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Searle BC, Chien A, Koller A, Hawke D, Herren AW, Kim Kim J, Lee KA, Leib RD, Nelson AJ, Patel P, Ren JM, Stemmer PM, Zhu Y, Neely BA, Patel B. A Multipathway Phosphopeptide Standard for Rapid Phosphoproteomics Assay Development. Mol Cell Proteomics 2023; 22:100639. [PMID: 37657519 PMCID: PMC10561125 DOI: 10.1016/j.mcpro.2023.100639] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 08/22/2023] [Accepted: 08/24/2023] [Indexed: 09/03/2023] Open
Abstract
Recent advances in methodology have made phosphopeptide analysis a tractable problem for many proteomics researchers. There are now a wide variety of robust and accessible enrichment strategies to generate phosphoproteomes while free or inexpensive software tools for quantitation and site localization have simplified phosphoproteome analysis workflow tremendously. As a research group under the Association for Biomolecular Resource Facilities umbrella, the Proteomics Standards Research Group has worked to develop a multipathway phosphopeptide standard based on a mixture of heavy-labeled phosphopeptides designed to enable researchers to rapidly develop assays. This mixture contains 131 mass spectrometry vetted phosphopeptides specifically chosen to cover as many known biologically interesting phosphosites as possible from seven different signaling networks: AMPK signaling, death and apoptosis signaling, ErbB signaling, insulin/insulin-like growth factor-1 signaling, mTOR signaling, PI3K/AKT signaling, and stress (p38/SAPK/JNK) signaling. Here, we describe a characterization of this mixture spiked into a HeLa tryptic digest stimulated with both epidermal growth factor and insulin-like growth factor-1 to activate the MAPK and PI3K/AKT/mTOR pathways. We further demonstrate a comparison of phosphoproteomic profiling of HeLa performed independently in five labs using this phosphopeptide mixture with data-independent acquisition. Despite different experimental and instrumentation processes, we found that labs could produce reproducible, harmonized datasets by reporting measurements as ratios to the standard, while intensity measurements showed lower consistency between labs even after normalization. Our results suggest that widely available, biologically relevant phosphopeptide standards can act as a quantitative "yardstick" across laboratories and sample preparations enabling experimental designs larger than a single laboratory can perform. Raw data files are publicly available in the MassIVE dataset MSV000090564.
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Affiliation(s)
- Brian C Searle
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, USA; Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio, USA.
| | - Allis Chien
- Mass Spectrometry Center, Stanford University, Stanford, California, USA
| | | | | | - Anthony W Herren
- UC Davis Genome Center, Proteomics Core, University of California Davis, Davis California, USA
| | - Jenny Kim Kim
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, New York, USA
| | - Kimberly A Lee
- Cell Signaling Technology, Inc, Danvers, Massachusetts, USA
| | - Ryan D Leib
- Mass Spectrometry Center, Stanford University, Stanford, California, USA
| | | | - Purvi Patel
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, New York, USA
| | - Jian Min Ren
- Cell Signaling Technology, Inc, Danvers, Massachusetts, USA
| | - Paul M Stemmer
- Department of Pharmaceutical Sciences, Wayne State University, Detroit, Michigan, USA
| | - Yiying Zhu
- Cell Signaling Technology, Inc, Danvers, Massachusetts, USA
| | - Benjamin A Neely
- National Institute of Standards and Technology, Charleston, South Carolina, USA
| | - Bhavin Patel
- Thermo Fisher Scientific, Rockford, Illinois, USA
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2
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Jagannadham MV, Gayatri P, Binny TM, Raman B, Kameshwari DB, Nagaraj R. Mass Spectral Analysis of Synthetic Peptides: Implications in Proteomics. J Biomol Tech 2021; 32:30-35. [PMID: 33953644 PMCID: PMC7704033 DOI: 10.7171/jbt.21-3201-001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Sequence determination of peptides is a crucial step in mass spectrometry-based proteomics. Peptide sequences are determined either by database search or by de novo sequencing using tandem mass spectrometry. Determination of all the theoretical expected peptide fragments and eliminating false discoveries remains a challenge in proteomics. Developing standards for evaluating the performance of mass spectrometers and algorithms used for identification of proteins is important for proteomics studies. The current study is focused on these aspects by using synthetic peptides. A total of 599 peptides were designed from in silico tryptic digest with 1 or 2 missed cleavages from 199 human proteins, and synthetic peptides corresponding to these sequences were obtained. The peptides were mixed together, and analysis was carried out using liquid chromatography-electrospray ionization tandem mass spectrometry on a Q-Exactive HF mass spectrometer. The peptides and proteins were identified with SEQUEST program. The analysis was carried out using the proteomics workflows. A total of 573 peptides representing 196 proteins could be identified, and a spectral library was created for these peptides. Analysis parameters such as "no enzyme selection" gave the maximum number of detected peptides as compared with trypsin in the selection. False discoveries could be identified. This study highlights the limitations of peptide detection and the need for developing powerful algorithms along with tools to evaluate mass spectrometers and algorithms. It also shows the limitations of peptide detection even with high-end mass spectrometers. The mass spectral data are available in ProteomeXchange with accession no. PXD017992.
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Affiliation(s)
| | - Pratap Gayatri
- CSIR-Centre for Cellular and Molecular Biology, Hyderabad 500007, India
| | - Taniya Mary Binny
- CSIR-Centre for Cellular and Molecular Biology, Hyderabad 500007, India
| | - Bathisaran Raman
- CSIR-Centre for Cellular and Molecular Biology, Hyderabad 500007, India
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3
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Jagannadham MV, Gayatri P, Binny TM, Raman B, Kameshwari DB, Nagaraj R. Mass Spectral Analysis of Synthetic Peptides: Implications in Proteomics. J Biomol Tech 2020:jbt.2020-3201-001. [PMID: 33304200 PMCID: PMC7704033 DOI: 10.7171/jbt.2020-3201-001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
Sequence determination of peptides is a crucial step in mass spectrometry-based proteomics. Peptide sequences are determined either by database search or by de novo sequencing using tandem mass spectrometry. Determination of all the theoretical expected peptide fragments and eliminating false discoveries remains a challenge in proteomics. Developing standards for evaluating the performance of mass spectrometers and algorithms used for identification of proteins is important for proteomics studies. The current study is focused on these aspects by using synthetic peptides. A total of 599 peptides were designed from in silico tryptic digest with 1 or 2 missed cleavages from 199 human proteins, and synthetic peptides corresponding to these sequences were obtained. The peptides were mixed together, and analysis was carried out using liquid chromatography-electrospray ionization tandem mass spectrometry on a Q-Exactive HF mass spectrometer. The peptides and proteins were identified with SEQUEST program. The analysis was carried out using the proteomics workflows. A total of 573 peptides representing 196 proteins could be identified, and a spectral library was created for these peptides. Analysis parameters such as "no enzyme selection" gave the maximum number of detected peptides as compared with trypsin in the selection. False discoveries could be identified. This study highlights the limitations of peptide detection and the need for developing powerful algorithms along with tools to evaluate mass spectrometers and algorithms. It also shows the limitations of peptide detection even with high-end mass spectrometers. The mass spectral data are available in ProteomeXchange with accession no. PXD017992.
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Affiliation(s)
| | - Pratap Gayatri
- CSIR-Centre for Cellular and Molecular Biology, Hyderabad 500007,
India
| | - Taniya Mary Binny
- CSIR-Centre for Cellular and Molecular Biology, Hyderabad 500007,
India
| | - Bathisaran Raman
- CSIR-Centre for Cellular and Molecular Biology, Hyderabad 500007,
India
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4
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Mische SM, Fisher NC, Meyn SM, Sol-Church K, Hegstad-Davies RL, Weis-Garcia F, Adams M, Ashton JM, Delventhal KM, Dragon JA, Holmes L, Jagtap P, Kubow KE, Mason CE, Palmblad M, Searle BC, Turck CW, Knudtson KL. A Review of the Scientific Rigor, Reproducibility, and Transparency Studies Conducted by the ABRF Research Groups. J Biomol Tech 2020; 31:11-26. [PMID: 31969795 PMCID: PMC6959150 DOI: 10.7171/jbt.20-3101-003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Shared research resource facilities, also known as core laboratories (Cores), are responsible for generating a significant and growing portion of the research data in academic biomedical research institutions. Cores represent a central repository for institutional knowledge management, with deep expertise in the strengths and limitations of technology and its applications. They inherently support transparency and scientific reproducibility by protecting against cognitive bias in research design and data analysis, and they have institutional responsibility for the conduct of research (research ethics, regulatory compliance, and financial accountability) performed in their Cores. The Association of Biomolecular Resource Facilities (ABRF) is a FASEB-member scientific society whose members are scientists and administrators that manage or support Cores. The ABRF Research Groups (RGs), representing expertise for an array of cutting-edge and established technology platforms, perform multicenter research studies to determine and communicate best practices and community-based standards. This review provides a summary of the contributions of the ABRF RGs to promote scientific rigor and reproducibility in Cores from the published literature, ABRF meetings, and ABRF RGs communications.
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Affiliation(s)
- Sheenah M. Mische
- New York University (NYU) Langone Medical Center, New
York, New York 10016, USA
| | - Nancy C. Fisher
- University of North Carolina at Chapel Hill, Chapel
Hill, North Carolina 27599, USA
| | - Susan M. Meyn
- Vanderbilt University Medical Center, Nashville,
Tennessee 37212, USA
| | - Katia Sol-Church
- University of Virginia School of Medicine,
Charlottesville, Virginia 22908, USA
| | | | | | - Marie Adams
- Van Andel Institute, Grand Rapids, Michigan 49503,
USA
| | - John M. Ashton
- University of Rochester Medical Center, West
Henrietta, New York 14642, USA
| | - Kym M. Delventhal
- Stowers Institute for Medical Research, Kansas City,
Missouri 64110, USA
| | | | - Laura Holmes
- Stowers Institute for Medical Research, Kansas City,
Missouri 64110, USA
| | - Pratik Jagtap
- University of Minnesota, Minneapolis, Minnesota
55455, USA
| | | | | | - Magnus Palmblad
- Leiden University Medical Center, Leiden 2333, The
Netherlands
| | - Brian C. Searle
- Institute for Systems Biology, Seattle, Washington
98109, USA
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5
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Lombard-Banek C, Schiel JE. Mass Spectrometry Advances and Perspectives for the Characterization of Emerging Adoptive Cell Therapies. Molecules 2020; 25:E1396. [PMID: 32204371 PMCID: PMC7144572 DOI: 10.3390/molecules25061396] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 03/06/2020] [Accepted: 03/11/2020] [Indexed: 12/12/2022] Open
Abstract
Adoptive cell therapy is an emerging anti-cancer modality, whereby the patient's own immune cells are engineered to express T-cell receptor (TCR) or chimeric antigen receptor (CAR). CAR-T cell therapies have advanced the furthest, with recent approvals of two treatments by the Food and Drug Administration of Kymriah (trisagenlecleucel) and Yescarta (axicabtagene ciloleucel). Recent developments in proteomic analysis by mass spectrometry (MS) make this technology uniquely suited to enable the comprehensive identification and quantification of the relevant biochemical architecture of CAR-T cell therapies and fulfill current unmet needs for CAR-T product knowledge. These advances include improved sample preparation methods, enhanced separation technologies, and extension of MS-based proteomic to single cells. Innovative technologies such as proteomic analysis of raw material quality attributes (MQA) and final product quality attributes (PQA) may provide insights that could ultimately fuel development strategies and lead to broad implementation.
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Affiliation(s)
- Camille Lombard-Banek
- National Institute of Standards and Technology, Gaithersburg, MD 20899, USA;
- Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA
| | - John E. Schiel
- National Institute of Standards and Technology, Gaithersburg, MD 20899, USA;
- Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA
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Mnatsakanyan R, Shema G, Basik M, Batist G, Borchers CH, Sickmann A, Zahedi RP. Detecting post-translational modification signatures as potential biomarkers in clinical mass spectrometry. Expert Rev Proteomics 2019; 15:515-535. [PMID: 29893147 DOI: 10.1080/14789450.2018.1483340] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
INTRODUCTION Numerous diseases are caused by changes in post-translational modifications (PTMs). Therefore, the number of clinical proteomics studies that include the analysis of PTMs is increasing. Combining complementary information-for example changes in protein abundance, PTM levels, with the genome and transcriptome (proteogenomics)-holds great promise for discovering important drivers and markers of disease, as variations in copy number, expression levels, or mutations without spatial/functional/isoform information is often insufficient or even misleading. Areas covered: We discuss general considerations, requirements, pitfalls, and future perspectives in applying PTM-centric proteomics to clinical samples. This includes samples obtained from a human subject, for instance (i) bodily fluids such as plasma, urine, or cerebrospinal fluid, (ii) primary cells such as reproductive cells, blood cells, and (iii) tissue samples/biopsies. Expert commentary: PTM-centric discovery proteomics can substantially contribute to the understanding of disease mechanisms by identifying signatures with potential diagnostic or even therapeutic relevance but may require coordinated efforts of interdisciplinary and eventually multi-national consortia, such as initiated in the cancer moonshot program. Additionally, robust and standardized mass spectrometry (MS) assays-particularly targeted MS, MALDI imaging, and immuno-MALDI-may be transferred to the clinic to improve patient stratification for precision medicine, and guide therapies.
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Affiliation(s)
- Ruzanna Mnatsakanyan
- a Protein Dynamics , Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V , Dortmund , 44227 , Germany
| | - Gerta Shema
- a Protein Dynamics , Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V , Dortmund , 44227 , Germany
| | - Mark Basik
- b Gerald Bronfman Department of Oncology , Jewish General Hospital, McGill University , Montreal , Quebec H4A 3T2 , Canada
| | - Gerald Batist
- b Gerald Bronfman Department of Oncology , Jewish General Hospital, McGill University , Montreal , Quebec H4A 3T2 , Canada
| | - Christoph H Borchers
- b Gerald Bronfman Department of Oncology , Jewish General Hospital, McGill University , Montreal , Quebec H4A 3T2 , Canada.,c University of Victoria-Genome British Columbia Proteomics Centre, University of Victoria , Victoria , British Columbia V8Z 7X8 , Canada.,d Department of Biochemistry and Microbiology , University of Victoria , Victoria , British Columbia , V8P 5C2 , Canada.,e Segal Cancer Proteomics Centre, Lady Davis Institute, Jewish General Hospital, McGill University , Montreal , Quebec H3T 1E2 , Canada
| | - Albert Sickmann
- a Protein Dynamics , Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V , Dortmund , 44227 , Germany.,f Medizinische Fakultät, Medizinische Proteom-Center (MPC), Ruhr-Universität Bochum , 44801 Bochum , Germany.,g Department of Chemistry , College of Physical Sciences, University of Aberdeen , Aberdeen AB24 3FX , Scotland , United Kingdom
| | - René P Zahedi
- a Protein Dynamics , Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V , Dortmund , 44227 , Germany.,b Gerald Bronfman Department of Oncology , Jewish General Hospital, McGill University , Montreal , Quebec H4A 3T2 , Canada.,e Segal Cancer Proteomics Centre, Lady Davis Institute, Jewish General Hospital, McGill University , Montreal , Quebec H3T 1E2 , Canada
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Bittremieux W, Tabb DL, Impens F, Staes A, Timmerman E, Martens L, Laukens K. Quality control in mass spectrometry-based proteomics. MASS SPECTROMETRY REVIEWS 2018; 37:697-711. [PMID: 28802010 DOI: 10.1002/mas.21544] [Citation(s) in RCA: 67] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2017] [Revised: 07/24/2017] [Accepted: 07/24/2017] [Indexed: 05/21/2023]
Abstract
Mass spectrometry is a highly complex analytical technique and mass spectrometry-based proteomics experiments can be subject to a large variability, which forms an obstacle to obtaining accurate and reproducible results. Therefore, a comprehensive and systematic approach to quality control is an essential requirement to inspire confidence in the generated results. A typical mass spectrometry experiment consists of multiple different phases including the sample preparation, liquid chromatography, mass spectrometry, and bioinformatics stages. We review potential sources of variability that can impact the results of a mass spectrometry experiment occurring in all of these steps, and we discuss how to monitor and remedy the negative influences on the experimental results. Furthermore, we describe how specialized quality control samples of varying sample complexity can be incorporated into the experimental workflow and how they can be used to rigorously assess detailed aspects of the instrument performance.
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Affiliation(s)
- Wout Bittremieux
- Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium
- Biomedical Informatics Research Center Antwerp (Biomina), University of Antwerp/Antwerp University Hospital, Edegem, Belgium
| | - David L Tabb
- Division of Molecular Biology and Human Genetics, Stellenbosch University Faculty of Medicine and Health Sciences, Tygerberg Hospital, Cape Town, South Africa
| | - Francis Impens
- VIB Proteomics Core, Ghent, Belgium
- VIB-UGent Center for Medical Biotechnology, Ghent, Belgium
- Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent, Belgium
| | - An Staes
- VIB Proteomics Core, Ghent, Belgium
- VIB-UGent Center for Medical Biotechnology, Ghent, Belgium
- Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent, Belgium
| | - Evy Timmerman
- VIB Proteomics Core, Ghent, Belgium
- VIB-UGent Center for Medical Biotechnology, Ghent, Belgium
- Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent, Belgium
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, Ghent, Belgium
- Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent, Belgium
- Bioinformatics Institute Ghent, Ghent University, Zwijnaarde, Belgium
| | - Kris Laukens
- Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium
- Biomedical Informatics Research Center Antwerp (Biomina), University of Antwerp/Antwerp University Hospital, Edegem, Belgium
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8
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Integrated Chemometrics and Statistics to Drive Successful Proteomics Biomarker Discovery. Proteomes 2018; 6:proteomes6020020. [PMID: 29701723 PMCID: PMC6027525 DOI: 10.3390/proteomes6020020] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Revised: 04/19/2018] [Accepted: 04/25/2018] [Indexed: 01/15/2023] Open
Abstract
Protein biomarkers are of great benefit for clinical research and applications, as they are powerful means for diagnosing, monitoring and treatment prediction of different diseases. Even though numerous biomarkers have been reported, the translation to clinical practice is still limited. This mainly due to: (i) incorrect biomarker selection, (ii) insufficient validation of potential biomarkers, and (iii) insufficient clinical use. In this review, we focus on the biomarker selection process and critically discuss the chemometrical and statistical decisions made in proteomics biomarker discovery to increase to selection of high value biomarkers. The characteristics of the data, the computational resources, the type of biomarker that is searched for and the validation strategy influence the decision making of the chemometrical and statistical methods and a decision made for one component directly influences the choice for another. Incorrect decisions could increase the false positive and negative rate of biomarkers which requires independent confirmation of outcome by other techniques and for comparison between different related studies. There are few guidelines for authors regarding data analysis documentation in peer reviewed journals, making it hard to reproduce successful data analysis strategies. Here we review multiple chemometrical and statistical methods for their value in proteomics-based biomarker discovery and propose to include key components in scientific documentation.
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9
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Bell L, Calder B, Hiller R, Klein A, Soares NC, Stoychev SH, Vorster BC, Tabb DL. Challenges and Opportunities for Biological Mass Spectrometry Core Facilities in the Developing World. J Biomol Tech 2018; 29:4-15. [PMID: 29623005 DOI: 10.7171/jbt.18-2901-003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
The developing world is seeing rapid growth in the availability of biological mass spectrometry (MS), particularly through core facilities. As proteomics and metabolomics becomes locally feasible for investigators in these nations, application areas associated with high burden in these nations, such as infectious disease, will see greatly increased research output. This article evaluates the rapid growth of MS in South Africa (currently approaching 20 laboratories) as a model for establishing MS core facilities in other nations of the developing world. Facilities should emphasize new services rather than new instruments. The reduction of the delays associated with reagent and other supply acquisition would benefit both facilities and the users who make use of their services. Instrument maintenance and repair, often mediated by an in-country business for an international vendor, is also likely to operate on a slower schedule than in the wealthiest nations. A key challenge to facilities in the developing world is educating potential facility users in how best to design experiments for proteomics and metabolomics, what reagents are most likely to introduce problematic artifacts, and how to interpret results from the facility. Here, we summarize the experience of 6 different institutions to raise the level of biological MS available to researchers in South Africa.
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Affiliation(s)
- Liam Bell
- Centre for Proteomic and Genomic Research, Observatory, Cape Town 7925, South Africa
| | - Bridget Calder
- University of Cape Town, Observatory, Cape Town 7925, South Africa
| | - Reinhard Hiller
- Centre for Proteomic and Genomic Research, Observatory, Cape Town 7925, South Africa
| | - Ashwil Klein
- University of the Western Cape, Bellville, Cape Town 7925, South Africa
| | - Nelson C Soares
- University of Cape Town, Observatory, Cape Town 7925, South Africa
| | - Stoyan H Stoychev
- Council for Scientific and Industrial Research, Pretoria 0001, South Africa
| | - Barend C Vorster
- Centre for Human Metabolomics, North-West University, Potchefstroom 2520, South Africa; and
| | - David L Tabb
- Department of Science and Technology/National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa
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10
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Bowden JA, Heckert A, Ulmer CZ, Jones CM, Koelmel JP, Abdullah L, Ahonen L, Alnouti Y, Armando AM, Asara JM, Bamba T, Barr JR, Bergquist J, Borchers CH, Brandsma J, Breitkopf SB, Cajka T, Cazenave-Gassiot A, Checa A, Cinel MA, Colas RA, Cremers S, Dennis EA, Evans JE, Fauland A, Fiehn O, Gardner MS, Garrett TJ, Gotlinger KH, Han J, Huang Y, Neo AH, Hyötyläinen T, Izumi Y, Jiang H, Jiang H, Jiang J, Kachman M, Kiyonami R, Klavins K, Klose C, Köfeler HC, Kolmert J, Koal T, Koster G, Kuklenyik Z, Kurland IJ, Leadley M, Lin K, Maddipati KR, McDougall D, Meikle PJ, Mellett NA, Monnin C, Moseley MA, Nandakumar R, Oresic M, Patterson R, Peake D, Pierce JS, Post M, Postle AD, Pugh R, Qiu Y, Quehenberger O, Ramrup P, Rees J, Rembiesa B, Reynaud D, Roth MR, Sales S, Schuhmann K, Schwartzman ML, Serhan CN, Shevchenko A, Somerville SE, St John-Williams L, Surma MA, Takeda H, Thakare R, Thompson JW, Torta F, Triebl A, Trötzmüller M, Ubhayasekera SJK, Vuckovic D, Weir JM, Welti R, Wenk MR, Wheelock CE, Yao L, Yuan M, Zhao XH, Zhou S. Harmonizing lipidomics: NIST interlaboratory comparison exercise for lipidomics using SRM 1950-Metabolites in Frozen Human Plasma. J Lipid Res 2017; 58:2275-2288. [PMID: 28986437 PMCID: PMC5711491 DOI: 10.1194/jlr.m079012] [Citation(s) in RCA: 264] [Impact Index Per Article: 37.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2017] [Revised: 10/02/2017] [Indexed: 12/22/2022] Open
Abstract
As the lipidomics field continues to advance, self-evaluation within the community is critical. Here, we performed an interlaboratory comparison exercise for lipidomics using Standard Reference Material (SRM) 1950-Metabolites in Frozen Human Plasma, a commercially available reference material. The interlaboratory study comprised 31 diverse laboratories, with each laboratory using a different lipidomics workflow. A total of 1,527 unique lipids were measured across all laboratories and consensus location estimates and associated uncertainties were determined for 339 of these lipids measured at the sum composition level by five or more participating laboratories. These evaluated lipids detected in SRM 1950 serve as community-wide benchmarks for intra- and interlaboratory quality control and method validation. These analyses were performed using nonstandardized laboratory-independent workflows. The consensus locations were also compared with a previous examination of SRM 1950 by the LIPID MAPS consortium. While the central theme of the interlaboratory study was to provide values to help harmonize lipids, lipid mediators, and precursor measurements across the community, it was also initiated to stimulate a discussion regarding areas in need of improvement.
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Affiliation(s)
- John A Bowden
- Marine Biochemical Sciences Group, Chemical Sciences Division, Hollings Marine Laboratory, National Institute of Standards and Technology, Charleston, SC
| | - Alan Heckert
- Statistical Engineering Division, National Institute of Standards and Technology, Gaithersburg, MD
| | - Candice Z Ulmer
- Marine Biochemical Sciences Group, Chemical Sciences Division, Hollings Marine Laboratory, National Institute of Standards and Technology, Charleston, SC
| | - Christina M Jones
- Marine Biochemical Sciences Group, Chemical Sciences Division, Hollings Marine Laboratory, National Institute of Standards and Technology, Charleston, SC
| | - Jeremy P Koelmel
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida, Gainesville, FL
| | | | - Linda Ahonen
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
| | - Yazen Alnouti
- Department of Pharmaceutical Sciences, University of Nebraska Medical Center, Omaha, NE
| | - Aaron M Armando
- Departments of Chemistry and Biochemistry and Pharmacology, School of Medicine, University of California, San Diego, La Jolla, CA
| | - John M Asara
- Division of Signal Transduction, Beth Israel Deaconess Medical Center, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Takeshi Bamba
- Division of Metabolomics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - John R Barr
- Division of Laboratory Sciences, Centers for Disease Control and Prevention, National Center for Environmental Health, Atlanta, GA
| | - Jonas Bergquist
- Department of Chemistry-BMC, Analytical Chemistry, Uppsala University, Uppsala, Sweden
| | - Christoph H Borchers
- University of Victoria-Genome British Columbia Proteomics Centre, University of Victoria, Victoria, British Columbia, Canada
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, British Columbia, Canada
- Gerald Bronfman Department of Oncology McGill University, Montreal, Quebec, Canada
- Proteomics Centre, Segal Cancer Centre, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Quebec, Canada
| | - Joost Brandsma
- Faculty of Medicine, Academic Unit of Clinical and Experimental Sciences, Southampton General Hospital, University of Southampton, Southampton, United Kingdom
| | - Susanne B Breitkopf
- Division of Signal Transduction, Beth Israel Deaconess Medical Center, Boston, MA
| | - Tomas Cajka
- National Institutes of Health West Coast Metabolomics Center, University of California Davis Genome Center, Davis, CA
| | - Amaury Cazenave-Gassiot
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore and Singapore Lipidomic Incubator (SLING), Life Sciences Institute, Singapore
| | - Antonio Checa
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Michelle A Cinel
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Romain A Colas
- Department of Anesthesiology, Perioperative and Pain Medicine, Center for Experimental Therapeutics and Reperfusion Injury, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Serge Cremers
- Biomarker Core Laboratory, Irving Institute for Clinical and Translational Research, Columbia University Medical Center, New York, NY
| | - Edward A Dennis
- Departments of Chemistry and Biochemistry and Pharmacology, School of Medicine, University of California, San Diego, La Jolla, CA
| | | | - Alexander Fauland
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Oliver Fiehn
- National Institutes of Health West Coast Metabolomics Center, University of California Davis Genome Center, Davis, CA
- Biochemistry Department, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Michael S Gardner
- Division of Laboratory Sciences, Centers for Disease Control and Prevention, National Center for Environmental Health, Atlanta, GA
| | - Timothy J Garrett
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida, Gainesville, FL
| | - Katherine H Gotlinger
- Department of Pharmacology, New York Medical College School of Medicine, Valhalla, NY
| | - Jun Han
- University of Victoria-Genome British Columbia Proteomics Centre, University of Victoria, Victoria, British Columbia, Canada
| | | | - Aveline Huipeng Neo
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore and Singapore Lipidomic Incubator (SLING), Life Sciences Institute, Singapore
| | | | - Yoshihiro Izumi
- Division of Metabolomics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - Hongfeng Jiang
- Biomarker Core Laboratory, Irving Institute for Clinical and Translational Research, Columbia University Medical Center, New York, NY
| | - Houli Jiang
- Department of Pharmacology, New York Medical College School of Medicine, Valhalla, NY
| | - Jiang Jiang
- Departments of Chemistry and Biochemistry and Pharmacology, School of Medicine, University of California, San Diego, La Jolla, CA
| | - Maureen Kachman
- Metabolomics Core, BRCF, University of Michigan, Ann Arbor, MI
| | | | | | | | - Harald C Köfeler
- Core Facility for Mass Spectrometry, Medical University of Graz, Graz, Austria
| | - Johan Kolmert
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | | | - Grielof Koster
- Faculty of Medicine, Academic Unit of Clinical and Experimental Sciences, Southampton General Hospital, University of Southampton, Southampton, United Kingdom
| | - Zsuzsanna Kuklenyik
- Division of Laboratory Sciences, Centers for Disease Control and Prevention, National Center for Environmental Health, Atlanta, GA
| | - Irwin J Kurland
- Stable Isotope and Metabolomics Core Facility, Diabetes Research Center, Albert Einstein College of Medicine, Bronx, NY
| | - Michael Leadley
- Analytical Facility of Bioactive Molecules, The Hospital for Sick Children Research Institute, Toronto, ON, Canada
| | - Karen Lin
- University of Victoria-Genome British Columbia Proteomics Centre, University of Victoria, Victoria, British Columbia, Canada
| | - Krishna Rao Maddipati
- Lipidomics Core Facility and Department of Pathology, Wayne State University, Detroit, MI
| | - Danielle McDougall
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida, Gainesville, FL
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | | | - Cian Monnin
- Department of Chemistry and Biochemistry, Concordia University, Montréal, Québec, Canada
| | - M Arthur Moseley
- Proteomics and Metabolomics Shared Resource, Levine Science Research Center, Duke University School of Medicine, Durham, NC
| | - Renu Nandakumar
- Biomarker Core Laboratory, Irving Institute for Clinical and Translational Research, Columbia University Medical Center, New York, NY
| | - Matej Oresic
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland
| | - Rainey Patterson
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida, Gainesville, FL
| | | | - Jason S Pierce
- Department of Biochemistry and Molecular Biology Medical University of South Carolina, Charleston, SC
| | - Martin Post
- Analytical Facility of Bioactive Molecules, The Hospital for Sick Children Research Institute, Toronto, ON, Canada
| | - Anthony D Postle
- Faculty of Medicine, Academic Unit of Clinical and Experimental Sciences, Southampton General Hospital, University of Southampton, Southampton, United Kingdom
| | - Rebecca Pugh
- Chemical Sciences Division, Environmental Specimen Bank Group, Hollings Marine Laboratory, National Institute of Standards and Technology, Charleston, SC
| | - Yunping Qiu
- Stable Isotope and Metabolomics Core Facility, Diabetes Research Center, Albert Einstein College of Medicine, Bronx, NY
| | - Oswald Quehenberger
- Departments of Medicine and Pharmacology, School of Medicine, University of California, San Diego, La Jolla, CA
| | - Parsram Ramrup
- Department of Chemistry and Biochemistry, Concordia University, Montréal, Québec, Canada
| | - Jon Rees
- Division of Laboratory Sciences, Centers for Disease Control and Prevention, National Center for Environmental Health, Atlanta, GA
| | - Barbara Rembiesa
- Department of Biochemistry and Molecular Biology Medical University of South Carolina, Charleston, SC
| | - Denis Reynaud
- Analytical Facility of Bioactive Molecules, The Hospital for Sick Children Research Institute, Toronto, ON, Canada
| | - Mary R Roth
- Division of Biology, Kansas Lipidomics Research Center, Kansas State University, Manhattan, KS
| | - Susanne Sales
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Kai Schuhmann
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | | | - Charles N Serhan
- Department of Anesthesiology, Perioperative and Pain Medicine, Center for Experimental Therapeutics and Reperfusion Injury, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Andrej Shevchenko
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Stephen E Somerville
- Hollings Marine Laboratory, Medical University of South Carolina, Charleston, SC
| | - Lisa St John-Williams
- Proteomics and Metabolomics Shared Resource, Levine Science Research Center, Duke University School of Medicine, Durham, NC
| | | | - Hiroaki Takeda
- Division of Metabolomics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - Rhishikesh Thakare
- Department of Pharmaceutical Sciences, University of Nebraska Medical Center, Omaha, NE
| | - J Will Thompson
- Proteomics and Metabolomics Shared Resource, Levine Science Research Center, Duke University School of Medicine, Durham, NC
| | - Federico Torta
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore and Singapore Lipidomic Incubator (SLING), Life Sciences Institute, Singapore
| | - Alexander Triebl
- Core Facility for Mass Spectrometry, Medical University of Graz, Graz, Austria
| | - Martin Trötzmüller
- Core Facility for Mass Spectrometry, Medical University of Graz, Graz, Austria
| | | | - Dajana Vuckovic
- Department of Chemistry and Biochemistry, Concordia University, Montréal, Québec, Canada
| | - Jacquelyn M Weir
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Ruth Welti
- Division of Biology, Kansas Lipidomics Research Center, Kansas State University, Manhattan, KS
| | - Markus R Wenk
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore and Singapore Lipidomic Incubator (SLING), Life Sciences Institute, Singapore
| | - Craig E Wheelock
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Libin Yao
- Division of Biology, Kansas Lipidomics Research Center, Kansas State University, Manhattan, KS
| | - Min Yuan
- Division of Signal Transduction, Beth Israel Deaconess Medical Center, Boston, MA
| | - Xueqing Heather Zhao
- Stable Isotope and Metabolomics Core Facility, Diabetes Research Center, Albert Einstein College of Medicine, Bronx, NY
| | - Senlin Zhou
- Lipidomics Core Facility and Department of Pathology, Wayne State University, Detroit, MI
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11
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Cheng L, Fan K, Huang Y, Wang D, Leung KS. Full Characterization of Localization Diversity in the Human Protein Interactome. J Proteome Res 2017; 16:3019-3029. [DOI: 10.1021/acs.jproteome.7b00306] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Lixin Cheng
- Department
of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Kaili Fan
- College
of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yan Huang
- College
of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Dong Wang
- College
of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- Center
for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Kwong-Sak Leung
- Department
of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
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12
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Abstract
Because proteomics experiments are so complex they can readily fail, and do so without clear cause. Using standard experimental design techniques and incorporating quality control can greatly increase the chances of success. This chapter introduces the relevant concepts and provides examples specific to proteomic workflows. Applying these notions to design successful proteomics experiments is straightforward. It can help identify failure causes and greatly increase the likelihood of inter-laboratory reproducibility.
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Affiliation(s)
- Daniel Ruderman
- Lawrence J. Ellison Institute for Transformative Medicine of USC, Keck School of Medicine of USC, 2250 Alcazar St. CSC-240, Los Angeles, CA, 90033, USA.
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13
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Holman SW, McLean L, Eyers CE. RePLiCal: A QconCAT Protein for Retention Time Standardization in Proteomics Studies. J Proteome Res 2016; 15:1090-102. [PMID: 26775667 DOI: 10.1021/acs.jproteome.5b00988] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
This study introduces a new reversed-phase liquid chromatography retention time (RT) standard, RePLiCal (Reversed-phase liquid chromatography calibrant), produced using QconCAT technology. The synthetic protein contains 27 lysine-terminating calibrant peptides, meaning that the same complement of standards can be generated using either Lys-C or trypsin-based digestion protocols. RePLiCal was designed such that each constituent peptide is unique with respect to all eukaryotic proteomes, thereby enabling integration into a wide range of proteomic analyses. RePLiCal has been benchmarked against three commercially available peptide RT standard kits and outperforms all in terms of LC gradient coverage. RePLiCal also provides a higher number of calibrant points for chromatographic retention time standardization and normalization. The standard provides stable RTs over long analysis times and can be readily transferred between different LC gradients and nUHPLC instruments. Moreover, RePLiCal can be used to predict RTs for other peptides in a timely manner. Furthermore, it is shown that RePLiCal can be used effectively to evaluate trapping column performance for nUHPLC instruments using trap-elute configurations, to optimize gradients to maximize peptide and protein identification rates, and to recalibrate the m/z scale of mass spectrometry data post-acquisition.
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Affiliation(s)
- Stephen W Holman
- Centre for Proteome Research, Department of Biochemistry, Institute of Integrative Biology, University of Liverpool , Crown Street, Liverpool L69 7ZB, United Kingdom
| | - Lynn McLean
- Centre for Proteome Research, Department of Biochemistry, Institute of Integrative Biology, University of Liverpool , Crown Street, Liverpool L69 7ZB, United Kingdom
| | - Claire E Eyers
- Centre for Proteome Research, Department of Biochemistry, Institute of Integrative Biology, University of Liverpool , Crown Street, Liverpool L69 7ZB, United Kingdom
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14
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Gahoual R, Beck A, François YN, Leize-Wagner E. Independent highly sensitive characterization of asparagine deamidation and aspartic acid isomerization by sheathless CZE-ESI-MS/MS. JOURNAL OF MASS SPECTROMETRY : JMS 2016; 51:150-158. [PMID: 26889931 DOI: 10.1002/jms.3735] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2015] [Revised: 11/02/2015] [Accepted: 11/15/2015] [Indexed: 06/05/2023]
Abstract
Amino acids residues are commonly submitted to various physicochemical modifications occurring at physiological pH and temperature. Post-translational modifications (PTMs) require comprehensive characterization because of their major influence on protein structure and involvement in numerous in vivo process or signaling. Mass spectrometry (MS) has gradually become an analytical tool of choice to characterize PTMs; however, some modifications are still challenging because of sample faint modification levels or difficulty to separate an intact peptide from modified counterparts before their transfer to the ionization source. Here, we report the implementation of capillary zone electrophoresis coupled to electrospray ionization tandem mass spectrometry (CZE-ESI-MS/MS) by the intermediate of a sheathless interfacing for independent and highly sensitive characterization of asparagine deamidation (deaN) and aspartic acid isomerization (isoD). CZE selectivity regarding deaN and isoD was studied extensively using different sets of synthetic peptides based on actual tryptic peptides. Results demonstrated CZE ability to separate the unmodified peptide from modified homologous exhibiting deaN, isoD or both independently with a resolution systematically superior to 1.29. Developed CZE-ESI-MS/MS method was applied for the characterization of monoclonal antibodies and complex protein mixture. Conserved CZE selectivity could be demonstrated even for complex samples, and foremost results obtained showed that CZE selectivity is similar regardless of the composition of the peptide. Separation of modified peptides prior to the MS analysis allowed to characterize and estimate modification levels of the sample independently for deaN and isoD even for peptides affected by both modifications and, as a consequence, enables to distinguish the formation of l-aspartic acid or d-aspartic acid generated from deaN. Separation based on peptide modification allowed, as supported by the ESI efficiency provided by CZE-ESI-MS/MS properties, and enabled to characterize and estimate studied PTMs with an unprecedented sensitivity and proved the relevance of implementing an electrophoretic driven separation for MS-based peptide analysis.
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Affiliation(s)
- Rabah Gahoual
- Laboratoire de Spectrométrie de Masse des Interactions et des Systèmes (LSMIS), UMR 7140 (UdS-CNRS), Université de Strasbourg, Strasbourg, France
- Division of BioAnalytical Chemistry, AIMMS Research Group BioMolecular Analysis, VU University Amsterdam, De Boelelaan 1083, 1081 HV, Amsterdam, The Netherlands
| | - Alain Beck
- Centre d'Immunologie Pierre Fabre, Saint-Julien-en-Genevois, France
| | - Yannis-Nicolas François
- Laboratoire de Spectrométrie de Masse des Interactions et des Systèmes (LSMIS), UMR 7140 (UdS-CNRS), Université de Strasbourg, Strasbourg, France
| | - Emmanuelle Leize-Wagner
- Division of BioAnalytical Chemistry, AIMMS Research Group BioMolecular Analysis, VU University Amsterdam, De Boelelaan 1083, 1081 HV, Amsterdam, The Netherlands
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15
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Hare DJ, New EJ. On the outside looking in: redefining the role of analytical chemistry in the biosciences. Chem Commun (Camb) 2016; 52:8918-34. [DOI: 10.1039/c6cc00128a] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Analytical chemistry has much to offer to an improved understanding of biological systems.
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Affiliation(s)
- Dominic J. Hare
- Elemental Bio-imaging Facility
- University of Technology Sydney
- Broadway
- Australia
- The Florey Institute of Neuroscience and Mental Health
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16
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Proteomics Is Analytical Chemistry: Fitness-for-Purpose in the Application of Top-Down and Bottom-Up Analyses. Proteomes 2015; 3:440-453. [PMID: 28248279 PMCID: PMC5217385 DOI: 10.3390/proteomes3040440] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2015] [Revised: 11/21/2015] [Accepted: 11/26/2015] [Indexed: 11/17/2022] Open
Abstract
Molecular mechanisms underlying health and disease function at least in part based on the flexibility and fine-tuning afforded by protein isoforms and post-translational modifications. The ability to effectively and consistently resolve these protein species or proteoforms, as well as assess quantitative changes is therefore central to proteomic analyses. Here we discuss the pros and cons of currently available and developing analytical techniques from the perspective of the full spectrum of available tools and their current applications, emphasizing the concept of fitness-for-purpose in experimental design based on consideration of sample size and complexity; this necessarily also addresses analytical reproducibility and its variance. Data quality is considered the primary criterion, and we thus emphasize that the standards of Analytical Chemistry must apply throughout any proteomic analysis.
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17
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A standardized kit for automated quantitative assessment of candidate protein biomarkers in human plasma. Bioanalysis 2015; 7:2991-3004. [DOI: 10.4155/bio.15.222] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Background: An increasingly popular mass spectrometry-based quantitative approach for health-related research in the biomedical field involves the use of stable isotope-labeled standards (SIS) and multiple/selected reaction monitoring (MRM/SRM). To improve inter-laboratory precision and enable more widespread use of this ‘absolute’ quantitative technique in disease-biomarker assessment studies, methods must be standardized. Results/methodology: Using this MRM-with-SIS-peptide approach, we developed an automated method (encompassing sample preparation, processing and analysis) for quantifying 76 candidate protein markers (spanning >4 orders of magnitude in concentration) in neat human plasma. Discussion/conclusion: The assembled biomarker assessment kit – the ‘BAK-76’ – contains the essential materials (SIS mixes), methods (for acquisition and analysis), and tools (Qualis-SIS software) for performing biomarker discovery or verification studies in a rapid and standardized manner.
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18
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Abstract
Mass spectrometry-based clinical proteomics approaches were introduced into the biomedical field more than two decades ago. Despite recent developments both in the field of mass spectrometry and bioinformatics, the gap between proteomics results and their translation into clinical practice still needs to be closed, as implementation of proteomics results in the clinic appears to be scarce. An extra focus on the importance of the experimental design is therefore of crucial importance.
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Affiliation(s)
- Evelyne Maes
- Center for Proteomics, University of Antwerp/Flemish Institute for Technological Research (VITO), Groenenborgerlaan 171, 2020 Antwerp, Belgium
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19
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Lebert D, Louwagie M, Goetze S, Picard G, Ossola R, Duquesne C, Basler K, Ferro M, Rinner O, Aebersold R, Garin J, Mouz N, Brunner E, Brun V. DIGESTIF: a universal quality standard for the control of bottom-up proteomics experiments. J Proteome Res 2014; 14:787-803. [PMID: 25495225 DOI: 10.1021/pr500834z] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
In bottom-up mass spectrometry-based proteomics analyses, variability at any step of the process, particularly during sample proteolysis, directly affects the sensitivity, accuracy, and precision of peptide detection and quantification. Currently, no generic internal standards are available to control the quality of sample processing steps. This makes it difficult to assess the comparability of MS proteomic data obtained under different experimental conditions. Here, we describe the design, synthesis, and validation of a universal protein standard, called DIGESTIF, that can be added to any biological sample. The DIGESTIF standard consists of a soluble recombinant protein scaffold to which a set of 11 artificial peptides (iRT peptides) with good ionization properties has been incorporated. In the protein scaffold, the amino acids flanking iRT peptide cleavage sites were selected either to favor or hinder protease cleavage. After sample processing, the retention time and relative intensity pattern of the released iRT peptides can be used to assess the quality of sample workup, the extent of digestion, and the performance of the LC-MS system. Thus, DIGESTIF can be used to standardize a broad spectrum of applications, ranging from simple replicate measurements to large-scale biomarker screening in biomedical applications.
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20
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Bereman MS. Tools for monitoring system suitability in LC MS/MS centric proteomic experiments. Proteomics 2014; 15:891-902. [DOI: 10.1002/pmic.201400373] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2014] [Revised: 09/12/2014] [Accepted: 10/13/2014] [Indexed: 11/06/2022]
Affiliation(s)
- Michael S. Bereman
- Department of Biological Sciences, Center for Human Health and the Environment; North Carolina State University; Raleigh NC USA
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21
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Boja ES, Kinsinger CR, Rodriguez H, Srinivas P. Integration of omics sciences to advance biology and medicine. Clin Proteomics 2014. [PMCID: PMC4274684 DOI: 10.1186/1559-0275-11-45] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
In the past two decades, our ability to study cellular and molecular systems has been transformed through the development of omics sciences. While unlimited potential lies within massive omics datasets, the success of omics sciences to further our understanding of human disease and/or translating these findings to clinical utility remains elusive due to a number of factors. A significant limiting factor is the integration of different omics datasets (i.e., integromics) for extraction of biological and clinical insights. To this end, the National Cancer Institute (NCI) and the National Heart, Lung and Blood Institute (NHLBI) organized a joint workshop in June 2012 with the focus on integration issues related to multi-omics technologies that needed to be resolved in order to realize the full utility of integrating omics datasets by providing a glimpse into the disease as an integrated “system”. The overarching goals were to (1) identify challenges and roadblocks in omics integration, and (2) facilitate the full maturation of ‘integromics’ in biology and medicine. Participants reached a consensus on the most significant barriers for integrating omics sciences and provided recommendations on viable approaches to overcome each of these barriers within the areas of technology, bioinformatics and clinical medicine.
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22
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Quandt A, Espona L, Balasko A, Weisser H, Brusniak MY, Kunszt P, Aebersold R, Malmström L. Using synthetic peptides to benchmark peptide identification software and search parameters for MS/MS data analysis. EUPA OPEN PROTEOMICS 2014. [DOI: 10.1016/j.euprot.2014.10.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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23
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Walzer M, Pernas LE, Nasso S, Bittremieux W, Nahnsen S, Kelchtermans P, Pichler P, van den Toorn HWP, Staes A, Vandenbussche J, Mazanek M, Taus T, Scheltema RA, Kelstrup CD, Gatto L, van Breukelen B, Aiche S, Valkenborg D, Laukens K, Lilley KS, Olsen JV, Heck AJR, Mechtler K, Aebersold R, Gevaert K, Vizcaíno JA, Hermjakob H, Kohlbacher O, Martens L. qcML: an exchange format for quality control metrics from mass spectrometry experiments. Mol Cell Proteomics 2014; 13:1905-13. [PMID: 24760958 PMCID: PMC4125725 DOI: 10.1074/mcp.m113.035907] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2013] [Revised: 03/13/2014] [Indexed: 12/22/2022] Open
Abstract
Quality control is increasingly recognized as a crucial aspect of mass spectrometry based proteomics. Several recent papers discuss relevant parameters for quality control and present applications to extract these from the instrumental raw data. What has been missing, however, is a standard data exchange format for reporting these performance metrics. We therefore developed the qcML format, an XML-based standard that follows the design principles of the related mzML, mzIdentML, mzQuantML, and TraML standards from the HUPO-PSI (Proteomics Standards Initiative). In addition to the XML format, we also provide tools for the calculation of a wide range of quality metrics as well as a database format and interconversion tools, so that existing LIMS systems can easily add relational storage of the quality control data to their existing schema. We here describe the qcML specification, along with possible use cases and an illustrative example of the subsequent analysis possibilities. All information about qcML is available at http://code.google.com/p/qcml.
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Affiliation(s)
- Mathias Walzer
- From the ‡Applied Bioinformatics, Center for Bioinformatics, Quantitative Biology Center, and Dept. of Computer Science, University of Tuebingen, Germany
| | - Lucia Espona Pernas
- §Department of Biology, Institute of Molecular Systems Biology, Eidgenössische Technische Hochschule Zürich, 8092 Zurich, Switzerland
| | - Sara Nasso
- ¶Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland; §Department of Biology, Institute of Molecular Systems Biology, Eidgenössische Technische Hochschule Zürich, 8092 Zurich, Switzerland
| | - Wout Bittremieux
- ‖Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium; **Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp/Antwerp University Hospital, Antwerp, Belgium
| | - Sven Nahnsen
- From the ‡Applied Bioinformatics, Center for Bioinformatics, Quantitative Biology Center, and Dept. of Computer Science, University of Tuebingen, Germany
| | - Pieter Kelchtermans
- ‡‡Department of Medical Protein Research, VIB, B-9000 Ghent, Belgium; §§Department of Biochemistry, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium; ¶¶Flemish Institute for Technological Research (VITO), Boeretang 200, B-2400 Mol Belgium
| | - Peter Pichler
- ‖‖Research Institute of Molecular Pathology (IMP), Dr. Bohr-Gasse 7, A-1030 Vienna, Austria; Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Dr. Bohr-Gasse 3, A-1030 Vienna, Austria
| | - Henk W P van den Toorn
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584 CH Utrecht, Netherlands; Netherlands Proteomics Centre, Padualaan 8, 3584 CH Utrecht, Netherlands
| | - An Staes
- ‡‡Department of Medical Protein Research, VIB, B-9000 Ghent, Belgium; §§Department of Biochemistry, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Jonathan Vandenbussche
- ‡‡Department of Medical Protein Research, VIB, B-9000 Ghent, Belgium; §§Department of Biochemistry, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Michael Mazanek
- ‖‖Research Institute of Molecular Pathology (IMP), Dr. Bohr-Gasse 7, A-1030 Vienna, Austria; Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Dr. Bohr-Gasse 3, A-1030 Vienna, Austria
| | - Thomas Taus
- ‖‖Research Institute of Molecular Pathology (IMP), Dr. Bohr-Gasse 7, A-1030 Vienna, Austria; Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Dr. Bohr-Gasse 3, A-1030 Vienna, Austria
| | - Richard A Scheltema
- Department of Proteomics and Signal Transduction, Max-Planck Institute of Biochemistry, Am Klopferspitz 18, D-82152 Martinsried, Germany
| | - Christian D Kelstrup
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3b, DK-2200 Copenhagen, Denmark
| | - Laurent Gatto
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, CB2 1GA, United Kingdom; Computational Proteomics Unit, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1GA, UK
| | - Bas van Breukelen
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584 CH Utrecht, Netherlands; Netherlands Proteomics Centre, Padualaan 8, 3584 CH Utrecht, Netherlands
| | - Stephan Aiche
- Department of Mathematics and Computer Science, Freie Universität Berlin, Takustr. 9, 14195 Berlin, Germany
| | - Dirk Valkenborg
- ¶¶Flemish Institute for Technological Research (VITO), Boeretang 200, B-2400 Mol Belgium; I-BioStat, Hasselt University, Belgium; CFP-CeProMa, University of Antwerp, Belgium
| | - Kris Laukens
- ‖Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium; **Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp/Antwerp University Hospital, Antwerp, Belgium
| | - Kathryn S Lilley
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, CB2 1GA, United Kingdom
| | - Jesper V Olsen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3b, DK-2200 Copenhagen, Denmark
| | - Albert J R Heck
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584 CH Utrecht, Netherlands; Netherlands Proteomics Centre, Padualaan 8, 3584 CH Utrecht, Netherlands
| | - Karl Mechtler
- ‖‖Research Institute of Molecular Pathology (IMP), Dr. Bohr-Gasse 7, A-1030 Vienna, Austria; Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Dr. Bohr-Gasse 3, A-1030 Vienna, Austria
| | - Ruedi Aebersold
- §Department of Biology, Institute of Molecular Systems Biology, Eidgenössische Technische Hochschule Zürich, 8092 Zurich, Switzerland; Faculty of Science, University of Zurich, Zurich, Switzerland
| | - Kris Gevaert
- ‡‡Department of Medical Protein Research, VIB, B-9000 Ghent, Belgium; §§Department of Biochemistry, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Oliver Kohlbacher
- From the ‡Applied Bioinformatics, Center for Bioinformatics, Quantitative Biology Center, and Dept. of Computer Science, University of Tuebingen, Germany
| | - Lennart Martens
- ‡‡Department of Medical Protein Research, VIB, B-9000 Ghent, Belgium; §§Department of Biochemistry, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium;
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Vaudel M, Barsnes H, Martens L, Berven FS. Bioinformatics for proteomics: opportunities at the interface between the scientists, their experiments, and the community. Methods Mol Biol 2014; 1156:239-48. [PMID: 24791993 DOI: 10.1007/978-1-4939-0685-7_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2023]
Abstract
Within the last decade, bioinformatics has moved from command line scripts dedicated to single experiments towards production grade software integrated in experimental workflows providing a rich environment for biological investigation. Located at the interface between the scientists, their experiments, and the community, bioinformatics acts as a gateway to a wide source of information. This chapter does not list tools and methods, but rather hints at how bioinformatics can help in improving biological projects, all the way from their initial design to the dissemination of the results.
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Affiliation(s)
- Marc Vaudel
- Proteomics Unit, Department of Biomedicine, University of Bergen, Jonas Liesvei 91, Bergen, 5009, Norway,
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25
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Kelchtermans P, Bittremieux W, De Grave K, Degroeve S, Ramon J, Laukens K, Valkenborg D, Barsnes H, Martens L. Machine learning applications in proteomics research: how the past can boost the future. Proteomics 2014; 14:353-66. [PMID: 24323524 DOI: 10.1002/pmic.201300289] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2013] [Revised: 09/24/2013] [Accepted: 10/14/2013] [Indexed: 01/22/2023]
Abstract
Machine learning is a subdiscipline within artificial intelligence that focuses on algorithms that allow computers to learn solving a (complex) problem from existing data. This ability can be used to generate a solution to a particularly intractable problem, given that enough data are available to train and subsequently evaluate an algorithm on. Since MS-based proteomics has no shortage of complex problems, and since publicly available data are becoming available in ever growing amounts, machine learning is fast becoming a very popular tool in the field. We here therefore present an overview of the different applications of machine learning in proteomics that together cover nearly the entire wet- and dry-lab workflow, and that address key bottlenecks in experiment planning and design, as well as in data processing and analysis.
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Affiliation(s)
- Pieter Kelchtermans
- Department of Medical Protein Research, VIB, Ghent, Belgium; Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent, Belgium; Flemish Institute for Technological Research (VITO), Boeretang, Mol, Belgium
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26
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Percy AJ, Chambers AG, Yang J, Jackson AM, Domanski D, Burkhart J, Sickmann A, Borchers CH. Method and platform standardization in MRM-based quantitative plasma proteomics. J Proteomics 2013; 95:66-76. [DOI: 10.1016/j.jprot.2013.07.026] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2013] [Revised: 07/11/2013] [Accepted: 07/26/2013] [Indexed: 10/26/2022]
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Loroch S, Dickhut C, Zahedi RP, Sickmann A. Phosphoproteomics--more than meets the eye. Electrophoresis 2013; 34:1483-92. [PMID: 23576030 DOI: 10.1002/elps.201200710] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2012] [Revised: 02/22/2013] [Accepted: 03/10/2013] [Indexed: 12/16/2022]
Abstract
PTMs enable cells to adapt to internal and external stimuli in the milliseconds to seconds time regime. Protein phosphorylation is probably the most important of these modifications as it affects protein structure and interactions, critically influencing the life cycle of a cell. In the last 15 years, new insights into phosphorylation have been provided by highly sensitive MS-based approaches combined with specific phosphopeptide enrichment strategies. Although so far research has mainly focused on the discovery and characterization of O-phosphorylation, this review also briefly outlines the current knowledge about N-phosphorylation depicting its ubiquitous relevance. Further, common pitfalls in sample preparation, LC-MS analysis, and subsequent data analysis are discussed as well as issues regarding quality and comparability of studies on protein phosphorylation.
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Affiliation(s)
- Stefan Loroch
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V, Dortmund, Germany
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