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Mohammed Y, Goodlett D, Borchers CH. Bioinformatics Tools and Knowledgebases to Assist Generating Targeted Assays for Plasma Proteomics. Methods Mol Biol 2023; 2628:557-577. [PMID: 36781806 DOI: 10.1007/978-1-0716-2978-9_32] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2023]
Abstract
In targeted proteomics experiments, selecting the appropriate proteotypic peptides as surrogate for the target protein is a crucial pre-acquisition step. This step is largely a bioinformatics exercise that involves integrating information on the peptides and proteins and using various software tools and knowledgebases. We present here a few resources that automate and simplify the selection process to a great degree. These tools and knowledgebases were developed primarily to streamline targeted proteomics assay development and include PeptidePicker, PeptidePickerDB, MRMAssayDB, MouseQuaPro, and PeptideTracker. We have used these tools to develop and document thousands of targeted proteomics assays, many of them for plasma proteins with focus on human and mouse. An important aspect in all these resources is the integrative approach on which they are based. Using these tools in the first steps of designing a singleplexed or multiplexed targeted proteomic experiment can reduce the necessary experimental steps tremendously. All the tools and knowledgebases we describe here are Web-based and freely accessible so scientists can query the information conveniently from the browser. This chapter provides an overview of these software tools and knowledgebases, their content, and how to use them for targeted plasma proteomics. We further demonstrate how to use them with the results of the HUPO Human Plasma Proteome Project to produce a new database of 3.8 k targeted assays for known human plasma proteins. Upon experimental validation, these assays should help in the further quantitative characterizing of the plasma proteome.
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Affiliation(s)
- Yassene Mohammed
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, ZA, Netherlands. .,University of Victoria - Genome BC Proteomics Centre, Victoria, BC, Canada. .,Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC, Canada.
| | - David Goodlett
- University of Victoria - Genome BC Proteomics Centre, Victoria, BC, Canada.,Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC, Canada.,University of Gdansk, International Centre for Cancer Vaccine Science, Gdansk, Poland
| | - Christoph H Borchers
- Proteomics Centre, Segal Cancer Centre, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, Canada.,Gerald Bronfman Department of Oncology, Jewish General Hospital, Montreal, QC, Canada.,Division of Experimental Medicine, McGill University, Montreal, QC, Canada.,Department of Pathology, McGill University, Montreal, QC, Canada
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2
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Pauletti BA, Granato DC, M Carnielli C, Câmara GA, Normando AGC, Telles GP, Leme AFP. Typic: A Practical and Robust Tool to Rank Proteotypic Peptides for Targeted Proteomics. J Proteome Res 2023; 22:539-545. [PMID: 36480281 DOI: 10.1021/acs.jproteome.2c00585] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The selection of a suitable proteotypic peptide remains a challenge for designing a targeted quantitative proteomics assay. Although the criteria are well-established in the literature, the selection of these peptides is often performed in a subjective and time-consuming manner. Here, we have developed a practical and semiautomated workflow implemented in an open-source program named Typic. Typic is designed to run in a command line and a graphical interface to help selecting a list of proteotypic peptides for targeted quantitation. The tool combines the input data and downloads additional data from public repositories to produce a file per protein as output. Each output file includes relevant information to the selection of proteotypic peptides organized in a table, a colored ranking of peptides according to their potential value as targets for quantitation and auxiliary plots to assist users in the task of proteotypic peptides selection. Taken together, Typic leads to a practical and straightforward data extraction from multiple data sets, allowing the identification of most suitable proteotypic peptides based on established criteria, in an unbiased and standardized manner, ultimately leading to a more robust targeted proteomics assay.
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Affiliation(s)
- Bianca A Pauletti
- Laboratório de Espectrometria de Massas, Laboratório Nacional de Biociências (LNBio), Centro Nacional de Pesquisa em Energia e Materiais (CNPEM), Campinas, 13083-970 São Paulo, Brazil
| | - Daniela C Granato
- Laboratório de Espectrometria de Massas, Laboratório Nacional de Biociências (LNBio), Centro Nacional de Pesquisa em Energia e Materiais (CNPEM), Campinas, 13083-970 São Paulo, Brazil
| | - Carolina M Carnielli
- Laboratório de Espectrometria de Massas, Laboratório Nacional de Biociências (LNBio), Centro Nacional de Pesquisa em Energia e Materiais (CNPEM), Campinas, 13083-970 São Paulo, Brazil
| | - Guilherme A Câmara
- Laboratório de Espectrometria de Massas, Laboratório Nacional de Biociências (LNBio), Centro Nacional de Pesquisa em Energia e Materiais (CNPEM), Campinas, 13083-970 São Paulo, Brazil
| | - Ana Gabriela C Normando
- Laboratório de Espectrometria de Massas, Laboratório Nacional de Biociências (LNBio), Centro Nacional de Pesquisa em Energia e Materiais (CNPEM), Campinas, 13083-970 São Paulo, Brazil
| | - Guilherme P Telles
- Instituto de Computação, Universidade Estadual de Campinas (UNICAMP), Campinas, 13083-852 São Paulo, Brazil
| | - Adriana F Paes Leme
- Laboratório de Espectrometria de Massas, Laboratório Nacional de Biociências (LNBio), Centro Nacional de Pesquisa em Energia e Materiais (CNPEM), Campinas, 13083-970 São Paulo, Brazil
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3
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van Bentum M, Selbach M. An Introduction to Advanced Targeted Acquisition Methods. Mol Cell Proteomics 2021; 20:100165. [PMID: 34673283 PMCID: PMC8600983 DOI: 10.1016/j.mcpro.2021.100165] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Revised: 10/11/2021] [Accepted: 10/13/2021] [Indexed: 01/13/2023] Open
Abstract
Targeted proteomics via selected reaction monitoring (SRM) or parallel reaction monitoring (PRM) enables fast and sensitive detection of a preselected set of target peptides. However, the number of peptides that can be monitored in conventional targeting methods is usually rather small. Recently, a series of methods has been described that employ intelligent acquisition strategies to increase the efficiency of mass spectrometers to detect target peptides. These methods are based on one of two strategies. First, retention time adjustment-based methods enable intelligent scheduling of target peptide retention times. These include Picky, iRT, as well as spike-in free real-time adjustment methods such as MaxQuant.Live. Second, in spike-in triggered acquisition methods such as SureQuant, Pseudo-PRM, TOMAHAQ, and Scout-MRM, targeted scans are initiated by abundant labeled synthetic peptides added to samples before the run. Both strategies enable the mass spectrometer to better focus data acquisition time on target peptides. This either enables more sensitive detection or a higher number of targets per run. Here, we provide an overview of available advanced targeting methods and highlight their intrinsic strengths and weaknesses and compatibility with specific experimental setups. Our goal is to provide a basic introduction to advanced targeting methods for people starting to work in this field. Advanced acquisition methods improve focus of mass spectrometers on target peptides. This review discusses existing methods based on two strategies. Retention time adjustment-based methods enable intelligent scheduling of peptide RTs. In spike-in triggered acquisition methods targeted scans are initiated by spike-ins.
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Affiliation(s)
- Mirjam van Bentum
- Proteome Dynamics, Max Delbrück Center for Molecular Medicine, Berlin, Germany; Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Matthias Selbach
- Proteome Dynamics, Max Delbrück Center for Molecular Medicine, Berlin, Germany; Charité-Universitätsmedizin Berlin, Berlin, Germany.
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4
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Tarasova IA, Masselon CD, Gorshkov AV, Gorshkov MV. Predictive chromatography of peptides and proteins as a complementary tool for proteomics. Analyst 2018; 141:4816-4832. [PMID: 27419248 DOI: 10.1039/c6an00919k] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
In the last couple of decades, considerable effort has been focused on developing methods for quantitative and qualitative proteome characterization. The method of choice in this characterization is mass spectrometry used in combination with sample separation. One of the most widely used separation techniques at the front end of a mass spectrometer is high performance liquid chromatography (HPLC). A unique feature of HPLC is its specificity to the amino acid sequence of separated peptides and proteins. This specificity may provide additional information about the peptides or proteins under study which is complementary to the mass spectrometry data. The value of this information for proteomics has been recognized in the past few decades, which has stimulated significant effort in the development and implementation of computational and theoretical models for the prediction of peptide retention time for a given sequence. Here we review the advances in this area and the utility of predicted retention times for proteomic applications.
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Affiliation(s)
- Irina A Tarasova
- Institute for Energy Problems of Chemical Physics, Russian Academy of Sciences, Moscow 119334, Russia.
| | - Christophe D Masselon
- CEA, iRTSV-BGE, Laboratoire d'Etude de la Dynamique des Protéomes, Grenoble, F-38000, France and INSERM, U1038-BGE, F-38000, Grenoble, France
| | - Alexander V Gorshkov
- N.N. Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow 119991, Russia
| | - Mikhail V Gorshkov
- Institute for Energy Problems of Chemical Physics, Russian Academy of Sciences, Moscow 119334, Russia. and Moscow Institute of Physics and Technology (State University), Dolgoprudny, Moscow region 141700, Russia
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5
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Huang SY, Lin MH, Chen YH, Lai CC, Lee MS, Hu AYC, Sung WC. Application of stable isotope dimethyl labeling for MRM based absolute antigen quantification of influenza vaccine. J Chromatogr B Analyt Technol Biomed Life Sci 2018; 1104:40-48. [PMID: 30428430 DOI: 10.1016/j.jchromb.2018.09.020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Revised: 09/11/2018] [Accepted: 09/18/2018] [Indexed: 12/23/2022]
Abstract
Determining the precursor/product ion pair and optimal collision energy are the critical steps for developing a multiple reaction monitoring (MRM) assay using triple quadruple mass spectrometer for protein quantitation. In this study, a platform consisting of stable isotope dimethyl labeling coupled with triple-quadruple mass spectrometer was used to quantify the protein components of the influenza vaccines. Dimethyl labeling of both the peptide N-termini and the ϵ-amino group of lysine residues was achieved by reductive amination using formaldehyde and sodium cyanoborohydrate. Dimethylated peptides are known to exhibit dominant a1 ions under gas phase fragmentation in a mass spectrometer. These a1 ions can be predicted from the peptide N-terminal amino acids, and their signals do not vary significantly across a wide range of collision energies, which facilitates the determination of MRM transition settings for multiple protein targets. The intrinsic a1 ions provide sensitivity for acquiring MRM peaks that is superior to that of the typical b/y ions used for native peptides, and they also provided good linearity (R2 ≥ 0.99) at the detected concentration range for each peptide. These features allow for the simultaneous quantification of hemagglutinin and neuraminidase in vaccines derived from either embryo eggs or cell cultivation. Moreover, the low abundant ovalbumin residue originated from the manufacturing process can also be determined. The results demonstrate that the stable isotope dimethyl labeling coupled with MRM Mass spectrometry screening of a1 ions (i.e., SIDa-MS) can be used as a high-throughput platform for multiple protein quantification of vaccine products.
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Affiliation(s)
| | - Min-Han Lin
- National Health Research Institutes, National Institute of Infectious Diseases and Vaccinology, Miaoli 350, Taiwan
| | - Yo-Hsuan Chen
- National Health Research Institutes, National Institute of Infectious Diseases and Vaccinology, Miaoli 350, Taiwan
| | - Chia-Chun Lai
- National Health Research Institutes, National Institute of Infectious Diseases and Vaccinology, Miaoli 350, Taiwan
| | - Min-Shi Lee
- National Health Research Institutes, National Institute of Infectious Diseases and Vaccinology, Miaoli 350, Taiwan
| | - Alan Yung-Chih Hu
- National Health Research Institutes, National Institute of Infectious Diseases and Vaccinology, Miaoli 350, Taiwan
| | - Wang-Chou Sung
- National Health Research Institutes, National Institute of Infectious Diseases and Vaccinology, Miaoli 350, Taiwan.
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6
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Liu W, Wei L, Sun J, Feng J, Guo G, Liang L, Fu T, Liu M, Li K, Huang Y, Zhu W, Zhen B, Wang Y, Ding C, Qin J. A reference peptide database for proteome quantification based on experimental mass spectrum response curves. Bioinformatics 2018; 34:2766-2772. [PMID: 29617941 PMCID: PMC6084618 DOI: 10.1093/bioinformatics/bty201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Accepted: 03/29/2018] [Indexed: 11/18/2022] Open
Abstract
Motivation Mass spectrometry (MS) based quantification of proteins/peptides has become a powerful tool in biological research with high sensitivity and throughput. The accuracy of quantification, however, has been problematic as not all peptides are suitable for quantification. Several methods and tools have been developed to identify peptides that response well in mass spectrometry and they are mainly based on predictive models, and rarely consider the linearity of the response curve, limiting the accuracy and applicability of the methods. An alternative solution is to select empirically superior peptides that offer satisfactory MS response intensity and linearity in a wide dynamic range of peptide concentration. Results We constructed a reference database for proteome quantification based on experimental mass spectrum response curves. The intensity and dynamic range of over 2 647 773 transitions from 121 318 peptides were obtained from a set of dilution experiments, covering 11 040 gene products. These transitions and peptides were evaluated and presented in a database named SCRIPT-MAP. We showed that the best-responder (BR) peptide approach for quantification based on SCRIPT-MAP database is robust, repeatable and accurate in proteome-scale protein quantification. This study provides a reference database as well as a peptides/transitions selection method for quantitative proteomics. Availability and implementation SCRIPT-MAP database is available at http://www.firmiana.org/responders/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Wanlin Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Lai Wei
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Jianan Sun
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Jinwen Feng
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Gaigai Guo
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Lizhu Liang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Tianyi Fu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Mingwei Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Kai Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Yin Huang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Weimin Zhu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Bei Zhen
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Yi Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.,Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Chen Ding
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.,State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, and School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Jun Qin
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.,Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
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7
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Timmins-Schiffman E, Mikan MP, Ting YS, Harvey HR, Nunn BL. MS analysis of a dilution series of bacteria:phytoplankton to improve detection of low abundance bacterial peptides. Sci Rep 2018; 8:9276. [PMID: 29915279 PMCID: PMC6006377 DOI: 10.1038/s41598-018-27650-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Accepted: 06/06/2018] [Indexed: 11/17/2022] Open
Abstract
Assigning links between microbial activity and biogeochemical cycles in the ocean is a primary objective for ecologists and oceanographers. Bacteria represent a small ecosystem component by mass, but act as the nexus for both nutrient transformation and organic matter recycling. There are limited methods to explore the full suite of active bacterial proteins largely responsible for degradation. Mass spectrometry (MS)-based proteomics now has the potential to document bacterial physiology within these complex systems. Global proteome profiling using MS, known as data dependent acquisition (DDA), is limited by the stochastic nature of ion selection, decreasing the detection of low abundance peptides. The suitability of MS-based proteomics methods in revealing bacterial signatures outnumbered by phytoplankton proteins was explored using a dilution series of pure bacteria (Ruegeria pomeroyi) and diatoms (Thalassiosira pseudonana). Two common acquisition strategies were utilized: DDA and selected reaction monitoring (SRM). SRM improved detection of bacterial peptides at low bacterial cellular abundance that were undetectable with DDA from a wide range of physiological processes (e.g. amino acid synthesis, lipid metabolism, and transport). We demonstrate the benefits and drawbacks of two different proteomic approaches for investigating species-specific physiological processes across relative abundances of bacteria that vary by orders of magnitude.
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Affiliation(s)
| | - Molly P Mikan
- Old Dominion University, Department of Ocean, Earth, and Atmospheric Sciences, Norfolk, VA, 23529, USA
| | - Ying Sonia Ting
- University of Washington, Department of Genome Sciences, Seattle, WA, 98195, USA
- Neon Therapeutics, Boston, MA, 02139, USA
| | - H Rodger Harvey
- Old Dominion University, Department of Ocean, Earth, and Atmospheric Sciences, Norfolk, VA, 23529, USA
| | - Brook L Nunn
- University of Washington, Department of Genome Sciences, Seattle, WA, 98195, USA.
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8
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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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9
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Alghanem B, Nikitin F, Stricker T, Duchoslav E, Luban J, Strambio-De-Castillia C, Muller M, Lisacek F, Varesio E, Hopfgartner G. Optimization by infusion of multiple reaction monitoring transitions for sensitive quantification of peptides by liquid chromatography/mass spectrometry. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2017; 31:753-761. [PMID: 28199054 DOI: 10.1002/rcm.7839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2016] [Revised: 12/02/2016] [Accepted: 02/13/2017] [Indexed: 06/06/2023]
Abstract
RATIONALE In peptide quantification by liquid chromatography/mass spectrometry (LC/MS), the optimization of multiple reaction monitoring (MRM) parameters is essential for sensitive detection. We have compared different approaches to build MRM assays, based either on flow injection analysis (FIA) of isotopically labelled peptides, or on the knowledge and the prediction of the best settings for MRM transitions and collision energies (CE). In this context, we introduce MRMOptimizer, an open-source software tool that processes spectra and assists the user in selecting transitions in the FIA workflow. METHODS MS/MS spectral libraries with CE voltages from 10 to 70 V are automatically acquired in FIA mode for isotopically labelled peptides. Then MRMOptimizer determines the optimal MRM settings for each peptide. To assess the quantitative performance of our approach, 155 peptides, representing 84 proteins, were analysed by LC/MRM-MS and the peak areas were compared between: (A) the MRMOptimizer-based workflow, (B1) the SRMAtlas transitions set used 'as-is'; (B2) the same SRMAtlas set with CE parameters optimized by Skyline. RESULTS 51% of the three most intense transitions per peptide were shown to be common to both A and B1/B2 methods, and displayed similar sensitivity and peak area distributions. The peak areas obtained with MRMOptimizer for transitions sharing either the precursor ion charge state or the fragment ions with the SRMAtlas set at unique transitions were increased 1.8- to 2.3-fold. The gain in sensitivity using MRMOptimizer for transitions with different precursor ion charge state and fragment ions (8% of the total), reaches a ~ 11-fold increase. CONCLUSIONS Isotopically labelled peptides can be used to optimize MRM transitions more efficiently in FIA than by searching databases. The MRMOptimizer software is MS independent and enables the post-acquisition selection of MRM parameters. Coefficients of variation for optimal CE values are lower than those obtained with the SRMAtlas approach (B2) and one additional peptide was detected. Copyright © 2017 John Wiley & Sons, Ltd.
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Affiliation(s)
- Bandar Alghanem
- Life Sciences Mass Spectrometry, Department of Inorganic and Analytical Chemistry, University of Geneva, Geneva, Switzerland
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
| | - Frédéric Nikitin
- Proteome Informatics Group, Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Thomas Stricker
- Life Sciences Mass Spectrometry, Department of Inorganic and Analytical Chemistry, University of Geneva, Geneva, Switzerland
| | | | - Jeremy Luban
- Medical School, Program in Molecular Medicine, University of Massachusetts, Worcester, MA, USA
| | | | - Markus Muller
- Proteome Informatics Group, Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Frédérique Lisacek
- Proteome Informatics Group, Swiss Institute of Bioinformatics, Geneva, Switzerland
- Computer Science Department, University of Geneva, Geneva, Switzerland
| | - Emmanuel Varesio
- School of Pharmaceutical Sciences, University of Lausanne, University of Geneva, Geneva, Switzerland
| | - Gérard Hopfgartner
- Life Sciences Mass Spectrometry, Department of Inorganic and Analytical Chemistry, University of Geneva, Geneva, Switzerland
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10
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Li H, Han J, Pan J, Liu T, Parker CE, Borchers CH. Current trends in quantitative proteomics - an update. JOURNAL OF MASS SPECTROMETRY : JMS 2017; 52:319-341. [PMID: 28418607 DOI: 10.1002/jms.3932] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2017] [Revised: 03/28/2017] [Accepted: 04/06/2017] [Indexed: 05/11/2023]
Abstract
Proteins can provide insights into biological processes at the functional level, so they are very promising biomarker candidates. The quantification of proteins in biological samples has been routinely used for the diagnosis of diseases and monitoring the treatment. Although large-scale protein quantification in complex samples is still a challenging task, a great amount of effort has been made to advance the technologies that enable quantitative proteomics. Seven years ago, in 2009, we wrote an article about the current trends in quantitative proteomics. In writing this current paper, we realized that, today, we have an even wider selection of potential tools for quantitative proteomics. These tools include new derivatization reagents, novel sampling formats, new types of analyzers and scanning techniques, and recently developed software to assist in assay development and data analysis. In this review article, we will discuss these innovative methods, and their current and potential applications in proteomics. Copyright © 2017 John Wiley & Sons, Ltd.
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Affiliation(s)
- H Li
- University of Victoria - Genome British Columbia Proteomics Centre, University of Victoria, Victoria, BC, V8Z 7X8, Canada
| | - J Han
- University of Victoria - Genome British Columbia Proteomics Centre, University of Victoria, Victoria, BC, V8Z 7X8, Canada
| | - J Pan
- University of Victoria - Genome British Columbia Proteomics Centre, University of Victoria, Victoria, BC, V8Z 7X8, Canada
| | - T Liu
- University of Victoria - Genome British Columbia Proteomics Centre, University of Victoria, Victoria, BC, V8Z 7X8, Canada
| | - C E Parker
- University of Victoria - Genome British Columbia Proteomics Centre, University of Victoria, Victoria, BC, V8Z 7X8, Canada
| | - C H Borchers
- University of Victoria - Genome British Columbia Proteomics Centre, University of Victoria, Victoria, BC, V8Z 7X8, Canada
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC, V8P 5C2, Canada
- Proteomics Centre, Segal Cancer Centre, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, H3T 1E2, Canada
- Gerald Bronfman Department of Oncology, Jewish General Hospital, Montreal, Quebec, H3T 1E2, Canada
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11
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Röst HL, Liu Y, D'Agostino G, Zanella M, Navarro P, Rosenberger G, Collins BC, Gillet L, Testa G, Malmström L, Aebersold R. TRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomics. Nat Methods 2016; 13:777-83. [PMID: 27479329 PMCID: PMC5008461 DOI: 10.1038/nmeth.3954] [Citation(s) in RCA: 122] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2015] [Accepted: 06/14/2016] [Indexed: 12/16/2022]
Abstract
Large scale, quantitative proteomic studies have become essential for the analysis of clinical cohorts, large perturbation experiments and systems biology studies. While next-generation mass spectrometric techniques such as SWATH-MS have substantially increased throughput and reproducibility, ensuring consistent quantification of thousands of peptide analytes across multiple LC-MS/MS runs remains a challenging and laborious manual process. To produce highly consistent and quantitatively accurate proteomics data matrices in an automated fashion, we have developed the TRIC software which utilizes fragment ion data to perform cross-run alignment, consistent peak-picking and quantification for high throughput targeted proteomics. TRIC uses a graph-based alignment strategy based on non-linear retention time correction to integrate peak elution information from all LC-MS/MS runs acquired in a study. When compared to state-of-the-art SWATH-MS data analysis, the algorithm was able to reduce the identification error by more than 3-fold at constant recall, while correcting for highly non-linear chromatographic effects. On a pulsed-SILAC experiment performed on human induced pluripotent stem (iPS) cells, TRIC was able to automatically align and quantify thousands of light and heavy isotopic peak groups and substantially increased the quantitative completeness and biological information in the data, providing insights into protein dynamics of iPS cells. Overall, this study demonstrates the importance of consistent quantification in highly challenging experimental setups, and proposes an algorithm to automate this task, constituting the last missing piece in a pipeline for automated analysis of massively parallel targeted proteomics datasets.
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Affiliation(s)
- Hannes L Röst
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,Department of Genetics, Stanford University, Stanford, California, USA
| | - Yansheng Liu
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Giuseppe D'Agostino
- Department of Experimental Oncology, European Institute of Oncology, Milan, Italy
| | - Matteo Zanella
- Department of Experimental Oncology, European Institute of Oncology, Milan, Italy
| | - Pedro Navarro
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,Institute for Immunology, University Medical Center of the Johannes Gutenberg University of Mainz, Mainz, Germany
| | - George Rosenberger
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,PhD Program in Systems Biology, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Ben C Collins
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Ludovic Gillet
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Giuseppe Testa
- Department of Experimental Oncology, European Institute of Oncology, Milan, Italy.,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Lars Malmström
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,S3IT, University of 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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12
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Sabbagh B, Mindt S, Neumaier M, Findeisen P. Clinical applications of MS-based protein quantification. Proteomics Clin Appl 2016; 10:323-45. [DOI: 10.1002/prca.201500116] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2015] [Revised: 11/18/2015] [Accepted: 12/30/2015] [Indexed: 12/14/2022]
Affiliation(s)
- Bassel Sabbagh
- Institute for Clinical Chemistry; Medical Faculty Mannheim of the University of Heidelberg; University Hospital Mannheim; Mannheim Germany
| | - Sonani Mindt
- Institute for Clinical Chemistry; Medical Faculty Mannheim of the University of Heidelberg; University Hospital Mannheim; Mannheim Germany
| | - Michael Neumaier
- Institute for Clinical Chemistry; Medical Faculty Mannheim of the University of Heidelberg; University Hospital Mannheim; Mannheim Germany
| | - Peter Findeisen
- Institute for Clinical Chemistry; Medical Faculty Mannheim of the University of Heidelberg; University Hospital Mannheim; Mannheim Germany
- MVZ Labor Dr. Limbach und Kollegen; Heidelberg Germany
- Working Group Proteomics of the German United Society for Clinical Chemistry and Laboratory Medicine e.V. (DGKL); Bonn Germany
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13
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Abstract
Liquid chromatography coupled with mass spectrometry (LC-MS) has been widely used for profiling protein expression levels. This chapter is focused on LC-MS data preprocessing, which is a crucial step in the analysis of LC-MS based proteomics. We provide a high-level overview, highlight associated challenges, and present a step-by-step example for analysis of data from LC-MS based untargeted proteomic study. Furthermore, key procedures and relevant issues with the subsequent analysis by multiple reaction monitoring (MRM) are discussed.
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Affiliation(s)
- Tsung-Heng Tsai
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, 20057, USA.
- Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA, 22203, USA.
| | - Minkun Wang
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, 20057, USA
- Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA, 22203, USA
| | - Habtom W Ressom
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, 20057, USA
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14
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Zhao Y, Brasier AR. Qualification and Verification of Protein Biomarker Candidates. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2016; 919:493-514. [DOI: 10.1007/978-3-319-41448-5_23] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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15
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Hu LZ, Zhang WP, Zhou MT, Han QQ, Gao XL, Zeng HL, Guo L. Analysis of Salmonella PhoP/PhoQ regulation by dimethyl-SRM-based quantitative proteomics. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2015; 1864:20-8. [PMID: 26472331 DOI: 10.1016/j.bbapap.2015.10.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2015] [Revised: 09/29/2015] [Accepted: 10/09/2015] [Indexed: 02/01/2023]
Abstract
SRM (selected reaction monitoring), a tandem mass spectrometry-based method characterized by high repeatability and accuracy, is an effective tool for the quantification of predetermined proteins. In this study, we built a time-scheduled dimethyl-SRM method that can provide the precise relative quantification of 92 proteins in one run. By applying this method to the Salmonella PhoP/PhoQ two-component system, we found that the expression of selected PhoP/PhoQ-activated proteins in response to Mg(2+) concentrations could be divided into two distinct patterns. For the time-course SRM experiment, we found that the dynamics of the selected PhoP/PhoQ-activated proteins could be divided into three distinct patterns, providing a new clue regarding PhoP/PhoQ activation and regulation. Moreover, the results for iron homeostasis proteins in response to Mg(2+) concentrations revealed that the PhoP/PhoQ two-component system may serve as a repressor for iron uptake proteins. And ribosomal protein levels clearly showed a response to different Mg(2+) concentrations and to time.
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Affiliation(s)
- Li-Zhi Hu
- State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan, China
| | - Wei-Ping Zhang
- State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan, China
| | - Mao-Tian Zhou
- State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan, China
| | - Qiang-Qiang Han
- State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan, China
| | - Xiao-Li Gao
- State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan, China
| | - Hao-Long Zeng
- State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan, China
| | - Lin Guo
- State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan, China.
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16
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Mohammed Y, Borchers CH. An extensive library of surrogate peptides for all human proteins. J Proteomics 2015; 129:93-97. [PMID: 26232110 DOI: 10.1016/j.jprot.2015.07.025] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2015] [Revised: 07/22/2015] [Accepted: 07/24/2015] [Indexed: 02/02/2023]
Abstract
Selecting the most appropriate surrogate peptides to represent a target protein is a major component of experimental design in Multiple Reaction Monitoring (MRM). Our software PeptidePicker with its v-score remains distinctive in its approach of integrating information about the proteins, their tryptic peptides, and the suitability of these peptides for MRM that is available online in UniProtKB, NCBI's dbSNP, ExPASy, PeptideAtlas, PRIDE, and GPMDB. The scoring algorithm reflects our "best knowledge" for selecting candidate peptides for MRM, based on the uniqueness of the peptide in the targeted proteome, its physiochemical properties, and whether it has previously been observed. Here we present an updated approach where we have already compiled a list of all possible surrogate peptides of the human proteome. Using our stringent selection criteria, the list includes 165k suitable MRM peptides covering 17k proteins of the human reviewed proteins in UniProtKB. Compared to average of 2-4min per protein for retrieving and integrating the information, the precompiled list includes all peptides available instantly. This allows a more cohesive and faster design of a multiplexed MRM experiment and provides insights into evidence for a protein's existence. We will keep this list up-to-date as proteomics data repositories continue to grow. This article is part of a Special Issue entitled: Computational Proteomics.
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Affiliation(s)
- Yassene Mohammed
- University of Victoria-Genome British Columbia Proteomics Centre, University of Victoria, Victoria, BC V8Z 7X8, Canada; Center for Proteomics and Metabolomics, Leiden University Medical Center, The Netherlands
| | - Christoph H Borchers
- University of Victoria-Genome British Columbia Proteomics Centre, University of Victoria, Victoria, BC V8Z 7X8, Canada; Department of Biochemistry & Microbiology, University of Victoria, Victoria, BC V8P 5C2, Canada.
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17
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Horvatovich P, Lundberg EK, Chen YJ, Sung TY, He F, Nice EC, Goode RJ, Yu S, Ranganathan S, Baker MS, Domont GB, Velasquez E, Li D, Liu S, Wang Q, He QY, Menon R, Guan Y, Corrales FJ, Segura V, Casal JI, Pascual-Montano A, Albar JP, Fuentes M, Gonzalez-Gonzalez M, Diez P, Ibarrola N, Degano RM, Mohammed Y, Borchers CH, Urbani A, Soggiu A, Yamamoto T, Salekdeh GH, Archakov A, Ponomarenko E, Lisitsa A, Lichti CF, Mostovenko E, Kroes RA, Rezeli M, Végvári Á, Fehniger TE, Bischoff R, Vizcaíno JA, Deutsch EW, Lane L, Nilsson CL, Marko-Varga G, Omenn GS, Jeong SK, Lim JS, Paik YK, Hancock WS. Quest for Missing Proteins: Update 2015 on Chromosome-Centric Human Proteome Project. J Proteome Res 2015; 14:3415-31. [DOI: 10.1021/pr5013009] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Péter Horvatovich
- Analytical
Biochemistry, Department of Pharmacy, University of Groningen, A. Deusinglaan
1, 9713 AV Groningen, The Netherlands
| | - Emma K. Lundberg
- Science
for Life Laboratory, KTH - Royal Institute of Technology, SE-171 21 Stockholm, Sweden
| | - Yu-Ju Chen
- Institute
of Chemistry, Academia Sinica, 128 Academia Road Sec. 2, Taipei 115, Taiwan
| | - Ting-Yi Sung
- Institute
of Information Science, Academia Sinica, 128 Academia Road Sec. 2, Taipei 115, Taiwan
| | - Fuchu He
- The State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, No. 27 Taiping Road, Haidian District, Beijing 100850, China
| | - Edouard C. Nice
- Department
of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria 3800, Australia
| | - Robert J. Goode
- Department
of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria 3800, Australia
| | - Simon Yu
- Department
of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria 3800, Australia
| | - Shoba Ranganathan
- Department
of Chemistry and Biomolecular Sciences and ARC Centre of Excellence
in Bioinformatics, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Mark S. Baker
- Australian
School of Advanced Medicine, Macquarie University, Sydney, NSW 2109, Australia
| | - Gilberto B. Domont
- Proteomics Unit, Institute of Chemistry, Federal University of Rio de Janeiro, Cidade Universitária, Av Athos da Silveira Ramos 149, CT-A542, 21941-909 Rio de Janeriro, Rj, Brazil
| | - Erika Velasquez
- Proteomics Unit, Institute of Chemistry, Federal University of Rio de Janeiro, Cidade Universitária, Av Athos da Silveira Ramos 149, CT-A542, 21941-909 Rio de Janeriro, Rj, Brazil
| | - Dong Li
- The State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, No. 27 Taiping Road, Haidian District, Beijing 100850, China
| | - Siqi Liu
- Beijing Institute of Genomics and BGI Shenzhen, No. 1 Beichen West Road, Chaoyang District, Beijing 100101, China
- BGI Shenzhen, Beishan Road, Yantian District, Shenzhen, 518083, China
| | - Quanhui Wang
- Beijing Institute of Genomics and BGI Shenzhen, No. 1 Beichen West Road, Chaoyang District, Beijing 100101, China
| | - Qing-Yu He
- Key Laboratory of Functional Protein
Research of Guangdong
Higher Education Institutes, College of Life Science and Technology, Jinan University, Guangzhou 510632, China
| | - Rajasree Menon
- Department of Computational Medicine & Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, Michigan 48109-2218, United States
| | - Yuanfang Guan
- Departments of Computational Medicine & Bioinformatics and Computer Sciences, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, Michigan 48109-2218, United States
| | - Fernando J. Corrales
- ProteoRed-ISCIII,
Biomolecular and Bioinformatics Resources Platform (PRB2), Spanish
Consortium of C-HPP (Chr-16), CIMA, University of Navarra, 31008 Pamplona, Spain
- Chr16 SpHPP Consortium, CIMA, University of Navarra, 31008 Pamplona, Spain
| | - Victor Segura
- ProteoRed-ISCIII,
Biomolecular and Bioinformatics Resources Platform (PRB2), Spanish
Consortium of C-HPP (Chr-16), CIMA, University of Navarra, 31008 Pamplona, Spain
- Chr16 SpHPP Consortium, CIMA, University of Navarra, 31008 Pamplona, Spain
| | - J. Ignacio Casal
- Department
of Cellular and Molecular Medicine, Centro de Investigaciones Biológicas (CIB-CSIC), 28040 Madrid, Spain
| | | | - Juan P. Albar
- Centro Nacional de Biotecnologia (CNB-CSIC), Cantoblanco, 28049 Madrid, Spain
| | - Manuel Fuentes
- Cancer
Research Center. Proteomics Unit and General Service of Cytometry,
Department of Medicine, University of Salmanca-CSIC, IBSAL, Campus Miguel de Unamuno
s/n, 37007 Salamanca, Spain
| | - Maria Gonzalez-Gonzalez
- Cancer
Research Center. Proteomics Unit and General Service of Cytometry,
Department of Medicine, University of Salmanca-CSIC, IBSAL, Campus Miguel de Unamuno
s/n, 37007 Salamanca, Spain
| | - Paula Diez
- Cancer
Research Center. Proteomics Unit and General Service of Cytometry,
Department of Medicine, University of Salmanca-CSIC, IBSAL, Campus Miguel de Unamuno
s/n, 37007 Salamanca, Spain
| | - Nieves Ibarrola
- Cancer
Research Center. Proteomics Unit and General Service of Cytometry,
Department of Medicine, University of Salmanca-CSIC, IBSAL, Campus Miguel de Unamuno
s/n, 37007 Salamanca, Spain
| | - Rosa M. Degano
- Cancer
Research Center. Proteomics Unit and General Service of Cytometry,
Department of Medicine, University of Salmanca-CSIC, IBSAL, Campus Miguel de Unamuno
s/n, 37007 Salamanca, Spain
| | - Yassene Mohammed
- University of Victoria-Genome British Columbia Proteomics
Centre, Vancouver Island
Technology Park, #3101−4464 Markham Street, Victoria, British Columbia V8Z 7X8, Canada
- Center
for Proteomics and Metabolomics, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Christoph H. Borchers
- University of Victoria-Genome British Columbia Proteomics
Centre, Vancouver Island
Technology Park, #3101−4464 Markham Street, Victoria, British Columbia V8Z 7X8, Canada
| | - Andrea Urbani
- Proteomics
and Metabonomic, Laboratory, Fondazione Santa Lucia, Rome, Italy
- Department
of Experimental Medicine and Surgery, University of Rome “Tor Vergata”, Rome, Italy
| | - Alessio Soggiu
- Department
of Veterinary Science and Public Health (DIVET), University of Milano, via Celoria 10, 20133 Milano, Italy
| | - Tadashi Yamamoto
- Institute
of Nephrology, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan
| | - Ghasem Hosseini Salekdeh
- Department of Molecular Systems Biology at Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
- Department of Systems Biology, Agricultural Biotechnology Research Institute of Iran, Karaj, Iran
| | | | | | - Andrey Lisitsa
- Orechovich Institute of Biomedical Chemistry, Moscow, Russia
| | - Cheryl F. Lichti
- Department
of Pharmacology and Toxicology, The University of Texas Medical Branch, Galveston, Texas 77555-0617, United States
| | - Ekaterina Mostovenko
- Department
of Pharmacology and Toxicology, The University of Texas Medical Branch, Galveston, Texas 77555-0617, United States
| | - Roger A. Kroes
- Falk Center for Molecular Therapeutics, Department of Biomedical Engineering, Northwestern University, 1801 Maple Ave., Suite 4300, Evanston, Illinois 60201, United States
| | - Melinda Rezeli
- Clinical Protein Science & Imaging, Department of Biomedical Engineering, Lund University, BMC D13, 221 84 Lund, Sweden
| | - Ákos Végvári
- Clinical Protein Science & Imaging, Department of Biomedical Engineering, Lund University, BMC D13, 221 84 Lund, Sweden
| | - Thomas E. Fehniger
- Clinical Protein Science & Imaging, Department of Biomedical Engineering, Lund University, BMC D13, 221 84 Lund, Sweden
| | - Rainer Bischoff
- Analytical
Biochemistry, Department of Pharmacy, University of Groningen, A. Deusinglaan
1, 9713 AV Groningen, The Netherlands
| | - Juan Antonio Vizcaíno
- European Molecular
Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, CB10 1SD, Hinxton, Cambridge, United Kingdom
| | - Eric W. Deutsch
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, Washington 98109, United States
| | - Lydie Lane
- SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
- Department
of Human Protein Science, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Carol L. Nilsson
- Department
of Pharmacology and Toxicology, The University of Texas Medical Branch, Galveston, Texas 77555-0617, United States
| | - György Marko-Varga
- Clinical Protein Science & Imaging, Department of Biomedical Engineering, Lund University, BMC D13, 221 84 Lund, Sweden
| | - Gilbert S. Omenn
- Departments of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics and School of Public Health, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, Michigan 48109-2218, United States
| | - Seul-Ki Jeong
- Departments of Integrated Omics for Biomedical Science & Biochemistry, College of Life Science and Technology, Yonsei Proteome Research Center, Yonsei University, Seoul, 120-749, Korea
| | - Jong-Sun Lim
- Departments of Integrated Omics for Biomedical Science & Biochemistry, College of Life Science and Technology, Yonsei Proteome Research Center, Yonsei University, Seoul, 120-749, Korea
| | - Young-Ki Paik
- Departments of Integrated Omics for Biomedical Science & Biochemistry, College of Life Science and Technology, Yonsei Proteome Research Center, Yonsei University, Seoul, 120-749, Korea
| | - William S. Hancock
- The
Barnett Institute of Chemical and Biological Analysis, Northeastern University, 140 The Fenway, Boston, Massachusetts 02115, United States
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18
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Reumer A, Maes E, Mertens I, Cho WCS, Landuyt B, Valkenborg D, Schoofs L, Baggerman G. Colorectal cancer biomarker discovery and validation using LC-MS/MS-based proteomics in blood: truth or dare? Expert Rev Proteomics 2014; 11:449-463. [PMID: 24702250 DOI: 10.1586/14789450.2014.905743] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Globally, colorectal cancer (CRC) is the third most common malignant neoplasm. However, highly sensitive, specific, noninvasive tests that allow CRC diagnosis at an early stage are still needed. As circulatory blood reflects the physiological status of an individual and/or the disease status for several disorders, efforts have been undertaken to identify candidate diagnostic CRC markers in plasma and serum. In this review, the challenges, bottlenecks and promising properties of mass spectrometry (MS)-based proteomics in blood are discussed. More specifically, important aspects in clinical design, sample retrieval, sample preparation, and MS analysis are presented. The recent developments in targeted MS approaches in plasma or serum are highlighted as well.
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Affiliation(s)
- Ank Reumer
- KU Leuven, Animal Physiology and Neurobiology Section, Naamsestraat 59, BE-3000 Leuven, Belgium
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19
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Mohammed Y, Domański D, Jackson AM, Smith DS, Deelder AM, Palmblad M, Borchers CH. PeptidePicker: A scientific workflow with web interface for selecting appropriate peptides for targeted proteomics experiments. J Proteomics 2014; 106:151-61. [DOI: 10.1016/j.jprot.2014.04.018] [Citation(s) in RCA: 76] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2014] [Revised: 04/08/2014] [Accepted: 04/10/2014] [Indexed: 01/08/2023]
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20
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Perez-Riverol Y, Wang R, Hermjakob H, Müller M, Vesada V, Vizcaíno JA. Open source libraries and frameworks for mass spectrometry based proteomics: a developer's perspective. BIOCHIMICA ET BIOPHYSICA ACTA 2014; 1844:63-76. [PMID: 23467006 PMCID: PMC3898926 DOI: 10.1016/j.bbapap.2013.02.032] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2012] [Revised: 02/05/2013] [Accepted: 02/22/2013] [Indexed: 12/23/2022]
Abstract
Data processing, management and visualization are central and critical components of a state of the art high-throughput mass spectrometry (MS)-based proteomics experiment, and are often some of the most time-consuming steps, especially for labs without much bioinformatics support. The growing interest in the field of proteomics has triggered an increase in the development of new software libraries, including freely available and open-source software. From database search analysis to post-processing of the identification results, even though the objectives of these libraries and packages can vary significantly, they usually share a number of features. Common use cases include the handling of protein and peptide sequences, the parsing of results from various proteomics search engines output files, and the visualization of MS-related information (including mass spectra and chromatograms). In this review, we provide an overview of the existing software libraries, open-source frameworks and also, we give information on some of the freely available applications which make use of them. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.
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Affiliation(s)
- Yasset Perez-Riverol
- EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
- Department of Proteomics, Center for Genetic Engineering and Biotechnology, Ciudad de la Habana, Cuba
| | - Rui Wang
- EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Henning Hermjakob
- EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Markus Müller
- Proteome Informatics Group, Swiss Institute of Bioinformatics, CMU - 1, rue Michel Servet CH-1211 Geneva, Switzerland
| | - Vladimir Vesada
- Department of Proteomics, Center for Genetic Engineering and Biotechnology, Ciudad de la Habana, Cuba
| | - Juan Antonio Vizcaíno
- EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
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21
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Archakov AI. Chromosomocentric approach to overcoming difficulties in implementation of international project Human Proteome. UKRAINIAN BIOCHEMICAL JOURNAL 2013. [DOI: 10.15407/ubj85.06.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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22
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Ponomarenko EA, Kopylov AT, Lisitsa AV, Radko SP, Kiseleva YY, Kurbatov LK, Ptitsyn KG, Tikhonova OV, Moisa AA, Novikova SE, Poverennaya EV, Ilgisonis EV, Filimonov AD, Bogolubova NA, Averchuk VV, Karalkin PA, Vakhrushev IV, Yarygin KN, Moshkovskii SA, Zgoda VG, Sokolov AS, Mazur AM, Prokhortchouck EB, Skryabin KG, Ilina EN, Kostrjukova ES, Alexeev DG, Tyakht AV, Gorbachev AY, Govorun VM, Archakov AI. Chromosome 18 transcriptoproteome of liver tissue and HepG2 cells and targeted proteome mapping in depleted plasma: update 2013. J Proteome Res 2013; 13:183-90. [PMID: 24328317 DOI: 10.1021/pr400883x] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
We report the results obtained in 2012-2013 by the Russian Consortium for the Chromosome-centric Human Proteome Project (C-HPP). The main scope of this work was the transcriptome profiling of genes on human chromosome 18 (Chr 18), as well as their encoded proteome, from three types of biomaterials: liver tissue, the hepatocellular carcinoma-derived cell line HepG2, and blood plasma. The transcriptome profiling for liver tissue was independently performed using two RNaseq platforms (SOLiD and Illumina) and also by droplet digital PCR (ddPCR) and quantitative RT-PCR. The proteome profiling of Chr 18 was accomplished by quantitatively measuring protein copy numbers in the three types of biomaterial (the lowest protein concentration measured was 10(-13) M) using selected reaction monitoring (SRM). In total, protein copy numbers were estimated for 228 master proteins, including quantitative data on 164 proteins in plasma, 171 in the HepG2 cell line, and 186 in liver tissue. Most proteins were present in plasma at 10(8) copies/μL, while the median abundance was 10(4) and 10(5) protein copies per cell in HepG2 cells and liver tissue, respectively. In summary, for liver tissue and HepG2 cells a "transcriptoproteome" was produced that reflects the relationship between transcript and protein copy numbers of the genes on Chr 18. The quantitative data acquired by RNaseq, PCR, and SRM were uploaded into the "Update_2013" data set of our knowledgebase (www.kb18.ru) and investigated for linear correlations.
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Affiliation(s)
- Elena A Ponomarenko
- Orekhovich Institute of Biomedical Chemistry of the Russian Academy of Medical Sciences , 10 Pogodinskaya Street, Moscow 119121, Russia
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23
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Bruce C, Stone K, Gulcicek E, Williams K. Proteomics and the analysis of proteomic data: 2013 overview of current protein-profiling technologies. ACTA ACUST UNITED AC 2013; Chapter 13:13.21.1-13.21.17. [PMID: 23504934 DOI: 10.1002/0471250953.bi1321s41] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Mass spectrometry has become a major tool in the study of proteomes. The analysis of proteolytic peptides and their fragment ions by this technique enables the identification and quantitation of the precursor proteins in a mixture. However, deducing chemical structures and then protein sequences from mass-to-charge ratios is a challenging computational task. Software tools incorporating powerful algorithms and statistical methods improved our ability to process the large quantities of proteomics data. Repositories of spectral data make both data analysis and experimental design more efficient. New approaches in quantitative and statistical proteomics make possible a greater coverage of the proteome, the identification of more post-translational modifications, and a greater sensitivity in the quantitation of targeted proteins.
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Affiliation(s)
- Can Bruce
- W.M. Keck Foundation Biotechnology Resource Laboratory and Molecular Biochemistry and Biophysics Department, Yale University, New Haven, Connecticut, USA
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24
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Archakov A, Zgoda V, Kopylov A, Naryzhny S, Chernobrovkin A, Ponomarenko E, Lisitsa A. Chromosome-centric approach to overcoming bottlenecks in the Human Proteome Project. Expert Rev Proteomics 2013; 9:667-76. [PMID: 23256676 DOI: 10.1586/epr.12.54] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
The international Human Proteome Project (HPP), a logical continuation of the Human Genome Project, was launched on 23 September 2010 in Sydney, Australia. In accordance with the gene-centric approach, the goals of the HPP are to prepare an inventory of all human proteins and decipher the network of cellular protein interactions. The greater complexity of the proteome in comparison to the genome gives rise to three bottlenecks in the implementation of the HPP. The main bottleneck is the insufficient sensitivity of proteomic technologies, hampering the detection of proteins with low- and ultra-low copy numbers. The second bottleneck is related to poor reproducibility of proteomic methods and the lack of a so-called 'gold' standard. The last bottleneck is the dynamic nature of the proteome: its instability over time. The authors here discuss approaches to overcome these bottlenecks in order to improve the success of the HPP.
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Affiliation(s)
- Alexander Archakov
- Orekhovich Institute of Biomedical Chemistry, Russian Academy of Medical Sciences, 119121, Pogodinskaya Street 10, Moscow, Russia.
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Colangelo CM, Chung L, Bruce C, Cheung KH. Review of software tools for design and analysis of large scale MRM proteomic datasets. Methods 2013; 61:287-98. [PMID: 23702368 DOI: 10.1016/j.ymeth.2013.05.004] [Citation(s) in RCA: 62] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2013] [Revised: 05/06/2013] [Accepted: 05/11/2013] [Indexed: 12/13/2022] Open
Abstract
Selective or Multiple Reaction monitoring (SRM/MRM) is a liquid-chromatography (LC)/tandem-mass spectrometry (MS/MS) method that enables the quantitation of specific proteins in a sample by analyzing precursor ions and the fragment ions of their selected tryptic peptides. Instrumentation software has advanced to the point that thousands of transitions (pairs of primary and secondary m/z values) can be measured in a triple quadrupole instrument coupled to an LC, by a well-designed scheduling and selection of m/z windows. The design of a good MRM assay relies on the availability of peptide spectra from previous discovery-phase LC-MS/MS studies. The tedious aspect of manually developing and processing MRM assays involving thousands of transitions has spurred to development of software tools to automate this process. Software packages have been developed for project management, assay development, assay validation, data export, peak integration, quality assessment, and biostatistical analysis. No single tool provides a complete end-to-end solution, thus this article reviews the current state and discusses future directions of these software tools in order to enable researchers to combine these tools for a comprehensive targeted proteomics workflow.
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Affiliation(s)
- Christopher M Colangelo
- W.M. Keck Foundation Biotechnology Resource Laboratory, School of Medicine, Yale University, New Haven, CT 06510, USA.
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26
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Sandin M, Teleman J, Malmström J, Levander F. Data processing methods and quality control strategies for label-free LC-MS protein quantification. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2013; 1844:29-41. [PMID: 23567904 DOI: 10.1016/j.bbapap.2013.03.026] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2012] [Revised: 01/18/2013] [Accepted: 03/08/2013] [Indexed: 12/20/2022]
Abstract
Protein quantification using different LC-MS techniques is becoming a standard practice. However, with a multitude of experimental setups to choose from, as well as a wide array of software solutions for subsequent data processing, it is non-trivial to select the most appropriate workflow for a given biological question. In this review, we highlight different issues that need to be addressed by software for quantitative LC-MS experiments and describe different approaches that are available. With focus on label-free quantification, examples are discussed both for LC-MS/MS and LC-SRM data processing. We further elaborate on current quality control methodology for performing accurate protein quantification experiments. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.
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Affiliation(s)
- Marianne Sandin
- Department of Immunotechnology, Lund University, BMC D13, 22184 Lund, Sweden
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27
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Abstract
![]()
Quantitative
measurement of proteins is one of the most fundamental analytical
tasks in a biochemistry laboratory, but widely used immunochemical
methods often have limited specificity and high measurement variation.
In this review, we discuss applications of multiple-reaction monitoring
(MRM) mass spectrometry, which allows sensitive, precise quantitative
analyses of peptides and the proteins from which they are derived.
Systematic development of MRM assays is permitted by databases of
peptide mass spectra and sequences, software tools for analysis design
and data analysis, and rapid evolution of tandem mass spectrometer
technology. Key advantages of MRM assays are the ability to target
specific peptide sequences, including variants and modified forms,
and the capacity for multiplexing that allows analysis of dozens to
hundreds of peptides. Different quantitative standardization methods
provide options that balance precision, sensitivity, and assay cost.
Targeted protein quantitation by MRM and related mass spectrometry
methods can advance biochemistry by transforming approaches to protein
measurement.
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Affiliation(s)
- Daniel C Liebler
- Department of Biochemistry and Jim Ayers Institute for Precancer Detection and Diagnosis, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee 37232-6350, United States.
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28
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Abstract
Multiple reaction monitoring (MRM), sometimes called selected reaction monitoring (SRM), is a directed tandem mass spectrometric technique performed on to triple quadrupole mass spectrometers. MRM assays can be used to sensitively and specifically quantify proteins based on peptides that are specific to the target protein. Stable-isotope-labeled standard peptide analogues (SIS peptides) of target peptides are added to enzymatic digests of samples, and quantified along with the native peptides during MRM analysis. Monitoring of the intact peptide and a collision-induced fragment of this peptide (an ion pair) can be used to provide information on the absolute peptide concentration of the peptide in the sample and, by inference, the concentration of the intact protein. This technique provides high specificity by selecting for biophysical parameters that are unique to the target peptides: (1) the molecular weight of the peptide, (2) the generation of a specific fragment from the peptide, and (3) the HPLC retention time during LC/MRM-MS analysis. MRM is a highly sensitive technique that has been shown to be capable of detecting attomole levels of target peptides in complex samples such as tryptic digests of human plasma. This chapter provides a detailed description of how to develop and use an MRM protein assay. It includes sections on the critical "first step" of selecting the target peptides, as well as optimization of MRM acquisition parameters for maximum sensitivity of the ion pairs that will be used in the final method, and characterization of the final MRM assay.
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Van Riper SK, de Jong EP, Carlis JV, Griffin TJ. Mass Spectrometry-Based Proteomics: Basic Principles and Emerging Technologies and Directions. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2013; 990:1-35. [DOI: 10.1007/978-94-007-5896-4_1] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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30
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Zgoda VG, Kopylov AT, Tikhonova OV, Moisa AA, Pyndyk NV, Farafonova TE, Novikova SE, Lisitsa AV, Ponomarenko EA, Poverennaya EV, Radko SP, Khmeleva SA, Kurbatov LK, Filimonov AD, Bogolyubova NA, Ilgisonis EV, Chernobrovkin AL, Ivanov AS, Medvedev AE, Mezentsev YV, Moshkovskii SA, Naryzhny SN, Ilina EN, Kostrjukova ES, Alexeev DG, Tyakht AV, Govorun VM, Archakov AI. Chromosome 18 transcriptome profiling and targeted proteome mapping in depleted plasma, liver tissue and HepG2 cells. J Proteome Res 2012; 12:123-34. [PMID: 23256950 DOI: 10.1021/pr300821n] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The final goal of the Russian part of the Chromosome-centric Human Proteome Project (C-HPP) was established as the analysis of the chromosome 18 (Chr 18) protein complement in plasma, liver tissue and HepG2 cells with the sensitivity of 10(-18) M. Using SRM, we have recently targeted 277 Chr 18 proteins in plasma, liver, and HepG2 cells. On the basis of the results of the survey, the SRM assays were drafted for 250 proteins: 41 proteins were found only in the liver tissue, 82 proteins were specifically detected in depleted plasma, and 127 proteins were mapped in both samples. The targeted analysis of HepG2 cells was carried out for 49 proteins; 41 of them were successfully registered using ordinary SRM and 5 additional proteins were registered using a combination of irreversible binding of proteins on CN-Br Sepharose 4B with SRM. Transcriptome profiling of HepG2 cells performed by RNAseq and RT-PCR has shown a significant correlation (r = 0.78) for 42 gene transcripts. A pilot affinity-based interactome analysis was performed for cytochrome b5 using analytical and preparative optical biosensor fishing followed by MS analysis of the fished proteins. All of the data on the proteome complement of the Chr 18 have been integrated into our gene-centric knowledgebase ( www.kb18.ru ).
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Affiliation(s)
- Victor G Zgoda
- Orekhovich Institute of Biomedical Chemistry of the Russian Academy of Medical Sciences, Russia
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31
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Methods and Progress of Mass Spectrometry-based Selected Reaction Monitoring*. PROG BIOCHEM BIOPHYS 2012. [DOI: 10.3724/sp.j.1206.2012.00009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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32
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Label-free quantitative proteomics trends for protein-protein interactions. J Proteomics 2012; 81:91-101. [PMID: 23153790 DOI: 10.1016/j.jprot.2012.10.027] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2012] [Revised: 10/24/2012] [Accepted: 10/31/2012] [Indexed: 12/14/2022]
Abstract
Understanding protein interactions within the complexity of a living cell is challenging, but techniques coupling affinity purification and mass spectrometry have enabled important progress to be made in the past 15 years. As identification of protein-protein interactions is becoming easier, the quantification of the interaction dynamics is the next frontier. Several quantitative mass spectrometric approaches have been developed to address this issue that vary in their strengths and weaknesses. While isotopic labeling approaches continue to contribute to the identification of regulated interactions, techniques that do not require labeling are becoming increasingly used in the field. Here, we describe the major types of label-free quantification used in interaction proteomics, and discuss the relative merits of data dependent and data independent acquisition approaches in label-free quantification. This article is part of a Special Issue entitled: From protein structures to clinical applications.
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33
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Cohen Freue GV, Borchers CH. Multiple reaction monitoring (MRM): principles and application to coronary artery disease. ACTA ACUST UNITED AC 2012; 5:378. [PMID: 22715283 DOI: 10.1161/circgenetics.111.959528] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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34
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Picotti P, Aebersold R. Selected reaction monitoring-based proteomics: workflows, potential, pitfalls and future directions. Nat Methods 2012; 9:555-66. [PMID: 22669653 DOI: 10.1038/nmeth.2015] [Citation(s) in RCA: 935] [Impact Index Per Article: 77.9] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Selected reaction monitoring (SRM) is a targeted mass spectrometry technique that is emerging in the field of proteomics as a complement to untargeted shotgun methods. SRM is particularly useful when predetermined sets of proteins, such as those constituting cellular networks or sets of candidate biomarkers, need to be measured across multiple samples in a consistent, reproducible and quantitatively precise manner. Here we describe how SRM is applied in proteomics, review recent advances, present selected applications and provide a perspective on the future of this powerful technology.
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Affiliation(s)
- Paola Picotti
- Department of Biology, Institute of Biochemistry, ETH Zurich, Switzerland.
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35
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Röst H, Malmström L, Aebersold R. A computational tool to detect and avoid redundancy in selected reaction monitoring. Mol Cell Proteomics 2012; 11:540-9. [PMID: 22535207 DOI: 10.1074/mcp.m111.013045] [Citation(s) in RCA: 72] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Selected reaction monitoring (SRM), also called multiple reaction monitoring, has become an invaluable tool for targeted quantitative proteomic analyses, but its application can be compromised by nonoptimal selection of transitions. In particular, complex backgrounds may cause ambiguities in SRM measurement results because peptides with interfering transitions similar to those of the target peptide may be present in the sample. Here, we developed a computer program, the SRMCollider, that calculates nonredundant theoretical SRM assays, also known as unique ion signatures (UIS), for a given proteomic background. We show theoretically that UIS of three transitions suffice to conclusively identify 90% of all yeast peptides and 85% of all human peptides. Using predicted retention times, the SRMCollider also simulates time-scheduled SRM acquisition, which reduces the number of interferences to consider and leads to fewer transitions necessary to construct an assay. By integrating experimental fragment ion intensities from large scale proteome synthesis efforts (SRMAtlas) with the information content-based UIS, we combine two orthogonal approaches to create high quality SRM assays ready to be deployed. We provide a user friendly, open source implementation of an algorithm to calculate UIS of any order that can be accessed online at http://www.srmcollider.org to find interfering transitions. Finally, our tool can also simulate the specificity of novel data-independent MS acquisition methods in Q1-Q3 space. This allows us to predict parameters for these methods that deliver a specificity comparable with that of SRM. Using SRM interference information in addition to other sources of information can increase the confidence in an SRM measurement. We expect that the consideration of information content will become a standard step in SRM assay design and analysis, facilitated by the SRMCollider.
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Affiliation(s)
- Hannes Röst
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich CH 8093, Switzerland
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36
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Rauh M. LC–MS/MS for protein and peptide quantification in clinical chemistry. J Chromatogr B Analyt Technol Biomed Life Sci 2012; 883-884:59-67. [DOI: 10.1016/j.jchromb.2011.09.030] [Citation(s) in RCA: 82] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2011] [Revised: 09/16/2011] [Accepted: 09/19/2011] [Indexed: 10/17/2022]
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37
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SHENG QUANHU, WU CHAOCHAO, SU ZHIDUAN, ZENG RONG. SRMBUILDER: A USER-FRIENDLY TOOL FOR SELECTED REACTION MONITORING DATA ANALYSIS. J Bioinform Comput Biol 2012; 9 Suppl 1:51-62. [DOI: 10.1142/s0219720011005756] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2011] [Revised: 09/05/2011] [Accepted: 09/15/2011] [Indexed: 11/18/2022]
Abstract
With high sensitivity and reproducibility, selected reaction monitoring (SRM) has become increasingly popular in proteome research for targeted quantification of low abundance proteins and post translational modification. SRM is also well accepted in other mass-spectrometry based research areas such as lipidomics and metabolomics, which necessitates the development of easy-to-use software for both post-acquisition SRM data analysis and quantification result validation. Here, we introduce a software tool SRMBuilder, which can automatically parse SRM data in multiple file formats, assign transitions to compounds, match light/heavy transition/compound pairs and provide a user-friendly graphic interface to manually validate the quantification result at transition/compound/sample level. SRMBuilder will greatly facilitate processing of the post-acquisition data files and validation of quantification result for SRM. The software can be downloaded for free from as part of the software suite ProteomicsTools.
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Affiliation(s)
- QUANHU SHENG
- Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - CHAOCHAO WU
- Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - ZHIDUAN SU
- Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - RONG ZENG
- Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
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38
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Abstract
A crucial part of optimization of metabolically engineered organisms is producing balanced levels of pathway proteins. Typically, protein levels are monitored by Western blot analysis; however, application to multiple enzyme pathways can be difficult without unique antibodies for each enzyme in the pathway. Furthermore, it can be time consuming, and cost prohibitive during exploratory stages of pathway design when many different proteins must be monitored simultaneously. We present here a targeted proteomics approach that uses selected-reaction monitoring (SRM) mass spectrometry to quantify multiple proteins in a sample. SRM methods provide high selectivity and high sensitivity to enable rapid quantification of multiple proteins in an engineered pathway regardless of sequence or organism of origin.
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Affiliation(s)
- Tanveer S Batth
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Joint BioEnergy Institute (JBEI), Berkeley, CA, USA
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39
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Krisp C, Randall SA, McKay MJ, Molloy MP. Towards clinical applications of selected reaction monitoring for plasma protein biomarker studies. Proteomics Clin Appl 2011; 6:42-59. [PMID: 22213646 DOI: 10.1002/prca.201100062] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2011] [Revised: 10/21/2011] [Accepted: 10/25/2011] [Indexed: 01/13/2023]
Abstract
The widespread clinical adoption of protein biomarkers with diagnostic, prognostic and/or predictive value remains a formidable challenge for the biomedical community. From discovery to validation, the path to biomarkers of clinical relevance abounds with many protein candidates, yet so few concrete examples have been substantiated. In this review, we focus on the recent adoption of selected reaction monitoring (SRM) of plasma proteins in the path to clinical use for a broad range of diseases including cancer, cardiovascular disease, genetic disorders and various metabolic disorders. Recent progress reveals a promising outlook for clinical applications using SRM, which now provides the routine analysis of clinically relevant protein markers at low nanogram per millilitre in plasma.
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Affiliation(s)
- Christoph Krisp
- Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, Australia
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40
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Austin RJ, Smidansky HM, Holstein CA, Chang DK, Epp A, Josephson NC, Martin DB. Proteomic analysis of the androgen receptor via MS-compatible purification of biotinylated protein on streptavidin resin. Proteomics 2011; 12:43-53. [PMID: 22116683 DOI: 10.1002/pmic.201100348] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2011] [Revised: 09/19/2011] [Accepted: 10/25/2011] [Indexed: 11/09/2022]
Abstract
The strength of the streptavidin/biotin interaction poses challenges for the recovery of biotinylated molecules from streptavidin resins. As an alternative to high-temperature elution in urea-containing buffers, we show that mono-biotinylated proteins can be released with relatively gentle heating in the presence of biotin and 2% SDS/Rapigest, avoiding protein carbamylation and minimizing streptavidin dissociation. We demonstrate the utility of this mild elution strategy in two studies of the human androgen receptor (AR). In the first, in which formaldehyde cross-linked complexes are analyzed in yeast, a mass spectrometry-based comparison of the AR complex using SILAC reveals an association between the androgen-activated AR and the Hsp90 chaperonin, while Hsp70 chaperonins associate specifically with the unliganded complex. In the second study, the endogenous AR is quantified in the LNCaP cell line by absolute SILAC and MRM-MS showing approximately 127,000 AR copies per cell, substantially more than previously measured using radioligand binding.
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Affiliation(s)
- Ryan J Austin
- Institute for Systems Biology, Seattle, WA 98109, USA
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41
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Lam H. Building and searching tandem mass spectral libraries for peptide identification. Mol Cell Proteomics 2011; 10:R111.008565. [PMID: 21900153 DOI: 10.1074/mcp.r111.008565] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Spectral library searching is an emerging approach in peptide identifications from tandem mass spectra, a critical step in proteomic data analysis. Conceptually, the premise of this approach is that the tandem MS fragmentation pattern of a peptide under some fixed conditions is a reproducible fingerprint of that peptide, such that unknown spectra acquired under the same conditions can be identified by spectral matching. In actual practice, a spectral library is first meticulously compiled from a large collection of previously observed and identified tandem MS spectra, usually obtained from shotgun proteomics experiments of complex mixtures. Then, a query spectrum is then identified by spectral matching using recently developed spectral search engines. This review discusses the basic principles of the two pillars of this approach: spectral library construction, and spectral library searching. An overview of the software tools available for these two tasks, as well as a high-level description of the underlying algorithms, will be given. Finally, several new methods that utilize spectral libraries for peptide identification in ways other than straightforward spectral matching will also be described.
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Affiliation(s)
- Henry Lam
- Department of Chemical and Biomolecular Engineering, the Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
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42
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Calvo E, Camafeita E, Fernández-Gutiérrez B, López JA. Applying selected reaction monitoring to targeted proteomics. Expert Rev Proteomics 2011; 8:165-73. [PMID: 21501010 DOI: 10.1586/epr.11.11] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Selected reaction monitoring (SRM) is a highly selective and sensitive mass spectrometric methodology for precise and accurate quantification of low-abundant proteins in complex mixtures and for characterization of modified peptides, and constitutes the method of choice in targeted proteomics. Owing to its outstanding features, SRM arises as an alternative to antibody-based assays for discovery and validation of clinically relevant biomarkers, a topic that is tackled in this article. Several of the obstacles encountered in SRM experiments, mainly those derived from shared physicochemical properties of peptides (e.g., mass, charge and chromatographic retention time), can compromise selectivity and quantitation. We illustrate how to circumvent these limitations on the basis of using time-scheduled chromatographic approaches and choosing appropriate spectrometric conditions, including the careful selection of the precursor and diagnostic ions.
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Affiliation(s)
- Enrique Calvo
- Unidad de Proteómica, Centro Nacional de Investigaciones Cardiovasculares, CNIC, Melchor Fernández Almagro 3, E-28029 Madrid, Spain
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43
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Biomarker validation in blood specimens by selected reaction monitoring mass spectrometry of N-glycosites. Methods Mol Biol 2011; 728:179-94. [PMID: 21468948 DOI: 10.1007/978-1-61779-068-3_11] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Targeted mass spectrometry using selected reaction monitoring (SRM) has emerged as the method of choice for the validation in blood serum, plasma, or other clinically relevant specimens of biomarker candidates arising from comparative proteomics or other discovery strategies. Here, we describe a method in which N-glycosites are selectively enriched from biological specimens by solid phase capture and PNGase F release, and then analyzed by SRM. Focusing the highly sensitive targeted mass spectrometry method on a subproteome enriched for secreted and shed proteins reproducibly identifies and quantifies such proteins in serum and plasma at the low nanogram per milliliter (ng/mL) concentration range. This protocol is intended to give an introduction to SRM-based targeted mass spectrometry with a special focus on the validation of biomarker candidates.
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44
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Proteomics by mass spectrometry—Go big or go home? J Pharm Biomed Anal 2011; 55:832-41. [DOI: 10.1016/j.jpba.2011.02.012] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2010] [Revised: 02/03/2011] [Accepted: 02/10/2011] [Indexed: 11/20/2022]
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45
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Foster JM, Degroeve S, Gatto L, Visser M, Wang R, Griss J, Apweiler R, Martens L. A posteriori quality control for the curation and reuse of public proteomics data. Proteomics 2011; 11:2182-94. [PMID: 21538885 DOI: 10.1002/pmic.201000602] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2010] [Revised: 02/07/2011] [Accepted: 02/21/2011] [Indexed: 01/26/2023]
Abstract
Proteomics is a rapidly expanding field encompassing a multitude of complex techniques and data types. To date much effort has been devoted to achieving the highest possible coverage of proteomes with the aim to inform future developments in basic biology as well as in clinical settings. As a result, growing amounts of data have been deposited in publicly available proteomics databases. These data are in turn increasingly reused for orthogonal downstream purposes such as data mining and machine learning. These downstream uses however, need ways to a posteriori validate whether a particular data set is suitable for the envisioned purpose. Furthermore, the (semi-)automatic curation of repository data is dependent on analyses that can highlight misannotation and edge conditions for data sets. Such curation is an important prerequisite for efficient proteomics data reuse in the life sciences in general. We therefore present here a selection of quality control metrics and approaches for the a posteriori detection of potential issues encountered in typical proteomics data sets. We illustrate our metrics by relying on publicly available data from the Proteomics Identifications Database (PRIDE), and simultaneously show the usefulness of the large body of PRIDE data as a means to derive empirical background distributions for relevant metrics.
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Affiliation(s)
- Joseph M Foster
- EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
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46
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Ning Z, Zhou H, Wang F, Abu-Farha M, Figeys D. Analytical Aspects of Proteomics: 2009–2010. Anal Chem 2011; 83:4407-26. [DOI: 10.1021/ac200857t] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
| | - Hu Zhou
- Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China 201203
| | - Fangjun Wang
- Key Lab of Separation Sciences for Analytical Chemistry, National Chromatographic Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China 116023
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47
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Elschenbroich S, Ignatchenko V, Clarke B, Kalloger SE, Boutros PC, Gramolini AO, Shaw P, Jurisica I, Kislinger T. In-Depth Proteomics of Ovarian Cancer Ascites: Combining Shotgun Proteomics and Selected Reaction Monitoring Mass Spectrometry. J Proteome Res 2011; 10:2286-99. [DOI: 10.1021/pr1011087] [Citation(s) in RCA: 63] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Sarah Elschenbroich
- Ontario Cancer Institute, University Health Network, Toronto, Canada
- Department of Chemistry and Pharmacy, Friedrich-Alexander University, Erlangen, Germany
| | | | | | - Steve E. Kalloger
- Genetic Pathology Evaluation Centre of the Prostate Research Centre, Department of Pathology, Vancouver General Hospital and British Columbia Cancer Agency, Vancouver, Canada
| | - Paul C. Boutros
- Informatics and Biocomputing Platform, Ontario Institute for Cancer Research, Toronto, Canada
| | | | - Patricia Shaw
- Ontario Cancer Institute, University Health Network, Toronto, Canada
| | - Igor Jurisica
- Ontario Cancer Institute, University Health Network, Toronto, Canada
- Campbell Family Cancer Research Institute, Toronto, Canada
| | - Thomas Kislinger
- Ontario Cancer Institute, University Health Network, Toronto, Canada
- Campbell Family Cancer Research Institute, Toronto, Canada
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48
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Reiter L, Rinner O, Picotti P, Hüttenhain R, Beck M, Brusniak MY, Hengartner MO, Aebersold R. mProphet: automated data processing and statistical validation for large-scale SRM experiments. Nat Methods 2011; 8:430-5. [PMID: 21423193 DOI: 10.1038/nmeth.1584] [Citation(s) in RCA: 375] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2010] [Accepted: 02/11/2011] [Indexed: 11/09/2022]
Abstract
Selected reaction monitoring (SRM) is a targeted mass spectrometric method that is increasingly used in proteomics for the detection and quantification of sets of preselected proteins at high sensitivity, reproducibility and accuracy. Currently, data from SRM measurements are mostly evaluated subjectively by manual inspection on the basis of ad hoc criteria, precluding the consistent analysis of different data sets and an objective assessment of their error rates. Here we present mProphet, a fully automated system that computes accurate error rates for the identification of targeted peptides in SRM data sets and maximizes specificity and sensitivity by combining relevant features in the data into a statistical model.
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49
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Zhang X, Li Y, Shao W, Lam H. Understanding the improved sensitivity of spectral library searching over sequence database searching in proteomics data analysis. Proteomics 2011; 11:1075-85. [PMID: 21298786 DOI: 10.1002/pmic.201000492] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2010] [Revised: 11/05/2010] [Accepted: 11/29/2010] [Indexed: 01/13/2023]
Abstract
Spectral library searching has been recently proposed as an alternative to sequence database searching for peptide identification from MS/MS. We performed a systematic comparison between spectral library searching and sequence database searching using a wide variety of data to better demonstrate, and understand, the superior sensitivity of the former observed in preliminary studies. By decoupling the effect of search space, we demonstrated that the success of spectral library searching is primarily attributable to the use of real library spectra for matching, without which the sensitivity advantage largely disappears. We further determined the extent to which the use of real peak intensities and non-canonical fragments, both under-utilized information in sequence database searching, contributes to the sensitivity advantage. Lastly, we showed that spectral library searching is disproportionately more successful in identifying low-quality spectra, and complex spectra of higher- charged precursors, both important frontiers in peptide sequencing. Our results answered important outstanding questions about this promising yet unproven method using well-controlled computational experiments and sound statistical approaches.
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Affiliation(s)
- Xin Zhang
- Department of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
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50
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Building and searching tandem mass (MS/MS) spectral libraries for peptide identification in proteomics. Methods 2011; 54:424-31. [PMID: 21277371 DOI: 10.1016/j.ymeth.2011.01.007] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2010] [Revised: 01/19/2011] [Accepted: 01/20/2011] [Indexed: 01/03/2023] Open
Abstract
Spectral library searching is an emerging approach in peptide identifications from tandem mass spectra, a critical step in proteomic data analysis. In spectral library searching, a spectral library is first meticulously compiled from a large collection of previously observed peptide MS/MS spectra that are conclusively assigned to their corresponding amino acid sequence. An unknown spectrum is then identified by comparing it to all the candidates in the spectral library for the most similar match. This review discusses the basic principles of spectral library building and searching, describes its advantages and limitations, and provides a primer for researchers interested in adopting this new approach in their data analysis. It will also discuss the future outlook on the evolution and utility of spectral libraries in the field of proteomics.
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