1
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Fu Q, Vegesna M, Sundararaman N, Damoc E, Arrey TN, Pashkova A, Mengesha E, Debbas P, Joung S, Li D, Cheng S, Braun J, McGovern DPB, Murray C, Xuan Y, Eyk JEV. Paradigm shift in biomarker translation: a pipeline to generate clinical grade biomarker candidates from DIA-MS discovery. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.20.586018. [PMID: 38562888 PMCID: PMC10983901 DOI: 10.1101/2024.03.20.586018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Clinical biomarker development has been stymied by inaccurate protein quantification from mass spectrometry (MS) discovery data and a prolonged validation process. To mitigate these issues, we created the Targeted Extraction Assessment of Quantification (TEAQ) software package. This innovative tool uses the discovery cohort analysis to select precursors, peptides, and proteins that adhere to established targeted assay criteria. TEAQ was applied to Data-Independent Acquisition MS data from plasma samples acquired on an Orbitrap™ Astral™ MS. Identified precursors were evaluated for linearity, specificity, repeatability, reproducibility, and intra-protein correlation from 11-point loading curves under three throughputs, to develop a resource for clinical-grade targeted assays. From a clinical cohort of individuals with inflammatory bowel disease (n=492), TEAQ successfully identified 1116 signature peptides for 327 quantifiable proteins from 1180 identified proteins. Embedding stringent selection criteria adaptable to targeted assay development into the analysis of discovery data will streamline the transition to validation and clinical studies.
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2
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Neely BA, Dorfer V, Martens L, Bludau I, Bouwmeester R, Degroeve S, Deutsch EW, Gessulat S, Käll L, Palczynski P, Payne SH, Rehfeldt TG, Schmidt T, Schwämmle V, Uszkoreit J, Vizcaíno JA, Wilhelm M, Palmblad M. Toward an Integrated Machine Learning Model of a Proteomics Experiment. J Proteome Res 2023; 22:681-696. [PMID: 36744821 PMCID: PMC9990124 DOI: 10.1021/acs.jproteome.2c00711] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
In recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and machine learning experts in a workshop with the goals to evaluate and explore machine learning applications for realistic modeling of data from multidimensional mass spectrometry-based proteomics analysis of any sample or organism. Following this sample-to-data roadmap helped identify knowledge gaps and define needs. Being able to generate bespoke and realistic synthetic data has legitimate and important uses in system suitability, method development, and algorithm benchmarking, while also posing critical ethical questions. The interdisciplinary nature of the workshop informed discussions of what is currently possible and future opportunities and challenges. In the following perspective we summarize these discussions in the hope of conveying our excitement about the potential of machine learning in proteomics and to inspire future research.
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Affiliation(s)
- Benjamin A Neely
- National Institute of Standards and Technology, Charleston, South Carolina 29412, United States
| | - Viktoria Dorfer
- Bioinformatics Research Group, University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, Belgium.,Department of Biomolecular Medicine, Faculty of Health Sciences and Medicine, Ghent University, 9000 Ghent, Belgium
| | - Isabell Bludau
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany
| | - Robbin Bouwmeester
- VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, Belgium.,Department of Biomolecular Medicine, Faculty of Health Sciences and Medicine, Ghent University, 9000 Ghent, Belgium
| | - Sven Degroeve
- VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, Belgium.,Department of Biomolecular Medicine, Faculty of Health Sciences and Medicine, Ghent University, 9000 Ghent, Belgium
| | - Eric W Deutsch
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | | | - Lukas Käll
- Science for Life Laboratory, KTH - Royal Institute of Technology, 171 21 Solna, Sweden
| | - Pawel Palczynski
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, 5230 Odense, Denmark
| | - Samuel H Payne
- Department of Biology, Brigham Young University, Provo, Utah 84602, United States
| | - Tobias Greisager Rehfeldt
- Institute for Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark
| | | | - Veit Schwämmle
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, 5230 Odense, Denmark
| | - Julian Uszkoreit
- Medical Proteome Analysis, Center for Protein Diagnostics (ProDi), Ruhr University Bochum, 44801 Bochum, Germany.,Medizinisches Proteom-Center, Medical Faculty, Ruhr University Bochum, 44801 Bochum, Germany
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Mathias Wilhelm
- Computational Mass Spectrometry, Technical University of Munich (TUM), 85354 Freising, Germany
| | - Magnus Palmblad
- Leiden University Medical Center, Postbus 9600, 2300 RC Leiden, The Netherlands
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3
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Psatha K, Kollipara L, Voutyraki C, Divanach P, Sickmann A, Rassidakis GZ, Drakos E, Aivaliotis M. Deciphering lymphoma pathogenesis via state-of-the-art mass spectrometry-based quantitative proteomics. J Chromatogr B Analyt Technol Biomed Life Sci 2016; 1047:2-14. [PMID: 27979587 DOI: 10.1016/j.jchromb.2016.11.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Revised: 10/18/2016] [Accepted: 11/04/2016] [Indexed: 12/13/2022]
Abstract
Mass spectrometry-based quantitative proteomics specifically applied to comprehend the pathogenesis of lymphoma has incremental value in deciphering the heterogeneity in complex deregulated molecular mechanisms/pathways of the lymphoma entities, implementing the current diagnostic and therapeutic strategies. Essential global, targeted and functional differential proteomics analyses although still evolving, have been successfully implemented to shed light on lymphoma pathogenesis to discover and explore the role of potential lymphoma biomarkers and drug targets. This review aims to outline and appraise the present status of MS-based quantitative proteomic approaches in lymphoma research, introducing the current state-of-the-art MS-based proteomic technologies, the opportunities they offer in biological discovery in human lymphomas and the related limitation issues arising from sample preparation to data evaluation. It is a synopsis containing information obtained from recent research articles, reviews and public proteomics repositories (PRIDE). We hope that this review article will aid, assimilate and assess all the information aiming to accelerate the development and validation of diagnostic, prognostic or therapeutic targets for an improved and empowered clinical proteomics application in lymphomas in the nearby future.
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Affiliation(s)
- Konstantina Psatha
- Institute of Molecular Biology and Biotechnology, FORTH, Heraklion, Greece; School of Medicine, National and Kapodistrian University of Athens, Athens, Greece; Department of Pathology, School of Medicine, University of Crete, Heraklion, Greece
| | - Laxmikanth Kollipara
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
| | | | - Peter Divanach
- Institute of Molecular Biology and Biotechnology, FORTH, Heraklion, Greece
| | - Albert Sickmann
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany; Department of Chemistry, College of Physical Sciences, University of Aberdeen, Aberdeen, Scotland, United Kingdom; Medizinische Fakultät, Medizinische Proteom-Center (MPC), Ruhr-Universität Bochum, Bochum, Germany
| | - George Z Rassidakis
- School of Medicine, National and Kapodistrian University of Athens, Athens, Greece; Department of Pathology and Cytology, Karolinska University Hospital and Karolinska Institute, Radiumhemmet, Stockholm, SE-17176, Sweden
| | - Elias Drakos
- Department of Pathology, School of Medicine, University of Crete, Heraklion, Greece
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4
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Röst HL, Malmström L, Aebersold R. Reproducible quantitative proteotype data matrices for systems biology. Mol Biol Cell 2016; 26:3926-31. [PMID: 26543201 PMCID: PMC4710225 DOI: 10.1091/mbc.e15-07-0507] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Historically, many mass spectrometry–based proteomic studies have aimed at compiling an inventory of protein compounds present in a biological sample, with the long-term objective of creating a proteome map of a species. However, to answer fundamental questions about the behavior of biological systems at the protein level, accurate and unbiased quantitative data are required in addition to a list of all protein components. Fueled by advances in mass spectrometry, the proteomics field has thus recently shifted focus toward the reproducible quantification of proteins across a large number of biological samples. This provides the foundation to move away from pure enumeration of identified proteins toward quantitative matrices of many proteins measured across multiple samples. It is argued here that data matrices consisting of highly reproducible, quantitative, and unbiased proteomic measurements across a high number of conditions, referred to here as quantitative proteotype maps, will become the fundamental currency in the field and provide the starting point for downstream biological analysis. Such proteotype data matrices, for example, are generated by the measurement of large patient cohorts, time series, or multiple experimental perturbations. They are expected to have a large effect on systems biology and personalized medicine approaches that investigate the dynamic behavior of biological systems across multiple perturbations, time points, and individuals.
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Affiliation(s)
- Hannes L Röst
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, CH-8093 Zurich, Switzerland Department of Genetics, Stanford University, Stanford, CA 94305
| | - Lars Malmström
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, CH-8093 Zurich, Switzerland S3IT, University of Zurich, CH-8057 Zurich, Switzerland
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, CH-8093 Zurich, Switzerland Faculty of Science, University of Zurich, CH-8057 Zurich, Switzerland
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5
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Liu F, Koval M, Ranganathan S, Fanayan S, Hancock WS, Lundberg EK, Beavis RC, Lane L, Duek P, McQuade L, Kelleher NL, Baker MS. Systems Proteomics View of the Endogenous Human Claudin Protein Family. J Proteome Res 2016; 15:339-59. [PMID: 26680015 DOI: 10.1021/acs.jproteome.5b00769] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Claudins are the major transmembrane protein components of tight junctions in human endothelia and epithelia. Tissue-specific expression of claudin members suggests that this protein family is not only essential for sustaining the role of tight junctions in cell permeability control but also vital in organizing cell contact signaling by protein-protein interactions. How this protein family is collectively processed and regulated is key to understanding the role of junctional proteins in preserving cell identity and tissue integrity. The focus of this review is to first provide a brief overview of the functional context, on the basis of the extensive body of claudin biology research that has been thoroughly reviewed, for endogenous human claudin members and then ascertain existing and future proteomics techniques that may be applicable to systematically characterizing the chemical forms and interacting protein partners of this protein family in human. The ability to elucidate claudin-based signaling networks may provide new insight into cell development and differentiation programs that are crucial to tissue stability and manipulation.
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Affiliation(s)
| | - Michael Koval
- Department of Medicine, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, and Department of Cell Biology, Emory University School of Medicine , 205 Whitehead Biomedical Research Building, 615 Michael Street, Atlanta, Georgia 30322, United States
| | | | | | - William S Hancock
- Barnett Institute and Department of Chemistry and Chemical Biology, Northeastern University , Boston, Massachusetts 02115, United States
| | - Emma K Lundberg
- SciLifeLab, School of Biotechnology, Royal Institute of Technology (KTH) , SE-171 21 Solna, Stockholm, Sweden
| | - Ronald C Beavis
- Department of Biochemistry and Medical Genetics, University of Manitoba , 744 Bannatyne Avenue, Winnipeg, Manitoba R3E 0W3, Canada
| | - Lydie Lane
- SIB-Swiss Institute of Bioinformatics , CMU - Rue Michel-Servet 1, 1211 Geneva, Switzerland
| | - Paula Duek
- SIB-Swiss Institute of Bioinformatics , CMU - Rue Michel-Servet 1, 1211 Geneva, Switzerland
| | | | - Neil L Kelleher
- Department of Chemistry, Department of Molecular Biosciences, and Proteomics Center of Excellence, Northwestern University , 2145 North Sheridan Road, Evanston, Illinois 60208, United States
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6
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Wang H, Shi T, Qian WJ, Liu T, Kagan J, Srivastava S, Smith RD, Rodland KD, Camp DG. The clinical impact of recent advances in LC-MS for cancer biomarker discovery and verification. Expert Rev Proteomics 2015; 13:99-114. [PMID: 26581546 DOI: 10.1586/14789450.2016.1122529] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Mass spectrometry (MS) -based proteomics has become an indispensable tool with broad applications in systems biology and biomedical research. With recent advances in liquid chromatography (LC) and MS instrumentation, LC-MS is making increasingly significant contributions to clinical applications, especially in the area of cancer biomarker discovery and verification. To overcome challenges associated with analyses of clinical samples (for example, a wide dynamic range of protein concentrations in bodily fluids and the need to perform high throughput and accurate quantification of candidate biomarker proteins), significant efforts have been devoted to improve the overall performance of LC-MS-based clinical proteomics platforms. Reviewed here are the recent advances in LC-MS and its applications in cancer biomarker discovery and quantification, along with the potentials, limitations and future perspectives.
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Affiliation(s)
- Hui Wang
- a Biological Sciences Division , Pacific Northwest National Laboratory , Richland , WA , USA
| | - Tujin Shi
- a Biological Sciences Division , Pacific Northwest National Laboratory , Richland , WA , USA
| | - Wei-Jun Qian
- a Biological Sciences Division , Pacific Northwest National Laboratory , Richland , WA , USA
| | - Tao Liu
- a Biological Sciences Division , Pacific Northwest National Laboratory , Richland , WA , USA
| | - Jacob Kagan
- b Division of Cancer Prevention , National Cancer Institute (NCI) , Rockville , MD , USA
| | - Sudhir Srivastava
- b Division of Cancer Prevention , National Cancer Institute (NCI) , Rockville , MD , USA
| | - Richard D Smith
- a Biological Sciences Division , Pacific Northwest National Laboratory , Richland , WA , USA
| | - Karin D Rodland
- a Biological Sciences Division , Pacific Northwest National Laboratory , Richland , WA , USA
| | - David G Camp
- a Biological Sciences Division , Pacific Northwest National Laboratory , Richland , WA , USA
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7
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Chawade A, Sandin M, Teleman J, Malmström J, Levander F. Data Processing Has Major Impact on the Outcome of Quantitative Label-Free LC-MS Analysis. J Proteome Res 2014; 14:676-87. [DOI: 10.1021/pr500665j] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Aakash Chawade
- Department
of Immunotechnology, Medicon Village, Lund University, Scheelevägen
2, S-223 63 Lund, Sweden
| | - Marianne Sandin
- Department
of Immunotechnology, Medicon Village, Lund University, Scheelevägen
2, S-223 63 Lund, Sweden
| | - Johan Teleman
- Department
of Immunotechnology, Medicon Village, Lund University, Scheelevägen
2, S-223 63 Lund, Sweden
- Department
of Clinical Sciences, Faculty of Medicine, Lund University, SE-221
84 Lund, Sweden
| | - Johan Malmström
- Department
of Clinical Sciences, Faculty of Medicine, Lund University, SE-221
84 Lund, Sweden
| | - Fredrik Levander
- Department
of Immunotechnology, Medicon Village, Lund University, Scheelevägen
2, S-223 63 Lund, Sweden
- Bioinformatics
Infrastructure for Life Sciences (BILS), Lund University, P.O. Box 117, 221 00 Lund, Sweden
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8
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Gianazza E, Tremoli E, Banfi C. The selected reaction monitoring/multiple reaction monitoring-based mass spectrometry approach for the accurate quantitation of proteins: clinical applications in the cardiovascular diseases. Expert Rev Proteomics 2014; 11:771-88. [PMID: 25400095 DOI: 10.1586/14789450.2014.947966] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Selected reaction monitoring, also known as multiple reaction monitoring, is a powerful targeted mass spectrometry approach for a confident quantitation of proteins/peptides in complex biological samples. In recent years, its optimization and application have become pivotal and of great interest in clinical research to derive useful outcomes for patient care. Thus, selected reaction monitoring/multiple reaction monitoring is now used as a highly sensitive and selective method for the evaluation of protein abundances and biomarker verification with potential applications in medical screening. This review describes technical aspects for the development of a robust multiplex assay and discussing its recent applications in cardiovascular proteomics: verification of promising disease candidates to select only the highest quality peptides/proteins for a preclinical validation, as well as quantitation of protein isoforms and post-translational modifications.
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Affiliation(s)
- Erica Gianazza
- Laboratory of Cell Biology and Biochemistry of Atherothrombosis, Unit of Proteomics, Centro Cardiologico Monzino IRCCS, Via Parea 4, 20138 Milan, Italy
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9
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Acosta-Martin AE, Lane L. Combining bioinformatics and MS-based proteomics: clinical implications. Expert Rev Proteomics 2014; 11:269-84. [PMID: 24720436 DOI: 10.1586/14789450.2014.900446] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Clinical proteomics research aims at i) discovery of protein biomarkers for screening, diagnosis and prognosis of disease, ii) discovery of protein therapeutic targets for improvement of disease prevention, treatment and follow-up, and iii) development of mass spectrometry (MS)-based assays that could be implemented in clinical chemistry, microbiology or hematology laboratories. MS has been increasingly applied in clinical proteomics studies for the identification and quantification of proteins. Bioinformatics plays a key role in the exploitation of MS data in several aspects such as the generation and curation of protein sequence databases, the development of appropriate software for MS data treatment and integration with other omics data and the establishment of adequate standard files for data sharing. In this article, we discuss the main MS approaches and bioinformatics solutions that are currently applied to accomplish the objectives of clinical proteomic research.
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10
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Gorini G, Adron Harris R, Dayne Mayfield R. Proteomic approaches and identification of novel therapeutic targets for alcoholism. Neuropsychopharmacology 2014; 39:104-30. [PMID: 23900301 PMCID: PMC3857647 DOI: 10.1038/npp.2013.182] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2013] [Revised: 07/02/2013] [Accepted: 07/04/2013] [Indexed: 01/01/2023]
Abstract
Recent studies have shown that gene regulation is far more complex than previously believed and does not completely explain changes at the protein level. Therefore, the direct study of the proteome, considerably different in both complexity and dynamicity to the genome/transcriptome, has provided unique insights to an increasing number of researchers. During the past decade, extraordinary advances in proteomic techniques have changed the way we can analyze the composition, regulation, and function of protein complexes and pathways underlying altered neurobiological conditions. When combined with complementary approaches, these advances provide the contextual information for decoding large data sets into meaningful biologically adaptive processes. Neuroproteomics offers potential breakthroughs in the field of alcohol research by leading to a deeper understanding of how alcohol globally affects protein structure, function, interactions, and networks. The wealth of information gained from these advances can help pinpoint relevant biomarkers for early diagnosis and improved prognosis of alcoholism and identify future pharmacological targets for the treatment of this addiction.
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Affiliation(s)
- Giorgio Gorini
- Waggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, Austin, TX, USA
| | - R Adron Harris
- Waggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, Austin, TX, USA
| | - R Dayne Mayfield
- Waggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, Austin, TX, USA
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11
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Vandermarliere E, Mueller M, Martens L. Getting intimate with trypsin, the leading protease in proteomics. MASS SPECTROMETRY REVIEWS 2013; 32:453-65. [PMID: 23775586 DOI: 10.1002/mas.21376] [Citation(s) in RCA: 136] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2012] [Revised: 02/15/2013] [Accepted: 02/15/2013] [Indexed: 05/21/2023]
Abstract
Nowadays, mass spectrometry-based proteomics is carried out primarily in a bottom-up fashion, with peptides obtained after proteolytic digest of a whole proteome lysate as the primary analytes instead of the proteins themselves. This experimental setup crucially relies on a protease to digest an abundant and complex protein mixture into a far more complex peptide mixture. Full knowledge of the working mechanism and specificity of the used proteases is therefore crucial, both for the digestion step itself as well as for the downstream identification and quantification of the (fragmentation) mass spectra acquired for the peptides in the mixture. Targeted protein analysis through selected reaction monitoring, a relative newcomer in the specific field of mass spectrometry-based proteomics, even requires a priori understanding of protease behavior for the proteins of interest. Because of the rapidly increasing popularity of proteomics as an analytical tool in the life sciences, there is now a renewed demand for detailed knowledge on trypsin, the workhorse protease in proteomics. This review addresses this need and provides an overview on the structure and working mechanism of trypsin, followed by a critical analysis of its cleavage behavior, typically simply accepted to occur exclusively yet consistently after Arg and Lys, unless they are followed by a Pro. In this context, shortcomings in our ability to understand and predict the behavior of trypsin will be highlighted, along with the downstream implications. Furthermore, an analysis is carried out on the inherent shortcomings of trypsin with regard to whole proteome analysis, and alternative approaches will be presented that can alleviate these issues. Finally, some reflections on the future of trypsin as the workhorse protease in mass spectrometry-based proteomics will be provided.
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Affiliation(s)
- Elien Vandermarliere
- Department of Medical Protein Research, VIB, B-9000 Ghent, Belgium; Department of Biochemistry, Ghent University, B-9000 Ghent, Belgium
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12
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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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13
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Fannes T, Vandermarliere E, Schietgat L, Degroeve S, Martens L, Ramon J. Predicting Tryptic Cleavage from Proteomics Data Using Decision Tree Ensembles. J Proteome Res 2013; 12:2253-9. [DOI: 10.1021/pr4001114] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Affiliation(s)
- Thomas Fannes
- Department of Computer Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A,
B-3000, Leuven, Belgium
| | - Elien Vandermarliere
- Department
of Medical Protein
Research, VIB, B-9000 Ghent, Belgium
- Department of Biochemistry, Ghent University, B-9000 Ghent, Belgium
| | - Leander Schietgat
- Department of Computer Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A,
B-3000, Leuven, Belgium
| | - Sven Degroeve
- Department
of Medical Protein
Research, VIB, B-9000 Ghent, Belgium
- Department of Biochemistry, Ghent University, B-9000 Ghent, Belgium
| | - Lennart Martens
- Department
of Medical Protein
Research, VIB, B-9000 Ghent, Belgium
- Department of Biochemistry, Ghent University, B-9000 Ghent, Belgium
| | - Jan Ramon
- Department of Computer Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A,
B-3000, Leuven, Belgium
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14
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Current status and advances in quantitative proteomic mass spectrometry. INTERNATIONAL JOURNAL OF PROTEOMICS 2013; 2013:180605. [PMID: 23533757 PMCID: PMC3606794 DOI: 10.1155/2013/180605] [Citation(s) in RCA: 119] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2012] [Revised: 01/16/2013] [Accepted: 01/21/2013] [Indexed: 12/18/2022]
Abstract
The accurate quantitation of proteins and peptides in complex biological systems is one of the most challenging areas of proteomics. Mass spectrometry-based approaches have forged significant in-roads allowing accurate and sensitive quantitation and the ability to multiplex vastly complex samples through the application of robust bioinformatic tools. These relative and absolute quantitative measures using label-free, tags, or stable isotope labelling have their own strengths and limitations. The continuous development of these methods is vital for increasing reproducibility in the rapidly expanding application of quantitative proteomics in biomarker discovery and validation. This paper provides a critical overview of the primary mass spectrometry-based quantitative approaches and the current status of quantitative proteomics in biomedical research.
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