1
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Shi J, Liu Y, Xu YJ. MS based foodomics: An edge tool integrated metabolomics and proteomics for food science. Food Chem 2024; 446:138852. [PMID: 38428078 DOI: 10.1016/j.foodchem.2024.138852] [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: 11/29/2023] [Revised: 02/05/2024] [Accepted: 02/24/2024] [Indexed: 03/03/2024]
Abstract
Foodomics has become a popular methodology in food science studies. Mass spectrometry (MS) based metabolomics and proteomics analysis played indispensable roles in foodomics research. So far, several methodologies have been developed to detect the metabolites and proteins in diets and consumers, including sample preparation, MS data acquisition, annotation and interpretation. Moreover, multiomics analysis integrated metabolomics and proteomics have received considerable attentions in the field of food safety and nutrition, because of more comprehensive and deeply. In this context, we intended to review the emerging strategies and their applications in MS-based foodomics, as well as future challenges and trends. The principle and application of multiomics were also discussed, such as the optimization of data acquisition, development of analysis algorithm and exploration of systems biology.
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Affiliation(s)
- Jiachen Shi
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Functional Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, 1800 Lihu Road, Wuxi 214122, Jiangsu, People's Republic of China.
| | - Yuanfa Liu
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Functional Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, 1800 Lihu Road, Wuxi 214122, Jiangsu, People's Republic of China.
| | - Yong-Jiang Xu
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Functional Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, 1800 Lihu Road, Wuxi 214122, Jiangsu, People's Republic of China.
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2
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Lou R, Shui W. Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023. Mol Cell Proteomics 2024; 23:100712. [PMID: 38182042 PMCID: PMC10847697 DOI: 10.1016/j.mcpro.2024.100712] [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: 10/31/2023] [Revised: 12/27/2023] [Accepted: 01/02/2024] [Indexed: 01/07/2024] Open
Abstract
Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.
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Affiliation(s)
- Ronghui Lou
- iHuman Institute, ShanghaiTech University, Shanghai, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
| | - Wenqing Shui
- iHuman Institute, ShanghaiTech University, Shanghai, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
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3
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Kitata RB, Yang JC, Chen YJ. Advances in data-independent acquisition mass spectrometry towards comprehensive digital proteome landscape. MASS SPECTROMETRY REVIEWS 2023; 42:2324-2348. [PMID: 35645145 DOI: 10.1002/mas.21781] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 12/17/2021] [Accepted: 01/21/2022] [Indexed: 06/15/2023]
Abstract
The data-independent acquisition mass spectrometry (DIA-MS) has rapidly evolved as a powerful alternative for highly reproducible proteome profiling with a unique strength of generating permanent digital maps for retrospective analysis of biological systems. Recent advancements in data analysis software tools for the complex DIA-MS/MS spectra coupled to fast MS scanning speed and high mass accuracy have greatly expanded the sensitivity and coverage of DIA-based proteomics profiling. Here, we review the evolution of the DIA-MS techniques, from earlier proof-of-principle of parallel fragmentation of all-ions or ions in selected m/z range, the sequential window acquisition of all theoretical mass spectra (SWATH-MS) to latest innovations, recent development in computation algorithms for data informatics, and auxiliary tools and advanced instrumentation to enhance the performance of DIA-MS. We further summarize recent applications of DIA-MS and experimentally-derived as well as in silico spectra library resources for large-scale profiling to facilitate biomarker discovery and drug development in human diseases with emphasis on the proteomic profiling coverage. Toward next-generation DIA-MS for clinical proteomics, we outline the challenges in processing multi-dimensional DIA data set and large-scale clinical proteomics, and continuing need in higher profiling coverage and sensitivity.
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Affiliation(s)
| | - Jhih-Ci Yang
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
- Sustainable Chemical Science and Technology, Taiwan International Graduate Program, Academia Sinica and National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Applied Chemistry, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Yu-Ju Chen
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
- Sustainable Chemical Science and Technology, Taiwan International Graduate Program, Academia Sinica and National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Chemistry, National Taiwan University, Taipei, Taiwan
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4
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Xu F, Wang W, Ding L, Fang X, Ding CF. Synchronized Reverse Scan Collision Induced Dissociation in Digital Ion Trap Mass Spectrometer for Improving Fragment Ion Detection. Anal Chem 2022; 94:17827-17834. [PMID: 36512629 DOI: 10.1021/acs.analchem.2c03524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Development of fragment ion detection methods is of great importance for mass spectrometer advancement or substance identification. To date, collision induced dissociation (CID) remains the most commonly used ion activation method in MS/MS experiments, and the effectiveness of CID in an ion trap mass spectrometer is limited by low mass cutoff and weak fragmentation yields. Theoretically, controlling the q value is the key to maintain the fragment efficiency and trapping efficiency of MS/MS, thus improving the detection of fragment ion, while currently reported techniques usually require complex circuitry and often produce different CID patterns. In this paper, with the developed synchronized reversed scanning-collision induced dissociation (SRS-CID) technique, we demonstrate its effective improvement in fragment ion detection. The SRS-CID is implemented on a digital ion trap mass spectrometer (DITMS) by reverse scanning the q values during CID process, or specifically, the frequency is increased during the CID process. With the SRS-CID technique, the fragmentation efficiency of precursor ions can be slightly improved. For reserpine analyte, the trapping efficiency for low-mass fragment ions is improved at least 3 times, and for YGGFL, the trapping efficiency for low-mass fragment ions is improved at least 9 times. These experimental results can also be validated by simulations, and the kinetic energy variation plot suggests consecutive fragmentation occurs. In any case, the SRS-CID provides a solution to the low efficiency of fragment ion detection during tandem MS analysis, which will certainly be useful in the future.
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Affiliation(s)
- Fuxing Xu
- Key Laboratory of Advanced Mass Spectrometry and Molecular Analysis of Zhejiang Province, Institute of Mass Spectrometry, School of Material Science and Chemical Engineering, Ningbo University, Ningbo 315211, China
| | - Weimin Wang
- Key Laboratory of Advanced Mass Spectrometry and Molecular Analysis of Zhejiang Province, Institute of Mass Spectrometry, School of Material Science and Chemical Engineering, Ningbo University, Ningbo 315211, China
| | - Li Ding
- Key Laboratory of Advanced Mass Spectrometry and Molecular Analysis of Zhejiang Province, Institute of Mass Spectrometry, School of Material Science and Chemical Engineering, Ningbo University, Ningbo 315211, China
| | - Xiang Fang
- National Institute of Metrology, Chemical Metrology & Analytical Science Division, Beijing 100029, China
| | - Chuan-Fan Ding
- Key Laboratory of Advanced Mass Spectrometry and Molecular Analysis of Zhejiang Province, Institute of Mass Spectrometry, School of Material Science and Chemical Engineering, Ningbo University, Ningbo 315211, China
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5
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Deep representation features from DreamDIA XMBD improve the analysis of data-independent acquisition proteomics. Commun Biol 2021; 4:1190. [PMID: 34650228 PMCID: PMC8517002 DOI: 10.1038/s42003-021-02726-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Accepted: 09/27/2021] [Indexed: 12/24/2022] Open
Abstract
We developed DreamDIAXMBD (denoted as DreamDIA), a software suite based on a deep representation model for data-independent acquisition (DIA) data analysis. DreamDIA adopts a data-driven strategy to capture comprehensive information from elution patterns of peptides in DIA data and achieves considerable improvements on both identification and quantification performance compared with other state-of-the-art methods such as OpenSWATH, Skyline and DIA-NN. Specifically, in contrast to existing methods which use only 6 to 10 selected fragment ions from spectral libraries, DreamDIA extracts additional features from hundreds of theoretical elution profiles originated from different ions of each precursor using a deep representation network. To achieve higher coverage of target peptides without sacrificing specificity, the extracted features are further processed by nonlinear discriminative models under the framework of positive-unlabeled learning with decoy peptides as affirmative negative controls. DreamDIA is publicly available at https://github.com/xmuyulab/DreamDIA-XMBD for high coverage and accuracy DIA data analysis.
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6
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Xue VW, Yang C, Wong SCC, Cho WCS. Proteomic profiling in extracellular vesicles for cancer detection and monitoring. Proteomics 2021; 21:e2000094. [PMID: 33665903 DOI: 10.1002/pmic.202000094] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 02/24/2021] [Accepted: 03/02/2021] [Indexed: 12/12/2022]
Abstract
Extracellular vesicles (EVs) are nanometer-size lipid vesicles released by cells, which play essential biological functions in intercellular communication. Increasing evidence indicates that EVs participate in cancer development, including invasion, migration, metastasis, and cancer immune modulation. One of the key mechanisms is that EVs affect different cells in the tumor microenvironment through surface-anchor proteins and protein cargos. Moreover, proteins specifically expressed in tumor-derived EVs can be applied in cancer diagnosis and monitoring. Besides, the EV proteome also helps to understand drug resistance in cancers and to guide clinical medication. With the development of mass spectrometry and array-based multi-protein detection, the research of EV proteomics has entered a new era. The high-throughput parallel proteomic profiling based on these new platforms allows us to study the impact of EV proteome on cancer progression more comprehensively and to describe the proteomic landscape in cancers with more details. In this article, we review the role and function of different types of EVs in cancer progression. More importantly, we summarize the proteomic profiling of EVs based on different methods and the application of EV proteome in cancer detection and monitoring.
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Affiliation(s)
- Vivian Weiwen Xue
- School of Basic Medical Sciences, Shenzhen University Health Science Centre, Shenzhen University, Shenzhen, China
| | - Chenxi Yang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Sze Chuen Cesar Wong
- Faculty of Health and Social Sciences, Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
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7
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Wilburn DB, Richards AL, Swaney DL, Searle BC. CIDer: A Statistical Framework for Interpreting Differences in CID and HCD Fragmentation. J Proteome Res 2021; 20:1951-1965. [PMID: 33729787 DOI: 10.1021/acs.jproteome.0c00964] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Library searching is a powerful technique for detecting peptides using either data independent or data dependent acquisition. While both large-scale spectrum library curators and deep learning prediction approaches have focused on beam-type CID fragmentation (HCD), resonance CID fragmentation remains a popular technique. Here we demonstrate an approach to model the differences between HCD and CID spectra, and present a software tool, CIDer, for converting libraries between the two fragmentation methods. We demonstrate that just using a combination of simple linear models and basic principles of peptide fragmentation, we can explain up to 43% of the variation between ions fragmented by HCD and CID across an array of collision energy settings. We further show that in some circumstances, searching converted CID libraries can detect more peptides than searching existing CID libraries or libraries of machine learning predictions from FASTA databases. These results suggest that leveraging information in existing libraries by converting between HCD and CID libraries may be an effective interim solution while large-scale CID libraries are being developed.
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Affiliation(s)
- Damien B Wilburn
- Institute for Systems Biology, Seattle, Washington 98109, United States.,Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Alicia L Richards
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, California 94158, United States.,J. David Gladstone Institutes, San Francisco, California 94158, United States.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, California 94158, United States
| | - Danielle L Swaney
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, California 94158, United States.,J. David Gladstone Institutes, San Francisco, California 94158, United States.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, California 94158, United States
| | - Brian C Searle
- Institute for Systems Biology, Seattle, Washington 98109, United States
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8
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Willems P, Fels U, Staes A, Gevaert K, Van Damme P. Use of Hybrid Data-Dependent and -Independent Acquisition Spectral Libraries Empowers Dual-Proteome Profiling. J Proteome Res 2021; 20:1165-1177. [PMID: 33467856 PMCID: PMC7871992 DOI: 10.1021/acs.jproteome.0c00350] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Indexed: 01/01/2023]
Abstract
In the context of bacterial infections, it is imperative that physiological responses can be studied in an integrated manner, meaning a simultaneous analysis of both the host and the pathogen responses. To improve the sensitivity of detection, data-independent acquisition (DIA)-based proteomics was found to outperform data-dependent acquisition (DDA) workflows in identifying and quantifying low-abundant proteins. Here, by making use of representative bacterial pathogen/host proteome samples, we report an optimized hybrid library generation workflow for DIA mass spectrometry relying on the use of data-dependent and in silico-predicted spectral libraries. When compared to searching DDA experiment-specific libraries only, the use of hybrid libraries significantly improved peptide detection to an extent suggesting that infection-relevant host-pathogen conditions could be profiled in sufficient depth without the need of a priori bacterial pathogen enrichment when studying the bacterial proteome. Proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifiers PXD017904 and PXD017945.
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Affiliation(s)
- Patrick Willems
- Department
of Biochemistry and Microbiology, Ghent
University, Ghent 9000, Belgium
- Department
of Plant Biotechnology and Bioinformatics, Ghent University, Ghent 9000, Belgium
- VIB-UGent
Center for Plant Systems Biology, Ghent 9052, Belgium
| | - Ursula Fels
- Department
of Biochemistry and Microbiology, Ghent
University, Ghent 9000, Belgium
- VIB-UGent
Center for Medical Biotechnology, Ghent 9052, Belgium
| | - An Staes
- VIB-UGent
Center for Medical Biotechnology, Ghent 9052, Belgium
- Department
of Biomolecular Medicine, Ghent University, Ghent 9000, Belgium
| | - Kris Gevaert
- VIB-UGent
Center for Medical Biotechnology, Ghent 9052, Belgium
- Department
of Biomolecular Medicine, Ghent University, Ghent 9000, Belgium
| | - Petra Van Damme
- Department
of Biochemistry and Microbiology, Ghent
University, Ghent 9000, Belgium
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9
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Rozanova S, Barkovits K, Nikolov M, Schmidt C, Urlaub H, Marcus K. Quantitative Mass Spectrometry-Based Proteomics: An Overview. Methods Mol Biol 2021; 2228:85-116. [PMID: 33950486 DOI: 10.1007/978-1-0716-1024-4_8] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
In recent decades, mass spectrometry has moved more than ever before into the front line of protein-centered research. After being established at the qualitative level, the more challenging question of quantification of proteins and peptides using mass spectrometry has become a focus for further development. In this chapter, we discuss and review actual strategies and problems of the methods for the quantitative analysis of peptides, proteins, and finally proteomes by mass spectrometry. The common themes, the differences, and the potential pitfalls of the main approaches are presented in order to provide a survey of the emerging field of quantitative, mass spectrometry-based proteomics.
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Affiliation(s)
- Svitlana Rozanova
- Medizinisches Proteom-Center, Medical Faculty, Ruhr-University Bochum, Bochum, Germany.,Medical Proteome Analysis, Center for protein diagnostics (PRODI), Ruhr-University Bochum, Bochum, Germany
| | - Katalin Barkovits
- Medizinisches Proteom-Center, Medical Faculty, Ruhr-University Bochum, Bochum, Germany.,Medical Proteome Analysis, Center for protein diagnostics (PRODI), Ruhr-University Bochum, Bochum, Germany
| | - Miroslav Nikolov
- Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry, Goettingen, Germany
| | - Carla Schmidt
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Institute for Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Halle, Germany
| | - Henning Urlaub
- Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry, Goettingen, Germany.,Bioanalytics Group, Institute of Clinical Chemistry, University Medical Center Goettingen, Goettingen, Germany.,Hematology/Oncology, Department of Medicine II, Johann Wolfgang Goethe University, Frankfurt, Germany
| | - Katrin Marcus
- Medizinisches Proteom-Center, Medical Faculty, Ruhr-University Bochum, Bochum, Germany. .,Medical Proteome Analysis, Center for protein diagnostics (PRODI), Ruhr-University Bochum, Bochum, Germany.
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10
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Guerrero CR, Maier LA, Griffin TJ, Higgins L, Najt CP, Perlman DM, Bhargava M. Application of Proteomics in Sarcoidosis. Am J Respir Cell Mol Biol 2020; 63:727-738. [PMID: 32804537 DOI: 10.1165/rcmb.2020-0070ps] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 08/17/2020] [Indexed: 02/03/2023] Open
Abstract
Sarcoidosis is a multisystem disease with heterogeneity in manifestations and outcomes. System-level studies leveraging "omics" technologies are expected to define mechanisms contributing to sarcoidosis heterogeneous manifestations and course. With improvements in mass spectrometry (MS) and bioinformatics, it is possible to study protein abundance for a large number of proteins simultaneously. Contemporary fast-scanning MS enables the acquisition of spectral data for deep coverage of the proteins with data-dependent or data-independent acquisition MS modes. Studies leveraging MS-based proteomics in sarcoidosis have characterized BAL fluid (BALF), alveolar macrophages, plasma, and exosomes. These studies identified several differentially expressed proteins, including protocadherin-2 precursor, annexin A2, pulmonary surfactant A2, complement factors C3, vitamin-D-binding protein, cystatin B, and amyloid P, comparing subjects with sarcoidosis with control subjects. Other studies identified ceruloplasmin, complement factors B, C3, and 1, and others with differential abundance in sarcoidosis compared with other interstitial lung diseases. Using quantitative proteomics, most recent studies found differences in PI3K/Akt/mTOR, MAP kinase, pluripotency-associated transcriptional factor, and hypoxia response pathways. Other studies identified increased clathrin-mediated endocytosis and Fcγ receptor-mediated phagocytosis pathways in sarcoidosis alveolar macrophages. Although studies in mixed BAL and blood cells or plasma are limited, some of the changes in lung compartment are detected in the blood cells and plasma. We review proteomics for sarcoidosis with a focus on the existing MS data acquisition strategies, bioinformatics for spectral data analysis to infer protein identity and quantity, unique aspects about biospecimen collection and processing for lung-related proteomics, and proteomics studies conducted to date in sarcoidosis.
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Affiliation(s)
- Candance R Guerrero
- Department of Biochemistry, Molecular Biology, and Biophysics, College of Biological Sciences and
| | - Lisa A Maier
- Division of Environmental and Occupational Health Sciences, National Jewish Health, Denver, Colorado
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology, and Biophysics, College of Biological Sciences and
| | - LeeAnn Higgins
- Department of Biochemistry, Molecular Biology, and Biophysics, College of Biological Sciences and
| | - Charles P Najt
- Department of Biochemistry, Molecular Biology, and Biophysics, College of Biological Sciences and
| | - David M Perlman
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Minnesota, Minneapolis, Minnesota; and
| | - Maneesh Bhargava
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Minnesota, Minneapolis, Minnesota; and
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11
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Midha MK, Campbell DS, Kapil C, Kusebauch U, Hoopmann MR, Bader SL, Moritz RL. DIALib-QC an assessment tool for spectral libraries in data-independent acquisition proteomics. Nat Commun 2020; 11:5251. [PMID: 33067471 PMCID: PMC7567827 DOI: 10.1038/s41467-020-18901-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Accepted: 09/17/2020] [Indexed: 01/24/2023] Open
Abstract
Data-independent acquisition (DIA) mass spectrometry, also known as Sequential Window Acquisition of all Theoretical Mass Spectra (SWATH), is a popular label-free proteomics strategy to comprehensively quantify peptides/proteins utilizing mass spectral libraries to decipher inherently multiplexed spectra collected linearly across a mass range. Although there are many spectral libraries produced worldwide, the quality control of these libraries is lacking. We present the DIALib-QC (DIA library quality control) software tool for the systematic evaluation of a library’s characteristics, completeness and correctness across 62 parameters of compliance, and further provide the option to improve its quality. We demonstrate its utility in assessing and repairing spectral libraries for correctness, accuracy and sensitivity. Most data-independent acquisition (DIA) methods depend on mass spectral libraries for peptide identification but tools to assess library quality are lacking. Here, the authors develop DIALib- QC for the systematic evaluation and correction of spectral libraries.
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Affiliation(s)
- Mukul K Midha
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | | | - Charu Kapil
- Institute for Systems Biology, Seattle, WA, 98109, USA
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12
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Fernández-Costa C, Martínez-Bartolomé S, McClatchy DB, Saviola AJ, Yu NK, Yates JR. Impact of the Identification Strategy on the Reproducibility of the DDA and DIA Results. J Proteome Res 2020; 19:3153-3161. [PMID: 32510229 PMCID: PMC7898222 DOI: 10.1021/acs.jproteome.0c00153] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Data-independent acquisition (DIA) is a promising technique for the proteomic analysis of complex protein samples. A number of studies have claimed that DIA experiments are more reproducible than data-dependent acquisition (DDA), but these claims are unsubstantiated since different data analysis methods are used in the two methods. Data analysis in most DIA workflows depends on spectral library searches, whereas DDA typically employs sequence database searches. In this study, we examined the reproducibility of the DIA and DDA results using both sequence database and spectral library search. The comparison was first performed using a cell lysate and then extended to an interactome study. Protein overlap among the technical replicates in both DDA and DIA experiments was 30% higher with library-based identifications than with sequence database identifications. The reproducibility of quantification was also improved with library search compared to database search, with the mean of the coefficient of variation decreasing more than 30% and a reduction in the number of missing values of more than 35%. Our results show that regardless of the acquisition method, higher identification and quantification reproducibility is observed when library search was used.
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Affiliation(s)
- Carolina Fernández-Costa
- Departments of Molecular Medicine & Neurobiology, The Scripps Research Institute, La Jolla, CA, USA
| | | | - Daniel B. McClatchy
- Departments of Molecular Medicine & Neurobiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Anthony J. Saviola
- Departments of Molecular Medicine & Neurobiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Nam-Kyung Yu
- Departments of Molecular Medicine & Neurobiology, The Scripps Research Institute, La Jolla, CA, USA
| | - John R. Yates
- Departments of Molecular Medicine & Neurobiology, The Scripps Research Institute, La Jolla, CA, USA
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13
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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: 120] [Impact Index Per Article: 30.0] [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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14
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Raetz M, Bonner R, Hopfgartner G. SWATH-MS for metabolomics and lipidomics: critical aspects of qualitative and quantitative analysis. Metabolomics 2020; 16:71. [PMID: 32504120 DOI: 10.1007/s11306-020-01692-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 05/29/2020] [Indexed: 12/19/2022]
Abstract
INTRODUCTION While liquid chromatography coupled to mass spectrometric detection in the selected reaction monitoring detection mode offers the best quantification sensitivity for omics, the number of target analytes is limited, must be predefined and specific methods developed. Data independent acquisition (DIA), including SWATH using quadrupole time of flight or orbitrap mass spectrometers and generic acquisition methods, has emerged as a powerful alternative technique for quantitative and qualitative analyses since it can cover a wide range of analytes without predefinition. OBJECTIVES Here we review the current state of DIA, SWATH-MS and highlight novel acquisition strategies for metabolomics and lipidomics and opportunities for data analysis tools. METHOD Different databases were searched for papers that report developments and applications of DIA and in particular SWATH-MS in metabolomics and lipidomics. RESULTS DIA methods generate digital sample records that can be mined retrospectively as further knowledge is gained and, with standardized acquisition schemes, used in multiple studies. The different chemical spaces of metabolites and lipids require different specificities, hence different acquisition and data processing approaches must be considered for their analysis. CONCLUSIONS Although the hardware and acquisition modes are well defined for SWATH-MS, a major challenge for routine use remains the lack of appropriate software tools capable of handling large datasets and large numbers of analytes.
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Affiliation(s)
- Michel Raetz
- Life Sciences Mass Spectrometry, Department of Inorganic and Analytical Chemistry, University of Geneva, 24 Quai Ernest Ansermet, CH-1211, Geneva, Switzerland
| | - Ron Bonner
- Ron Bonner Consulting, Newmarket, ON, L3Y 3C7, Canada
| | - Gérard Hopfgartner
- Life Sciences Mass Spectrometry, Department of Inorganic and Analytical Chemistry, University of Geneva, 24 Quai Ernest Ansermet, CH-1211, Geneva, Switzerland.
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15
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Zhang F, Ge W, Ruan G, Cai X, Guo T. Data‐Independent Acquisition Mass Spectrometry‐Based Proteomics and Software Tools: A Glimpse in 2020. Proteomics 2020; 20:e1900276. [DOI: 10.1002/pmic.201900276] [Citation(s) in RCA: 116] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 03/27/2020] [Indexed: 01/02/2023]
Affiliation(s)
- Fangfei Zhang
- Key Laboratory of Structural Biology of Zhejiang ProvinceSchool of Life SciencesWestlake University 18 Shilongshan Road Hangzhou Zhejiang Province 310024 China
- Institute of Basic Medical SciencesWestlake Institute for Advanced Study 18 Shilongshan Road Hangzhou Zhejiang Province 310024 China
| | - Weigang Ge
- Key Laboratory of Structural Biology of Zhejiang ProvinceSchool of Life SciencesWestlake University 18 Shilongshan Road Hangzhou Zhejiang Province 310024 China
- Institute of Basic Medical SciencesWestlake Institute for Advanced Study 18 Shilongshan Road Hangzhou Zhejiang Province 310024 China
| | - Guan Ruan
- Key Laboratory of Structural Biology of Zhejiang ProvinceSchool of Life SciencesWestlake University 18 Shilongshan Road Hangzhou Zhejiang Province 310024 China
- Institute of Basic Medical SciencesWestlake Institute for Advanced Study 18 Shilongshan Road Hangzhou Zhejiang Province 310024 China
| | - Xue Cai
- Key Laboratory of Structural Biology of Zhejiang ProvinceSchool of Life SciencesWestlake University 18 Shilongshan Road Hangzhou Zhejiang Province 310024 China
- Institute of Basic Medical SciencesWestlake Institute for Advanced Study 18 Shilongshan Road Hangzhou Zhejiang Province 310024 China
| | - Tiannan Guo
- Key Laboratory of Structural Biology of Zhejiang ProvinceSchool of Life SciencesWestlake University 18 Shilongshan Road Hangzhou Zhejiang Province 310024 China
- Institute of Basic Medical SciencesWestlake Institute for Advanced Study 18 Shilongshan Road Hangzhou Zhejiang Province 310024 China
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16
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Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis. Int J Mol Sci 2020; 21:ijms21082873. [PMID: 32326049 PMCID: PMC7216093 DOI: 10.3390/ijms21082873] [Citation(s) in RCA: 114] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 04/16/2020] [Accepted: 04/18/2020] [Indexed: 01/15/2023] Open
Abstract
Recent advances in mass spectrometry (MS)-based proteomics have enabled tremendous progress in the understanding of cellular mechanisms, disease progression, and the relationship between genotype and phenotype. Though many popular bioinformatics methods in proteomics are derived from other omics studies, novel analysis strategies are required to deal with the unique characteristics of proteomics data. In this review, we discuss the current developments in the bioinformatics methods used in proteomics and how they facilitate the mechanistic understanding of biological processes. We first introduce bioinformatics software and tools designed for mass spectrometry-based protein identification and quantification, and then we review the different statistical and machine learning methods that have been developed to perform comprehensive analysis in proteomics studies. We conclude with a discussion of how quantitative protein data can be used to reconstruct protein interactions and signaling networks.
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17
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Searle BC, Swearingen KE, Barnes CA, Schmidt T, Gessulat S, Küster B, Wilhelm M. Generating high quality libraries for DIA MS with empirically corrected peptide predictions. Nat Commun 2020; 11:1548. [PMID: 32214105 PMCID: PMC7096433 DOI: 10.1038/s41467-020-15346-1] [Citation(s) in RCA: 112] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Accepted: 02/28/2020] [Indexed: 11/09/2022] Open
Abstract
Data-independent acquisition approaches typically rely on experiment-specific spectrum libraries, requiring offline fractionation and tens to hundreds of injections. We demonstrate a library generation workflow that leverages fragmentation and retention time prediction to build libraries containing every peptide in a proteome, and then refines those libraries with empirical data. Our method specifically enables rapid, experiment-specific library generation for non-model organisms, which we demonstrate using the malaria parasite Plasmodium falciparum, and non-canonical databases, which we show by detecting missense variants in HeLa.
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Affiliation(s)
- Brian C Searle
- Institute for Systems Biology, Seattle, WA, USA. .,Proteome Software, Inc., Portland, OR, USA.
| | | | | | | | - Siegfried Gessulat
- Technical University of Munich, Freising, Germany.,SAP SE, Potsdam, Germany
| | - Bernhard Küster
- Technical University of Munich, Freising, Germany.,Bavarian Center for Biomolecular Mass Spectrometry, Freising, Germany
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18
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Barkovits K, Pacharra S, Pfeiffer K, Steinbach S, Eisenacher M, Marcus K, Uszkoreit J. Reproducibility, Specificity and Accuracy of Relative Quantification Using Spectral Library-based Data-independent Acquisition. Mol Cell Proteomics 2020; 19:181-197. [PMID: 31699904 PMCID: PMC6944235 DOI: 10.1074/mcp.ra119.001714] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 10/17/2019] [Indexed: 12/14/2022] Open
Abstract
Currently data-dependent acquisition (DDA) is the method of choice for mass spectrometry-based proteomics discovery experiments, but data-independent acquisition (DIA) is steadily becoming more important. One of the most important requirements to perform a DIA analysis is the availability of suitable spectral libraries for peptide identification and quantification. Several studies were performed addressing the evaluation of spectral library performance for protein identification in DIA measurements. But so far only few experiments estimate the effect of these libraries on the quantitative level.In this work we created a gold standard spike-in sample set with known contents and ratios of proteins in a complex protein matrix that allowed a detailed comparison of DIA quantification data obtained with different spectral library approaches. We used in-house generated sample-specific spectral libraries created using varying sample preparation approaches and repeated DDA measurement. In addition, two different search engines were tested for protein identification from DDA data and subsequent library generation. In total, eight different spectral libraries were generated, and the quantification results compared with a library free method, as well as a default DDA analysis. Not only the number of identifications on peptide and protein level in the spectral libraries and the corresponding DIA analysis results was inspected, but also the number of expected and identified differentially abundant protein groups and their ratios.We found, that while libraries of prefractionated samples were generally larger, there was no significant increase in DIA identifications compared with repetitive non-fractionated measurements. Furthermore, we show that the accuracy of the quantification is strongly dependent on the applied spectral library and whether the quantification is based on peptide or protein level. Overall, the reproducibility and accuracy of DIA quantification is superior to DDA in all applied approaches.Data has been deposited to the ProteomeXchange repository with identifiers PXD012986, PXD012987, PXD012988 and PXD014956.
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Affiliation(s)
- Katalin Barkovits
- Ruhr University Bochum, Faculty of Medicine, Medizinisches Proteom-Center, Bochum, Germany
| | - Sandra Pacharra
- Ruhr University Bochum, Faculty of Medicine, Medizinisches Proteom-Center, Bochum, Germany
| | - Kathy Pfeiffer
- Ruhr University Bochum, Faculty of Medicine, Medizinisches Proteom-Center, Bochum, Germany
| | - Simone Steinbach
- Ruhr University Bochum, Faculty of Medicine, Medizinisches Proteom-Center, Bochum, Germany
| | - Martin Eisenacher
- Ruhr University Bochum, Faculty of Medicine, Medizinisches Proteom-Center, Bochum, Germany
| | - Katrin Marcus
- Ruhr University Bochum, Faculty of Medicine, Medizinisches Proteom-Center, Bochum, Germany.
| | - Julian Uszkoreit
- Ruhr University Bochum, Faculty of Medicine, Medizinisches Proteom-Center, Bochum, Germany.
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19
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20
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Mun DG, Nam D, Kim H, Pandey A, Lee SW. Accurate Precursor Mass Assignment Improves Peptide Identification in Data-Independent Acquisition Mass Spectrometry. Anal Chem 2019; 91:8453-8460. [DOI: 10.1021/acs.analchem.9b01474] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Dong-Gi Mun
- Department of Chemistry, Center for Proteogenome Research, Korea University, Seoul 136-701, Republic of Korea
| | - Dowoon Nam
- Department of Chemistry, Center for Proteogenome Research, Korea University, Seoul 136-701, Republic of Korea
| | - Hokeun Kim
- Department of Chemistry, Center for Proteogenome Research, Korea University, Seoul 136-701, Republic of Korea
| | - Akhilesh Pandey
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55902, United States
- Manipal Academy of Higher Education (MAHE), Manipal, 576104 Karnataka, India
| | - Sang-Won Lee
- Department of Chemistry, Center for Proteogenome Research, Korea University, Seoul 136-701, Republic of Korea
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21
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Amodei D, Egertson J, MacLean BX, Johnson R, Merrihew GE, Keller A, Marsh D, Vitek O, Mallick P, MacCoss MJ. Improving Precursor Selectivity in Data-Independent Acquisition Using Overlapping Windows. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2019; 30:669-684. [PMID: 30671891 PMCID: PMC6445824 DOI: 10.1007/s13361-018-2122-8] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 12/03/2018] [Accepted: 12/05/2018] [Indexed: 05/22/2023]
Abstract
A major goal of proteomics research is the accurate and sensitive identification and quantification of a broad range of proteins within a sample. Data-independent acquisition (DIA) approaches that acquire MS/MS spectra independently of precursor information have been developed to overcome the reproducibility challenges of data-dependent acquisition and the limited breadth of targeted proteomics strategies. Typical DIA implementations use wide MS/MS isolation windows to acquire comprehensive fragment ion data. However, wide isolation windows produce highly chimeric spectra, limiting the achievable sensitivity and accuracy of quantification and identification. Here, we present a DIA strategy in which spectra are collected with overlapping (rather than adjacent or random) windows and then computationally demultiplexed. This approach improves precursor selectivity by nearly a factor of 2, without incurring any loss in mass range, mass resolution, chromatographic resolution, scan speed, or other key acquisition parameters. We demonstrate a 64% improvement in sensitivity and a 17% improvement in peptides detected in a 6-protein bovine mix spiked into a yeast background. To confirm the method's applicability to a realistic biological experiment, we also analyze the regulation of the proteasome in yeast grown in rapamycin and show that DIA experiments with overlapping windows can help elucidate its adaptation toward the degradation of oxidatively damaged proteins. Our integrated computational and experimental DIA strategy is compatible with any DIA-capable instrument. The computational demultiplexing algorithm required to analyze the data has been made available as part of the open-source proteomics software tools Skyline and msconvert (Proteowizard), making it easy to apply as part of standard proteomics workflows. Graphical Abstract.
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Affiliation(s)
- Dario Amodei
- Department of Radiology, Stanford University, 3155 Porter Drive, Palo Alto, CA USA
| | - Jarrett Egertson
- Department of Genome Sciences, University of Washington, 3720 15th Ave. NE, Seattle, WA USA
| | - Brendan X. MacLean
- Department of Genome Sciences, University of Washington, 3720 15th Ave. NE, Seattle, WA USA
| | - Richard Johnson
- Department of Genome Sciences, University of Washington, 3720 15th Ave. NE, Seattle, WA USA
| | - Gennifer E. Merrihew
- Department of Genome Sciences, University of Washington, 3720 15th Ave. NE, Seattle, WA USA
| | - Austin Keller
- Department of Genome Sciences, University of Washington, 3720 15th Ave. NE, Seattle, WA USA
| | - Don Marsh
- Department of Genome Sciences, University of Washington, 3720 15th Ave. NE, Seattle, WA USA
| | - Olga Vitek
- College of Computer and Information Science, Northeastern University, 440 Huntington Ave, Boston, MA USA
| | - Parag Mallick
- Department of Radiology, Stanford University, 3155 Porter Drive, Palo Alto, CA USA
| | - Michael J. MacCoss
- Department of Genome Sciences, University of Washington, 3720 15th Ave. NE, Seattle, WA USA
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22
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Muntel J, Kirkpatrick J, Bruderer R, Huang T, Vitek O, Ori A, Reiter L. Comparison of Protein Quantification in a Complex Background by DIA and TMT Workflows with Fixed Instrument Time. J Proteome Res 2019; 18:1340-1351. [DOI: 10.1021/acs.jproteome.8b00898] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jan Muntel
- Biognosys AG, Wagistrasse 21, 8952 Schlieren, Switzerland
| | - Joanna Kirkpatrick
- Leibniz Institute on Aging, Fritz Lipmann Institute, Beutenbergstrasse 11, 07745 Jena, Germany
| | | | - Ting Huang
- Northeastern University, 360 Huntington Avenue, Boston, Massachusetts 02115, United States
| | - Olga Vitek
- Northeastern University, 360 Huntington Avenue, Boston, Massachusetts 02115, United States
| | - Alessandro Ori
- Leibniz Institute on Aging, Fritz Lipmann Institute, Beutenbergstrasse 11, 07745 Jena, Germany
| | - Lukas Reiter
- Biognosys AG, Wagistrasse 21, 8952 Schlieren, Switzerland
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23
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Mass Spectrometry-Based Biomarkers in Drug Development. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1140:435-449. [PMID: 31347063 DOI: 10.1007/978-3-030-15950-4_25] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Advances in mass spectrometry, proteomics, protein bioanalytical approaches, and biochemistry have led to a rapid evolution and expansion in the area of mass spectrometry-based biomarker discovery and development. The last decade has also seen significant progress in establishing accepted definitions, guidelines, and criteria for the analytical validation, acceptance and qualification of biomarkers. These advances have coincided with a decreased return on investment for pharmaceutical research and development and an increasing need for better early decision making tools. Empowering development teams with tools to measure a therapeutic interventions impact on disease state and progression, measure target engagement and to confirm predicted pharmacodynamic effects is critical to efficient data-driven decision making. Appropriate implementation of a biomarker or a combination of biomarkers can enhance understanding of a drugs mechanism, facilitate effective translation from the preclinical to clinical space, enable early proof of concept and dose selection, and increases the efficiency of drug development. Here we will provide descriptions of the different classes of biomarkers that have utility in the drug development process as well as review specific, protein-centric, mass spectrometry-based approaches for the discovery of biomarkers and development of targeted assays to measure these markers in a selective and analytically precise manner.
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24
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Smith BJ, Martins-de-Souza D, Fioramonte M. A Guide to Mass Spectrometry-Based Quantitative Proteomics. Methods Mol Biol 2019; 1916:3-39. [PMID: 30535679 DOI: 10.1007/978-1-4939-8994-2_1] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Proteomics has become an attractive science in the postgenomic era, given its capacity to identify up to thousands of molecules in a single, complex sample and quantify them in an absolute and/or relative manner. The use of these techniques enables understanding of cellular and molecular mechanisms of diseases and other biological conditions, as well as identification and screening of protein biomarkers. Here we provide a straightforward, up-to-date compilation and comparison of the main quantitation techniques used in comparative proteomics such as in vitro and in vivo stable isotope labeling and label-free techniques. Additionally, this chapter includes common methods for data acquisition in proteomics and some appropriate methods for data processing. This compilation can serve as a reference for scientists who are new to, or already familiar with, quantitative proteomics.
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Affiliation(s)
- Bradley J Smith
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
| | - Daniel Martins-de-Souza
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
- Center for Neurobiology, University of Campinas (UNICAMP), Campinas, Brazil
- Instituto Nacional de Biomarcadores em Neuropsiquiatria (INBION), Conselho Nacional de Desenvolvimento Cientifico e Tecnologico, Sao Paulo, Brazil
| | - Mariana Fioramonte
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil.
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25
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Chromatogram libraries improve peptide detection and quantification by data independent acquisition mass spectrometry. Nat Commun 2018; 9:5128. [PMID: 30510204 PMCID: PMC6277451 DOI: 10.1038/s41467-018-07454-w] [Citation(s) in RCA: 253] [Impact Index Per Article: 42.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Accepted: 10/16/2018] [Indexed: 01/27/2023] Open
Abstract
Data independent acquisition (DIA) mass spectrometry is a powerful technique that is improving the reproducibility and throughput of proteomics studies. Here, we introduce an experimental workflow that uses this technique to construct chromatogram libraries that capture fragment ion chromatographic peak shape and retention time for every detectable peptide in a proteomics experiment. These coordinates calibrate protein databases or spectrum libraries to a specific mass spectrometer and chromatography setup, facilitating DIA-only pipelines and the reuse of global resource libraries. We also present EncyclopeDIA, a software tool for generating and searching chromatogram libraries, and demonstrate the performance of our workflow by quantifying proteins in human and yeast cells. We find that by exploiting calibrated retention time and fragmentation specificity in chromatogram libraries, EncyclopeDIA can detect 20–25% more peptides from DIA experiments than with data dependent acquisition-based spectrum libraries alone. Data-independent acquisition (DIA)-based proteomics often relies on mass spectrum libraries from data-dependent acquisition experiments. Here, the authors present a method to generate DIA-based chromatogram libraries, enabling DIA-only workflows and detecting more peptides than with spectrum libraries alone.
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26
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Ludwig C, Gillet L, Rosenberger G, Amon S, Collins BC, Aebersold R. Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial. Mol Syst Biol 2018; 14:e8126. [PMID: 30104418 PMCID: PMC6088389 DOI: 10.15252/msb.20178126] [Citation(s) in RCA: 578] [Impact Index Per Article: 96.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Revised: 05/11/2018] [Accepted: 05/15/2018] [Indexed: 01/16/2023] Open
Abstract
Many research questions in fields such as personalized medicine, drug screens or systems biology depend on obtaining consistent and quantitatively accurate proteomics data from many samples. SWATH-MS is a specific variant of data-independent acquisition (DIA) methods and is emerging as a technology that combines deep proteome coverage capabilities with quantitative consistency and accuracy. In a SWATH-MS measurement, all ionized peptides of a given sample that fall within a specified mass range are fragmented in a systematic and unbiased fashion using rather large precursor isolation windows. To analyse SWATH-MS data, a strategy based on peptide-centric scoring has been established, which typically requires prior knowledge about the chromatographic and mass spectrometric behaviour of peptides of interest in the form of spectral libraries and peptide query parameters. This tutorial provides guidelines on how to set up and plan a SWATH-MS experiment, how to perform the mass spectrometric measurement and how to analyse SWATH-MS data using peptide-centric scoring. Furthermore, concepts on how to improve SWATH-MS data acquisition, potential trade-offs of parameter settings and alternative data analysis strategies are discussed.
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Affiliation(s)
- Christina Ludwig
- Bavarian Center for Biomolecular Mass Spectrometry (BayBioMS), Technical University of Munich (TUM), Freising, Germany
| | - Ludovic Gillet
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - George Rosenberger
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Sabine Amon
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Ben C Collins
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- Faculty of Science, University of Zurich, Zurich, Switzerland
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27
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Moseley MA, Hughes CJ, Juvvadi PR, Soderblom EJ, Lennon S, Perkins SR, Thompson JW, Steinbach WJ, Geromanos SJ, Wildgoose J, Langridge JI, Richardson K, Vissers JPC. Scanning Quadrupole Data-Independent Acquisition, Part A: Qualitative and Quantitative Characterization. J Proteome Res 2017; 17:770-779. [PMID: 28901143 DOI: 10.1021/acs.jproteome.7b00464] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
A novel data-independent acquisition (DIA) method incorporating a scanning quadrupole in front of a collision cell and orthogonal acceleration time-of-flight mass analyzer is described. The method has been characterized for the qualitative and quantitative label-free proteomic analysis of complex biological samples. The principle of the scanning quadrupole DIA method is discussed, and analytical instrument characteristics, such as the quadrupole transmission width, scan/integration time, and chromatographic separation, have been optimized in relation to sample complexity for a number of different model proteomes of varying complexity and dynamic range including human plasma, cell lines, and bacteria. In addition, the technological merits over existing DIA approaches are described and contrasted. The qualitative and semiquantitative performance of the method is illustrated for the analysis of relatively simple protein digest mixtures and a well-characterized human cell line sample using untargeted and targeted search strategies. Finally, the results from a human cell line were compared against publicly available data that used similar chromatographic conditions but were acquired with DDA technology and alternative mass analyzer systems. Qualitative comparison showed excellent concordance of results with >90% overlap of the detected proteins.
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Affiliation(s)
- M Arthur Moseley
- Proteomics and Metabolomics Shared Resource Center for Genomic and Computational Biology, Duke University Medical Center , Durham, North Carolina 27710, United States
| | | | - Praveen R Juvvadi
- Division of Pediatric Infectious Diseases, Department of Pediatrics, Duke University Medical Center , Durham, North Carolina 27710, United States
| | - Erik J Soderblom
- Proteomics and Metabolomics Shared Resource Center for Genomic and Computational Biology, Duke University Medical Center , Durham, North Carolina 27710, United States
| | - Sarah Lennon
- Waters Corporation , Wilmslow SK9 4AX, United Kingdom
| | - Simon R Perkins
- Institute of Integrative Biology, University of Liverpool , Liverpool L69 3BX, United Kingdom
| | - J Will Thompson
- Proteomics and Metabolomics Shared Resource Center for Genomic and Computational Biology, Duke University Medical Center , Durham, North Carolina 27710, United States
| | - William J Steinbach
- Division of Pediatric Infectious Diseases, Department of Pediatrics, Duke University Medical Center , Durham, North Carolina 27710, United States.,Department of Molecular Genetics and Microbiology, Duke University Medical Center , Durham, North Carolina 27710, United States
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28
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Sandhu C, Qureshi A, Emili A. Panomics for Precision Medicine. Trends Mol Med 2017; 24:85-101. [PMID: 29217119 DOI: 10.1016/j.molmed.2017.11.001] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Revised: 11/11/2017] [Accepted: 11/13/2017] [Indexed: 12/24/2022]
Abstract
Medicine is poised to undergo a digital transformation. High-throughput platforms are creating terabytes of genomic, transcriptomic, proteomic, and metabolomic data. The challenge is to interpret these data in a meaningful manner - to uncover relationships that are not readily apparent between molecular profiles and states of health or disease. This will require the development of novel data pipelines and computational tools. The combined analysis of multi-dimensional data is referred to as 'panomics'. The ultimate hope of integrative panomics is that it will lead to the discovery and application of novel markers and targeted therapeutics that drive forward a new era of 'precision medicine' where inter-individual variation is accounted for in the treatment of patients.
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Affiliation(s)
| | - Alia Qureshi
- Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Andrew Emili
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
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29
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Bruderer R, Bernhardt OM, Gandhi T, Xuan Y, Sondermann J, Schmidt M, Gomez-Varela D, Reiter L. Optimization of Experimental Parameters in Data-Independent Mass Spectrometry Significantly Increases Depth and Reproducibility of Results. Mol Cell Proteomics 2017; 16:2296-2309. [PMID: 29070702 PMCID: PMC5724188 DOI: 10.1074/mcp.ra117.000314] [Citation(s) in RCA: 248] [Impact Index Per Article: 35.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Revised: 10/23/2017] [Indexed: 12/11/2022] Open
Abstract
Comprehensive, reproducible and precise analysis of large sample cohorts is one of the key objectives of quantitative proteomics. Here, we present an implementation of data-independent acquisition using its parallel acquisition nature that surpasses the limitation of serial MS2 acquisition of data-dependent acquisition on a quadrupole ultra-high field Orbitrap mass spectrometer. In deep single shot data-independent acquisition, we identified and quantified 6,383 proteins in human cell lines using 2-or-more peptides/protein and over 7100 proteins when including the 717 proteins that were identified on the basis of a single peptide sequence. 7739 proteins were identified in mouse tissues using 2-or-more peptides/protein and 8121 when including the 382 proteins that were identified based on a single peptide sequence. Missing values for proteins were within 0.3 to 2.1% and median coefficients of variation of 4.7 to 6.2% among technical triplicates. In very complex mixtures, we could quantify 10,780 proteins and 12,192 proteins when including the 1412 proteins that were identified based on a single peptide sequence. Using this optimized DIA, we investigated large-protein networks before and after the critical period for whisker experience-induced synaptic strength in the murine somatosensory cortex 1-barrel field. This work shows that parallel mass spectrometry enables proteome profiling for discovery with high coverage, reproducibility, precision and scalability.
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Affiliation(s)
- Roland Bruderer
- From the ‡Biognosys, Wagistrasse 21, 8952 Schlieren, Switzerland
| | | | - Tejas Gandhi
- From the ‡Biognosys, Wagistrasse 21, 8952 Schlieren, Switzerland
| | - Yue Xuan
- §Thermo Fisher Scientific, 28199 Bremen, Germany
| | - Julia Sondermann
- ¶Somatosensory Signaling and Systems Biology Group, Max Planck Institute of Experimental Medicine, Hermann-Rein-Strasse 3, 37075 Goettingen, Germany
| | - Manuela Schmidt
- ¶Somatosensory Signaling and Systems Biology Group, Max Planck Institute of Experimental Medicine, Hermann-Rein-Strasse 3, 37075 Goettingen, Germany
| | - David Gomez-Varela
- ¶Somatosensory Signaling and Systems Biology Group, Max Planck Institute of Experimental Medicine, Hermann-Rein-Strasse 3, 37075 Goettingen, Germany
| | - Lukas Reiter
- From the ‡Biognosys, Wagistrasse 21, 8952 Schlieren, Switzerland.
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30
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Williams BJ, Ciavarini SJ, Devlin C, Cohn SM, Xie R, Vissers JPC, Martin LB, Caswell A, Langridge JI, Geromanos SJ. Multi-mode acquisition (MMA): An MS/MS acquisition strategy for maximizing selectivity, specificity and sensitivity of DIA product ion spectra. Proteomics 2017; 16:2284-301. [PMID: 27296928 DOI: 10.1002/pmic.201500492] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2015] [Revised: 05/16/2016] [Accepted: 06/10/2016] [Indexed: 01/08/2023]
Abstract
In proteomics studies, it is generally accepted that depth of coverage and dynamic range is limited in data-directed acquisitions. The serial nature of the method limits both sensitivity and the number of precursor ions that can be sampled. To that end, a number of data-independent acquisition (DIA) strategies have been introduced with these methods, for the most part, immune to the sampling issue; nevertheless, some do have other limitations with respect to sensitivity. The major limitation with DIA approaches is interference, i.e., MS/MS spectra are highly chimeric and often incapable of being identified using conventional database search engines. Utilizing each available dimension of separation prior to ion detection, we present a new multi-mode acquisition (MMA) strategy multiplexing both narrowband and wideband DIA acquisitions in a single analytical workflow. The iterative nature of the MMA workflow limits the adverse effects of interference with minimal loss in sensitivity. Qualitative identification can be performed by selected ion chromatograms or conventional database search strategies.
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Affiliation(s)
| | | | | | | | - Rong Xie
- Waters Corporation, Milford, MA, USA
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31
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Shao W, Lam H. Tandem mass spectral libraries of peptides and their roles in proteomics research. MASS SPECTROMETRY REVIEWS 2017; 36:634-648. [PMID: 27403644 DOI: 10.1002/mas.21512] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2015] [Accepted: 05/21/2016] [Indexed: 05/15/2023]
Abstract
Proteomics is a rapidly maturing field aimed at the high-throughput identification and quantification of all proteins in a biological system. The cornerstone of proteomic technology is tandem mass spectrometry of peptides resulting from the digestion of protein mixtures. The fragmentation pattern of each peptide ion is captured in its tandem mass spectrum, which enables its identification and acts as a fingerprint for the peptide. Spectral libraries are simply searchable collections of these fingerprints, which have taken on an increasingly prominent role in proteomic data analysis. This review describes the historical development of spectral libraries in proteomics, details the computational procedures behind library building and searching, surveys the current applications of spectral libraries, and discusses the outstanding challenges. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 36:634-648, 2017.
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Affiliation(s)
- Wenguang Shao
- Department of Biology, Institute of Molecular Systems Biology, Eidgenössische Technische Hochschule (ETH) Zurich, Zurich, Switzerland
- Division of Biomedical Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
| | - Henry Lam
- Division of Biomedical Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
- Department of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
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32
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Huang R, Chen Z, He L, He N, Xi Z, Li Z, Deng Y, Zeng X. Mass spectrometry-assisted gel-based proteomics in cancer biomarker discovery: approaches and application. Theranostics 2017; 7:3559-3572. [PMID: 28912895 PMCID: PMC5596443 DOI: 10.7150/thno.20797] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2017] [Accepted: 07/12/2017] [Indexed: 12/13/2022] Open
Abstract
There is a critical need for the discovery of novel biomarkers for early detection and targeted therapy of cancer, a major cause of deaths worldwide. In this respect, proteomic technologies, such as mass spectrometry (MS), enable the identification of pathologically significant proteins in various types of samples. MS is capable of high-throughput profiling of complex biological samples including blood, tissues, urine, milk, and cells. MS-assisted proteomics has contributed to the development of cancer biomarkers that may form the foundation for new clinical tests. It can also aid in elucidating the molecular mechanisms underlying cancer. In this review, we discuss MS principles and instrumentation as well as approaches in MS-based proteomics, which have been employed in the development of potential biomarkers. Furthermore, the challenges in validation of MS biomarkers for their use in clinical practice are also reviewed.
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Affiliation(s)
- Rongrong Huang
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Zhongsi Chen
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Lei He
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Nongyue He
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
- Economical Forest Cultivation and Utilization of 2011 Collaborative Innovation Center in Hunan Province, Hunan Key Laboratory of Green Chemistry and Application of Biological Nanotechnology; Hunan University of Technology, Zhuzhou 412007, China
| | - Zhijiang Xi
- School of Medicine, Yangtze University, Jingzhou 434023, China
| | - Zhiyang Li
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
- Department of Clinical Laboratory, the Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - Yan Deng
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
- Economical Forest Cultivation and Utilization of 2011 Collaborative Innovation Center in Hunan Province, Hunan Key Laboratory of Green Chemistry and Application of Biological Nanotechnology; Hunan University of Technology, Zhuzhou 412007, China
| | - Xin Zeng
- Nanjing Maternity and Child Health Medical Institute, Obstetrics and Gynecology Hospital Affiliated to Nanjing Medical University, Nanjing 210004, China
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33
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Ting YS, Egertson JD, Bollinger JG, Searle BC, Payne SH, Noble WS, MacCoss MJ. PECAN: library-free peptide detection for data-independent acquisition tandem mass spectrometry data. Nat Methods 2017; 14:903-908. [PMID: 28783153 PMCID: PMC5578911 DOI: 10.1038/nmeth.4390] [Citation(s) in RCA: 111] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2016] [Accepted: 06/20/2017] [Indexed: 12/18/2022]
Abstract
Data-independent acquisition (DIA) is an emerging mass spectrometry (MS)-based technique for unbiased and reproducible measurement of protein mixtures. DIA tandem mass spectrometry spectra are often highly multiplexed, containing product ions from multiple cofragmenting precursors. Detecting peptides directly from DIA data is therefore challenging; most DIA data analyses require spectral libraries. Here we present PECAN (http://pecan.maccosslab.org), a library-free, peptide-centric tool that robustly and accurately detects peptides directly from DIA data. PECAN reports evidence of detection based on product ion scoring, which enables detection of low-abundance analytes with poor precursor ion signal. We demonstrate the chromatographic peak picking accuracy and peptide detection capability of PECAN, and we further validate its detection with data-dependent acquisition and targeted analyses. Lastly, we used PECAN to build a plasma proteome library from DIA data and to query known sequence variants.
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Affiliation(s)
- Ying S Ting
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | - Jarrett D Egertson
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | - James G Bollinger
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | - Brian C Searle
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | - Samuel H Payne
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - William Stafford Noble
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA.,Department of Computer Science and Engineering, University of Washington, Seattle, Washington, USA
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
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34
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Teleman J, Hauri S, Malmström J. Improvements in Mass Spectrometry Assay Library Generation for Targeted Proteomics. J Proteome Res 2017; 16:2384-2392. [PMID: 28516777 DOI: 10.1021/acs.jproteome.6b00928] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
In data-independent acquisition mass spectrometry (DIA-MS), targeted extraction of peptide signals in silico using mass spectrometry assay libraries is a successful method for the identification and quantification of proteins. However, it remains unclear if high quality assay libraries with more accurate peptide ion coordinates can improve peptide target identification rates in DIA analysis. In this study, we systematically improved and evaluated the common algorithmic steps for assay library generation and demonstrate that increased assay quality results in substantially higher identification rates of peptide targets from mouse organ protein lysates measured by DIA-MS. The introduced changes are (1) a new spectrum interpretation algorithm, (2) reapplication of segmented retention time normalization, (3) a ppm fragment mass error matching threshold, (4) usage of internal peptide fragments, and (5) a multilevel false discovery rate calculation. Taken together, these changes yielded 14-36% more identified peptide targets at 1% assay false discovery rate and are implemented in three new open source tools, Fraggle, Tramler, and Franklin, available at https://github.com/fickludd/eviltools . The improved algorithms provide ways to better utilize discovery MS data, translating to substantially increased DIA performance and ultimately better foundations for drawing biological conclusions in DIA-based experiments.
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Affiliation(s)
- Johan Teleman
- Department of Clinical Sciences, Lund University , BMC D13, 221 84 Lund, Sweden.,Department of Immunotechnology, Lund University , Medicon Village (Building 406), 223 81 Lund, Sweden
| | - Simon Hauri
- Department of Clinical Sciences, Lund University , BMC D13, 221 84 Lund, Sweden
| | - Johan Malmström
- Department of Clinical Sciences, Lund University , BMC D13, 221 84 Lund, Sweden
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35
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A review on mass spectrometry-based quantitative proteomics: Targeted and data independent acquisition. Anal Chim Acta 2017; 964:7-23. [DOI: 10.1016/j.aca.2017.01.059] [Citation(s) in RCA: 205] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2016] [Revised: 01/03/2017] [Accepted: 01/05/2017] [Indexed: 01/18/2023]
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36
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Anjo SI, Santa C, Manadas B. SWATH-MS as a tool for biomarker discovery: From basic research to clinical applications. Proteomics 2017; 17. [DOI: 10.1002/pmic.201600278] [Citation(s) in RCA: 102] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Revised: 01/05/2017] [Accepted: 01/23/2017] [Indexed: 12/16/2022]
Affiliation(s)
- Sandra Isabel Anjo
- CNC - Center for Neuroscience and Cell Biology; University of Coimbra; Coimbra Portugal
- Faculty of Sciences and Technology; University of Coimbra; Coimbra Portugal
| | - Cátia Santa
- CNC - Center for Neuroscience and Cell Biology; University of Coimbra; Coimbra Portugal
- Institute for Interdisciplinary Research (III); University of Coimbra; Coimbra Portugal
| | - Bruno Manadas
- CNC - Center for Neuroscience and Cell Biology; University of Coimbra; Coimbra Portugal
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37
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Shi T, Song E, Nie S, Rodland KD, Liu T, Qian WJ, Smith RD. Advances in targeted proteomics and applications to biomedical research. Proteomics 2016; 16:2160-82. [PMID: 27302376 PMCID: PMC5051956 DOI: 10.1002/pmic.201500449] [Citation(s) in RCA: 145] [Impact Index Per Article: 18.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2015] [Revised: 05/09/2016] [Accepted: 06/10/2016] [Indexed: 12/17/2022]
Abstract
Targeted proteomics technique has emerged as a powerful protein quantification tool in systems biology, biomedical research, and increasing for clinical applications. The most widely used targeted proteomics approach, selected reaction monitoring (SRM), also known as multiple reaction monitoring (MRM), can be used for quantification of cellular signaling networks and preclinical verification of candidate protein biomarkers. As an extension to our previous review on advances in SRM sensitivity (Shi et al., Proteomics, 12, 1074-1092, 2012) herein we review recent advances in the method and technology for further enhancing SRM sensitivity (from 2012 to present), and highlighting its broad biomedical applications in human bodily fluids, tissue and cell lines. Furthermore, we also review two recently introduced targeted proteomics approaches, parallel reaction monitoring (PRM) and data-independent acquisition (DIA) with targeted data extraction on fast scanning high-resolution accurate-mass (HR/AM) instruments. Such HR/AM targeted quantification with monitoring all target product ions addresses SRM limitations effectively in specificity and multiplexing; whereas when compared to SRM, PRM and DIA are still in the infancy with a limited number of applications. Thus, for HR/AM targeted quantification we focus our discussion on method development, data processing and analysis, and its advantages and limitations in targeted proteomics. Finally, general perspectives on the potential of achieving both high sensitivity and high sample throughput for large-scale quantification of hundreds of target proteins are discussed.
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Affiliation(s)
- Tujin Shi
- Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Ehwang Song
- Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Song Nie
- Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Karin D Rodland
- Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Tao Liu
- Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Wei-Jun Qian
- Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Richard D Smith
- Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
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38
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Gillet LC, Leitner A, Aebersold R. Mass Spectrometry Applied to Bottom-Up Proteomics: Entering the High-Throughput Era for Hypothesis Testing. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2016; 9:449-72. [PMID: 27049628 DOI: 10.1146/annurev-anchem-071015-041535] [Citation(s) in RCA: 211] [Impact Index Per Article: 26.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Proteins constitute a key class of molecular components that perform essential biochemical reactions in living cells. Whether the aim is to extensively characterize a given protein or to perform high-throughput qualitative and quantitative analysis of the proteome content of a sample, liquid chromatography coupled to tandem mass spectrometry has become the technology of choice. In this review, we summarize the current state of mass spectrometry applied to bottom-up proteomics, the approach that focuses on analyzing peptides obtained from proteolytic digestion of proteins. With the recent advances in instrumentation and methodology, we show that the field is moving away from providing qualitative identification of long lists of proteins to delivering highly consistent and accurate quantification values for large numbers of proteins across large numbers of samples. We believe that this shift will have a profound impact for the field of proteomics and life science research in general.
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Affiliation(s)
- Ludovic C Gillet
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, 8093 Zürich, Switzerland;
| | - Alexander Leitner
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, 8093 Zürich, Switzerland;
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, 8093 Zürich, Switzerland;
- Faculty of Science, University of Zürich, 8057 Zürich, Switzerland
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39
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White FM, Wolf-Yadlin A. Methods for the Analysis of Protein Phosphorylation-Mediated Cellular Signaling Networks. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2016; 9:295-315. [PMID: 27049636 DOI: 10.1146/annurev-anchem-071015-041542] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Protein phosphorylation-mediated cellular signaling networks regulate almost all aspects of cell biology, including the responses to cellular stimulation and environmental alterations. These networks are highly complex and comprise hundreds of proteins and potentially thousands of phosphorylation sites. Multiple analytical methods have been developed over the past several decades to identify proteins and protein phosphorylation sites regulating cellular signaling, and to quantify the dynamic response of these sites to different cellular stimulation. Here we provide an overview of these methods, including the fundamental principles governing each method, their relative strengths and weaknesses, and some examples of how each method has been applied to the analysis of complex signaling networks. When applied correctly, each of these techniques can provide insight into the topology, dynamics, and regulation of protein phosphorylation signaling networks.
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Affiliation(s)
- Forest M White
- Department of Biological Engineering and David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139;
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40
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Wu JX, Song X, Pascovici D, Zaw T, Care N, Krisp C, Molloy MP. SWATH Mass Spectrometry Performance Using Extended Peptide MS/MS Assay Libraries. Mol Cell Proteomics 2016; 15:2501-14. [PMID: 27161445 DOI: 10.1074/mcp.m115.055558] [Citation(s) in RCA: 78] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2015] [Indexed: 12/26/2022] Open
Abstract
The use of data-independent acquisition methods such as SWATH for mass spectrometry based proteomics is usually performed with peptide MS/MS assay libraries which enable identification and quantitation of peptide peak areas. Reference assay libraries can be generated locally through information dependent acquisition, or obtained from community data repositories for commonly studied organisms. However, there have been no studies performed to systematically evaluate how locally generated or repository-based assay libraries affect SWATH performance for proteomic studies. To undertake this analysis, we developed a software workflow, SwathXtend, which generates extended peptide assay libraries by integration with a local seed library and delivers statistical analysis of SWATH-quantitative comparisons. We designed test samples using peptides from a yeast extract spiked into peptides from human K562 cell lysates at three different ratios to simulate protein abundance change comparisons. SWATH-MS performance was assessed using local and external assay libraries of varying complexities and proteome compositions. These experiments demonstrated that local seed libraries integrated with external assay libraries achieve better performance than local assay libraries alone, in terms of the number of identified peptides and proteins and the specificity to detect differentially abundant proteins. Our findings show that the performance of extended assay libraries is influenced by the MS/MS feature similarity of the seed and external libraries, while statistical analysis using multiple testing corrections increases the statistical rigor needed when searching against large extended assay libraries.
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Affiliation(s)
- Jemma X Wu
- From the ‡Australian Proteome Analysis Facility (APAF), Dept. Chemistry & Biomolecular Sciences, Macquarie University, Sydney, Australia
| | - Xiaomin Song
- From the ‡Australian Proteome Analysis Facility (APAF), Dept. Chemistry & Biomolecular Sciences, Macquarie University, Sydney, Australia
| | - Dana Pascovici
- From the ‡Australian Proteome Analysis Facility (APAF), Dept. Chemistry & Biomolecular Sciences, Macquarie University, Sydney, Australia
| | - Thiri Zaw
- From the ‡Australian Proteome Analysis Facility (APAF), Dept. Chemistry & Biomolecular Sciences, Macquarie University, Sydney, Australia
| | - Natasha Care
- From the ‡Australian Proteome Analysis Facility (APAF), Dept. Chemistry & Biomolecular Sciences, Macquarie University, Sydney, Australia
| | - Christoph Krisp
- From the ‡Australian Proteome Analysis Facility (APAF), Dept. Chemistry & Biomolecular Sciences, Macquarie University, Sydney, Australia
| | - Mark P Molloy
- From the ‡Australian Proteome Analysis Facility (APAF), Dept. Chemistry & Biomolecular Sciences, Macquarie University, Sydney, Australia
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41
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Liu G, Knight JDR, Zhang JP, Tsou CC, Wang J, Lambert JP, Larsen B, Tyers M, Raught B, Bandeira N, Nesvizhskii AI, Choi H, Gingras AC. Data Independent Acquisition analysis in ProHits 4.0. J Proteomics 2016; 149:64-68. [PMID: 27132685 DOI: 10.1016/j.jprot.2016.04.042] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2015] [Revised: 03/18/2016] [Accepted: 04/27/2016] [Indexed: 11/30/2022]
Abstract
Affinity purification coupled with mass spectrometry (AP-MS) is a powerful technique for the identification and quantification of physical interactions. AP-MS requires careful experimental design, appropriate control selection and quantitative workflows to successfully identify bona fide interactors amongst a large background of contaminants. We previously introduced ProHits, a Laboratory Information Management System for interaction proteomics, which tracks all samples in a mass spectrometry facility, initiates database searches and provides visualization tools for spectral counting-based AP-MS approaches. More recently, we implemented Significance Analysis of INTeractome (SAINT) within ProHits to provide scoring of interactions based on spectral counts. Here, we provide an update to ProHits to support Data Independent Acquisition (DIA) with identification software (DIA-Umpire and MSPLIT-DIA), quantification tools (through DIA-Umpire, or externally via targeted extraction), and assessment of quantitative enrichment (through mapDIA) and scoring of interactions (through SAINT-intensity). With additional improvements, notably support of the iProphet pipeline, facilitated deposition into ProteomeXchange repositories and enhanced export and viewing functions, ProHits 4.0 offers a comprehensive suite of tools to facilitate affinity proteomics studies. SIGNIFICANCE It remains challenging to score, annotate and analyze proteomics data in a transparent manner. ProHits was previously introduced as a LIMS to enable storing, tracking and analysis of standard AP-MS data. In this revised version, we expand ProHits to include integration with a number of identification and quantification tools based on Data-Independent Acquisition (DIA). ProHits 4.0 also facilitates data deposition into public repositories, and the transfer of data to new visualization tools.
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Affiliation(s)
- Guomin Liu
- Centre for Systems Biology, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
| | - James D R Knight
- Centre for Systems Biology, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
| | - Jian Ping Zhang
- Centre for Systems Biology, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
| | - Chih-Chiang Tsou
- Department of Pathology, University of Michigan, Ann Arbor, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, USA
| | - Jian Wang
- Center for Computational Mass Spectrometry, University of California, San Diego, La Jolla, California, USA; Department of Computer Science and Engineering, University of California, San Diego, La Jolla, California, USA
| | - Jean-Philippe Lambert
- Centre for Systems Biology, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
| | - Brett Larsen
- Centre for Systems Biology, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
| | - Mike Tyers
- Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, Québec, Canada
| | - Brian Raught
- Princess Margaret Cancer Institute, Department of Medical Biophysics, University of Toronto, Ontario, Canada
| | - Nuno Bandeira
- Center for Computational Mass Spectrometry, University of California, San Diego, La Jolla, California, USA; Department of Computer Science and Engineering, University of California, San Diego, La Jolla, California, USA; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California, USA
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, USA
| | - Hyungwon Choi
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore; Institute of Molecular and Cell Biology, Agency for Science, Technology, and Research, Singapore, Singapore
| | - Anne-Claude Gingras
- Centre for Systems Biology, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
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42
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Abstract
The ultimate aim of proteomics is to fully identify and quantify the entire complement of proteins and post-translational modifications in biological samples of interest. For the last 15 years, liquid chromatography-tandem mass spectrometry (LC-MS/MS) in data-dependent acquisition (DDA) mode has been the standard for proteomics when sampling breadth and discovery were the main objectives; multiple reaction monitoring (MRM) LC-MS/MS has been the standard for targeted proteomics when precise quantification, reproducibility, and validation were the main objectives. Recently, improvements in mass spectrometer design and bioinformatics algorithms have resulted in the rediscovery and development of another sampling method: data-independent acquisition (DIA). DIA comprehensively and repeatedly samples every peptide in a protein digest, producing a complex set of mass spectra that is difficult to interpret without external spectral libraries. Currently, DIA approaches the identification breadth of DDA while achieving the reproducible quantification characteristic of MRM or its newest version, parallel reaction monitoring (PRM). In comparative
de novo identification and quantification studies in human cell lysates, DIA identified up to 89% of the proteins detected in a comparable DDA experiment while providing reproducible quantification of over 85% of them. DIA analysis aided by spectral libraries derived from prior DIA experiments or auxiliary DDA data produces identification and quantification as reproducible and precise as that achieved by MRM/PRM, except on low‑abundance peptides that are obscured by stronger signals. DIA is still a work in progress toward the goal of sensitive, reproducible, and precise quantification without external spectral libraries. New software tools applied to DIA analysis have to deal with deconvolution of complex spectra as well as proper filtering of false positives and false negatives. However, the future outlook is positive, and various researchers are working on novel bioinformatics techniques to address these issues and increase the reproducibility, fidelity, and identification breadth of DIA.
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Affiliation(s)
- Alex Hu
- Department of Genome Sciences, University of Washington, Seattle, WA, 98109, USA
| | - William S Noble
- Department of Genome Sciences, University of Washington, Seattle, WA, 98109, USA
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43
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Affiliation(s)
- Nicholas M. Riley
- Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Joshua J. Coon
- Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
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44
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Malmström L, Bakochi A, Svensson G, Kilsgård O, Lantz H, Petersson AC, Hauri S, Karlsson C, Malmström J. Quantitative proteogenomics of human pathogens using DIA-MS. J Proteomics 2015; 129:98-107. [PMID: 26381203 DOI: 10.1016/j.jprot.2015.09.012] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2015] [Revised: 09/04/2015] [Accepted: 09/09/2015] [Indexed: 11/26/2022]
Abstract
The increasing number of bacterial genomes in combination with reproducible quantitative proteome measurements provides new opportunities to explore how genetic differences modulate proteome composition and virulence. It is challenging to combine genome and proteome data as the underlying genome influences the proteome. We present a strategy to facilitate the integration of genome data from several genetically similar bacterial strains with data-independent analysis mass spectrometry (DIA-MS) for rapid interrogation of the combined data sets. The strategy relies on the construction of a composite genome combining all genetic data in a compact format, which can accommodate the fusion with quantitative peptide and protein information determined via DIA-MS. We demonstrate the method by combining data sets from whole genome sequencing, shotgun MS and DIA-MS from 34 clinical isolates of Streptococcus pyogenes. The data structure allows for fast exploration of the data showing that undetected proteins are on average more amenable to amino acid substitution than expressed proteins. We identified several significantly differentially expressed proteins between invasive and non-invasive strains. The work underlines how integration of whole genome sequencing with accurately quantified proteomes can further advance the interpretation of the relationship between genomes, proteomes and virulence. This article is part of a Special Issue entitled: Computational Proteomics.
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Affiliation(s)
| | - Anahita Bakochi
- Division of Infection Medicine, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Gabriel Svensson
- Division of Infection Medicine, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Ola Kilsgård
- Division of Infection Medicine, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Henrik Lantz
- Department of Medical Biochemistry and Microbiology/BILS, Uppsala University, Uppsala, Sweden
| | - Ann Cathrine Petersson
- Department of Clinical Microbiology, Division of Laboratory Medicine, Region Skåne, Lund, Sweden
| | - Simon Hauri
- Division of Infection Medicine, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Christofer Karlsson
- Division of Infection Medicine, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Johan Malmström
- Division of Infection Medicine, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
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45
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Porter CJ, Bereman MS. Data-independent-acquisition mass spectrometry for identification of targeted-peptide site-specific modifications. Anal Bioanal Chem 2015; 407:6627-35. [PMID: 26105512 PMCID: PMC5257204 DOI: 10.1007/s00216-015-8819-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2015] [Revised: 05/17/2015] [Accepted: 06/01/2015] [Indexed: 12/22/2022]
Abstract
We present a novel strategy based on data-independent acquisition coupled to targeted data extraction for the detection and identification of site-specific modifications of targeted peptides in a completely unbiased manner. This method requires prior knowledge of the site of the modification along the peptide backbone from the protein of interest, but not the mass of the modification. The procedure, named multiplex adduct peptide profiling (MAPP), consists of three steps: 1) A fragment-ion tag is extracted from the data, consisting of the b-type and y-type ion series from the N and C-terminus, respectively, up to the amino-acid position that is believed to be modified; 2) MS1 features are matched to the fragment-ion tag in retention-time space, using the isolation window as a pre-filter to enable calculation of the mass of the modification; and 3) modified fragment ions are overlaid with the unmodified fragment ions to verify the mass calculated in step 2. We discuss the development, applications, and limitations of this new method for detection of unknown peptide modifications. We present an application of the method in profiling adducted peptides derived from abundant proteins in biological fluids with the ultimate objective of detecting biomarkers of exposure to reactive species.
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Affiliation(s)
- Caleb J Porter
- Department of Chemistry, North Carolina State University, Raleigh, NC, 27695, USA
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46
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Ting YS, Egertson JD, Payne SH, Kim S, MacLean B, Käll L, Aebersold R, Smith RD, Noble WS, MacCoss MJ. Peptide-Centric Proteome Analysis: An Alternative Strategy for the Analysis of Tandem Mass Spectrometry Data. Mol Cell Proteomics 2015. [PMID: 26217018 DOI: 10.1074/mcp.o114.047035] [Citation(s) in RCA: 104] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
In mass spectrometry-based bottom-up proteomics, data-independent acquisition is an emerging technique because of its comprehensive and unbiased sampling of precursor ions. However, current data-independent acquisition methods use wide precursor isolation windows, resulting in cofragmentation and complex mixture spectra. Thus, conventional database searching tools that identify peptides by interpreting individual tandem MS spectra are inherently limited in analyzing data-independent acquisition data. Here we discuss an alternative approach, peptide-centric analysis, which tests directly for the presence and absence of query peptides. We discuss how peptide-centric analysis resolves some limitations of traditional spectrum-centric analysis, and we outline the unique characteristics of peptide-centric analysis in general.
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Affiliation(s)
- Ying S Ting
- From the ‡Department of Genome Sciences, University of Washington, Seattle, Washington
| | - Jarrett D Egertson
- From the ‡Department of Genome Sciences, University of Washington, Seattle, Washington
| | - Samuel H Payne
- §Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington
| | - Sangtae Kim
- §Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington
| | - Brendan MacLean
- From the ‡Department of Genome Sciences, University of Washington, Seattle, Washington
| | - Lukas Käll
- ¶Science for Life Laboratory, Royal Institute of Technology (KTH), Stockholm, Sweden
| | - Ruedi Aebersold
- ‖Department of Biology, Institute of Molecular Systems Biology, Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland; ‡‡Faculty of Science, University of Zurich, Zurich, Switzerland
| | - Richard D Smith
- §Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington
| | - William Stafford Noble
- From the ‡Department of Genome Sciences, University of Washington, Seattle, Washington; **Department of Computer Science and Engineering, University of Washington, Seattle, Washington
| | - Michael J MacCoss
- From the ‡Department of Genome Sciences, University of Washington, Seattle, Washington;
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47
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Large-scale label-free phosphoproteomics: from technology to data interpretation. Bioanalysis 2015; 6:2403-20. [PMID: 25384593 DOI: 10.4155/bio.14.188] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Protein phosphorylation plays a central role in the dynamic intracellular signaling and the control of biochemical pathways in all living cells. Recent advances in high-performance MS/MS-based technology make the large-scale identification and quantification of phosphorylation sites possible. Here, we review the full data generation pipeline, starting from sample preparation methods and LC-MS detection procedures, through to data processing and analysis software tools that facilitate the systematic comparative profiling of thousands of phosphoproteins in different biological specimens in a single experiment. We emphasize current challenges and promising avenues for the mechanistic interpretation and visualization of global phosphorylation networks and their relevance to human health and disease.
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48
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Parker BL, Yang G, Humphrey SJ, Chaudhuri R, Ma X, Peterman S, James DE. Targeted phosphoproteomics of insulin signaling using data-independent acquisition mass spectrometry. Sci Signal 2015; 8:rs6. [DOI: 10.1126/scisignal.aaa3139] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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49
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Szabo Z, Janaky T. Challenges and developments in protein identification using mass spectrometry. Trends Analyt Chem 2015. [DOI: 10.1016/j.trac.2015.03.007] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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50
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Wu X, Held K, Zheng C, Staudinger BJ, Chavez JD, Weisbrod CR, Eng JK, Singh PK, Manoil C, Bruce JE. Dynamic Proteome Response of Pseudomonas aeruginosa to Tobramycin Antibiotic Treatment. Mol Cell Proteomics 2015; 14:2126-37. [PMID: 26018413 DOI: 10.1074/mcp.m115.050161] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2015] [Indexed: 11/06/2022] Open
Abstract
Genetically susceptible bacteria become antibiotic tolerant during chronic infections, and the mechanisms responsible are poorly understood. One factor that may contribute to differential sensitivity in vitro and in vivo is differences in the time-dependent tobramycin concentration profile experienced by the bacteria. Here, we examine the proteome response induced by subinhibitory concentrations of tobramycin in Pseudomonas aeruginosa cells grown under planktonic conditions. These efforts revealed increased levels of heat shock proteins and proteases were present at higher dosage treatments (0.5 and 1 μg/ml), while less dramatic at 0.1 μg/ml dosage. In contrast, many metabolic enzymes were significantly induced by lower dosages (0.1 and 0.5 μg/ml) but not at 1 μg/ml dosage. Time course proteome analysis further revealed that the increase of heat shock proteins and proteases was most rapid from 15 min to 60 min, and the increased levels sustained till 6 h (last time point tested). Heat shock protein IbpA exhibited the greatest induction by tobramycin, up to 90-fold. Nevertheless, deletion of ibpA did not enhance sensitivity to tobramycin. It seemed possible that the absence of sensitization could be due to redundant functioning of IbpA with other proteins that protect cells from tobramycin. Indeed, inactivation of two heat shock chaperones/proteases in addition to ibpA in double mutants (ibpA/clpB, ibpA/PA0779 and ibpA/hslV) did increase tobramycin sensitivity. Collectively, these results demonstrate the time- and concentration-dependent nature of the P. aeruginosa proteome response to tobramycin and that proteome modulation and protein redundancy are protective mechanisms to help bacteria resist antibiotic treatments.
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Affiliation(s)
- Xia Wu
- From the ‡Department of Genome Sciences
| | | | | | - Benjamin J Staudinger
- ¶Department of Medicine and Microbiology, University of Washington, Seattle, WA 98195
| | | | | | | | - Pradeep K Singh
- ¶Department of Medicine and Microbiology, University of Washington, Seattle, WA 98195
| | | | - James E Bruce
- From the ‡Department of Genome Sciences, §Department of Chemistry,
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