101
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Enhanced differential expression statistics for data-independent acquisition proteomics. Sci Rep 2017; 7:5869. [PMID: 28724900 PMCID: PMC5517573 DOI: 10.1038/s41598-017-05949-y] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2016] [Accepted: 06/07/2017] [Indexed: 01/28/2023] Open
Abstract
We describe a new reproducibility-optimization method ROPECA for statistical analysis of proteomics data with a specific focus on the emerging data-independent acquisition (DIA) mass spectrometry technology. ROPECA optimizes the reproducibility of statistical testing on peptide-level and aggregates the peptide-level changes to determine differential protein-level expression. Using a ‘gold standard’ spike-in data and a hybrid proteome benchmark data we show the competitive performance of ROPECA over conventional protein-based analysis as well as state-of-the-art peptide-based tools especially in DIA data with consistent peptide measurements. Furthermore, we also demonstrate the improved accuracy of our method in clinical studies using proteomics data from a longitudinal human twin study.
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102
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Abstract
The quantitative measurement of the proteome has been shown to yield new insights into physiology and cell biology that cannot be determined from the genome and transcriptome because the quantitative relationship between transcriptome and proteome is complex. MS-based proteomics techniques, such as SWATH-MS, have recently advanced to the point at which they may be reliably applied by biologists who are not specialists in mass spectrometry. Here we provide standard protocols for preparation of tissue samples for input into the SWATH-MS analytical pipeline. These protocols are designed for high-throughput processing of tissues with ≥5 mg of sample available for analysis. Studies with extremely limited amounts of tissue should consider PCT-SWATH. An experienced single user should be able to process 48 samples per day for injection into the mass spectrometer, or up to 144 samples a week. The machine time necessary for running these samples with SWATH is approximately 1.5 hr per sample. Data acquisition protocols are also provided. © 2017 by John Wiley & Sons, Inc.
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Affiliation(s)
- Yibo Wu
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Evan G Williams
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,Faculty of Science, University of Zurich, Zurich, Switzerland
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103
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Rosenberger G, Liu Y, Röst HL, Ludwig C, Buil A, Bensimon A, Soste M, Spector TD, Dermitzakis ET, Collins BC, Malmström L, Aebersold R. Inference and quantification of peptidoforms in large sample cohorts by SWATH-MS. Nat Biotechnol 2017; 35:781-788. [PMID: 28604659 PMCID: PMC5593115 DOI: 10.1038/nbt.3908] [Citation(s) in RCA: 88] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Accepted: 05/22/2017] [Indexed: 01/01/2023]
Abstract
Consistent detection and quantification of protein post-translational modifications (PTMs) across sample cohorts is a prerequisite for functional analysis of biological processes. Data-independent acquisition (DIA) is a bottom-up mass spectrometry approach that provides complete information on precursor and fragment ions. However, owing to the convoluted structure of DIA data sets, confident, systematic identification and quantification of peptidoforms has remained challenging. Here, we present inference of peptidoforms (IPF), a fully automated algorithm that uses spectral libraries to query, validate and quantify peptidoforms in DIA data sets. The method was developed on data acquired by the DIA method SWATH-MS and benchmarked using a synthetic phosphopeptide reference data set and phosphopeptide-enriched samples. IPF reduced false site-localization by more than sevenfold compared with previous approaches, while recovering 85.4% of the true signals. Using IPF, we quantified peptidoforms in DIA data acquired from >200 samples of blood plasma of a human twin cohort and assessed the contribution of heritable, environmental and longitudinal effects on their PTMs.
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Affiliation(s)
- George Rosenberger
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,PhD Program in Systems Biology, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Yansheng Liu
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Hannes L Röst
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,Department of Genetics, Stanford University, Stanford, California, USA
| | - Christina Ludwig
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,Bavarian Biomolecular Mass Spectrometry Center (BayBioMS), Technical University Munich, Freising, Germany
| | - Alfonso Buil
- Research Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Roskilde, Denmark
| | - Ariel Bensimon
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Martin Soste
- Department of Biology, Institute of Biochemistry, ETH Zurich, Zurich, Switzerland
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Hospital Campus, London, UK
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Ben C Collins
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Lars Malmström
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,S3IT, University of Zurich, Zurich, Switzerland
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,Faculty of Science, University of Zurich, Zurich, Switzerland
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104
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Teleman J, Hauri S, Malmström J. Improvements in Mass Spectrometry Assay Library Generation for Targeted Proteomics. J Proteome Res 2017; 16:2384-2392. [PMID: 28516777 DOI: 10.1021/acs.jproteome.6b00928] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
In data-independent acquisition mass spectrometry (DIA-MS), targeted extraction of peptide signals in silico using mass spectrometry assay libraries is a successful method for the identification and quantification of proteins. However, it remains unclear if high quality assay libraries with more accurate peptide ion coordinates can improve peptide target identification rates in DIA analysis. In this study, we systematically improved and evaluated the common algorithmic steps for assay library generation and demonstrate that increased assay quality results in substantially higher identification rates of peptide targets from mouse organ protein lysates measured by DIA-MS. The introduced changes are (1) a new spectrum interpretation algorithm, (2) reapplication of segmented retention time normalization, (3) a ppm fragment mass error matching threshold, (4) usage of internal peptide fragments, and (5) a multilevel false discovery rate calculation. Taken together, these changes yielded 14-36% more identified peptide targets at 1% assay false discovery rate and are implemented in three new open source tools, Fraggle, Tramler, and Franklin, available at https://github.com/fickludd/eviltools . The improved algorithms provide ways to better utilize discovery MS data, translating to substantially increased DIA performance and ultimately better foundations for drawing biological conclusions in DIA-based experiments.
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Affiliation(s)
- Johan Teleman
- Department of Clinical Sciences, Lund University , BMC D13, 221 84 Lund, Sweden.,Department of Immunotechnology, Lund University , Medicon Village (Building 406), 223 81 Lund, Sweden
| | - Simon Hauri
- Department of Clinical Sciences, Lund University , BMC D13, 221 84 Lund, Sweden
| | - Johan Malmström
- Department of Clinical Sciences, Lund University , BMC D13, 221 84 Lund, Sweden
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105
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Meyer JG, Schilling B. Clinical applications of quantitative proteomics using targeted and untargeted data-independent acquisition techniques. Expert Rev Proteomics 2017; 14:419-429. [PMID: 28436239 PMCID: PMC5671767 DOI: 10.1080/14789450.2017.1322904] [Citation(s) in RCA: 100] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
INTRODUCTION While selected/multiple-reaction monitoring (SRM or MRM) is considered the gold standard for quantitative protein measurement, emerging data-independent acquisition (DIA) using high-resolution scans have opened a new dimension of high-throughput, comprehensive quantitative proteomics. These newer methodologies are particularly well suited for discovery of biomarker candidates from human disease samples, and for investigating and understanding human disease pathways. Areas covered: This article reviews the current state of targeted and untargeted DIA mass spectrometry-based proteomic workflows, including SRM, parallel-reaction monitoring (PRM) and untargeted DIA (e.g., SWATH). Corresponding bioinformatics strategies, as well as application in biological and clinical studies are presented. Expert commentary: Nascent application of highly-multiplexed untargeted DIA, such as SWATH, for accurate protein quantification from clinically relevant and disease-related samples shows great potential to comprehensively investigate biomarker candidates and understand disease.
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Affiliation(s)
- Jesse G Meyer
- a Mass Spectrometry Core , Buck Institute for Research on Aging , Novato , CA , USA
| | - Birgit Schilling
- a Mass Spectrometry Core , Buck Institute for Research on Aging , Novato , CA , USA
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106
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Chen G, Walmsley S, Cheung GCM, Chen L, Cheng CY, Beuerman RW, Wong TY, Zhou L, Choi H. Customized Consensus Spectral Library Building for Untargeted Quantitative Metabolomics Analysis with Data Independent Acquisition Mass Spectrometry and MetaboDIA Workflow. Anal Chem 2017; 89:4897-4906. [PMID: 28391692 DOI: 10.1021/acs.analchem.6b05006] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Data independent acquisition-mass spectrometry (DIA-MS) coupled with liquid chromatography is a promising approach for rapid, automatic sampling of MS/MS data in untargeted metabolomics. However, wide isolation windows in DIA-MS generate MS/MS spectra containing a mixed population of fragment ions together with their precursor ions. This precursor-fragment ion map in a comprehensive MS/MS spectral library is crucial for relative quantification of fragment ions uniquely representative of each precursor ion. However, existing reference libraries are not sufficient for this purpose since the fragmentation patterns of small molecules can vary in different instrument setups. Here we developed a bioinformatics workflow called MetaboDIA to build customized MS/MS spectral libraries using a user's own data dependent acquisition (DDA) data and to perform MS/MS-based quantification with DIA data, thus complementing conventional MS1-based quantification. MetaboDIA also allows users to build a spectral library directly from DIA data in studies of a large sample size. Using a marine algae data set, we show that quantification of fragment ions extracted with a customized MS/MS library can provide as reliable quantitative data as the direct quantification of precursor ions based on MS1 data. To test its applicability in complex samples, we applied MetaboDIA to a clinical serum metabolomics data set, where we built a DDA-based spectral library containing consensus spectra for 1829 compounds. We performed fragment ion quantification using DIA data using this library, yielding sensitive differential expression analysis.
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Affiliation(s)
- Gengbo Chen
- Saw Swee Hock School of Public Health, National University of Singapore , Singapore, Singapore 117547
| | - Scott Walmsley
- Department of Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus , Aurora, Colorado 80045, United States.,Computational Biosciences Program, University of Colorado Anschutz Medical Campus , Aurora, Colorado 80045, United States
| | - Gemmy C M Cheung
- Singapore National Eye Centre , Singapore 168751.,Ophthalmology and Visual Sciences Academic Clinical Research Program, Duke-NUS Medical School, National University of Singapore , Singapore 169857
| | - Liyan Chen
- Singapore Eye Research Institute , Singapore 168756
| | - Ching-Yu Cheng
- Ophthalmology and Visual Sciences Academic Clinical Research Program, Duke-NUS Medical School, National University of Singapore , Singapore 169857.,Singapore Eye Research Institute , Singapore 168756.,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore , Singapore 119228
| | - Roger W Beuerman
- Ophthalmology and Visual Sciences Academic Clinical Research Program, Duke-NUS Medical School, National University of Singapore , Singapore 169857.,Singapore Eye Research Institute , Singapore 168756.,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore , Singapore 119228.,Neuroscience and Behavioural Disorders Programme, Duke-NUS Medical School, National University of Singapore , Singapore 169857
| | - Tien Yin Wong
- Singapore National Eye Centre , Singapore 168751.,Ophthalmology and Visual Sciences Academic Clinical Research Program, Duke-NUS Medical School, National University of Singapore , Singapore 169857.,Singapore Eye Research Institute , Singapore 168756.,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore , Singapore 119228
| | - Lei Zhou
- Ophthalmology and Visual Sciences Academic Clinical Research Program, Duke-NUS Medical School, National University of Singapore , Singapore 169857.,Singapore Eye Research Institute , Singapore 168756.,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore , Singapore 119228
| | - Hyungwon Choi
- Saw Swee Hock School of Public Health, National University of Singapore , Singapore, Singapore 117547.,Institute of Molecular and Cell Biology, Agency for Science, Technology, and Research , Singapore, Singapore 138632
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107
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Nemeth J, Vongrad V, Metzner KJ, Strouvelle VP, Weber R, Pedrioli P, Aebersold R, Günthard HF, Collins BC. In Vivo and in Vitro Proteome Analysis of Human Immunodeficiency Virus (HIV)-1-infected, Human CD4 + T Cells. Mol Cell Proteomics 2017; 16:S108-S123. [PMID: 28223351 DOI: 10.1074/mcp.m116.065235] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Revised: 02/03/2017] [Indexed: 01/06/2023] Open
Abstract
Host-directed therapies against HIV-1 are thought to be critical for long term containment of the HIV-1 pandemic but remain elusive. Because HIV-1 infects and manipulates important effectors of both the innate and adaptive immune system, identifying modulations of the host cell systems in humans during HIV-1 infection may be crucial for the development of immune based therapies. Here, we quantified the changes of the proteome in human CD4+ T cells upon HIV-1 infection, both in vitro and in vivo A SWATH-MS approach was used to measure the proteome of human primary CD4+ T cells infected with HIV-1 in vitro as well as CD4+ T cells from HIV-1-infected patients with paired samples on and off antiretroviral treatment. In the in vitro experiment, the proteome of CD4+ T cells was quantified over a time course following HIV-1 infection. 1,725 host cell proteins and 4 HIV-1 proteins were quantified, with 145 proteins changing significantly during the time course. Changes in the proteome peaked 24 h after infection, concomitantly with significant HIV-1 protein production. In the in vivo branch of the study, CD4+ T cells from viremic patients and those with no detectable viral load after treatment were sorted, and the proteomes were quantified. We consistently detected 895 proteins, 172 of which were considered to be significantly different between the viremic patients and patients undergoing successful treatment. The proteome of the in vitro-infected CD4+ T cells was modulated on multiple functional levels, including TLR-4 signaling and the type 1 interferon signaling pathway. Perturbations in the type 1 interferon signaling pathway were recapitulated in CD4+ T cells from patients. The study shows that proteome maps generated by SWATH-MS indicate a range of functionally significant changes in the proteome of HIV-infected human CD4+ T cells. Exploring these perturbations in more detail may help identify new targets for immune based interventions.
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Affiliation(s)
- Johannes Nemeth
- From the ‡Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich 8091.,§Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich CH-8093
| | - Valentina Vongrad
- From the ‡Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich 8091.,‖Institute of Medical Virology, University of Zurich, Zurich 8057, Switzerland
| | - Karin J Metzner
- From the ‡Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich 8091.,‖Institute of Medical Virology, University of Zurich, Zurich 8057, Switzerland
| | - Victoria P Strouvelle
- From the ‡Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich 8091.,‖Institute of Medical Virology, University of Zurich, Zurich 8057, Switzerland
| | - Rainer Weber
- From the ‡Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich 8091
| | - Patrick Pedrioli
- §Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich CH-8093
| | - Ruedi Aebersold
- §Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich CH-8093.,**Faculty of Science, University of Zurich, Zurich 8057; and
| | - Huldrych F Günthard
- From the ‡Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich 8091; .,‖Institute of Medical Virology, University of Zurich, Zurich 8057, Switzerland
| | - Ben C Collins
- §Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich CH-8093;
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108
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Sabherwal S, English JA, Föcking M, Cagney G, Cotter DR. Blood biomarker discovery in drug-free schizophrenia: the contribution of proteomics and multiplex immunoassays. Expert Rev Proteomics 2016; 13:1141-1155. [DOI: 10.1080/14789450.2016.1252262] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Sophie Sabherwal
- Department of Psychiatry, Royal College of Surgeons in Ireland, ERC Beaumont Hospital, Dublin, Ireland
| | - Jane A. English
- Department of Psychiatry, Royal College of Surgeons in Ireland, ERC Beaumont Hospital, Dublin, Ireland
| | - Melanie Föcking
- Department of Psychiatry, Royal College of Surgeons in Ireland, ERC Beaumont Hospital, Dublin, Ireland
| | - Gerard Cagney
- Proteome Research Centre, UCD Conway Institute of Biomolecular and Biomedical Research, School of Medicine, and Medical Sciences, University College Dublin, Dublin, Ireland
| | - David R. Cotter
- Department of Psychiatry, Royal College of Surgeons in Ireland, ERC Beaumont Hospital, Dublin, Ireland
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109
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Tsou CC, Tsai CF, Teo GC, Chen YJ, Nesvizhskii AI. Untargeted, spectral library-free analysis of data-independent acquisition proteomics data generated using Orbitrap mass spectrometers. Proteomics 2016; 16:2257-71. [PMID: 27246681 DOI: 10.1002/pmic.201500526] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2015] [Revised: 04/11/2016] [Accepted: 05/30/2016] [Indexed: 12/12/2022]
Abstract
We describe an improved version of the data-independent acquisition (DIA) computational analysis tool DIA-Umpire, and show that it enables highly sensitive, untargeted, and direct (spectral library-free) analysis of DIA data obtained using the Orbitrap family of mass spectrometers. DIA-Umpire v2 implements an improved feature detection algorithm with two additional filters based on the isotope pattern and fractional peptide mass analysis. The targeted re-extraction step of DIA-Umpire is updated with an improved scoring function and a more robust, semiparametric mixture modeling of the resulting scores for computing posterior probabilities of correct peptide identification in a targeted setting. Using two publicly available Q Exactive DIA datasets generated using HEK-293 cells and human liver microtissues, we demonstrate that DIA-Umpire can identify similar number of peptide ions, but with better identification reproducibility between replicates and samples, as with conventional data-dependent acquisition. We further demonstrate the utility of DIA-Umpire using a series of Orbitrap Fusion DIA experiments with HeLa cell lysates profiled using conventional data-dependent acquisition and using DIA with different isolation window widths.
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Affiliation(s)
- Chih-Chiang Tsou
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | | | - Guo Ci Teo
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Yu-Ju Chen
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
| | - Alexey I Nesvizhskii
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.,Department of Pathology, University of Michigan, Ann Arbor, MI, USA
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110
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Gillet LC, Leitner A, Aebersold R. Mass Spectrometry Applied to Bottom-Up Proteomics: Entering the High-Throughput Era for Hypothesis Testing. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2016; 9:449-72. [PMID: 27049628 DOI: 10.1146/annurev-anchem-071015-041535] [Citation(s) in RCA: 218] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Proteins constitute a key class of molecular components that perform essential biochemical reactions in living cells. Whether the aim is to extensively characterize a given protein or to perform high-throughput qualitative and quantitative analysis of the proteome content of a sample, liquid chromatography coupled to tandem mass spectrometry has become the technology of choice. In this review, we summarize the current state of mass spectrometry applied to bottom-up proteomics, the approach that focuses on analyzing peptides obtained from proteolytic digestion of proteins. With the recent advances in instrumentation and methodology, we show that the field is moving away from providing qualitative identification of long lists of proteins to delivering highly consistent and accurate quantification values for large numbers of proteins across large numbers of samples. We believe that this shift will have a profound impact for the field of proteomics and life science research in general.
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Affiliation(s)
- Ludovic C Gillet
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, 8093 Zürich, Switzerland;
| | - Alexander Leitner
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, 8093 Zürich, Switzerland;
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, 8093 Zürich, Switzerland;
- Faculty of Science, University of Zürich, 8057 Zürich, Switzerland
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111
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Teo G, Koh H, Fermin D, Lambert JP, Knight JDR, Gingras AC, Choi H. SAINTq: Scoring protein-protein interactions in affinity purification - mass spectrometry experiments with fragment or peptide intensity data. Proteomics 2016; 16:2238-45. [PMID: 27119218 DOI: 10.1002/pmic.201500499] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Revised: 03/21/2016] [Accepted: 04/12/2016] [Indexed: 11/10/2022]
Abstract
SAINT (Significance Analysis of INTeractome) is a probabilistic method for scoring bait-prey interactions against negative controls in affinity purification - mass spectrometry (AP-MS) experiments. Our published SAINT algorithms use spectral counts or protein intensities as the input for calculating the probability of true interaction, which enables objective selection of high-confidence interactions with false discovery control. With the advent of new protein quantification methods such as Data Independent Acquisition (DIA), we redeveloped the scoring method to utilize the reproducibility information embedded in the peptide or fragment intensity data as a key scoring criterion, bypassing protein intensity summarization required in the previous SAINT workflow. The new software package, SAINTq, addresses key issues in the interaction scoring based on intensity data, including treatment of missing values and selection of peptides and fragments for scoring each prey protein. We applied SAINTq to two independent DIA AP-MS data sets profiling the interactome of MEPCE and EIF4A2 and that of 14-3-3β, and benchmarked the performance in terms of recovering previously reported literature interactions in the iRefIndex database. In both data sets, the SAINTq analysis using the fragment-level intensity data led to the most sensitive detection of literature interactions at the same level of specificity. This analysis outperforms the analysis using protein intensity data summed from fragment intensity data that is equivalent to the model in SAINTexpress.
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Affiliation(s)
- Guoci Teo
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Hiromi Koh
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Damian Fermin
- Department of Pathology, Yale University, New Haven, CT, USA
| | | | - James D R Knight
- Lunenfeld-Tanenbaum Research Institute, Sinai Health Service, Ontario, Canada
| | - Anne-Claude Gingras
- Lunenfeld-Tanenbaum Research Institute, Sinai Health Service, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Ontario, Canada
| | - Hyungwon Choi
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.,Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, Singapore
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112
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Liu G, Knight JDR, Zhang JP, Tsou CC, Wang J, Lambert JP, Larsen B, Tyers M, Raught B, Bandeira N, Nesvizhskii AI, Choi H, Gingras AC. Data Independent Acquisition analysis in ProHits 4.0. J Proteomics 2016; 149:64-68. [PMID: 27132685 DOI: 10.1016/j.jprot.2016.04.042] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2015] [Revised: 03/18/2016] [Accepted: 04/27/2016] [Indexed: 11/30/2022]
Abstract
Affinity purification coupled with mass spectrometry (AP-MS) is a powerful technique for the identification and quantification of physical interactions. AP-MS requires careful experimental design, appropriate control selection and quantitative workflows to successfully identify bona fide interactors amongst a large background of contaminants. We previously introduced ProHits, a Laboratory Information Management System for interaction proteomics, which tracks all samples in a mass spectrometry facility, initiates database searches and provides visualization tools for spectral counting-based AP-MS approaches. More recently, we implemented Significance Analysis of INTeractome (SAINT) within ProHits to provide scoring of interactions based on spectral counts. Here, we provide an update to ProHits to support Data Independent Acquisition (DIA) with identification software (DIA-Umpire and MSPLIT-DIA), quantification tools (through DIA-Umpire, or externally via targeted extraction), and assessment of quantitative enrichment (through mapDIA) and scoring of interactions (through SAINT-intensity). With additional improvements, notably support of the iProphet pipeline, facilitated deposition into ProteomeXchange repositories and enhanced export and viewing functions, ProHits 4.0 offers a comprehensive suite of tools to facilitate affinity proteomics studies. SIGNIFICANCE It remains challenging to score, annotate and analyze proteomics data in a transparent manner. ProHits was previously introduced as a LIMS to enable storing, tracking and analysis of standard AP-MS data. In this revised version, we expand ProHits to include integration with a number of identification and quantification tools based on Data-Independent Acquisition (DIA). ProHits 4.0 also facilitates data deposition into public repositories, and the transfer of data to new visualization tools.
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Affiliation(s)
- Guomin Liu
- Centre for Systems Biology, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
| | - James D R Knight
- Centre for Systems Biology, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
| | - Jian Ping Zhang
- Centre for Systems Biology, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
| | - Chih-Chiang Tsou
- Department of Pathology, University of Michigan, Ann Arbor, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, USA
| | - Jian Wang
- Center for Computational Mass Spectrometry, University of California, San Diego, La Jolla, California, USA; Department of Computer Science and Engineering, University of California, San Diego, La Jolla, California, USA
| | - Jean-Philippe Lambert
- Centre for Systems Biology, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
| | - Brett Larsen
- Centre for Systems Biology, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
| | - Mike Tyers
- Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, Québec, Canada
| | - Brian Raught
- Princess Margaret Cancer Institute, Department of Medical Biophysics, University of Toronto, Ontario, Canada
| | - Nuno Bandeira
- Center for Computational Mass Spectrometry, University of California, San Diego, La Jolla, California, USA; Department of Computer Science and Engineering, University of California, San Diego, La Jolla, California, USA; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California, USA
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, USA
| | - Hyungwon Choi
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore; Institute of Molecular and Cell Biology, Agency for Science, Technology, and Research, Singapore, Singapore
| | - Anne-Claude Gingras
- Centre for Systems Biology, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
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Blattmann P, Heusel M, Aebersold R. SWATH2stats: An R/Bioconductor Package to Process and Convert Quantitative SWATH-MS Proteomics Data for Downstream Analysis Tools. PLoS One 2016; 11:e0153160. [PMID: 27054327 PMCID: PMC4824525 DOI: 10.1371/journal.pone.0153160] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2016] [Accepted: 03/24/2016] [Indexed: 11/19/2022] Open
Abstract
SWATH-MS is an acquisition and analysis technique of targeted proteomics that enables measuring several thousand proteins with high reproducibility and accuracy across many samples. OpenSWATH is popular open-source software for peptide identification and quantification from SWATH-MS data. For downstream statistical and quantitative analysis there exist different tools such as MSstats, mapDIA and aLFQ. However, the transfer of data from OpenSWATH to the downstream statistical tools is currently technically challenging. Here we introduce the R/Bioconductor package SWATH2stats, which allows convenient processing of the data into a format directly readable by the downstream analysis tools. In addition, SWATH2stats allows annotation, analyzing the variation and the reproducibility of the measurements, FDR estimation, and advanced filtering before submitting the processed data to downstream tools. These functionalities are important to quickly analyze the quality of the SWATH-MS data. Hence, SWATH2stats is a new open-source tool that summarizes several practical functionalities for analyzing, processing, and converting SWATH-MS data and thus facilitates the efficient analysis of large-scale SWATH/DIA datasets.
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Affiliation(s)
- Peter Blattmann
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093, Zurich, Switzerland
- * E-mail:
| | - Moritz Heusel
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093, Zurich, Switzerland
- PhD program in Molecular and Translational Biomedicine, Competence Center Personalized Medicine UZH/ETH & Life Science Zurich Graduate School, ETH Zurich and University of Zurich, 8044, Zurich, Switzerland
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093, Zurich, Switzerland
- Faculty of Science, University of Zurich, 8057, Zurich, Switzerland
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