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Demeulemeester N, Gébelin M, Caldi Gomes L, Lingor P, Carapito C, Martens L, Clement L. msqrob2PTM: Differential Abundance and Differential Usage Analysis of MS-Based Proteomics Data at the Posttranslational Modification and Peptidoform Level. Mol Cell Proteomics 2024; 23:100708. [PMID: 38154689 PMCID: PMC10875266 DOI: 10.1016/j.mcpro.2023.100708] [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: 07/06/2023] [Revised: 12/19/2023] [Accepted: 12/24/2023] [Indexed: 12/30/2023] Open
Abstract
In the era of open-modification search engines, more posttranslational modifications than ever can be detected by LC-MS/MS-based proteomics. This development can switch proteomics research into a higher gear, as PTMs are key in many cellular pathways important in cell proliferation, migration, metastasis, and aging. However, despite these advances in modification identification, statistical methods for PTM-level quantification and differential analysis have yet to catch up. This absence can partly be explained by statistical challenges inherent to the data, such as the confounding of PTM intensities with its parent protein abundance. Therefore, we have developed msqrob2PTM, a new workflow in the msqrob2 universe capable of differential abundance analysis at the PTM and at the peptidoform level. The latter is important for validating PTMs found as significantly differential. Indeed, as our method can deal with multiple PTMs per peptidoform, there is a possibility that significant PTMs stem from one significant peptidoform carrying another PTM, hinting that it might be the other PTM driving the perceived differential abundance. Our workflows can flag both differential peptidoform abundance (DPA) and differential peptidoform usage (DPU). This enables a distinction between direct assessment of differential abundance of peptidoforms (DPA) and differences in the relative usage of peptidoforms corrected for corresponding protein abundances (DPU). For DPA, we directly model the log2-transformed peptidoform intensities, while for DPU, we correct for parent protein abundance by an intermediate normalization step which calculates the log2-ratio of the peptidoform intensities to their summarized parent protein intensities. We demonstrated the utility and performance of msqrob2PTM by applying it to datasets with known ground truth, as well as to biological PTM-rich datasets. Our results show that msqrob2PTM is on par with, or surpassing the performance of, the current state-of-the-art methods. Moreover, msqrob2PTM is currently unique in providing output at the peptidoform level.
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Affiliation(s)
- Nina Demeulemeester
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, Ghent, Belgium; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Marie Gébelin
- Laboratoire de Spectrométrie de Masse BioOrganique, IPHC UMR 7178, CNRS, Infrastructure Nationale de Protéomique ProFI - FR2048, Université de Strasbourg, Strasbourg, France
| | - Lucas Caldi Gomes
- Department of Neurology, Technical University Munich, Munich, Germany
| | - Paul Lingor
- Department of Neurology, Technical University Munich, Munich, Germany
| | - Christine Carapito
- Laboratoire de Spectrométrie de Masse BioOrganique, IPHC UMR 7178, CNRS, Infrastructure Nationale de Protéomique ProFI - FR2048, Université de Strasbourg, Strasbourg, France
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
| | - Lieven Clement
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.
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2
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Toghrayee Z, Montazeri H. Uncovering hidden cancer self-dependencies through analysis of shRNA-level dependency scores. Sci Rep 2024; 14:856. [PMID: 38195844 PMCID: PMC10776685 DOI: 10.1038/s41598-024-51453-5] [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: 08/02/2023] [Accepted: 01/05/2024] [Indexed: 01/11/2024] Open
Abstract
Large-scale short hairpin RNA (shRNA) screens on well-characterized human cancer cell lines have been widely used to identify novel cancer dependencies. However, the off-target effects of shRNA reagents pose a significant challenge in the analysis of these screens. To mitigate these off-target effects, various approaches have been proposed that aggregate different shRNA viability scores targeting a gene into a single gene-level viability score. Most computational methods for discovering cancer dependencies rely on these gene-level scores. In this paper, we propose a computational method, named NBDep, to find cancer self-dependencies by directly analyzing shRNA-level dependency scores instead of gene-level scores. The NBDep algorithm begins by removing known batch effects of the shRNAs and selecting a subset of concordant shRNAs for each gene. It then uses negative binomial random effects models to statistically assess the dependency between genetic alterations and the viabilities of cell lines by incorporating all shRNA dependency scores of each gene into the model. We applied NBDep to the shRNA dependency scores available at Project DRIVE, which covers 26 different types of cancer. The proposed method identified more well-known and putative cancer genes compared to alternative gene-level approaches in pan-cancer and cancer-specific analyses. Additionally, we demonstrated that NBDep controls type-I error and outperforms statistical tests based on gene-level scores in simulation studies.
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Affiliation(s)
- Zohreh Toghrayee
- Department of Bioinformatics, Institute Biochemistry and Biophysics, University of Tehran, Tehran, Iran
- Department of Bioinformatics, Kish International Campus University of Tehran, Kish, Iran
| | - Hesam Montazeri
- Department of Bioinformatics, Institute Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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3
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Zhang Y. pepDESC: A method for the detection of differentially expressed proteins for mass spectrometry-based single-cell proteomics using peptide-level information. Mol Cell Proteomics 2023:100583. [PMID: 37236439 PMCID: PMC10316082 DOI: 10.1016/j.mcpro.2023.100583] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 04/21/2023] [Accepted: 05/20/2023] [Indexed: 05/28/2023] Open
Abstract
Single-cell proteomics as an emerging field has exhibited potential in revealing cellular heterogeneity at the functional level. However, accurate interpretation of single-cell proteomics data suffers from challenges such as measurement noise, internal heterogeneity, and the limited sample size of label-free quantitative mass spectrometry. Herein, the author describes pepDESC, a method for detecting differentially expressed proteins using peptide-level information designed for label-free quantitative mass spectrometry-based single-cell proteomics. While in this study, the author focuses on the heterogeneity among limited number of samples, pepDESC is also applicable to regular-size proteomics data. By balancing proteome coverage and quantification accuracy using peptide quantification, pepDESC is demonstrated to be effective in real-world single-cell and spike -in benchmark datasets. By applying pepDESC to a published single-mouse macrophages data, the author found a large fraction of differentially expressed proteins among three types of cells, which remarkably revealed distinct dynamics of different cellular functions responding to lipopolysaccharide stimulation.
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Affiliation(s)
- Yutong Zhang
- Collage of Chemistry and Molecular Engineering, Peking University, 100871 Beijing, China; Beijing Advanced Innovation Center for Genomics, Peking University,100871 Beijing, China; Biomedical Pioneering Innovation Center, Peking University, 100871 Beijing, China
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4
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Tardif M, Fremy E, Hesse AM, Burger T, Couté Y, Wieczorek S. Statistical Analysis of Quantitative Peptidomics and Peptide-Level Proteomics Data with Prostar. Methods Mol Biol 2023; 2426:163-196. [PMID: 36308690 DOI: 10.1007/978-1-0716-1967-4_9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Prostar is a software tool dedicated to the processing of quantitative data resulting from mass spectrometry-based label-free proteomics. Practically, once biological samples have been analyzed by bottom-up proteomics, the raw mass spectrometer outputs are processed by bioinformatics tools, so as to identify peptides and quantify them, notably by means of precursor ion chromatogram integration. From that point, the classical workflows aggregate these pieces of peptide-level information to infer protein-level identities and amounts. Finally, protein abundances can be statistically analyzed to find out proteins that are significantly differentially abundant between compared conditions. Prostar original workflow has been developed based on this strategy. However, recent works have demonstrated that processing peptide-level information is often more accurate when searching for differentially abundant proteins, as the aggregation step tends to hide some of the data variabilities and biases. As a result, Prostar has been extended by workflows that manage peptide-level data, and this protocol details their use. The first one, deemed "peptidomics," implies that the differential analysis is conducted at peptide level, independently of the peptide-to-protein relationship. The second workflow proposes to aggregate the peptide abundances after their preprocessing (i.e., after filtering, normalization, and imputation), so as to minimize the amount of protein-level preprocessing prior to differential analysis.
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Affiliation(s)
- Marianne Tardif
- Univ. Grenoble Alpes, INSERM, CEA, UMR BioSanté U1292, CNRS, Grenoble, France
| | - Enora Fremy
- Univ. Grenoble Alpes, INSERM, CEA, UMR BioSanté U1292, CNRS, Grenoble, France
| | - Anne-Marie Hesse
- Univ. Grenoble Alpes, INSERM, CEA, UMR BioSanté U1292, CNRS, Grenoble, France
| | - Thomas Burger
- Univ. Grenoble Alpes, CNRS, INSERM, CEA, Grenoble, France
| | - Yohann Couté
- Univ. Grenoble Alpes, INSERM, CEA, UMR BioSanté U1292, CNRS, Grenoble, France
| | - Samuel Wieczorek
- Univ. Grenoble Alpes, INSERM, CEA, UMR BioSanté U1292, CNRS, Grenoble, France.
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5
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Vincent D, Bui A, Ezernieks V, Shahinfar S, Luke T, Ram D, Rigas N, Panozzo J, Rochfort S, Daetwyler H, Hayden M. A community resource to mass explore the wheat grain proteome and its application to the late-maturity alpha-amylase (LMA) problem. Gigascience 2022; 12:giad084. [PMID: 37919977 PMCID: PMC10627334 DOI: 10.1093/gigascience/giad084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 08/02/2023] [Accepted: 09/19/2023] [Indexed: 11/04/2023] Open
Abstract
BACKGROUND Late-maturity alpha-amylase (LMA) is a wheat genetic defect causing the synthesis of high isoelectric point alpha-amylase following a temperature shock during mid-grain development or prolonged cold throughout grain development, both leading to starch degradation. While the physiology is well understood, the biochemical mechanisms involved in grain LMA response remain unclear. We have applied high-throughput proteomics to 4,061 wheat flours displaying a range of LMA activities. Using an array of statistical analyses to select LMA-responsive biomarkers, we have mined them using a suite of tools applicable to wheat proteins. RESULTS We observed that LMA-affected grains activated their primary metabolisms such as glycolysis and gluconeogenesis; TCA cycle, along with DNA- and RNA- binding mechanisms; and protein translation. This logically transitioned to protein folding activities driven by chaperones and protein disulfide isomerase, as well as protein assembly via dimerisation and complexing. The secondary metabolism was also mobilized with the upregulation of phytohormones and chemical and defence responses. LMA further invoked cellular structures, including ribosomes, microtubules, and chromatin. Finally, and unsurprisingly, LMA expression greatly impacted grain storage proteins, as well as starch and other carbohydrates, with the upregulation of alpha-gliadins and starch metabolism, whereas LMW glutenin, stachyose, sucrose, UDP-galactose, and UDP-glucose were downregulated. CONCLUSIONS To our knowledge, this is not only the first proteomics study tackling the wheat LMA issue but also the largest plant-based proteomics study published to date. Logistics, technicalities, requirements, and bottlenecks of such an ambitious large-scale high-throughput proteomics experiment along with the challenges associated with big data analyses are discussed.
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Affiliation(s)
- Delphine Vincent
- Agriculture Victoria Research, AgriBio, Center Centre for AgriBioscience, Bundoora, VIC 3083, Australia
| | - AnhDuyen Bui
- Agriculture Victoria Research, AgriBio, Center Centre for AgriBioscience, Bundoora, VIC 3083, Australia
| | - Vilnis Ezernieks
- Agriculture Victoria Research, AgriBio, Center Centre for AgriBioscience, Bundoora, VIC 3083, Australia
| | - Saleh Shahinfar
- Agriculture Victoria Research, AgriBio, Center Centre for AgriBioscience, Bundoora, VIC 3083, Australia
| | - Timothy Luke
- Agriculture Victoria Research, AgriBio, Center Centre for AgriBioscience, Bundoora, VIC 3083, Australia
| | - Doris Ram
- Agriculture Victoria Research, AgriBio, Center Centre for AgriBioscience, Bundoora, VIC 3083, Australia
| | - Nicholas Rigas
- Agriculture Victoria Research, Grains Innovation Park, Horsham, VIC 3400, Australia
| | - Joe Panozzo
- Agriculture Victoria Research, Grains Innovation Park, Horsham, VIC 3400, Australia
- Centre for Agricultural Innovation, University of Melbourne, Parkville, VIC 3010, Australia
| | - Simone Rochfort
- Agriculture Victoria Research, AgriBio, Center Centre for AgriBioscience, Bundoora, VIC 3083, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
| | - Hans Daetwyler
- Agriculture Victoria Research, AgriBio, Center Centre for AgriBioscience, Bundoora, VIC 3083, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
| | - Matthew Hayden
- Agriculture Victoria Research, AgriBio, Center Centre for AgriBioscience, Bundoora, VIC 3083, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
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Chion M, Carapito C, Bertrand F. Accounting for multiple imputation-induced variability for differential analysis in mass spectrometry-based label-free quantitative proteomics. PLoS Comput Biol 2022; 18:e1010420. [PMID: 36037245 PMCID: PMC9462777 DOI: 10.1371/journal.pcbi.1010420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 09/09/2022] [Accepted: 07/21/2022] [Indexed: 11/20/2022] Open
Abstract
Imputing missing values is common practice in label-free quantitative proteomics. Imputation aims at replacing a missing value with a user-defined one. However, the imputation itself may not be optimally considered downstream of the imputation process, as imputed datasets are often considered as if they had always been complete. Hence, the uncertainty due to the imputation is not adequately taken into account. We provide a rigorous multiple imputation strategy, leading to a less biased estimation of the parameters’ variability thanks to Rubin’s rules. The imputation-based peptide’s intensities’ variance estimator is then moderated using Bayesian hierarchical models. This estimator is finally included in moderated t-test statistics to provide differential analyses results. This workflow can be used both at peptide and protein-level in quantification datasets. Indeed, an aggregation step is included for protein-level results based on peptide-level quantification data. Our methodology, named mi4p, was compared to the state-of-the-art limma workflow implemented in the DAPARR package, both on simulated and real datasets. We observed a trade-off between sensitivity and specificity, while the overall performance of mi4p outperforms DAPAR in terms of F-Score. Statistical inference methods commonly used in quantitative proteomics are based on the measurement of peptide intensities. They allow the deduction of protein abundances provided that sufficient peptides per protein are available. However, they do not satisfactorily consider peptides or proteins whose intensities are missing under certain conditions, even though they are particularly interesting from a biological or medical point of view, since they may explain a difference between the groups being compared. Some state-of-the-art statistical proteomics data processing software proposes to impute these missing values, while others simply remove proteins with too many missing peptides. The statistical treatment is not entirely satisfactory when imputation methods are used, notably multiple imputation techniques. Indeed, even if these statistical tools are relevant in this context, the data sets once imputed are considered as having always been complete in the subsequent analyses: the uncertainty caused by the imputation is not taken into account. These analyses generally conclude with a study of the differences in protein abundances between the different conditions, either using Student’s or Welch’s test for the most rudimentary approaches or using the t-tempered testing techniques based on empirical Bayesian approaches. Thus, we propose a new methodology that starts by imputing missing values at the peptide level and estimating the uncertainty associated with this imputation and naturally extends by incorporating this uncertainty into the current moderated variance estimation techniques.
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Affiliation(s)
- Marie Chion
- Institut de Recherche Mathématique Avancée, UMR 7501, CNRS-Université de Strasbourg, Strasbourg, France
- Laboratoire de Spectrométrie de Masse Bio-Organique, Institut Pluridisciplinaire Hubert Curien, UMR 7178, CNRS-Université de Strasbourg, Strasbourg, France
- Laboratoire Mathématiques appliquées à Paris 5, UMR 8145, CNRS-Université Paris Cité, Paris, France
- * E-mail:
| | - Christine Carapito
- Laboratoire de Spectrométrie de Masse Bio-Organique, Institut Pluridisciplinaire Hubert Curien, UMR 7178, CNRS-Université de Strasbourg, Strasbourg, France
- Infrastructure Nationale de Protéomique ProFi - FR2048, 67087 Strasbourg, France
| | - Frédéric Bertrand
- Institut de Recherche Mathématique Avancée, UMR 7501, CNRS-Université de Strasbourg, Strasbourg, France
- Laboratoire Informatique et Société Numérique, Université de Technologie de Troyes, Troyes, France
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7
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Tsiamis V, Schwämmle V. VIQoR: a web service for visually supervised protein inference and protein quantification. Bioinformatics 2022; 38:2757-2764. [PMID: 35561162 DOI: 10.1093/bioinformatics/btac182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 03/07/2022] [Accepted: 03/22/2022] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION In quantitative bottom-up mass spectrometry (MS)-based proteomics, the reliable estimation of protein concentration changes from peptide quantifications between different biological samples is essential. This estimation is not a single task but comprises the two processes of protein inference and protein abundance summarization. Furthermore, due to the high complexity of proteomics data and associated uncertainty about the performance of these processes, there is a demand for comprehensive visualization methods able to integrate protein with peptide quantitative data including their post-translational modifications. Hence, there is a lack of a suitable tool that provides post-identification quantitative analysis of proteins with simultaneous interactive visualization. RESULTS In this article, we present VIQoR, a user-friendly web service that accepts peptide quantitative data of both labeled and label-free experiments and accomplishes the crucial components protein inference and summarization and interactive visualization modules, including the novel VIQoR plot. We implemented two different parsimonious algorithms to solve the protein inference problem, while protein summarization is facilitated by a well-established factor analysis algorithm called fast-FARMS followed by a weighted average summarization function that minimizes the effect of missing values. In addition, summarization is optimized by the so-called Global Correlation Indicator (GCI). We test the tool on three publicly available ground truth datasets and demonstrate the ability of the protein inference algorithms to handle shared peptides. We furthermore show that GCI increases the accuracy of the quantitative analysis in datasets with replicated design. AVAILABILITY AND IMPLEMENTATION VIQoR is accessible at: http://computproteomics.bmb.sdu.dk/Apps/VIQoR/. The source code is available at: https://bitbucket.org/veitveit/viqor/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Vasileios Tsiamis
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, 5230 Odense, Denmark
| | - Veit Schwämmle
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, 5230 Odense, Denmark
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8
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Suomi T, Elo LL. Statistical and machine learning methods to study human CD4+ T cell proteome profiles. Immunol Lett 2022; 245:8-17. [DOI: 10.1016/j.imlet.2022.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 03/11/2022] [Accepted: 03/15/2022] [Indexed: 11/05/2022]
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9
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Klann K, Münch C. PBLMM: Peptide-based linear mixed models for differential expression analysis of shotgun proteomics data. J Cell Biochem 2022; 123:691-696. [PMID: 35132673 DOI: 10.1002/jcb.30225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 01/23/2022] [Accepted: 01/27/2022] [Indexed: 11/07/2022]
Abstract
Here, we present a peptide-based linear mixed models tool-PBLMM, a standalone desktop application for differential expression analysis of proteomics data. We also provide a Python package that allows streamlined data analysis workflows implementing the PBLMM algorithm. PBLMM is easy to use without scripting experience and calculates differential expression by peptide-based linear mixed regression models. We show that peptide-based models outperform classical methods of statistical inference of differentially expressed proteins. In addition, PBLMM exhibits superior statistical power in situations of low effect size and/or low sample size. Taken together our tool provides an easy-to-use, high-statistical-power method to infer differentially expressed proteins from proteomics data.
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Affiliation(s)
- Kevin Klann
- Institute of Biochemistry II, Faculty of Medicine, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Christian Münch
- Institute of Biochemistry II, Faculty of Medicine, Goethe University Frankfurt, Frankfurt am Main, Germany
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10
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Schäfer JA, Bozkurt S, Michaelis JB, Klann K, Münch C. Global mitochondrial protein import proteomics reveal distinct regulation by translation and translocation machinery. Mol Cell 2021; 82:435-446.e7. [PMID: 34847359 PMCID: PMC8791276 DOI: 10.1016/j.molcel.2021.11.004] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 09/09/2021] [Accepted: 11/02/2021] [Indexed: 01/03/2023]
Abstract
Most mitochondrial proteins are translated in the cytosol and imported into mitochondria. Mutations in the mitochondrial protein import machinery cause human pathologies. However, a lack of suitable tools to measure protein uptake across the mitochondrial proteome has prevented the identification of specific proteins affected by import perturbation. Here, we introduce mePRODmt, a pulsed-SILAC based proteomics approach that includes a booster signal to increase the sensitivity for mitochondrial proteins selectively, enabling global dynamic analysis of endogenous mitochondrial protein uptake in cells. We applied mePRODmt to determine protein uptake kinetics and examined how inhibitors of mitochondrial import machineries affect protein uptake. Monitoring changes in translation and uptake upon mitochondrial membrane depolarization revealed that protein uptake was extensively modulated by the import and translation machineries via activation of the integrated stress response. Strikingly, uptake changes were not uniform, with subsets of proteins being unaffected or decreased due to changes in translation or import capacity. Proteomics approach to quantify protein uptake into mitochondria Determination of protein uptake rates for >700 mitochondrial proteins Characterization of differential protein uptake changes during mitochondrial stress Protein translation and translocation integrate to change protein uptake
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Affiliation(s)
- Jasmin Adriana Schäfer
- Institute of Biochemistry II, Goethe University Frankfurt, Theodor-Stern-Kai 7, Haus 75, 60590 Frankfurt am Main, Germany
| | - Süleyman Bozkurt
- Institute of Biochemistry II, Goethe University Frankfurt, Theodor-Stern-Kai 7, Haus 75, 60590 Frankfurt am Main, Germany
| | - Jonas Benjamin Michaelis
- Institute of Biochemistry II, Goethe University Frankfurt, Theodor-Stern-Kai 7, Haus 75, 60590 Frankfurt am Main, Germany
| | - Kevin Klann
- Institute of Biochemistry II, Goethe University Frankfurt, Theodor-Stern-Kai 7, Haus 75, 60590 Frankfurt am Main, Germany
| | - Christian Münch
- Institute of Biochemistry II, Goethe University Frankfurt, Theodor-Stern-Kai 7, Haus 75, 60590 Frankfurt am Main, Germany; Frankfurt Cancer Institute, Frankfurt am Main, Germany; Cardio-Pulmonary Institute, Frankfurt am Main, Germany.
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11
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Dowell JA, Wright LJ, Armstrong EA, Denu JM. Benchmarking Quantitative Performance in Label-Free Proteomics. ACS OMEGA 2021; 6:2494-2504. [PMID: 33553868 PMCID: PMC7859943 DOI: 10.1021/acsomega.0c04030] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 01/11/2021] [Indexed: 05/07/2023]
Abstract
Previous benchmarking studies have demonstrated the importance of instrument acquisition methodology and statistical analysis on quantitative performance in label-free proteomics. However, the effects of these parameters in combination with replicate number and false discovery rate (FDR) corrections are not known. Using a benchmarking standard, we systematically evaluated the combined impact of acquisition methodology, replicate number, statistical approach, and FDR corrections. These analyses reveal a complex interaction between these parameters that greatly impacts the quantitative fidelity of protein- and peptide-level quantification. At a high replicate number (n = 8), both data-dependent acquisition (DDA) and data-independent acquisition (DIA) methodologies yield accurate protein quantification across statistical approaches. However, at a low replicate number (n = 4), only DIA in combination with linear models for microarrays (LIMMA) and reproducibility-optimized test statistic (ROTS) produced a high level of quantitative fidelity. Quantitative accuracy at low replicates is also greatly impacted by FDR corrections, with Benjamini-Hochberg and Storey corrections yielding variable true positive rates for DDA workflows. For peptide quantification, replicate number and acquisition methodology are even more critical. A higher number of replicates in combination with DIA and LIMMA produce high quantitative fidelity, while DDA performs poorly regardless of replicate number or statistical approach. These results underscore the importance of pairing instrument acquisition methodology with the appropriate replicate number and statistical approach for optimal quantification performance.
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Affiliation(s)
- James A. Dowell
- Wisconsin
Institute for Discovery, University of Wisconsin−Madison, 330 North Orchard Street, Madison, Wisconsin 53715, United States
| | - Logan J. Wright
- Wisconsin
Institute for Discovery, University of Wisconsin−Madison, 330 North Orchard Street, Madison, Wisconsin 53715, United States
| | - Eric A. Armstrong
- Wisconsin
Institute for Discovery, University of Wisconsin−Madison, 330 North Orchard Street, Madison, Wisconsin 53715, United States
| | - John M. Denu
- Wisconsin
Institute for Discovery, University of Wisconsin−Madison, 330 North Orchard Street, Madison, Wisconsin 53715, United States
- Department
of Biomolecular Chemistry, University of
Wisconsin−Madison, 420 Henry Mall Room 1135 Biochemistry Building, Madison, Wisconsin 53706, United States
- .
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Burgos R, Weber M, Martinez S, Lluch‐Senar M, Serrano L. Protein quality control and regulated proteolysis in the genome-reduced organism Mycoplasma pneumoniae. Mol Syst Biol 2020; 16:e9530. [PMID: 33320415 PMCID: PMC7737663 DOI: 10.15252/msb.20209530] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 11/04/2020] [Accepted: 11/08/2020] [Indexed: 12/14/2022] Open
Abstract
Protein degradation is a crucial cellular process in all-living systems. Here, using Mycoplasma pneumoniae as a model organism, we defined the minimal protein degradation machinery required to maintain proteome homeostasis. Then, we conditionally depleted the two essential ATP-dependent proteases. Whereas depletion of Lon results in increased protein aggregation and decreased heat tolerance, FtsH depletion induces cell membrane damage, suggesting a role in quality control of membrane proteins. An integrative comparative study combining shotgun proteomics and RNA-seq revealed 62 and 34 candidate substrates, respectively. Cellular localization of substrates and epistasis studies supports separate functions for Lon and FtsH. Protein half-life measurements also suggest a role for Lon-modulated protein decay. Lon plays a key role in protein quality control, degrading misfolded proteins and those not assembled into functional complexes. We propose that regulating complex assembly and degradation of isolated proteins is a mechanism that coordinates important cellular processes like cell division. Finally, by considering the entire set of proteases and chaperones, we provide a fully integrated view of how a minimal cell regulates protein folding and degradation.
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Affiliation(s)
- Raul Burgos
- Centre for Genomic Regulation (CRG)The Barcelona Institute of Science and TechnologyBarcelonaSpain
| | - Marc Weber
- Centre for Genomic Regulation (CRG)The Barcelona Institute of Science and TechnologyBarcelonaSpain
| | - Sira Martinez
- Centre for Genomic Regulation (CRG)The Barcelona Institute of Science and TechnologyBarcelonaSpain
| | - Maria Lluch‐Senar
- Centre for Genomic Regulation (CRG)The Barcelona Institute of Science and TechnologyBarcelonaSpain
| | - Luis Serrano
- Centre for Genomic Regulation (CRG)The Barcelona Institute of Science and TechnologyBarcelonaSpain
- Universitat Pompeu Fabra (UPF)BarcelonaSpain
- ICREABarcelonaSpain
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13
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Sticker A, Goeminne L, Martens L, Clement L. Robust Summarization and Inference in Proteome-wide Label-free Quantification. Mol Cell Proteomics 2020; 19:1209-1219. [PMID: 32321741 PMCID: PMC7338080 DOI: 10.1074/mcp.ra119.001624] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 04/20/2020] [Indexed: 12/27/2022] Open
Abstract
Label-Free Quantitative mass spectrometry based workflows for differential expression (DE) analysis of proteins impose important challenges on the data analysis because of peptide-specific effects and context dependent missingness of peptide intensities. Peptide-based workflows, like MSqRob, test for DE directly from peptide intensities and outperform summarization methods which first aggregate MS1 peptide intensities to protein intensities before DE analysis. However, these methods are computationally expensive, often hard to understand for the non-specialized end-user, and do not provide protein summaries, which are important for visualization or downstream processing. In this work, we therefore evaluate state-of-the-art summarization strategies using a benchmark spike-in dataset and discuss why and when these fail compared with the state-of-the-art peptide based model, MSqRob. Based on this evaluation, we propose a novel summarization strategy, MSqRobSum, which estimates MSqRob's model parameters in a two-stage procedure circumventing the drawbacks of peptide-based workflows. MSqRobSum maintains MSqRob's superior performance, while providing useful protein expression summaries for plotting and downstream analysis. Summarizing peptide to protein intensities considerably reduces the computational complexity, the memory footprint and the model complexity, and makes it easier to disseminate DE inferred on protein summaries. Moreover, MSqRobSum provides a highly modular analysis framework, which provides researchers with full flexibility to develop data analysis workflows tailored toward their specific applications.
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Affiliation(s)
- Adriaan Sticker
- Department of Applied Mathematics, Computer Science & Statistics, Ghent University, Belgium; VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, Ghent, Belgium; Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium
| | - Ludger Goeminne
- Department of Applied Mathematics, Computer Science & Statistics, Ghent University, Belgium; VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, Ghent, Belgium; Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, Ghent, Belgium; Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium.
| | - Lieven Clement
- Department of Applied Mathematics, Computer Science & Statistics, Ghent University, Belgium; Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium.
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14
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Ocken AR, Ku MM, Kinzer-Ursem TL, Calve S. Perlecan Knockdown Significantly Alters Extracellular Matrix Composition and Organization During Cartilage Development. Mol Cell Proteomics 2020; 19:1220-1235. [PMID: 32381549 PMCID: PMC7338092 DOI: 10.1074/mcp.ra120.001998] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 05/05/2020] [Indexed: 02/05/2023] Open
Abstract
Perlecan is a critical proteoglycan found in the extracellular matrix (ECM) of cartilage. In healthy cartilage, perlecan regulates cartilage biomechanics and we previously demonstrated perlecan deficiency leads to reduced cellular and ECM stiffness in vivo This change in mechanics may lead to the early onset osteoarthritis seen in disorders resulting from perlecan knockdown such as Schwartz-Jampel syndrome (SJS). To identify how perlecan knockdown affects the material properties of developing cartilage, we used imaging and liquid chromatography-tandem mass spectrometry (LC-MS/MS) to study the ECM in a murine model of SJS, Hspg2C1532Y-Neo Perlecan knockdown led to defective pericellular matrix formation, whereas the abundance of bulk ECM proteins, including many collagens, increased. Post-translational modifications and ultrastructure of collagens were not significantly different; however, LC-MS/MS analysis showed more protein was secreted by Hspg2C1532Y-Neo cartilage in vitro, suggesting that the incorporation of newly synthesized ECM was impaired. In addition, glycosaminoglycan deposition was atypical, which may explain the previously observed decrease in mechanics. Overall, these findings provide insight into the influence of perlecan on functional cartilage assembly and the progression of osteoarthritis in SJS.
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Affiliation(s)
- Alexander R Ocken
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana
| | - Madeline M Ku
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana
| | | | - Sarah Calve
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana.
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15
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Mallikarjun V, Richardson SM, Swift J. BayesENproteomics: Bayesian Elastic Nets for Quantification of Peptidoforms in Complex Samples. J Proteome Res 2020; 19:2167-2184. [PMID: 32319298 DOI: 10.1021/acs.jproteome.9b00468] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Multivariate regression modelling provides a statistically powerful means of quantifying the effects of a given treatment while compensating for sources of variation and noise, such as variability between human donors and the behavior of different peptides during mass spectrometry. However, methods to quantify endogenous post-translational modifications (PTMs) are typically reliant on summary statistical methods that fail to consider sources of variability such as changes in the levels of the parent protein. Here, we compare three multivariate regression methods, including a novel Bayesian elastic net algorithm (BayesENproteomics) that enables assessment of relative protein abundances while also quantifying identified PTMs for each protein. We tested the ability of these methods to accurately quantify expression of proteins in a mixed-species benchmark experiment and to quantify synthetic PTMs induced by stable isotope labelling. Finally, we extended our regression pipeline to calculate fold changes at the pathway level, providing a complement to commonly used enrichment analysis. Our results show that BayesENproteomics can quantify changes to protein levels across a broad dynamic range while also accurately quantifying PTM and pathway-level fold changes.
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Affiliation(s)
- Venkatesh Mallikarjun
- Wellcome Centre for Cell-Matrix Research, University of Manchester, Oxford Road, Manchester M13 9PT, U.K.,Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Oxford Road, Manchester M13 9PL, U.K
| | - Stephen M Richardson
- Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Oxford Road, Manchester M13 9PL, U.K
| | - Joe Swift
- Wellcome Centre for Cell-Matrix Research, University of Manchester, Oxford Road, Manchester M13 9PT, U.K.,Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Oxford Road, Manchester M13 9PL, U.K
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16
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Goeminne LJE, Sticker A, Martens L, Gevaert K, Clement L. MSqRob Takes the Missing Hurdle: Uniting Intensity- and Count-Based Proteomics. Anal Chem 2020; 92:6278-6287. [PMID: 32227882 DOI: 10.1021/acs.analchem.9b04375] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Missing values are a major issue in quantitative data-dependent mass spectrometry-based proteomics. We therefore present an innovative solution to this key issue by introducing a hurdle model, which is a mixture between a binomial peptide count and a peptide intensity-based model component. It enables dramatically enhanced quantification of proteins with many missing values without having to resort to harmful assumptions for missingness. We demonstrate the superior performance of our method by comparing it with state-of-the-art methods in the field.
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Affiliation(s)
- Ludger J E Goeminne
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281-S9, B9000 Ghent, Belgium.,VIB-UGent Center for Medical Biotechnology, VIB, Albert Baertsoenkaai 3, B9000 Ghent, Belgium.,Department of Biomolecular Medicine, Ghent University, Albert Baertsoenkaai 3, B9000 Ghent, Belgium.,Bioinformatics Institute Ghent, Ghent University, Technologiepark 927, B9052 Ghent, Belgium
| | - Adriaan Sticker
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281-S9, B9000 Ghent, Belgium.,VIB-UGent Center for Medical Biotechnology, VIB, Albert Baertsoenkaai 3, B9000 Ghent, Belgium.,Department of Biomolecular Medicine, Ghent University, Albert Baertsoenkaai 3, B9000 Ghent, Belgium.,Bioinformatics Institute Ghent, Ghent University, Technologiepark 927, B9052 Ghent, Belgium
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, Albert Baertsoenkaai 3, B9000 Ghent, Belgium.,Department of Biomolecular Medicine, Ghent University, Albert Baertsoenkaai 3, B9000 Ghent, Belgium.,Bioinformatics Institute Ghent, Ghent University, Technologiepark 927, B9052 Ghent, Belgium
| | - Kris Gevaert
- VIB-UGent Center for Medical Biotechnology, VIB, Albert Baertsoenkaai 3, B9000 Ghent, Belgium.,Department of Biomolecular Medicine, Ghent University, Albert Baertsoenkaai 3, B9000 Ghent, Belgium
| | - Lieven Clement
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281-S9, B9000 Ghent, Belgium.,Bioinformatics Institute Ghent, Ghent University, Technologiepark 927, B9052 Ghent, Belgium
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17
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Jacob L, Combes F, Burger T. PEPA test: fast and powerful differential analysis from relative quantitative proteomics data using shared peptides. Biostatistics 2019; 20:632-647. [PMID: 29917055 DOI: 10.1093/biostatistics/kxy021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2017] [Revised: 04/12/2018] [Accepted: 05/06/2018] [Indexed: 11/13/2022] Open
Abstract
We propose a new hypothesis test for the differential abundance of proteins in mass-spectrometry based relative quantification. An important feature of this type of high-throughput analyses is that it involves an enzymatic digestion of the sample proteins into peptides prior to identification and quantification. Due to numerous homology sequences, different proteins can lead to peptides with identical amino acid chains, so that their parent protein is ambiguous. These so-called shared peptides make the protein-level statistical analysis a challenge and are often not accounted for. In this article, we use a linear model describing peptide-protein relationships to build a likelihood ratio test of differential abundance for proteins. We show that the likelihood ratio statistic can be computed in linear time with the number of peptides. We also provide the asymptotic null distribution of a regularized version of our statistic. Experiments on both real and simulated datasets show that our procedures outperforms state-of-the-art methods. The procedures are available via the pepa.test function of the DAPAR Bioconductor R package.
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Affiliation(s)
- Laurent Jacob
- Université de Lyon, Université Lyon 1, CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Évolutive, 43 bd du 11 novembre 1918, Villeurbanne Cedex, France
| | - Florence Combes
- Université Grenoble Alpes, CNRS, CEA, INSERM, BIG-BGE, 17 avenue des Martyrs, Grenoble Cedex 9, France
| | - Thomas Burger
- Université Grenoble Alpes, CNRS, CEA, INSERM, BIG-BGE, 17 avenue des Martyrs, Grenoble Cedex 9, France
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18
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Ammar C, Gruber M, Csaba G, Zimmer R. MS-EmpiRe Utilizes Peptide-level Noise Distributions for Ultra-sensitive Detection of Differentially Expressed Proteins. Mol Cell Proteomics 2019; 18:1880-1892. [PMID: 31235637 PMCID: PMC6731086 DOI: 10.1074/mcp.ra119.001509] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 06/12/2019] [Indexed: 11/06/2022] Open
Abstract
Mass spectrometry based proteomics is the method of choice for quantifying genome-wide differential changes of protein expression in a wide range of biological and biomedical applications. Protein expression changes need to be reliably derived from many measured peptide intensities and their corresponding peptide fold changes. These peptide fold changes vary considerably for a given protein. Numerous instrumental setups aim to reduce this variability, whereas current computational methods only implicitly account for this problem. We introduce a new method, MS-EmpiRe, which explicitly accounts for the noise underlying peptide fold changes. We derive data set-specific, intensity-dependent empirical error fold change distributions, which are used for individual weighing of peptide fold changes to detect differentially expressed proteins (DEPs).In a recently published proteome-wide benchmarking data set, MS-EmpiRe doubles the number of correctly identified DEPs at an estimated FDR cutoff compared with state-of-the-art tools. We additionally confirm the superior performance of MS-EmpiRe on simulated data. MS-EmpiRe requires only peptide intensities mapped to proteins and, thus, can be applied to any common quantitative proteomics setup. We apply our method to diverse MS data sets and observe consistent increases in sensitivity with more than 1000 additional significant proteins in deep data sets, including a clinical study over multiple patients. At the same time, we observe that even the proteins classified as most insignificant by other methods but significant by MS-EmpiRe show very clear regulation on the peptide intensity level. MS-EmpiRe provides rapid processing (< 2 min for 6 LC-MS/MS runs (3 h gradients)) and is publicly available under github.com/zimmerlab/MS-EmpiRe with a manual including examples.
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Affiliation(s)
- Constantin Ammar
- ‡Ludwig-Maximilians-Universität München, Department of Informatics, Amalienstrasse 17, 80333 München, Germany; §Graduate School of Quantitative Biosciences (QBM), Ludwig-Maximillians-Universität München, Feodor-Lynen-Strasse 25, 81377 Munich, Germany
| | - Markus Gruber
- ‡Ludwig-Maximilians-Universität München, Department of Informatics, Amalienstrasse 17, 80333 München, Germany
| | - Gergely Csaba
- ‡Ludwig-Maximilians-Universität München, Department of Informatics, Amalienstrasse 17, 80333 München, Germany
| | - Ralf Zimmer
- ‡Ludwig-Maximilians-Universität München, Department of Informatics, Amalienstrasse 17, 80333 München, Germany; §Graduate School of Quantitative Biosciences (QBM), Ludwig-Maximillians-Universität München, Feodor-Lynen-Strasse 25, 81377 Munich, Germany.
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19
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Wieczorek S, Combes F, Borges H, Burger T. Protein-Level Statistical Analysis of Quantitative Label-Free Proteomics Data with ProStaR. Methods Mol Biol 2019; 1959:225-246. [PMID: 30852826 DOI: 10.1007/978-1-4939-9164-8_15] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
ProStaR is a software tool dedicated to differential analysis in label-free quantitative proteomics. Practically, once biological samples have been analyzed by bottom-up mass spectrometry-based proteomics, the raw mass spectrometer outputs are processed by bioinformatics tools, so as to identify peptides and quantify them, by means of precursor ion chromatogram integration. Then, it is classical to use these peptide-level pieces of information to derive the identity and quantity of the sample proteins before proceeding with refined statistical processing at protein-level, so as to bring out proteins which abundance is significantly different between different groups of samples. To achieve this statistical step, it is possible to rely on ProStaR, which allows the user to (1) load correctly formatted data, (2) clean them by means of various filters, (3) normalize the sample batches, (4) impute the missing values, (5) perform null hypothesis significance testing, (6) check the well-calibration of the resulting p-values, (7) select a subset of differentially abundant proteins according to some false discovery rate, and (8) contextualize these selected proteins into the Gene Ontology. This chapter provides a detailed protocol on how to perform these eight processing steps with ProStaR.
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Affiliation(s)
- Samuel Wieczorek
- Université Grenoble Alpes, CEA, Inserm, BGE U1038, Grenoble, France
| | - Florence Combes
- Université Grenoble Alpes, CEA, Inserm, BGE U1038, Grenoble, France
| | - Hélène Borges
- Université Grenoble Alpes, CEA, Inserm, BGE U1038, Grenoble, France
| | - Thomas Burger
- Université Grenoble Alpes, CEA, Inserm, BGE U1038, Grenoble, France. .,CNRS, BIG-BGE, Grenoble, France.
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20
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Slama P, Hoopmann MR, Moritz RL, Geman D. Robust determination of differential abundance in shotgun proteomics using nonparametric statistics. Mol Omics 2018; 14:424-436. [PMID: 30259924 PMCID: PMC6490964 DOI: 10.1039/c8mo00077h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Label-free shotgun mass spectrometry enables the detection of significant changes in protein abundance between different conditions. Due to often limited cohort sizes or replication, large ratios of potential protein markers to number of samples, as well as multiple null measurements pose important technical challenges to conventional parametric models. From a statistical perspective, a scenario similar to that of unlabeled proteomics is encountered in genomics when looking for differentially expressed genes. Still, the difficulty of detecting a large fraction of the true positives without a high false discovery rate is arguably greater in proteomics due to even smaller sample sizes and peptide-to-peptide variability in detectability. These constraints argue for nonparametric (or distribution-free) tests on normalized peptide values, thus minimizing the number of free parameters, as well as for measuring significance with permutation testing. We propose such a procedure with a class-based statistic, no parametric assumptions, and no parameters to select other than a nominal false discovery rate. Our method was tested on a new dataset which is available via ProteomeXchange with identifier PXD006447. The dataset was prepared using a standard proteolytic digest of a human protein mixture at 1.5-fold to 3-fold protein concentration changes and diluted into a constant background of yeast proteins. We demonstrate its superiority relative to other approaches in terms of the realized sensitivity and realized false discovery rates determined by ground truth, and recommend it for detecting differentially abundant proteins from MS data.
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Affiliation(s)
- Patrick Slama
- Center for Imaging Science, Institute for Computational Medicine, Johns Hopkins University, USA.
- Independent Researcher, Paris, France
| | | | - Robert L. Moritz
- Institute for Systems Biology, 401 Terry Avenue N, Seattle, WA, USA 98109
| | - Donald Geman
- Center for Imaging Science, Institute for Computational Medicine, Johns Hopkins University, USA.
- Department of Applied Mathematics and Statistics, Johns Hopkins University, 3400 N. Charles St., Baltimore MD, 21218
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21
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Causey DR, Kim JH, Stead DA, Martin SAM, Devlin RH, Macqueen DJ. Proteomic comparison of selective breeding and growth hormone transgenesis in fish: Unique pathways to enhanced growth. J Proteomics 2018; 192:114-124. [PMID: 30153513 PMCID: PMC7086150 DOI: 10.1016/j.jprot.2018.08.013] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2018] [Accepted: 08/23/2018] [Indexed: 12/14/2022]
Abstract
In fish used for food production and scientific research, fast growth can be achieved via selective breeding or induced instantaneously via growth hormone (GH) transgenesis (GHT). The proteomic basis for these distinct routes towards a similar higher phenotype remains uncharacterized, as are associated implications for health parameters. We addressed this knowledge gap using skeletal muscle proteomics in coho salmon (Oncorhynchus kisutch), hypothesising that i) selective breeding and GHT are underpinned by both parallel and unique changes in growth systems, and ii) rapidly-growing fish strains have lowered scope to allocate resources towards immune function. Quantitative profiling of GHT and growth-selected strains was done in comparison to wild-type after injection with PBS (control) or Poly I:C (to mimic infection). We identified remodelling of the muscle proteome in each growth-enhanced strain that was strikingly non-overlapping. GHT was characterized by focal upregulation of systems driving protein synthesis, while the growth-selected fish presented a larger and more diverse set of changes, consistent with complex alterations to many metabolic and cellular pathways. Poly I:C had little detectable effect on the muscle proteome. This study demonstrates that distinct proteome profiles can explain outwardly similar enhanced growth phenotypes, improving our understanding of growth mechanisms in anthropogenic animal strains. Significance This work provides the first proteomic insights into mechanisms underpinning different anthropogenic routes to rapid growth in salmon. High-throughput proteomic profiling was used to reveal changes supporting enhanced growth, comparing skeletal muscle of growth hormone transgenic (GHT) and selectively-bred salmon strains with their wild-type counterparts. Contrasting past mRNA-level comparisons of the same fish strains, our data reveals a surprisingly substantial proteomic divergence between the GHT and selectively bred strains. The findings demonstrate that many unique molecular mechanisms underlie growth-enhanced phenotypes in different types of fish strain used for food production and scientific research. Mechanistic basis for rapid growth poorly understood in fish. Comparative proteomic profiling done in fish strains showing highly enhanced growth. Distinct basis for enhanced growth comparing transgenic and domesticated fish strains. Highly distinct proteome profiles may explain outwardly similar growth phenotypes. Study enhances understanding of how rapid growth is achieved in anthropogenic animal strains.
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Affiliation(s)
- Dwight R Causey
- School of Biological Sciences, University of Aberdeen, Aberdeen, UK
| | - Jin-Hyoung Kim
- Fisheries and Oceans Canada, West Vancouver, British Columbia V7V 1N6, Canada; Korea Polar Research Institute (KOPRI), Yeonsu-gu, Incheon 21990, Republic of Korea
| | - David A Stead
- Aberdeen Proteomics, University of Aberdeen, Rowett Institute, Aberdeen, UK
| | | | - Robert H Devlin
- Fisheries and Oceans Canada, West Vancouver, British Columbia V7V 1N6, Canada
| | - Daniel J Macqueen
- School of Biological Sciences, University of Aberdeen, Aberdeen, UK.
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22
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Firmino M, Weis SN, Souza JMF, Gomes BRB, Mól AR, Mortari MR, Souza GEP, Coca GC, Williams TCR, Fontes W, Ricart CAO, de Sousa MV, Veiga-Souza FH. Label-free quantitative proteomics of rat hypothalamus under fever induced by LPS and PGE 2. J Proteomics 2018; 187:182-199. [PMID: 30056254 DOI: 10.1016/j.jprot.2018.07.018] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Revised: 07/13/2018] [Accepted: 07/24/2018] [Indexed: 12/21/2022]
Abstract
Fever is a brain-mediated increase in body temperature mainly during inflammatory or infectious challenges. Although there is considerable data regarding the inflammation pathways involved in fever, metabolic alterations necessary to orchestrate the complex inflammatory response are not totally understood. We performed proteomic analysis of rat hypothalamus using label-free LC-MS/MS in a model of fever induced by lipopolysaccharide (LPS) or prostaglandin E2 (PGE2). In total, 7021 proteins were identified. As far as we know, this is the largest rat hypothalamus proteome dataset available to date. Pathway analysis showed proteins from both stimuli associated with inflammatory and metabolic pathways. Concerning metabolic pathways, rats exposed to LPS or PGE2 presented lower relative abundance of proteins involved in glycolysis, pentose phosphate pathway and tricarboxylic acid cycle. Mitochondrial function may also be altered by both stimuli because significant downregulation of several proteins was found, mainly in complexes I and IV. LPS was able to induce downregulation of important proteins in the enzymatic antioxidant system, thereby contributing to oxidative stress. The results offered comprehensive information about fever responses and helped to reveal new insights into proteins potentially involved in inflammatory signaling and metabolic changes in the hypothalamus during systemic LPS and central PGE2 administration. SIGNIFICANCE The evolutionary persistence of fever, despite the elevated cost for maintenance of this response, suggests that elevation in core temperature may represent an interesting strategy for survival. Fever response is achieved through the integrated behavioral, physiological, immunological and biochemical processes that determine the balance between heat generation and elimination. The development of such complex response arouses interest in studying how the cell metabolism responds or even contributes to promote fever. Our results offered comprehensive information about fever responses, including metabolic and inflammatory pathways, providing new insights into candidate proteins potentially involved in inflammatory signaling and metabolic changes in the hypothalamus during fever induced by systemic LPS and central PGE2 perturbation.
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Affiliation(s)
- Marina Firmino
- Laboratory of Protein Chemistry and Biochemistry, Department of Cell Biology, Institute of Biology, University of Brasilia, Brasília, DF 70910-900, Brazil
| | - Simone N Weis
- Laboratory of Protein Chemistry and Biochemistry, Department of Cell Biology, Institute of Biology, University of Brasilia, Brasília, DF 70910-900, Brazil
| | - Jaques M F Souza
- Laboratory of Protein Chemistry and Biochemistry, Department of Cell Biology, Institute of Biology, University of Brasilia, Brasília, DF 70910-900, Brazil
| | - Bruna R B Gomes
- Laboratory of Protein Chemistry and Biochemistry, Department of Cell Biology, Institute of Biology, University of Brasilia, Brasília, DF 70910-900, Brazil
| | - Alan R Mól
- Laboratory of Protein Chemistry and Biochemistry, Department of Cell Biology, Institute of Biology, University of Brasilia, Brasília, DF 70910-900, Brazil
| | - Márcia R Mortari
- Laboratory of Neuropharmacology, Department of Physiological Sciences, Institute of Biological Sciences, University of Brasilia, Brasília, DF 70910-900, Brazil
| | - Gloria E P Souza
- Laboratory of Pharmacology, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP 14040-903, Brazil
| | - Guilherme C Coca
- Laboratory of Plant Biochemistry, Department of Botany, University of Brasilia, Brasília, DF 70910-900, Brazil
| | - Thomas C R Williams
- Laboratory of Plant Biochemistry, Department of Botany, University of Brasilia, Brasília, DF 70910-900, Brazil
| | - Wagner Fontes
- Laboratory of Protein Chemistry and Biochemistry, Department of Cell Biology, Institute of Biology, University of Brasilia, Brasília, DF 70910-900, Brazil
| | - Carlos André O Ricart
- Laboratory of Protein Chemistry and Biochemistry, Department of Cell Biology, Institute of Biology, University of Brasilia, Brasília, DF 70910-900, Brazil
| | - Marcelo V de Sousa
- Laboratory of Protein Chemistry and Biochemistry, Department of Cell Biology, Institute of Biology, University of Brasilia, Brasília, DF 70910-900, Brazil.
| | - Fabiane H Veiga-Souza
- Laboratory of Protein Chemistry and Biochemistry, Department of Cell Biology, Institute of Biology, University of Brasilia, Brasília, DF 70910-900, Brazil; School of Ceilandia, University of Brasilia, Brasília, DF 72220-275, Brazil.
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23
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Mass Spectrometry Analysis of Lysine Posttranslational Modifications of Tau Protein from Alzheimer's Disease Brain. Methods Mol Biol 2018; 1523:161-177. [PMID: 27975250 DOI: 10.1007/978-1-4939-6598-4_10] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Recent advances in mass spectrometry (MS)-based proteomics have greatly facilitated the robust identification and quantification of posttranslational modifications (PTMs), including those that are present at substoichiometric site occupancies. The abnormal posttranslational modification and accumulation of the microtubule-associated protein tau has been implicated in the pathogenesis of Alzheimer's disease (AD), and it is thought that the primary mode of regulation of tau occurs through PTMs. Several studies have been published regarding tau phosphorylation; however, other tau PTMs such as ubiquitylation, acetylation, methylation, oxidation, sumoylation, nitration, and glycosylation have not been analyzed as extensively. The comprehensive detection and delineation of these PTMs is critical for drug target discovery and validation. Lysine-directed PTMs including ubiquitylation, acetylation, and methylation play key regulatory roles with respect to the rates of tau turnover and aggregation. MS-based analytical approaches have been used to gain insight into the tau lysine-directed PTM signature that is most closely associated with neurofibrillary lesion formation. This chapter provides details pertaining to the liquid chromatography tandem mass spectrometry (LC-MS/MS)-based analysis of the lysine-directed posttranslational modification of tau.
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24
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D’Angelo G, Chaerkady R, Yu W, Hizal DB, Hess S, Zhao W, Lekstrom K, Guo X, White WI, Roskos L, Bowen MA, Yang H. Statistical Models for the Analysis of Isobaric Tags Multiplexed Quantitative Proteomics. J Proteome Res 2017; 16:3124-3136. [DOI: 10.1021/acs.jproteome.6b01050] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Gina D’Angelo
- Statistical
Sciences, ‡Antibody Discovery and Protein Engineering, Protein Sciences, §Research Bioinformatics, ∥Clinical Biomarkers
and Computational Biology, and ⊥Clinical Pharmacology, Pharmacometrics, and
DMPK, MedImmune, Gaithersburg, Maryland 20878, United States
| | - Raghothama Chaerkady
- Statistical
Sciences, ‡Antibody Discovery and Protein Engineering, Protein Sciences, §Research Bioinformatics, ∥Clinical Biomarkers
and Computational Biology, and ⊥Clinical Pharmacology, Pharmacometrics, and
DMPK, MedImmune, Gaithersburg, Maryland 20878, United States
| | - Wen Yu
- Statistical
Sciences, ‡Antibody Discovery and Protein Engineering, Protein Sciences, §Research Bioinformatics, ∥Clinical Biomarkers
and Computational Biology, and ⊥Clinical Pharmacology, Pharmacometrics, and
DMPK, MedImmune, Gaithersburg, Maryland 20878, United States
| | - Deniz Baycin Hizal
- Statistical
Sciences, ‡Antibody Discovery and Protein Engineering, Protein Sciences, §Research Bioinformatics, ∥Clinical Biomarkers
and Computational Biology, and ⊥Clinical Pharmacology, Pharmacometrics, and
DMPK, MedImmune, Gaithersburg, Maryland 20878, United States
| | - Sonja Hess
- Statistical
Sciences, ‡Antibody Discovery and Protein Engineering, Protein Sciences, §Research Bioinformatics, ∥Clinical Biomarkers
and Computational Biology, and ⊥Clinical Pharmacology, Pharmacometrics, and
DMPK, MedImmune, Gaithersburg, Maryland 20878, United States
| | - Wei Zhao
- Statistical
Sciences, ‡Antibody Discovery and Protein Engineering, Protein Sciences, §Research Bioinformatics, ∥Clinical Biomarkers
and Computational Biology, and ⊥Clinical Pharmacology, Pharmacometrics, and
DMPK, MedImmune, Gaithersburg, Maryland 20878, United States
| | - Kristen Lekstrom
- Statistical
Sciences, ‡Antibody Discovery and Protein Engineering, Protein Sciences, §Research Bioinformatics, ∥Clinical Biomarkers
and Computational Biology, and ⊥Clinical Pharmacology, Pharmacometrics, and
DMPK, MedImmune, Gaithersburg, Maryland 20878, United States
| | - Xiang Guo
- Statistical
Sciences, ‡Antibody Discovery and Protein Engineering, Protein Sciences, §Research Bioinformatics, ∥Clinical Biomarkers
and Computational Biology, and ⊥Clinical Pharmacology, Pharmacometrics, and
DMPK, MedImmune, Gaithersburg, Maryland 20878, United States
| | - Wendy I. White
- Statistical
Sciences, ‡Antibody Discovery and Protein Engineering, Protein Sciences, §Research Bioinformatics, ∥Clinical Biomarkers
and Computational Biology, and ⊥Clinical Pharmacology, Pharmacometrics, and
DMPK, MedImmune, Gaithersburg, Maryland 20878, United States
| | - Lorin Roskos
- Statistical
Sciences, ‡Antibody Discovery and Protein Engineering, Protein Sciences, §Research Bioinformatics, ∥Clinical Biomarkers
and Computational Biology, and ⊥Clinical Pharmacology, Pharmacometrics, and
DMPK, MedImmune, Gaithersburg, Maryland 20878, United States
| | - Michael A. Bowen
- Statistical
Sciences, ‡Antibody Discovery and Protein Engineering, Protein Sciences, §Research Bioinformatics, ∥Clinical Biomarkers
and Computational Biology, and ⊥Clinical Pharmacology, Pharmacometrics, and
DMPK, MedImmune, Gaithersburg, Maryland 20878, United States
| | - Harry Yang
- Statistical
Sciences, ‡Antibody Discovery and Protein Engineering, Protein Sciences, §Research Bioinformatics, ∥Clinical Biomarkers
and Computational Biology, and ⊥Clinical Pharmacology, Pharmacometrics, and
DMPK, MedImmune, Gaithersburg, Maryland 20878, United States
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25
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Goeminne LJE, Gevaert K, Clement L. Experimental design and data-analysis in label-free quantitative LC/MS proteomics: A tutorial with MSqRob. J Proteomics 2017; 171:23-36. [PMID: 28391044 DOI: 10.1016/j.jprot.2017.04.004] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Revised: 03/29/2017] [Accepted: 04/01/2017] [Indexed: 12/14/2022]
Abstract
Label-free shotgun proteomics is routinely used to assess proteomes. However, extracting relevant information from the massive amounts of generated data remains difficult. This tutorial provides a strong foundation on analysis of quantitative proteomics data. We provide key statistical concepts that help researchers to design proteomics experiments and we showcase how to analyze quantitative proteomics data using our recent free and open-source R package MSqRob, which was developed to implement the peptide-level robust ridge regression method for relative protein quantification described by Goeminne et al. MSqRob can handle virtually any experimental proteomics design and outputs proteins ordered by statistical significance. Moreover, its graphical user interface and interactive diagnostic plots provide easy inspection and also detection of anomalies in the data and flaws in the data analysis, allowing deeper assessment of the validity of results and a critical review of the experimental design. Our tutorial discusses interactive preprocessing, data analysis and visualization of label-free MS-based quantitative proteomics experiments with simple and more complex designs. We provide well-documented scripts to run analyses in bash mode on GitHub, enabling the integration of MSqRob in automated pipelines on cluster environments (https://github.com/statOmics/MSqRob). SIGNIFICANCE The concepts outlined in this tutorial aid in designing better experiments and analyzing the resulting data more appropriately. The two case studies using the MSqRob graphical user interface will contribute to a wider adaptation of advanced peptide-based models, resulting in higher quality data analysis workflows and more reproducible results in the proteomics community. We also provide well-documented scripts for experienced users that aim at automating MSqRob on cluster environments.
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Affiliation(s)
- Ludger J E Goeminne
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Belgium; VIB-UGent Center for Medical Biotechnology, VIB, Belgium; Department of Biochemistry, Ghent University, Belgium; Bioinformatics Institute Ghent, Ghent University, Belgium.
| | - Kris Gevaert
- VIB-UGent Center for Medical Biotechnology, VIB, Belgium; Department of Biochemistry, Ghent University, Belgium; Bioinformatics Institute Ghent, Ghent University, Belgium.
| | - Lieven Clement
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Belgium; Bioinformatics Institute Ghent, Ghent University, Belgium.
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26
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Dowle AA, Wilson J, Thomas JR. Comparing the Diagnostic Classification Accuracy of iTRAQ, Peak-Area, Spectral-Counting, and emPAI Methods for Relative Quantification in Expression Proteomics. J Proteome Res 2016; 15:3550-3562. [PMID: 27546623 DOI: 10.1021/acs.jproteome.6b00308] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Diagnostic classification accuracy is critical in expression proteomics to ensure that as many true differences as possible are identified with acceptable false-positive rates. We present a comparison of the diagnostic accuracy of iTRAQ with three label-free methods, peak area, spectral counting, and emPAI, for relative quantification using a spiked proteome standard. We provide the first validation of emPAI for intersample relative quantification and find clear differences among the four quantification approaches that could be considered when designing an experiment. Spectral counting was observed to perform surprisingly well in all regards. Peak area performed best for smaller fold differences and was shown to be capable of discerning a 1.1-fold difference with acceptable specificity and sensitivity. The performance of iTRAQ was dramatically worse than the label-free methods with low abundance proteins. Using the iTRAQ data set for validation, we also demonstrate a novel iTRAQ analysis regime that avoids the use of ratios in significance testing and outperforms a common commercial alternative.
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Affiliation(s)
- Adam A Dowle
- Bioscience Technology Facility, Department of Biology, University of York , York YO10 5DD, United Kingdom
| | - Julie Wilson
- Departments of Mathematics and Chemistry, University of York , York YO10 5DD, United Kingdom
| | - Jerry R Thomas
- Bioscience Technology Facility, Department of Biology, University of York , York YO10 5DD, United Kingdom
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27
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Ahrné E, Glatter T, Viganò C, Schubert CV, Nigg EA, Schmidt A. Evaluation and Improvement of Quantification Accuracy in Isobaric Mass Tag-Based Protein Quantification Experiments. J Proteome Res 2016; 15:2537-47. [DOI: 10.1021/acs.jproteome.6b00066] [Citation(s) in RCA: 99] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Erik Ahrné
- Biozentrum, University of Basel, Klingelbergstrasse
50/70, 4056 Basel, Switzerland
| | - Timo Glatter
- Biozentrum, University of Basel, Klingelbergstrasse
50/70, 4056 Basel, Switzerland
| | - Cristina Viganò
- Biozentrum, University of Basel, Klingelbergstrasse
50/70, 4056 Basel, Switzerland
| | - Conrad von Schubert
- Biozentrum, University of Basel, Klingelbergstrasse
50/70, 4056 Basel, Switzerland
| | - Erich A. Nigg
- Biozentrum, University of Basel, Klingelbergstrasse
50/70, 4056 Basel, Switzerland
| | - Alexander Schmidt
- Biozentrum, University of Basel, Klingelbergstrasse
50/70, 4056 Basel, Switzerland
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28
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Ning Z, Zhang X, Mayne J, Figeys D. Peptide-Centric Approaches Provide an Alternative Perspective To Re-Examine Quantitative Proteomic Data. Anal Chem 2016; 88:1973-8. [DOI: 10.1021/acs.analchem.5b04148] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Zhibin Ning
- Ottawa
Institute of Systems
Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, Ontario Canada, K1H8M5
| | - Xu Zhang
- Ottawa
Institute of Systems
Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, Ontario Canada, K1H8M5
| | - Janice Mayne
- Ottawa
Institute of Systems
Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, Ontario Canada, K1H8M5
| | - Daniel Figeys
- Ottawa
Institute of Systems
Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, Ontario Canada, K1H8M5
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29
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Goeminne LJE, Gevaert K, Clement L. Peptide-level Robust Ridge Regression Improves Estimation, Sensitivity, and Specificity in Data-dependent Quantitative Label-free Shotgun Proteomics. Mol Cell Proteomics 2015; 15:657-68. [PMID: 26566788 DOI: 10.1074/mcp.m115.055897] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Indexed: 01/22/2023] Open
Abstract
Peptide intensities from mass spectra are increasingly used for relative quantitation of proteins in complex samples. However, numerous issues inherent to the mass spectrometry workflow turn quantitative proteomic data analysis into a crucial challenge. We and others have shown that modeling at the peptide level outperforms classical summarization-based approaches, which typically also discard a lot of proteins at the data preprocessing step. Peptide-based linear regression models, however, still suffer from unbalanced datasets due to missing peptide intensities, outlying peptide intensities and overfitting. Here, we further improve upon peptide-based models by three modular extensions: ridge regression, improved variance estimation by borrowing information across proteins with empirical Bayes and M-estimation with Huber weights. We illustrate our method on the CPTAC spike-in study and on a study comparing wild-type and ArgP knock-out Francisella tularensis proteomes. We show that the fold change estimates of our robust approach are more precise and more accurate than those from state-of-the-art summarization-based methods and peptide-based regression models, which leads to an improved sensitivity and specificity. We also demonstrate that ionization competition effects come already into play at very low spike-in concentrations and confirm that analyses with peptide-based regression methods on peptide intensity values aggregated by charge state and modification status (e.g. MaxQuant's peptides.txt file) are slightly superior to analyses on raw peptide intensity values (e.g. MaxQuant's evidence.txt file).
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Affiliation(s)
- Ludger J E Goeminne
- From the ‡Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Belgium; §VIB Medical Biotechnology Center, Ghent University, Belgium; ¶Department of Biochemistry, Ghent University, Belgium
| | - Kris Gevaert
- §VIB Medical Biotechnology Center, Ghent University, Belgium; ¶Department of Biochemistry, Ghent University, Belgium
| | - Lieven Clement
- From the ‡Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Belgium;
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30
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Suomi T, Corthals GL, Nevalainen OS, Elo LL. Using Peptide-Level Proteomics Data for Detecting Differentially Expressed Proteins. J Proteome Res 2015; 14:4564-70. [PMID: 26380941 DOI: 10.1021/acs.jproteome.5b00363] [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: 12/29/2022]
Abstract
The expression of proteins can be quantified in high-throughput means using different types of mass spectrometers. In recent years, there have emerged label-free methods for determining protein abundance. Although the expression is initially measured at the peptide level, a common approach is to combine the peptide-level measurements into protein-level values before differential expression analysis. However, this simple combination is prone to inconsistencies between peptides and may lose valuable information. To this end, we introduce here a method for detecting differentially expressed proteins by combining peptide-level expression-change statistics. Using controlled spike-in experiments, we show that the approach of averaging peptide-level expression changes yields more accurate lists of differentially expressed proteins than does the conventional protein-level approach. This is particularly true when there are only few replicate samples or the differences between the sample groups are small. The proposed technique is implemented in the Bioconductor package PECA, and it can be downloaded from http://www.bioconductor.org.
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Affiliation(s)
| | - Garry L Corthals
- Van't Hoff Institute for Molecular Sciences, University of Amsterdam , 1090 GD Amsterdam , The Netherlands
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