1
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Abdul-Khalek N, Picciani M, Shouman O, Wimmer R, Overgaard MT, Wilhelm M, Gregersen Echers S. To Fly, or Not to Fly, That Is the Question: A Deep Learning Model for Peptide Detectability Prediction in Mass Spectrometry. J Proteome Res 2025. [PMID: 40344201 DOI: 10.1021/acs.jproteome.4c00973] [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: 05/11/2025]
Abstract
Identifying detectable peptides, known as flyers, is key in mass spectrometry-based proteomics. Peptide detectability is strongly related to peptide sequences and their resulting physicochemical properties. Moreover, the high variability in MS data challenges the development of a generic model for detectability prediction, underlining the need for customizable tools. We present Pfly, a deep learning model developed to predict peptide detectability based solely on peptide sequence. Pfly is a versatile and reliable state-of-the-art tool, offering high performance, accessibility, and easy customizability for end-users. This adaptability allows researchers to tailor Pfly to specific experimental conditions, improving accuracy and expanding applicability across various research fields. Pfly is an encoder-decoder with an attention mechanism, classifying peptides as flyers or non-flyers, and providing both binary and categorical probabilities for four distinct classes defined in this study. The model was initially trained on a synthetic peptide library and subsequently fine-tuned with a biological dataset to mitigate bias toward synthesizability, improving predictive capacity and outperforming state-of-the-art predictors in benchmark comparisons across different human and cross-species datasets. The study further investigates the influence of protein abundance and rescoring, illustrating the negative impact on peptide identification due to misclassification. Pfly has been integrated into the DLOmix framework and is accessible on GitHub at https://github.com/wilhelm-lab/dlomix.
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Affiliation(s)
- Naim Abdul-Khalek
- Department of Chemistry and Bioscience, Aalborg University, 9220 Aalborg, Denmark
| | - Mario Picciani
- Computational Mass Spectrometry, School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
| | - Omar Shouman
- Computational Mass Spectrometry, School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
| | - Reinhard Wimmer
- Department of Chemistry and Bioscience, Aalborg University, 9220 Aalborg, Denmark
| | | | - Mathias Wilhelm
- Computational Mass Spectrometry, School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
- Munich Data Science Institute, Technical University of Munich, 85748 Garching, Germany
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2
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Wang Z, Liu PK, Li L. A Tutorial Review of Labeling Methods in Mass Spectrometry-Based Quantitative Proteomics. ACS MEASUREMENT SCIENCE AU 2024; 4:315-337. [PMID: 39184361 PMCID: PMC11342459 DOI: 10.1021/acsmeasuresciau.4c00007] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 04/01/2024] [Accepted: 04/03/2024] [Indexed: 08/27/2024]
Abstract
Recent advancements in mass spectrometry (MS) have revolutionized quantitative proteomics, with multiplex isotope labeling emerging as a key strategy for enhancing accuracy, precision, and throughput. This tutorial review offers a comprehensive overview of multiplex isotope labeling techniques, including precursor-based, mass defect-based, reporter ion-based, and hybrid labeling methods. It details their fundamental principles, advantages, and inherent limitations along with strategies to mitigate the limitation of ratio-distortion. This review will also cover the applications and latest progress in these labeling techniques across various domains, including cancer biomarker discovery, neuroproteomics, post-translational modification analysis, cross-linking MS, and single-cell proteomics. This Review aims to provide guidance for researchers on selecting appropriate methods for their specific goals while also highlighting the potential future directions in this rapidly evolving field.
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Affiliation(s)
- Zicong Wang
- School
of Pharmacy, University of Wisconsin—Madison, Madison, Wisconsin 53705, United States
| | - Peng-Kai Liu
- Biophysics
Graduate program, University of Wisconsin—Madison, Madison, Wisconsin 53705, United States
| | - Lingjun Li
- School
of Pharmacy, University of Wisconsin—Madison, Madison, Wisconsin 53705, United States
- Biophysics
Graduate program, University of Wisconsin—Madison, Madison, Wisconsin 53705, United States
- Department
of Chemistry, University of Wisconsin—Madison, Madison, Wisconsin 53706, United States
- Lachman
Institute for Pharmaceutical Development, School of Pharmacy, University of Wisconsin—Madison, Madison, Wisconsin 53705, United States
- Wisconsin
Center for NanoBioSystems, School of Pharmacy, University of Wisconsin—Madison, Madison, Wisconsin 53705, United States
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3
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Kalhor M, Lapin J, Picciani M, Wilhelm M. Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors Into Peptide Identification. Mol Cell Proteomics 2024; 23:100798. [PMID: 38871251 PMCID: PMC11269915 DOI: 10.1016/j.mcpro.2024.100798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 05/26/2024] [Accepted: 06/09/2024] [Indexed: 06/15/2024] Open
Abstract
Rescoring of peptide spectrum matches originating from database search engines enabled by peptide property predictors is exceeding the performance of peptide identification from traditional database search engines. In contrast to the peptide spectrum match scores calculated by traditional database search engines, rescoring peptide spectrum matches generates scores based on comparing observed and predicted peptide properties, such as fragment ion intensities and retention times. These newly generated scores enable a more efficient discrimination between correct and incorrect peptide spectrum matches. This approach was shown to lead to substantial improvements in the number of confidently identified peptides, facilitating the analysis of challenging datasets in various fields such as immunopeptidomics, metaproteomics, proteogenomics, and single-cell proteomics. In this review, we summarize the key elements leading up to the recent introduction of multiple data-driven rescoring pipelines. We provide an overview of relevant post-processing rescoring tools, introduce prominent data-driven rescoring pipelines for various applications, and highlight limitations, opportunities, and future perspectives of this approach and its impact on mass spectrometry-based proteomics.
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Affiliation(s)
- Mostafa Kalhor
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Joel Lapin
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Mario Picciani
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Mathias Wilhelm
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany; Munich Data Science Institute, Technical University of Munich, Garching, Germany.
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4
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Abdul-Khalek N, Wimmer R, Overgaard MT, Gregersen Echers S. Insight on physicochemical properties governing peptide MS1 response in HPLC-ESI-MS/MS: A deep learning approach. Comput Struct Biotechnol J 2023; 21:3715-3727. [PMID: 37560124 PMCID: PMC10407266 DOI: 10.1016/j.csbj.2023.07.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 07/13/2023] [Accepted: 07/19/2023] [Indexed: 08/11/2023] Open
Abstract
Accurate and absolute quantification of peptides in complex mixtures using quantitative mass spectrometry (MS)-based methods requires foreground knowledge and isotopically labeled standards, thereby increasing analytical expenses, time consumption, and labor, thus limiting the number of peptides that can be accurately quantified. This originates from differential ionization efficiency between peptides and thus, understanding the physicochemical properties that influence the ionization and response in MS analysis is essential for developing less restrictive label-free quantitative methods. Here, we used equimolar peptide pool repository data to develop a deep learning model capable of identifying amino acids influencing the MS1 response. By using an encoder-decoder with an attention mechanism and correlating attention weights with amino acid physicochemical properties, we obtain insight on properties governing the peptide-level MS1 response within the datasets. While the problem cannot be described by one single set of amino acids and properties, distinct patterns were reproducibly obtained. Properties are grouped in three main categories related to peptide hydrophobicity, charge, and structural propensities. Moreover, our model can predict MS1 intensity output under defined conditions based solely on peptide sequence input. Using a refined training dataset, the model predicted log-transformed peptide MS1 intensities with an average error of 9.7 ± 0.5% based on 5-fold cross validation, and outperformed random forest and ridge regression models on both log-transformed and real scale data. This work demonstrates how deep learning can facilitate identification of physicochemical properties influencing peptide MS1 responses, but also illustrates how sequence-based response prediction and label-free peptide-level quantification may impact future workflows within quantitative proteomics.
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Affiliation(s)
- Naim Abdul-Khalek
- Department of Chemistry and Bioscience, Aalborg University, Aalborg 9220, Denmark
| | - Reinhard Wimmer
- Department of Chemistry and Bioscience, Aalborg University, Aalborg 9220, Denmark
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5
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Rusilowicz M, Newman DW, Creamer DR, Johnson J, Adair K, Harman VM, Grant CM, Beynon RJ, Hubbard SJ. AlacatDesigner─Computational Design of Peptide Concatamers for Protein Quantitation. J Proteome Res 2023; 22:594-604. [PMID: 36688735 PMCID: PMC9903321 DOI: 10.1021/acs.jproteome.2c00608] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Protein quantitation via mass spectrometry relies on peptide proxies for the parent protein from which abundances are estimated. Owing to the variability in signal from individual peptides, accurate absolute quantitation usually relies on the addition of an external standard. Typically, this involves stable isotope-labeled peptides, delivered singly or as a concatenated recombinant protein. Consequently, the selection of the most appropriate surrogate peptides and the attendant design in recombinant proteins termed QconCATs are challenges for proteome science. QconCATs can now be built in a "a-la-carte" assembly method using synthetic biology: ALACATs. To assist their design, we present "AlacatDesigner", a tool that supports the peptide selection for recombinant protein standards based on the user's target protein. The user-customizable tool considers existing databases, occurrence in the literature, potential post-translational modifications, predicted miscleavage, predicted divergence of the peptide and protein quantifications, and ionization potential within the mass spectrometer. We show that peptide selections are enriched for good proteotypic and quantotypic candidates compared to empirical data. The software is freely available to use either via a web interface AlacatDesigner, downloaded as a Desktop application or imported as a Python package for the command line interface or in scripts.
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Affiliation(s)
- Martin Rusilowicz
- Division
of Evolution, Infection and Genomics, School of Biological Sciences,
Faculty of Biology, Medicine and Health, Manchester Academic Health
Science Centre, University of Manchester, Manchester M13 9PT, United Kingdom
| | - David W. Newman
- Division
of Evolution, Infection and Genomics, School of Biological Sciences,
Faculty of Biology, Medicine and Health, Manchester Academic Health
Science Centre, University of Manchester, Manchester M13 9PT, United Kingdom
| | - Declan R. Creamer
- Division
of Molecular and Cellular Function, School of Biological Sciences,
Faculty of Biology, Medicine and Health, Manchester Academic Health
Science Centre, University of Manchester, Manchester M13 9PT, United Kingdom
| | - James Johnson
- GeneMill,
Institute of Systems Molecular and Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, United
Kingdom
| | - Kareena Adair
- Centre
for Proteome Research, Institute of Systems and Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, United
Kingdom
| | - Victoria M. Harman
- Centre
for Proteome Research, Institute of Systems and Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, United
Kingdom
| | - Chris M. Grant
- Division
of Molecular and Cellular Function, School of Biological Sciences,
Faculty of Biology, Medicine and Health, Manchester Academic Health
Science Centre, University of Manchester, Manchester M13 9PT, United Kingdom
| | - Robert J. Beynon
- Centre
for Proteome Research, Institute of Systems and Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, United
Kingdom
| | - Simon J. Hubbard
- Division
of Evolution, Infection and Genomics, School of Biological Sciences,
Faculty of Biology, Medicine and Health, Manchester Academic Health
Science Centre, University of Manchester, Manchester M13 9PT, United Kingdom,
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6
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Dincer AB, Lu Y, Schweppe DK, Oh S, Noble WS. Reducing Peptide Sequence Bias in Quantitative Mass Spectrometry Data with Machine Learning. J Proteome Res 2022; 21:1771-1782. [PMID: 35696663 DOI: 10.1021/acs.jproteome.2c00211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Quantitative mass spectrometry measurements of peptides necessarily incorporate sequence-specific biases that reflect the behavior of the peptide during enzymatic digestion and liquid chromatography and in a mass spectrometer. These sequence-specific effects impair quantification accuracy, yielding peptide quantities that are systematically under- or overestimated. We provide empirical evidence for the existence of such biases, and we use a deep neural network, called Pepper, to automatically identify and reduce these biases. The model generalizes to new proteins and new runs within a related set of tandem mass spectrometry experiments, and the learned coefficients themselves reflect expected physicochemical properties of the corresponding peptide sequences. The resulting adjusted abundance measurements are more correlated with mRNA-based gene expression measurements than the unadjusted measurements. Pepper is suitable for data generated on a variety of mass spectrometry instruments and can be used with labeled or label-free approaches and with data-independent or data-dependent acquisition.
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Affiliation(s)
- Ayse B Dincer
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Yang Lu
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Devin K Schweppe
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Sewoong Oh
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - William Stafford Noble
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States.,Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
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7
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Abstract
Miniaturization is an important trend in modern analytical instrument development, including miniaturized gas chromatography and liquid chromatography, as well as micro bore columns and capillary-to-microfluidics-based platforms. Apart from the miniaturization of the separation column, which is the core part of a chromatographic system, other parts of the system, including the sampler, pumping system, gradient generation, and detection systems, have been miniaturized. Miniaturized liquid chromatography significantly reduces solvent and sample consumption while providing comparable or even better separation efficiency. When liquid chromatography is coupled with mass spectroscopy, a low flow rate can increase the ionization efficiency, leading to enhanced sensitivity of the mass spectrometer. In contrast, normal-scale liquid chromatography suffers from its relatively high volumetric flow rate, which challenges the scanning frequency of the mass spectrometer. On the other hand because of the small sample size, other detection strategies such as spectrometric methods cannot provide sufficient sensitivity and limits of detection. In this sense, mass spectrometry has become the detection method of choice for micro-scale liquid-phase chromatography. Miniaturized liquid chromatography can diminish sample dilution efficiently when extremely small amounts of samples are used. The main driving force for this miniaturization trend, especially in liquid-phase separations, is the desperate need for microscale analyses of biological and clinical samples, given these samples are precious and the sample size is usually very small. At present, microscale liquid-phase chromatography is the only method of choice for such small, precious, and highly informative samples. The miniaturization of liquid chromatography systems, especially chromatographic columns, would be advantageous to the modularization and integration of liquid chromatography instrumental systems. Chip liquid chromatography is an integration of chromatography columns, liquid control systems, and detection methods on a single microfluidic chip. Chip liquid chromatography is an excellent format for the miniaturization of liquid chromatography systems, and it has already attracted significant attention from academia and industry. However, this attempt is challenging, and great effort is required on fundamental techniques, such as the substrate material of the microfluidic chip, structure of the micro-chromatography column, fluid control method, and detection methods, in order to make the chips suitable for liquid chromatography. Currently, the major problem in chip liquid chromatography is that the properties of the chip substrate materials cannot meet the requirements for further miniaturization and integration of chip liquid chromatography. The strength of the existing chip substrate materials is generally below 60 MPa, and the material properties limit further advances in the miniaturization and integration of chromatographic chips. Therefore, new chip substrate materials and the standard of chip channel design such as channel size and channel structure should be the key for further development of chip liquid chromatography. Mainstream instrumentation companies as well as new start-up innovation companies are now undertaking efforts toward the development of microchip liquid chromatographic products. Agilent, the first instrumentation company that introduced commercial microchip liquid chromatographic columns to the market, has led this field. Apart from microchip-based columns, Agilent introduced trap columns for different kinds of biological molecules as well as gradient generation systems for microchip-based liquid phase chromatography. Recently, another start-up company introduced microchip columns based on the in situ microfabrication of the column bed rather than packing the column with a particulate material. Such developments in microfabrication may further propel the advancement of micro-scale liquid-phase chromatography to an unprecedented level, which is beyond the conventional components and materials employed in normal-scale liquid chromatography. This review introduces the recent research progress in microchip liquid chromatography technologies, and briefly discusses the current state of commercialization of microchips for liquid chromatography by major instrumentation companies.
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Affiliation(s)
- Hanrong WEN
- 厦门大学化学化工学院, 福建 厦门 361005
- College of Chemistry & Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Jue ZHU
- 厦门大学化学化工学院, 福建 厦门 361005
- College of Chemistry & Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Bo ZHANG
- 厦门大学化学化工学院, 福建 厦门 361005
- College of Chemistry & Chemical Engineering, Xiamen University, Xiamen 361005, China
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8
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Wilburn DB, Richards AL, Swaney DL, Searle BC. CIDer: A Statistical Framework for Interpreting Differences in CID and HCD Fragmentation. J Proteome Res 2021; 20:1951-1965. [PMID: 33729787 DOI: 10.1021/acs.jproteome.0c00964] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Library searching is a powerful technique for detecting peptides using either data independent or data dependent acquisition. While both large-scale spectrum library curators and deep learning prediction approaches have focused on beam-type CID fragmentation (HCD), resonance CID fragmentation remains a popular technique. Here we demonstrate an approach to model the differences between HCD and CID spectra, and present a software tool, CIDer, for converting libraries between the two fragmentation methods. We demonstrate that just using a combination of simple linear models and basic principles of peptide fragmentation, we can explain up to 43% of the variation between ions fragmented by HCD and CID across an array of collision energy settings. We further show that in some circumstances, searching converted CID libraries can detect more peptides than searching existing CID libraries or libraries of machine learning predictions from FASTA databases. These results suggest that leveraging information in existing libraries by converting between HCD and CID libraries may be an effective interim solution while large-scale CID libraries are being developed.
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Affiliation(s)
- Damien B Wilburn
- Institute for Systems Biology, Seattle, Washington 98109, United States.,Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Alicia L Richards
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, California 94158, United States.,J. David Gladstone Institutes, San Francisco, California 94158, United States.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, California 94158, United States
| | - Danielle L Swaney
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, California 94158, United States.,J. David Gladstone Institutes, San Francisco, California 94158, United States.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, California 94158, United States
| | - Brian C Searle
- Institute for Systems Biology, Seattle, Washington 98109, United States
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9
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Waas M, Littrell J, Gundry RL. CIRFESS: An Interactive Resource for Querying the Set of Theoretically Detectable Peptides for Cell Surface and Extracellular Enrichment Proteomic Studies. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2020; 31:1389-1397. [PMID: 32212654 PMCID: PMC8116119 DOI: 10.1021/jasms.0c00021] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Cell surface transmembrane, extracellular, and secreted proteins are high value targets for immunophenotyping, drug development, and studies related to intercellular communication in health and disease. As the number of specific and validated affinity reagents that target this subproteome are limited, mass spectrometry (MS)-based approaches will continue to play a critical role in enabling discovery and quantitation of these molecules. Given the technical considerations that make MS-based cell surface proteome studies uniquely challenging, it can be difficult to select an appropriate experimental approach. To this end, we have integrated multiple prediction strategies and annotations into a single online resource, Compiled Interactive Resource for Extracellular and Surface Studies (CIRFESS). CIRFESS enables rapid interrogation of the human proteome to reveal the cell surface proteome theoretically detectable by current approaches and highlights where current prediction strategies provide concordant and discordant information. We applied CIRFESS to identify the percentage of various subsets of the proteome which are expected to be captured by targeted enrichment strategies, including two established methods and one that is possible but not yet demonstrated. These results will inform the selection of available proteomic strategies and development of new strategies to enhance coverage of the cell surface and extracellular proteome. CIRFESS is available at www.cellsurfer.net/cirfess.
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Affiliation(s)
- Matthew Waas
- CardiOmics Program, Center for Heart and Vascular Research, Division of Cardiovascular Medicine, and Department of Cellular and Integrative Physiology, University of Nebraska Medical Center, Omaha, Nebraska 68198, United States
| | - Jack Littrell
- CardiOmics Program, Center for Heart and Vascular Research, Division of Cardiovascular Medicine, and Department of Cellular and Integrative Physiology, University of Nebraska Medical Center, Omaha, Nebraska 68198, United States
| | - Rebekah L Gundry
- CardiOmics Program, Center for Heart and Vascular Research, Division of Cardiovascular Medicine, and Department of Cellular and Integrative Physiology, University of Nebraska Medical Center, Omaha, Nebraska 68198, United States
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10
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Pino LK, Searle BC, Bollinger JG, Nunn B, MacLean B, MacCoss MJ. The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics. MASS SPECTROMETRY REVIEWS 2020; 39:229-244. [PMID: 28691345 PMCID: PMC5799042 DOI: 10.1002/mas.21540] [Citation(s) in RCA: 507] [Impact Index Per Article: 101.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Accepted: 06/01/2017] [Indexed: 05/03/2023]
Abstract
Skyline is a freely available, open-source Windows client application for accelerating targeted proteomics experimentation, with an emphasis on the proteomics and mass spectrometry community as users and as contributors. This review covers the informatics encompassed by the Skyline ecosystem, from computationally assisted targeted mass spectrometry method development, to raw acquisition file data processing, and quantitative analysis and results sharing.
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Affiliation(s)
- Lindsay K Pino
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington
| | - Brian C Searle
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington
| | - James G Bollinger
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington
| | - Brook Nunn
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington
| | - Brendan MacLean
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington
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11
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Gao Z, Chang C, Yang J, Zhu Y, Fu Y. AP3: An Advanced Proteotypic Peptide Predictor for Targeted Proteomics by Incorporating Peptide Digestibility. Anal Chem 2019; 91:8705-8711. [DOI: 10.1021/acs.analchem.9b02520] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Affiliation(s)
- Zhiqiang Gao
- National Center for Mathematics and Interdisciplinary Sciences, Key Laboratory of Random Complex Structures and Data Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Cheng Chang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Jinghan Yang
- National Center for Mathematics and Interdisciplinary Sciences, Key Laboratory of Random Complex Structures and Data Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yunping Zhu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
- Anhui Medical University, Hefei 230032, China
| | - Yan Fu
- National Center for Mathematics and Interdisciplinary Sciences, Key Laboratory of Random Complex Structures and Data Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
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12
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Pappireddi N, Martin L, Wühr M. A Review on Quantitative Multiplexed Proteomics. Chembiochem 2019; 20:1210-1224. [PMID: 30609196 PMCID: PMC6520187 DOI: 10.1002/cbic.201800650] [Citation(s) in RCA: 200] [Impact Index Per Article: 33.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Revised: 12/27/2018] [Indexed: 12/11/2022]
Abstract
Over the last few decades, mass spectrometry-based proteomics has become an increasingly powerful tool that is now able to routinely detect and quantify thousands of proteins. A major advance for global protein quantification was the introduction of isobaric tags, which, in a single experiment, enabled the global quantification of proteins across multiple samples. Herein, these methods are referred to as multiplexed proteomics. The principles, advantages, and drawbacks of various multiplexed proteomics techniques are discussed and compared with alternative approaches. We also discuss how the emerging combination of multiplexing with targeted proteomics might enable the reliable and high-quality quantification of very low abundance proteins across multiple conditions. Lastly, we suggest that fusing multiplexed proteomics with data-independent acquisition approaches might enable the comparison of hundreds of different samples without missing values, while maintaining the superb measurement precision and accuracy obtainable with isobaric tag quantification.
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Affiliation(s)
- Nishant Pappireddi
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
- The Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Lance Martin
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
- The Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Martin Wühr
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
- The Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA
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13
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Kecskemeti A, Gaspar A. Particle-based liquid chromatographic separations in microfluidic devices - A review. Anal Chim Acta 2018; 1021:1-19. [DOI: 10.1016/j.aca.2018.01.064] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Revised: 01/18/2018] [Accepted: 01/21/2018] [Indexed: 01/06/2023]
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14
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Haghighi F, Talebpour Z, Nezhad AS. Towards fully integrated liquid chromatography on a chip: Evolution and evaluation. Trends Analyt Chem 2018. [DOI: 10.1016/j.trac.2018.05.002] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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15
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Jonckheere W, Dermauw W, Khalighi M, Pavlidi N, Reubens W, Baggerman G, Tirry L, Menschaert G, Kant MR, Vanholme B, Van Leeuwen T. A Gene Family Coding for Salivary Proteins (SHOT) of the Polyphagous Spider Mite Tetranychus urticae Exhibits Fast Host-Dependent Transcriptional Plasticity. MOLECULAR PLANT-MICROBE INTERACTIONS : MPMI 2018; 31:112-124. [PMID: 29094648 DOI: 10.1094/mpmi-06-17-0139-r] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
The salivary protein repertoire released by the herbivorous pest Tetranychus urticae is assumed to hold keys to its success on diverse crops. We report on a spider mite-specific protein family that is expanded in T. urticae. The encoding genes have an expression pattern restricted to the anterior podocephalic glands, while peptide fragments were found in the T. urticae secretome, supporting the salivary nature of these proteins. As peptide fragments were identified in a host-dependent manner, we designated this family as the SHOT (secreted host-responsive protein of Tetranychidae) family. The proteins were divided in three groups based on sequence similarity. Unlike TuSHOT3 genes, TuSHOT1 and TuSHOT2 genes were highly expressed when feeding on a subset of family Fabaceae, while expression was depleted on other hosts. TuSHOT1 and TuSHOT2 expression was induced within 24 h after certain host transfers, pointing toward transcriptional plasticity rather than selection as the cause. Transfer from an 'inducer' to a 'noninducer' plant was associated with slow yet strong downregulation of TuSHOT1 and TuSHOT2, occurring over generations rather than hours. This asymmetric on and off regulation points toward host-specific effects of SHOT proteins, which is further supported by the diversity of SHOT genes identified in Tetranychidae with a distinct host repertoire.
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Affiliation(s)
- Wim Jonckheere
- 1 Laboratory of Agrozoology, Department of Crop Protection, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, 9000 Ghent, Belgium
- 2 Department of Evolutionary Biology, Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, The Netherlands
| | - Wannes Dermauw
- 1 Laboratory of Agrozoology, Department of Crop Protection, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, 9000 Ghent, Belgium
| | - Mousaalreza Khalighi
- 1 Laboratory of Agrozoology, Department of Crop Protection, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, 9000 Ghent, Belgium
| | - Nena Pavlidi
- 2 Department of Evolutionary Biology, Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, The Netherlands
| | - Wim Reubens
- 1 Laboratory of Agrozoology, Department of Crop Protection, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, 9000 Ghent, Belgium
| | - Geert Baggerman
- 3 Center for Proteomics (CFP), University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium
- 4 Flemish Institute for Technological Research (VITO), Boeretang 200, 2400 Mol, Belgium
| | - Luc Tirry
- 1 Laboratory of Agrozoology, Department of Crop Protection, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, 9000 Ghent, Belgium
| | - Gerben Menschaert
- 5 Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University
| | - Merijn R Kant
- 6 Department of Population Biology, Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam
| | - Bartel Vanholme
- 7 Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 927, 9052 Gent, Belgium; and
- 8 Centre for Plant Systems Biology, VIB, Technologiepark 927, 9052 Gent, Belgium
| | - Thomas Van Leeuwen
- 1 Laboratory of Agrozoology, Department of Crop Protection, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, 9000 Ghent, Belgium
- 2 Department of Evolutionary Biology, Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, The Netherlands
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16
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Abelin JG, Keskin DB, Sarkizova S, Hartigan CR, Zhang W, Sidney J, Stevens J, Lane W, Zhang GL, Eisenhaure TM, Clauser KR, Hacohen N, Rooney MS, Carr SA, Wu CJ. Mass Spectrometry Profiling of HLA-Associated Peptidomes in Mono-allelic Cells Enables More Accurate Epitope Prediction. Immunity 2017; 46:315-326. [PMID: 28228285 DOI: 10.1016/j.immuni.2017.02.007] [Citation(s) in RCA: 451] [Impact Index Per Article: 56.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2016] [Revised: 11/29/2016] [Accepted: 12/29/2016] [Indexed: 12/11/2022]
Abstract
Identification of human leukocyte antigen (HLA)-bound peptides by liquid chromatography-tandem mass spectrometry (LC-MS/MS) is poised to provide a deep understanding of rules underlying antigen presentation. However, a key obstacle is the ambiguity that arises from the co-expression of multiple HLA alleles. Here, we have implemented a scalable mono-allelic strategy for profiling the HLA peptidome. By using cell lines expressing a single HLA allele, optimizing immunopurifications, and developing an application-specific spectral search algorithm, we identified thousands of peptides bound to 16 different HLA class I alleles. These data enabled the discovery of subdominant binding motifs and an integrative analysis quantifying the contribution of factors critical to epitope presentation, such as protein cleavage and gene expression. We trained neural-network prediction algorithms with our large dataset (>24,000 peptides) and outperformed algorithms trained on datasets of peptides with measured affinities. We thus demonstrate a strategy for systematically learning the rules of endogenous antigen presentation.
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Affiliation(s)
| | - Derin B Keskin
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA; Harvard Medical School, Boston, MA, 02115, USA; Department of Computer Science, Metropolitan College, Boston University, Boston, MA, 02215, USA
| | - Siranush Sarkizova
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02142, USA
| | | | - Wandi Zhang
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - John Sidney
- La Jolla Institute for Allergy and Immunology, 92037, La Jolla, CA
| | - Jonathan Stevens
- Tissue Typing Laboratory, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - William Lane
- Tissue Typing Laboratory, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Guang Lan Zhang
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA; Harvard Medical School, Boston, MA, 02115, USA; Department of Computer Science, Metropolitan College, Boston University, Boston, MA, 02215, USA
| | | | | | - Nir Hacohen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA; Center for Cancer Immunology, Massachusetts General Hospital, Boston, MA, 02114, USA.
| | - Michael S Rooney
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Harvard/MIT Division of Health Sciences and Technology, Cambridge, Massachusetts, 02139 USA; Neon Therapeutics, Cambridge, MA, 02139, USA.
| | - Steven A Carr
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Catherine J Wu
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA; Harvard Medical School, Boston, MA, 02115, USA.
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17
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Targeted proteomic assays for quantitation of proteins identified by proteogenomic analysis of ovarian cancer. Sci Data 2017; 4:170091. [PMID: 28722704 PMCID: PMC5516542 DOI: 10.1038/sdata.2017.91] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2016] [Accepted: 06/01/2017] [Indexed: 02/06/2023] Open
Abstract
Mass spectrometry (MS) based targeted proteomic methods such as selected reaction monitoring (SRM) are emerging as a promising tool for verification of candidate proteins in biological and biomedical applications. The Clinical Proteomic Tumor Analysis Consortium (CPTAC) of the National Cancer Institute has investigated the standardization and analytical validation of the SRM assays and demonstrated robust analytical performance on different instruments across different laboratories. An Assay Portal has also been established by CPTAC to provide the research community a resource consisting of large sets of targeted MS-based assays, and a depository to share assays publicly. Herein, we report the development of 98 SRM assays that have been thoroughly characterized according to the CPTAC Assay Characterization Guidance Document; 37 of these passed all five experimental tests. The assays cover 70 proteins previously identified at the protein level in ovarian tumors. The experiments, methods and results for characterizing these SRM assays for their MS response, repeatability, selectivity, stability, and endogenous detection are described in detail. Data are available via PeptideAtlas, Panorama and the CPTAC Assay Portal.
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18
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Jarnuczak AF, Lee DCH, Lawless C, Holman SW, Eyers CE, Hubbard SJ. Analysis of Intrinsic Peptide Detectability via Integrated Label-Free and SRM-Based Absolute Quantitative Proteomics. J Proteome Res 2016; 15:2945-59. [PMID: 27454336 DOI: 10.1021/acs.jproteome.6b00048] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Quantitative mass spectrometry-based proteomics of complex biological samples remains challenging in part due to the variability and charge competition arising during electrospray ionization (ESI) of peptides and the subsequent transfer and detection of ions. These issues preclude direct quantification from signal intensity alone in the absence of a standard. A deeper understanding of the governing principles of peptide ionization and exploitation of the inherent ionization and detection parameters of individual peptides is thus of great value. Here, using the yeast proteome as a model system, we establish the concept of peptide F-factor as a measure of detectability, closely related to ionization efficiency. F-factor is calculated by normalizing peptide precursor ion intensity by absolute abundance of the parent protein. We investigated F-factor characteristics in different shotgun proteomics experiments, including across multiple ESI-based LC-MS platforms. We show that F-factors mirror previously observed physicochemical predictors as peptide detectability but demonstrate a nonlinear relationship between hydrophobicity and peptide detectability. Similarly, we use F-factors to show how peptide ion coelution adversely affects detectability and ionization. We suggest that F-factors have great utility for understanding peptide detectability and gas-phase ion chemistry in complex peptide mixtures, selection of surrogate peptides in targeted MS studies, and for calibration of peptide ion signal in label-free workflows. Data are available via ProteomeXchange with identifier PXD003472.
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Affiliation(s)
- Andrew F Jarnuczak
- Faculty of Biology, Medicine and Health, University of Manchester , Michael Smith Building, Oxford Road, Manchester M13 9PT, United Kingdom
| | - Dave C H Lee
- Faculty of Biology, Medicine and Health, University of Manchester , Second Floor, Wolfson Molecular Imaging Centre, 27 Palatine Road, Withington, Manchester, M20 3JL, United Kingdom
| | - Craig Lawless
- Faculty of Biology, Medicine and Health, University of Manchester , Michael Smith Building, Oxford Road, Manchester M13 9PT, United Kingdom
| | - Stephen W Holman
- Centre for Proteome Research, University of Liverpool , Department of Biochemistry, Institute of Integrative Biology, Crown Street, Liverpool, L69 7ZB, United Kingdom
| | - Claire E Eyers
- Centre for Proteome Research, University of Liverpool , Department of Biochemistry, Institute of Integrative Biology, Crown Street, Liverpool, L69 7ZB, United Kingdom
| | - Simon J Hubbard
- Faculty of Biology, Medicine and Health, University of Manchester , Michael Smith Building, Oxford Road, Manchester M13 9PT, United Kingdom
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19
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Singh SA, Aikawa E, Aikawa M. Current Trends and Future Perspectives of State-of-the-Art Proteomics Technologies Applied to Cardiovascular Disease Research. Circ J 2016; 80:1674-83. [PMID: 27430298 DOI: 10.1253/circj.cj-16-0499] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The use of mass spectrometry (MS)-dependent protein research is increasing in the cardiovascular sciences. A major reason for this is the versatility of and ability for MS technologies to accommodate a variety of biological questions such as those pertaining to basic research and clinical applications. In addition, mass spectrometers are becoming easier to operate, and require less expertise to run standard proteomics experiments. Nonetheless, despite the increasing interest in proteomics, many non-expert end users may not be as familiar with the variety of mass spectrometric tools and workflows available to them. We therefore review the major strategies used in unbiased and targeted MS, while providing specific applications in cardiovascular research. Because MS technologies are developing rapidly, it is important to understand the core concepts, strengths and weaknesses. Most importantly, we hope to inspire the further integration of this exciting technology into everyday research in the cardiovascular sciences. (Circ J 2016; 80: 1674-1683).
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Affiliation(s)
- Sasha A Singh
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School
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20
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Hoofnagle AN, Whiteaker JR, Carr SA, Kuhn E, Liu T, Massoni SA, Thomas SN, Townsend RR, Zimmerman LJ, Boja E, Chen J, Crimmins DL, Davies SR, Gao Y, Hiltke TR, Ketchum KA, Kinsinger CR, Mesri M, Meyer MR, Qian WJ, Schoenherr RM, Scott MG, Shi T, Whiteley GR, Wrobel JA, Wu C, Ackermann BL, Aebersold R, Barnidge DR, Bunk DM, Clarke N, Fishman JB, Grant RP, Kusebauch U, Kushnir MM, Lowenthal MS, Moritz RL, Neubert H, Patterson SD, Rockwood AL, Rogers J, Singh RJ, Van Eyk JE, Wong SH, Zhang S, Chan DW, Chen X, Ellis MJ, Liebler DC, Rodland KD, Rodriguez H, Smith RD, Zhang Z, Zhang H, Paulovich AG. Recommendations for the Generation, Quantification, Storage, and Handling of Peptides Used for Mass Spectrometry-Based Assays. Clin Chem 2016; 62:48-69. [PMID: 26719571 DOI: 10.1373/clinchem.2015.250563] [Citation(s) in RCA: 162] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND For many years, basic and clinical researchers have taken advantage of the analytical sensitivity and specificity afforded by mass spectrometry in the measurement of proteins. Clinical laboratories are now beginning to deploy these work flows as well. For assays that use proteolysis to generate peptides for protein quantification and characterization, synthetic stable isotope-labeled internal standard peptides are of central importance. No general recommendations are currently available surrounding the use of peptides in protein mass spectrometric assays. CONTENT The Clinical Proteomic Tumor Analysis Consortium of the National Cancer Institute has collaborated with clinical laboratorians, peptide manufacturers, metrologists, representatives of the pharmaceutical industry, and other professionals to develop a consensus set of recommendations for peptide procurement, characterization, storage, and handling, as well as approaches to the interpretation of the data generated by mass spectrometric protein assays. Additionally, the importance of carefully characterized reference materials-in particular, peptide standards for the improved concordance of amino acid analysis methods across the industry-is highlighted. The alignment of practices around the use of peptides and the transparency of sample preparation protocols should allow for the harmonization of peptide and protein quantification in research and clinical care.
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Affiliation(s)
| | | | | | | | - Tao Liu
- Pacific Northwest National Laboratory, Richland, WA
| | | | | | | | | | | | - Jing Chen
- Johns Hopkins University, Baltimore, MD
| | | | | | - Yuqian Gao
- Pacific Northwest National Laboratory, Richland, WA
| | | | | | | | | | | | - Wei-Jun Qian
- Pacific Northwest National Laboratory, Richland, WA
| | | | | | - Tujin Shi
- Pacific Northwest National Laboratory, Richland, WA
| | | | - John A Wrobel
- University of North Carolina School of Medicine, Chapel Hill, NC
| | - Chaochao Wu
- Pacific Northwest National Laboratory, Richland, WA
| | | | - Ruedi Aebersold
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | | | | | | | | | - Russ P Grant
- Laboratory Corporation of America Holdings, Inc., Burlington, NC
| | | | - Mark M Kushnir
- University of Utah and ARUP Laboratories, Salt Lake City, UT
| | | | | | | | | | - Alan L Rockwood
- University of Utah and ARUP Laboratories, Salt Lake City, UT
| | | | | | | | | | | | | | - Xian Chen
- University of North Carolina School of Medicine, Chapel Hill, NC
| | | | | | | | | | | | | | - Hui Zhang
- Johns Hopkins University, Baltimore, MD
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21
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Searle BC, Egertson JD, Bollinger JG, Stergachis AB, MacCoss MJ. Using Data Independent Acquisition (DIA) to Model High-responding Peptides for Targeted Proteomics Experiments. Mol Cell Proteomics 2015; 14:2331-40. [PMID: 26100116 DOI: 10.1074/mcp.m115.051300] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2015] [Indexed: 11/06/2022] Open
Abstract
Targeted mass spectrometry is an essential tool for detecting quantitative changes in low abundant proteins throughout the proteome. Although selected reaction monitoring (SRM) is the preferred method for quantifying peptides in complex samples, the process of designing SRM assays is laborious. Peptides have widely varying signal responses dictated by sequence-specific physiochemical properties; one major challenge is in selecting representative peptides to target as a proxy for protein abundance. Here we present PREGO, a software tool that predicts high-responding peptides for SRM experiments. PREGO predicts peptide responses with an artificial neural network trained using 11 minimally redundant, maximally relevant properties. Crucial to its success, PREGO is trained using fragment ion intensities of equimolar synthetic peptides extracted from data independent acquisition experiments. Because of similarities in instrumentation and the nature of data collection, relative peptide responses from data independent acquisition experiments are a suitable substitute for SRM experiments because they both make quantitative measurements from integrated fragment ion chromatograms. Using an SRM experiment containing 12,973 peptides from 724 synthetic proteins, PREGO exhibits a 40-85% improvement over previously published approaches at selecting high-responding peptides. These results also represent a dramatic improvement over the rules-based peptide selection approaches commonly used in the literature.
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Affiliation(s)
- Brian C Searle
- From the ‡Department of Genome Sciences, University of Washington, Seattle, Washington 98195; §Proteome Software Inc., Portland, OR 97219
| | - Jarrett D Egertson
- From the ‡Department of Genome Sciences, University of Washington, Seattle, Washington 98195
| | - James G Bollinger
- From the ‡Department of Genome Sciences, University of Washington, Seattle, Washington 98195
| | - Andrew B Stergachis
- From the ‡Department of Genome Sciences, University of Washington, Seattle, Washington 98195
| | - Michael J MacCoss
- From the ‡Department of Genome Sciences, University of Washington, Seattle, Washington 98195;
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