-Computing/PSpecteR), and a PSpecteR Docker container (https://hub.docker.com/r/emslcomputing/pspecter) is available for easy local installation.
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16
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Gehrig PM, Nowak K, Panse C, Leutert M, Grossmann J, Schlapbach R, Hottiger MO. Gas-Phase Fragmentation of ADP-Ribosylated Peptides: Arginine-Specific Side-Chain Losses and Their Implication in Database Searches. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:157-168. [PMID: 33140951 DOI: 10.1021/jasms.0c00040] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
ADP-ribosylation is a reversible post-translational modification of proteins that has been linked to many biological processes. The identification of ADP-ribosylated proteins and particularly of their acceptor amino acids remains a major challenge. The attachment sites of the modification are difficult to localize by mass spectrometry (MS) because of the labile nature of the linkage and the complex fragmentation pattern of the ADP-ribose in MS/MS experiments. In this study we performed a comprehensive analysis of higher-energy collisional dissociation (HCD) spectra acquired from ADP-ribosylated peptides which were modified on arginine, serine, glutamic acid, aspartic acid, tyrosine, or lysine residues. In addition to the fragmentation of the peptide backbone, various cleavages of the ADP-ribosylated amino acid side chains were investigated. We focused on gas-phase fragmentations that were specific either to ADP-ribosylated arginine or to ADP-ribosylated serine and other O-linked ADP-ribosylations. The O-glycosidic linkage between ADP-ribose and serine, glutamic acid, or aspartic acid was the major cleavage site, making localization of these modification sites difficult. In contrast, the bond between ADP-ribose and arginine was relatively stable. The main cleavage site was the inner bond of the guanidine group, which resulted in the formation of ADP-ribosylated carbodiimide and of ornithine in place of modified arginine. Taking peptide fragment ions resulting from this specific cleavage into account, a considerably larger number of peptides containing ADP-ribosylated arginine were identified in database searches. Furthermore, the presence of diagnostic ions and of losses of fragments from peptide ions allowed us, in most cases, to distinguish between ADP-ribosylated arginine and serine residues.
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Affiliation(s)
- Peter M Gehrig
- Functional Genomics Center Zurich, University of Zurich and ETH Zurich, 8057 Zurich, Switzerland
| | - Kathrin Nowak
- Department of Molecular Mechanisms of Disease, University of Zurich, 8057 Zurich, Switzerland
- Molecular Life Science PhD Program of the Life Science Zurich Graduate School, University of Zurich, 8057 Zurich, Switzerland
| | - Christian Panse
- Functional Genomics Center Zurich, University of Zurich and ETH Zurich, 8057 Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Quartier Sorge-Batiment Amphipole 1015, Lausanne, Switzerland
| | - Mario Leutert
- Department of Molecular Mechanisms of Disease, University of Zurich, 8057 Zurich, Switzerland
| | - Jonas Grossmann
- Functional Genomics Center Zurich, University of Zurich and ETH Zurich, 8057 Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Quartier Sorge-Batiment Amphipole 1015, Lausanne, Switzerland
| | - Ralph Schlapbach
- Functional Genomics Center Zurich, University of Zurich and ETH Zurich, 8057 Zurich, Switzerland
| | - Michael O Hottiger
- Department of Molecular Mechanisms of Disease, University of Zurich, 8057 Zurich, Switzerland
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17
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Ultraviolet Photodissociation for Non-Target Screening-Based Identification of Organic Micro-Pollutants in Water Samples. Molecules 2020; 25:molecules25184189. [PMID: 32932695 PMCID: PMC7570901 DOI: 10.3390/molecules25184189] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 09/08/2020] [Accepted: 09/10/2020] [Indexed: 11/22/2022] Open
Abstract
Non-target screening (NTS) based on the combination of liquid chromatography coupled to high-resolution mass spectrometry has become the key method to identify organic micro-pollutants (OMPs) in water samples. However, a large number of compounds remains unidentified with current NTS approaches due to poor quality fragmentation spectra generated by suboptimal fragmentation methods. Here, the potential of the alternative fragmentation technique ultraviolet photodissociation (UVPD) to improve identification of OMPs in water samples was investigated. A diverse set of water-relevant OMPs was selected based on k-means clustering and unsupervised artificial neural networks. The selected OMPs were analyzed using an Orbitrap Fusion Lumos equipped with UVPD. Therewith, information-rich MS2 fragmentation spectra of compounds that fragment poorly with higher-energy collisional dissociation (HCD) could be attained. Development of an R-based data analysis workflow and user interface facilitated the characterization and comparison of HCD and UVPD fragmentation patterns. UVPD and HCD generated both unique and common fragments, demonstrating that some fragmentation pathways are specific to the respective fragmentation method, while others seem more generic. Application of UVPD fragmentation to the analysis of surface water enabled OMP identification using existing HCD spectral libraries. However, high-throughput applications still require optimization of informatics workflows and spectral libraries tailored to UVPD.
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18
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Takemori A, Butcher DS, Harman VM, Brownridge P, Shima K, Higo D, Ishizaki J, Hasegawa H, Suzuki J, Yamashita M, Loo JA, Loo RRO, Beynon RJ, Anderson LC, Takemori N. PEPPI-MS: Polyacrylamide-Gel-Based Prefractionation for Analysis of Intact Proteoforms and Protein Complexes by Mass Spectrometry. J Proteome Res 2020; 19:3779-3791. [PMID: 32538093 DOI: 10.1021/acs.jproteome.0c00303] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Prefractionation of complex mixtures of proteins derived from biological samples is indispensable for proteome analysis via top-down mass spectrometry (MS). Polyacrylamide gel electrophoresis (PAGE), which enables high-resolution protein separation based on molecular size, is a widely used technique in biochemical experiments and has the potential to be useful in sample fractionation for top-down MS analysis. However, the lack of a means to efficiently recover the separated proteins in-gel has always been a barrier to its use in sample prefractionation. In this study, we present a novel experimental workflow, called Passively Eluting Proteins from Polyacrylamide gels as Intact species for MS ("PEPPI-MS"), which allows top-down MS of PAGE-separated proteins. The optimization of Coomassie brilliant blue staining followed by the passive extraction step in the PEPPI-MS workflow enabled the efficient recovery of proteins, separated on commercial precast gels, from a wide range of molecular weight regions in under 10 min. Two-dimensional separation combining offline PEPPI-MS with online reversed-phase liquid chromatographic separation resulted in identification of over 1000 proteoforms recovered from the target region of the gel (≤50 kDa). Given the widespread availability and relatively low cost of traditional sodium dodecyl sulfate (SDS)-PAGE equipment, the PEPPI-MS workflow will be a powerful prefractionation strategy for top-down proteomics.
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Affiliation(s)
- Ayako Takemori
- Division of Analytical Bio-Medicine, Advanced Research Support Center, Ehime University, Toon 791-0295, Ehime, Japan
| | - David S Butcher
- Ion Cyclotron Resonance Program, National High Magnetic Field Laboratory, Florida State University, Tallahassee, Florida 32310, United States
| | - Victoria M Harman
- Centre for Proteome Research, Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, U.K
| | - Philip Brownridge
- Centre for Proteome Research, Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, U.K
| | - Keisuke Shima
- Shimadzu Corporation, Nakagyo-ku, Kyoto 604-8511, Japan
| | - Daisuke Higo
- Thermo Fisher Scientific K.K., Yokohama 221-0022, Kanagawa, Japan
| | - Jun Ishizaki
- Department of Hematology, Clinical Immunology and Infectious Diseases, Graduate School of Medicine, Ehime University, Toon 791-0295, Ehime, Japan
| | - Hitoshi Hasegawa
- Department of Hematology, Clinical Immunology and Infectious Diseases, Graduate School of Medicine, Ehime University, Toon 791-0295, Ehime, Japan
| | - Junpei Suzuki
- Department of Immunology, Graduate School of Medicine, Ehime University, Toon 791-0295, Ehime, Japan
| | - Masakatsu Yamashita
- Department of Immunology, Graduate School of Medicine, Ehime University, Toon 791-0295, Ehime, Japan
| | - Joseph A Loo
- Department of Chemistry and Biochemistry, University of California-Los Angeles, Los Angeles, California 90095, United States.,Department of Biological Chemistry, UCLA/DOE Institute for Genomics and Proteomics, and UCLA Molecular Biology Institute, University of California-Los Angeles, Los Angeles, California 90095, United States
| | - Rachel R Ogorzalek Loo
- Department of Biological Chemistry, UCLA/DOE Institute for Genomics and Proteomics, and UCLA Molecular Biology Institute, University of California-Los Angeles, Los Angeles, California 90095, United States
| | - Robert J Beynon
- Centre for Proteome Research, Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, U.K
| | - Lissa C Anderson
- Ion Cyclotron Resonance Program, National High Magnetic Field Laboratory, Florida State University, Tallahassee, Florida 32310, United States
| | - Nobuaki Takemori
- Division of Analytical Bio-Medicine, Advanced Research Support Center, Ehime University, Toon 791-0295, Ehime, Japan
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19
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Pino LK, Just SC, MacCoss MJ, Searle BC. Acquiring and Analyzing Data Independent Acquisition Proteomics Experiments without Spectrum Libraries. Mol Cell Proteomics 2020; 19:1088-1103. [PMID: 32312845 PMCID: PMC7338082 DOI: 10.1074/mcp.p119.001913] [Citation(s) in RCA: 135] [Impact Index Per Article: 33.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 04/14/2020] [Indexed: 11/06/2022] Open
Abstract
Data independent acquisition (DIA) is an attractive alternative to standard shotgun proteomics methods for quantitative experiments. However, most DIA methods require collecting exhaustive, sample-specific spectrum libraries with data dependent acquisition (DDA) to detect and quantify peptides. In addition to working with non-human samples, studies of splice junctions, sequence variants, or simply working with small sample yields can make developing DDA-based spectrum libraries impractical. Here we illustrate how to acquire, queue, and validate DIA data without spectrum libraries, and provide a workflow to efficiently generate DIA-only chromatogram libraries using gas-phase fractionation (GPF). We present best-practice methods for collecting DIA data using Orbitrap-based instruments and develop an understanding for why DIA using an Orbitrap mass spectrometer should be approached differently than when using time-of-flight instruments. Finally, we discuss several methods for analyzing DIA data without libraries.
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Affiliation(s)
- Lindsay K Pino
- Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Seth C Just
- Proteome Software, Inc. Portland, Oregon, USA
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | - Brian C Searle
- Institute for Systems Biology, Seattle, Washington, USA.
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20
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Ebner JN, Ritz D, von Fumetti S. Comparative proteomics of stenotopic caddisfly Crunoecia irrorata identifies acclimation strategies to warming. Mol Ecol 2019; 28:4453-4469. [PMID: 31478292 PMCID: PMC6856850 DOI: 10.1111/mec.15225] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 07/28/2019] [Accepted: 07/29/2019] [Indexed: 12/23/2022]
Abstract
Species' ecological preferences are often deduced from habitat characteristics thought to represent more or less optimal conditions for physiological functioning. Evolution has led to stenotopic and eurytopic species, the former having decreased niche breadths and lower tolerances to environmental variability. Species inhabiting freshwater springs are often described as being stenotopic specialists, adapted to the stable thermal conditions found in these habitats. Whether due to past local adaptation these species have evolved or have lost intra-generational adaptive mechanisms to cope with increasing thermal variability has, to our knowledge, never been investigated. By studying how the proteome of a stenotopic species changes as a result of increasing temperatures, we investigate if the absence or attenuation of molecular mechanisms is indicative of local adaptation to freshwater springs. An understanding of compensatory mechanisms is especially relevant as spring specialists will experience thermal conditions beyond their physiological limits due to climate change. In this study, the stenotopic species Crunoecia irrorata (Trichoptera: Lepidostomatidae, Curtis 1834) was acclimated to 10, 15 and 20°C for 168 hr. We constructed a homology-based database and via liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based shotgun proteomics identified 1,358 proteins. Differentially abundant proteins and protein norms of reaction revealed candidate proteins and molecular mechanisms facilitating compensatory responses such as trehalose metabolism, tracheal system alteration and heat-shock protein regulation. A species-specific understanding of compensatory physiologies challenges the characterization of species as having narrow tolerances to environmental variability if that characterization is based on occurrences and habitat characteristics alone.
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Affiliation(s)
- Joshua N. Ebner
- Geoecology Research GroupDepartment of Environmental SciencesUniversity of BaselBaselSwitzerland
| | - Danilo Ritz
- Proteomics Core FacilityBiozentrumUniversity of BaselBaselSwitzerland
| | - Stefanie von Fumetti
- Geoecology Research GroupDepartment of Environmental SciencesUniversity of BaselBaselSwitzerland
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21
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Kiweler M, Looso M, Graumann J. MARMoSET - Extracting Publication-ready Mass Spectrometry Metadata from RAW Files. Mol Cell Proteomics 2019; 18:1700-1702. [PMID: 31097673 PMCID: PMC6683000 DOI: 10.1074/mcp.tir119.001505] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 05/07/2019] [Indexed: 11/06/2022] Open
Abstract
In the context of publishing data sets acquired by mass spectrometry or works based on such molecular screens, metadata documenting the instrument settings are of central importance to the evaluation and reproduction of results. A single experiment may be linked to hundreds of data acquisitions, which are frequently stored in proprietary file formats. Together with community-, repository-, as well as publisher-specific reporting standards, this state of affairs frequently leads to manual -and thus error prone-metadata extraction and formatting. Data extracted from a single file also often stand in for an entire file set, implying a risk for unreported parameter divergence. To support quality control and data reporting, the C# application MARMoSET extracts and reduces publication relevant metadata from Thermo Fischer Scientific RAW files. It is integrated with an R package for easy reporting. The tool is expected to be particularly useful to high throughput environments such as service facilities with large project numbers and/or sizes.
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Affiliation(s)
- Marina Kiweler
- Bioinformatics Core Unit, Max Planck Institute for Heart and Lung Research, W.G. Kerckhoff Institute, Ludwigstr. 43, Bad Nauheim, Germany
| | - Mario Looso
- Bioinformatics Core Unit, Max Planck Institute for Heart and Lung Research, W.G. Kerckhoff Institute, Ludwigstr. 43, Bad Nauheim, Germany; The German Center for Cardiovascular Research (DZHK), Partner Site Rhine-Main.
| | - Johannes Graumann
- Scientific Service Group Biomolecular Mass Spectrometry, Max Planck Institute for Heart and Lung Research, W.G. Kerckhoff Institute, Ludwigstr. 43, Bad Nauheim, Germany; The German Center for Cardiovascular Research (DZHK), Partner Site Rhine-Main.
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22
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Nguyen CDL, Malchow S, Reich S, Steltgens S, Shuvaev KV, Loroch S, Lorenz C, Sickmann A, Knobbe-Thomsen CB, Tews B, Medenbach J, Ahrends R. A sensitive and simple targeted proteomics approach to quantify transcription factor and membrane proteins of the unfolded protein response pathway in glioblastoma cells. Sci Rep 2019; 9:8836. [PMID: 31222112 PMCID: PMC6586633 DOI: 10.1038/s41598-019-45237-5] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Accepted: 05/31/2019] [Indexed: 12/27/2022] Open
Abstract
Many cellular events are driven by changes in protein expression, measurable by mass spectrometry or antibody-based assays. However, using conventional technology, the analysis of transcription factor or membrane receptor expression is often limited by an insufficient sensitivity and specificity. To overcome this limitation, we have developed a high-resolution targeted proteomics strategy, which allows quantification down to the lower attomol range in a straightforward way without any prior enrichment or fractionation approaches. The method applies isotope-labeled peptide standards for quantification of the protein of interest. As proof of principle, we applied the improved workflow to proteins of the unfolded protein response (UPR), a signaling pathway of great clinical importance, and could for the first time detect and quantify all major UPR receptors, transducers and effectors that are not readily detectable via antibody-based-, SRM- or conventional PRM assays. As transcription and translation is central to the regulation of UPR, quantification and determination of protein copy numbers in the cell is important for our understanding of the signaling process as well as how pharmacologic modulation of these pathways impacts on the signaling. These questions can be answered using our newly established workflow as exemplified in an experiment using UPR perturbation in a glioblastoma cell lines.
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Affiliation(s)
- Chi D L Nguyen
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., 44227, Dortmund, Germany
| | - Sebastian Malchow
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., 44227, Dortmund, Germany
| | - Stefan Reich
- Translational Control Group, Biochemistry I, University of Regensburg, 93053, Regensburg, Germany
| | - Sascha Steltgens
- Institute of Neuropathology, Medical Faculty, Heinrich-Heine-University Düsseldorf, 40225, Düsseldorf, Germany
| | - Konstantin V Shuvaev
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., 44227, Dortmund, Germany
| | - Stefan Loroch
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., 44227, Dortmund, Germany
| | - Christin Lorenz
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., 44227, Dortmund, Germany
| | - Albert Sickmann
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., 44227, Dortmund, Germany.,Medizinische Fakultät, Ruhr-Universität Bochum, Bochum, 44801, Germany.,College of Physical Sciences, University of Aberdeen, Old Aberdeen, AB24 3UE, UK
| | - Christiane B Knobbe-Thomsen
- Institute of Neuropathology, Medical Faculty, Heinrich-Heine-University Düsseldorf, 40225, Düsseldorf, Germany
| | - Björn Tews
- Schaller Research Group, University of Heidelberg and DKFZ, 69120, Heidelberg, Germany.,Molecular Mechanisms of Tumor Invasion, DKFZ, 69120, Heidelberg, Germany
| | - Jan Medenbach
- Translational Control Group, Biochemistry I, University of Regensburg, 93053, Regensburg, Germany
| | - Robert Ahrends
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., 44227, Dortmund, Germany.
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23
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Huffman RG, Chen A, Specht H, Slavov N. DO-MS: Data-Driven Optimization of Mass Spectrometry Methods. J Proteome Res 2019; 18:2493-2500. [PMID: 31081635 DOI: 10.1021/acs.jproteome.9b00039] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The performance of ultrasensitive liquid chromatography and tandem mass spectrometry (LC-MS/MS) methods, such as single-cell proteomics by mass spectrometry (SCoPE-MS), depends on multiple interdependent parameters. This interdependence makes it challenging to specifically pinpoint the sources of problems in the LC-MS/MS methods and approaches for resolving them. For example, a low signal at the MS2 level can be due to poor LC separation, ionization, apex targeting, ion transfer, or ion detection. We sought to specifically diagnose such problems by interactively visualizing data from all levels of bottom-up LC-MS/MS analysis. Many software packages, such as MaxQuant, already provide such data, and we developed an open source platform for their interactive visualization and analysis: Data-driven Optimization of MS (DO-MS). We found that in many cases DO-MS not only specifically diagnosed LC-MS/MS problems but also enabled us to rationally optimize them. For example, by using DO-MS to optimize the sampling of the elution peak apexes, we increased ion accumulation times and apex sampling, which resulted in a 370% more efficient delivery of ions for MS2 analysis. DO-MS is easy to install and use, and its GUI allows for interactive data subsetting and high-quality figure generation. The modular design of DO-MS facilitates customization and expansion. DO-MS v1.0.8 is available for download from GitHub: https://github.com/SlavovLab/DO-MS . Additional documentation is available at https://do-ms.slavovlab.net .
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Affiliation(s)
- R Gray Huffman
- Department of Bioengineering , Northeastern University , Boston , Massachusetts 02115 , United States.,Barnett Institute , Northeastern University , Boston , Massachusetts 02115 , United States
| | - Albert Chen
- Department of Bioengineering , Northeastern University , Boston , Massachusetts 02115 , United States.,Barnett Institute , Northeastern University , Boston , Massachusetts 02115 , United States
| | - Harrison Specht
- Department of Bioengineering , Northeastern University , Boston , Massachusetts 02115 , United States.,Barnett Institute , Northeastern University , Boston , Massachusetts 02115 , United States
| | - Nikolai Slavov
- Department of Bioengineering , Northeastern University , Boston , Massachusetts 02115 , United States.,Barnett Institute , Northeastern University , Boston , Massachusetts 02115 , United States.,Department of Biology , Northeastern University , Boston , Massachusetts 02115 , United States
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Barshop WD, Rayatpisheh S, Kim HJ, Wohlschlegel JA. Sequential Windowed Acquisition of Reporter Masses for Quantitation-First Proteomics. J Proteome Res 2019; 18:1893-1901. [PMID: 30781952 DOI: 10.1021/acs.jproteome.8b00884] [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: 11/28/2022]
Abstract
The standard approach for proteomic data acquisition of isobaric-tagged samples by mass spectrometry is data-dependent acquisition. This semistochastic, identification-first paradigm generates a wealth of peptide-level data without regard to relative abundance. We introduce a data acquisition concept called sequential windowed acquisition of reporter masses (SWARM). This approach performs quantitation first, thereby allowing subsequent acquisition decisions to be predicated on user-defined patterns of reporter ion intensities. The efficacy of this approach is validated through experiments with both synthetic mixtures of Escherichia coli ribosomes spiked into human cell lysates at known ratios and the quantitative evaluation of the human proteome's response to the inhibition of cullin-based protein ubiquitination via the small molecule MLN4924. We find that SWARM-informed parallel reaction monitoring acquisitions display effective acquisition biasing toward analytes displaying quantitative characteristics of interest, resulting in an improvement in the detection of differentially abundant analytes. The SWARM concept provides a flexible platform for the further development of new acquisition methods.
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Affiliation(s)
- William D Barshop
- Department of Biological Chemistry, David Geffen School of Medicine , University of California, Los Angeles , Los Angeles , California 90095 , United States
| | - Shima Rayatpisheh
- Department of Biological Chemistry, David Geffen School of Medicine , University of California, Los Angeles , Los Angeles , California 90095 , United States
| | - Hee Jong Kim
- Department of Biological Chemistry, David Geffen School of Medicine , University of California, Los Angeles , Los Angeles , California 90095 , United States
| | - James A Wohlschlegel
- Department of Biological Chemistry, David Geffen School of Medicine , University of California, Los Angeles , Los Angeles , California 90095 , United States
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Kovalchik KA, Colborne S, Spencer SE, Sorensen PH, Chen DDY, Morin GB, Hughes CS. RawTools: Rapid and Dynamic Interrogation of Orbitrap Data Files for Mass Spectrometer System Management. J Proteome Res 2018; 18:700-708. [PMID: 30462513 DOI: 10.1021/acs.jproteome.8b00721] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Optimizing the quality of proteomics data collected from a mass spectrometer (MS) requires careful selection of acquisition parameters and proper assessment of instrument performance. Software tools capable of extracting a broad set of information from raw files, including meta, scan, quantification, and identification data, are needed to provide guidance for MS system management. In this work, direct extraction and utilization of these data is demonstrated using RawTools, a standalone tool for extracting meta and scan data directly from raw MS files generated on Thermo Orbitrap instruments. RawTools generates summarized and detailed plain text outputs after parsing individual raw files, including scan rates and durations, duty cycle characteristics, precursor and reporter ion quantification, and chromatography performance. RawTools also contains a diagnostic module that includes an optional "preview" database search for facilitating informed decision-making related to optimization of MS performance based on a variety of metrics. RawTools has been developed in C# and utilizes the Thermo RawFileReader library and thus can process raw MS files with high speed and high efficiency on all major operating systems (Windows, MacOS, Linux). To demonstrate the utility of RawTools, the extraction of meta and scan data from both individual and large collections of raw MS files was carried out to identify problematic characteristics of instrument performance. Taken together, the combined rich feature-set of RawTools with the capability for interrogation of MS and experiment performance makes this software a valuable tool for proteomics researchers.
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Affiliation(s)
- Kevin A Kovalchik
- Department of Chemistry , University of British Columbia , Vancouver , British Columbia V6T 1Z3 , Canada.,Canada's Michael Smith Genome Sciences Centre , British Columbia Cancer Agency , Vancouver , British Columbia V5Z 1L3 , Canada
| | - Shane Colborne
- Canada's Michael Smith Genome Sciences Centre , British Columbia Cancer Agency , Vancouver , British Columbia V5Z 1L3 , Canada
| | - Sandra Elizabeth Spencer
- Canada's Michael Smith Genome Sciences Centre , British Columbia Cancer Agency , Vancouver , British Columbia V5Z 1L3 , Canada
| | - Poul H Sorensen
- Department of Molecular Oncology , British Columbia Cancer Research Centre , Vancouver , British Columbia V5Z 1L3 , Canada
| | - David D Y Chen
- Department of Chemistry , University of British Columbia , Vancouver , British Columbia V6T 1Z3 , Canada
| | - Gregg B Morin
- Canada's Michael Smith Genome Sciences Centre , British Columbia Cancer Agency , Vancouver , British Columbia V5Z 1L3 , Canada.,Department of Medical Genetics , University of British Columbia , Vancouver , British Columbia V6T 1Z3 , Canada
| | - Christopher S Hughes
- Canada's Michael Smith Genome Sciences Centre , British Columbia Cancer Agency , Vancouver , British Columbia V5Z 1L3 , Canada.,Department of Molecular Oncology , British Columbia Cancer Research Centre , Vancouver , British Columbia V5Z 1L3 , Canada
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