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ABSTRACT: Selected Reaction Monitoring (SRM) is emerging as a standard tool for high-throughput protein quantification. For reliable and reproducible SRM protein quantification it is essential that system performance is stable. We present here a quality control workflow that is based on repeated analysis of a standard sample to allow insight into the stability of the key properties of a SRM setup. This is supported by automated software to monitor system performance and display information like signal intensities and retention time stability over time, and alert upon deviations from expected metrics. Utilizing the software to evaluate 407 repeated injections of a standard sample during half a year, outliers in relative peptide signal intensities and relative peptide fragment ratios are identified, indicating the need for instrument maintenance. We therefore believe the software could be a vital and powerful tool for any lab regularly performing SRM, increasing the reliability and quality of the SRM platform. This article is part of a Special Issue entitled: Standardization and Quality Control BIOLOGICAL SIGNIFICANCE: Selected Reaction Monitoring (SRM) mass spectrometry is becoming established as a standard technique for accurate protein quantification. However, to achieve the required quantification reproducibility of the liquid chromatography(LC)-SRM setup, system performance needs to be monitored over time. Here we introduce a workflow with associated software to enable automated monitoring of LC-SRM setups. We believe that usage of the presented concepts will further strengthen the role of SRM as a reliable tool for protein quantification.
Journal of proteomics 04/2013; · 5.07 Impact Factor
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ABSTRACT: Protein quantification using different LC-MS techniques is becoming standard practice. However, with a multitude of experimental setups to choose from, as well as a wide array of software solutions for subsequent data processing, it is non-trivial to select the most appropriate workflow for a given biological question. In this review, we highlight different issues that need to be addressed by software for quantitative LC-MS experiments and describe different approaches that are available. With focus on label-free quantification, examples are discussed both for LC-MS/MS and LC-SRM data processing. We further elaborate on current quality control methodology for performing accurate protein quantification experiments.
Biochimica et Biophysica Acta 04/2013; · 4.66 Impact Factor
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Gerhard Mayer,
Luisa Montecchi-Palazzi,
David Ovelleiro,
Andrew R Jones,
Pierre-Alain Binz,
Eric W Deutsch,
Matthew Chambers,
Marius Kallhardt, Fredrik Levander,
James Shofstahl,
Sandra Orchard,
Juan Antonio Vizcaíno,
Henning Hermjakob,
Christian Stephan,
Helmut E Meyer,
Martin Eisenacher
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ABSTRACT: Controlled vocabularies (CVs), i.e. a collection of predefined terms describing a modeling domain, used for the semantic annotation of data, and ontologies are used in structured data formats and databases to avoid inconsistencies in annotation, to have a unique (and preferably short) accession number and to give researchers and computer algorithms the possibility for more expressive semantic annotation of data. The Human Proteome Organization (HUPO)-Proteomics Standards Initiative (PSI) makes extensive use of ontologies/CVs in their data formats. The PSI-Mass Spectrometry (MS) CV contains all the terms used in the PSI MS-related data standards. The CV contains a logical hierarchical structure to ensure ease of maintenance and the development of software that makes use of complex semantics. The CV contains terms required for a complete description of an MS analysis pipeline used in proteomics, including sample labeling, digestion enzymes, instrumentation parts and parameters, software used for identification and quantification of peptides/proteins and the parameters and scores used to determine their significance. Owing to the range of topics covered by the CV, collaborative development across several PSI working groups, including proteomics research groups, instrument manufacturers and software vendors, was necessary. In this article, we describe the overall structure of the CV, the process by which it has been developed and is maintained and the dependencies on other ontologies. Database URL: http://psidev.cvs.sourceforge.net/viewvc/psidev/psi/psi-ms/mzML/controlledVocabulary/psi-ms.obo.
Database The Journal of Biological Databases and Curation 01/2013; 2013:bat009. · 2.07 Impact Factor
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ABSTRACT: Selected reaction monitoring (SRM) is a mass spectrometry method with documented ability to quantify proteins accurately and reproducibly using labeled reference peptides. However, the use of labeled reference peptides becomes impractical if large numbers of peptides are targeted and when high flexibility is desired when selecting peptides. We have developed a label-free quantitative SRM workflow that relies on a new automated algorithm, Anubis, for accurate peak detection. Anubis efficiently removes interfering signals from contaminating peptides to estimate the true signal of the targeted peptides. We evaluated the algorithm on a published multisite data set and achieved results in line with manual data analysis. In complex peptide mixtures from whole proteome digests of Streptococcus pyogenes we achieved a technical variability across the entire proteome abundance range of 6.5-19.2%, which was considerably below the total variation across biological samples. Our results show that the label-free SRM workflow with automated data analysis is feasible for large-scale biological studies, opening up new possibilities for quantitative proteomics and systems biology.
Journal of Proteome Research 06/2012; 11(7):3766-73. · 5.11 Impact Factor
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ABSTRACT: We present a comparison of two-dimensional separation methods and how they affect the degree of coverage of protein expression in complex mixtures. We investigated the relative merits of various protein and peptide separations prior to acidic reversed-phase chromatography directly coupled to an ion trap mass spectrometer. The first dimensions investigated were density gradient organelle fractionation of cell extracts, 1D SDS-PAGE protein separation followed by digestion by trypsin or GluC proteases, strong cation exchange chromatography, and off-gel isoelectric focusing of tryptic peptides. The number of fractions from each first dimension and the total data accumulation RP-HPLC-MS/MS time was kept constant and the experiments were run in triplicate. We find that the most critical parameters are the data accumulation time, which defines the level of under-sampling and the avoidance of peptides from high expression level proteins eluting over the entire gradient.
Journal of Proteome Research 03/2012; 11(5):2644-52. · 5.11 Impact Factor
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ABSTRACT: Approximately 25% of eukaryotic proteins possessing homology to at least two transmembrane domains are predicted to be embedded in biological membranes. Nevertheless, this group of proteins is not usually well represented in proteome-wide experiments due to their refractory nature. Here we present a quantitative mass spectrometry-based comparison of membrane protein expression in cerebellar granule neurons grown in primary culture that were isolated from wild-type mice and mice lacking the cellular prion protein. This protein is a cell-surface glycoprotein that is mainly expressed in the central nervous system and is involved in several neurodegenerative disorders, though its physiological role is unclear. We used a low specificity enzyme α-chymotrypsin to digest membrane proteins preparations that had been separated by SDS-PAGE. The resulting peptides were labeled with tandem mass tags and analyzed by MS. The differentially expressed proteins identified using this approach were further analyzed by multiple reaction monitoring to confirm the expression level changes.
Journal of Proteome Research 02/2012; 11(2):523-36. · 5.11 Impact Factor
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Eric W Deutsch,
Matthew Chambers,
Steffen Neumann, Fredrik Levander,
Pierre-Alain Binz,
Jim Shofstahl,
David S Campbell,
Luis Mendoza,
David Ovelleiro,
Kenny Helsens,
Lennart Martens,
Ruedi Aebersold,
Robert L Moritz,
Mi-Youn Brusniak
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ABSTRACT: Targeted proteomics via selected reaction monitoring is a powerful mass spectrometric technique affording higher dynamic range, increased specificity and lower limits of detection than other shotgun mass spectrometry methods when applied to proteome analyses. However, it involves selective measurement of predetermined analytes, which requires more preparation in the form of selecting appropriate signatures for the proteins and peptides that are to be targeted. There is a growing number of software programs and resources for selecting optimal transitions and the instrument settings used for the detection and quantification of the targeted peptides, but the exchange of this information is hindered by a lack of a standard format. We have developed a new standardized format, called TraML, for encoding transition lists and associated metadata. In addition to introducing the TraML format, we demonstrate several implementations across the community, and provide semantic validators, extensive documentation, and multiple example instances to demonstrate correctly written documents. Widespread use of TraML will facilitate the exchange of transitions, reduce time spent handling incompatible list formats, increase the reusability of previously optimized transitions, and thus accelerate the widespread adoption of targeted proteomics via selected reaction monitoring.
Molecular & Cellular Proteomics 12/2011; 11(4):R111.015040. · 7.40 Impact Factor
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Jürgen Cox,
Ron M A Heeren,
Peter James,
Jesús V Jorrin-Novo,
Eugene Kolker, Fredrik Levander,
Nicholas Morrice,
Paola Picotti,
Pier Giorgio Righetti,
Jean-Charles Sánchez, [......],
Roman Zubarev,
Bruno M Alexandre,
Fernando J Corrales,
György Marko-Varga,
Sinead O'Donovan,
Serena O'Neil,
Jozsef Prechl,
Tânia Simões,
Wolfram Weckwerth,
Deborah Penque
Journal of proteomics 05/2011; 75(1):4-17. · 5.07 Impact Factor
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ABSTRACT: Proteomic experiments can be difficult to handle because of the large amount of data in different formats that is generated. Samples need to be managed and generated, data needs to be integrated with samples and annotation information. A laboratory information management system (LIMS) can be used to overcome some of the data handling problems. In this chapter, we discuss the role of a LIMS in the proteomics laboratory, and show two step-by-step examples of usage of the Proteios Software Environment (ProSE) to handle two different proteomics workflows.
Methods in molecular biology (Clifton, N.J.) 01/2011; 696:79-92.
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Paolo Cifani,
Maria Bendz,
Kristofer Wårell,
Karin Hansson, Fredrik Levander,
Marianne Sandin,
Morten Krogh,
Marie Ovenberger,
Erik Fredlund,
Marica Vaapil,
Alexander Pietras,
Sven Påhlman,
Peter James
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ABSTRACT: Nontransient hypoxia is strongly associated with malignant lesions, resulting in aggressive behavior and resistance to treatment. We present an analysis of mRNA and protein expression changes in neuroblastoma cell lines occurring upon the transition from normoxia to hypoxia. The correlation between mRNA and protein level changes was poor, although some known hypoxia-driven genes and proteins correlated well. We present previously undescribed membrane proteins expressed under hypoxic conditions that are candidates for evaluation as biomarkers.
Journal of Proteome Research 01/2011; 10(4):1645-56. · 5.11 Impact Factor
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ABSTRACT: As high-resolution instruments are becoming standard in proteomics laboratories, label-free quantification using precursor measurements is becoming a viable option, and is consequently rapidly gaining popularity. Several software solutions have been presented for label-free analysis, but to our knowledge no conclusive studies regarding the sensitivity and reliability of each step of the analysis procedure has been described. Here, we use real complex samples to assess the reliability of label-free quantification using four different software solutions. A generic approach to quality test quantitative label-free LC-MS is introduced. Measures for evaluation are defined for feature detection, alignment and quantification. All steps of the analysis could be considered adequately performed by the utilized software solutions, although differences and possibilities for improvement could be identified. The described method provides an effective testing procedure, which can help the user to quickly pinpoint where in the workflow changes are needed.
Proteomics 01/2011; 11(6):1114-24. · 4.43 Impact Factor
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Lennart Martens,
Matthew Chambers,
Marc Sturm,
Darren Kessner, Fredrik Levander,
Jim Shofstahl,
Wilfred H Tang,
Andreas Römpp,
Steffen Neumann,
Angel D Pizarro,
Luisa Montecchi-Palazzi,
Natalie Tasman,
Mike Coleman,
Florian Reisinger,
Puneet Souda,
Henning Hermjakob,
Pierre-Alain Binz,
Eric W Deutsch
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ABSTRACT: Mass spectrometry is a fundamental tool for discovery and analysis in the life sciences. With the rapid advances in mass spectrometry technology and methods, it has become imperative to provide a standard output format for mass spectrometry data that will facilitate data sharing and analysis. Initially, the efforts to develop a standard format for mass spectrometry data resulted in multiple formats, each designed with a different underlying philosophy. To resolve the issues associated with having multiple formats, vendors, researchers, and software developers convened under the banner of the HUPO PSI to develop a single standard. The new data format incorporated many of the desirable technical attributes from the previous data formats, while adding a number of improvements, including features such as a controlled vocabulary with validation tools to ensure consistent usage of the format, improved support for selected reaction monitoring data, and immediately available implementations to facilitate rapid adoption by the community. The resulting standard data format, mzML, is a well tested open-source format for mass spectrometer output files that can be readily utilized by the community and easily adapted for incremental advances in mass spectrometry technology.
Molecular & Cellular Proteomics 01/2011; 10(1):R110.000133. · 7.40 Impact Factor
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ABSTRACT: Label-free LC-MS analysis allows determining the differential expression level of proteins in multiple samples, without the use of stable isotopes. This technique is based on the direct comparison of multiple runs, obtained by continuous detection in MS mode. Only differentially expressed peptides are selected for further fragmentation, thus avoiding the bias toward abundant peptides typical of data-dependent tandem MS. The computational framework includes detection, alignment, normalization and matching of peaks across multiple sets, and several software packages are available to address these processing steps. Yet, more care should be taken to improve the quality of the LC-MS maps entering the pipeline, as this parameter severely affects the results of all downstream analyses. In this paper we show how the inclusion of a preprocessing step of background subtraction in a common laboratory pipeline can lead to an enhanced inclusion list of peptides selected for fragmentation and consequently to better protein identification.
Journal of Biomedicine and Biotechnology 01/2010; 2010:131505. · 2.44 Impact Factor
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ABSTRACT: We present a mass spectrometry-based method for the identification and quantification of membrane proteins using the low-specificity protease Proteinase K, at very high pH, to digest proteins isolated by a modified SDS-PAGE protocol. The resulting peptides are modified with a fragmentation-directing isotope labeled tag. We apply the method to quantify differences in membrane protein expression of Bacillus subtilis grown in the presence or absence of glucose.
Journal of Proteome Research 11/2009; 8(12):5666-73. · 5.11 Impact Factor
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ABSTRACT: The identification of serum biomarkers for the diagnosis of inflammatory bowel diseases able to reduce the need for invasive tests represents a major goal in their therapy and follow-up. We report here a methodological approach for the evaluation of specific changes in the serum peptides abundance in healthy (H) and Crohn's disease (CD) subjects, based on a label-free LC ESI/Q-TOF differential mass spectrometry (MS) approach combined with targeted MS/MS analysis. The low molecular weight serum proteins were separated by RP nano-LC ESI/Q-TOF MS and the resulting datasets were aligned with msInspect software. The differently abundant peptides, evaluated using Proteios Software Environment, were identified by MS/MS analysis and database search. The identification of clusters of peptides resulting from proteins (such as fibrinogen-alpha) commonly involved in physiological processes lead to the evaluation of a possible role in CD of specific serum exoproteases. An assay based on synthetic peptides spiked into H, CD and ulcerative colitis (UC) serum samples as substrate, followed by MALDI MS and chemometric analysis of the metabolite patterns has been developed achieving a 100% discrimination between CD, UC and H subjects. The results are promising for the application of this approach as a simple tool for diagnostic aims and biomarker discovery in CD.
Journal of chromatography. B, Analytical technologies in the biomedical and life sciences 09/2009; 877(27):3127-36. · 2.78 Impact Factor
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Martin Eisenacher,
Lennart Martens,
Tanja Hardt,
Michael Kohl,
Harald Barsnes,
Kenny Helsens,
Jari Häkkinen, Fredrik Levander,
Ruedi Aebersold,
Joël Vandekerckhove, [......],
Frederique Lisacek,
Jennifer A Siepen,
Simon J Hubbard,
Pierre-Alain Binz,
Martin Blüggel,
Herbert Thiele,
John Cottrell,
Helmut E Meyer,
Rolf Apweiler,
Christian Stephan
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ABSTRACT: In proteomics, rapid developments in instrumentation led to the acquisition of increasingly large data sets. Correspondingly, ProDaC was founded in 2006 as a Coordination Action project within the 6th European Union Framework Programme to support data sharing and community-wide data collection. The objectives of ProDaC were the development of documentation and storage standards, setup of a standardized data submission pipeline and collection of data. Ending in March 2009, ProDaC has delivered a comprehensive toolbox of standards and computer programs to achieve these goals.
Proteomics 08/2009; 9(15):3928-33. · 4.43 Impact Factor
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ABSTRACT: Xylose fermentation in yeast has been a target of research for years, yet not all the factors that may affect xylose fermentation performance of yeast strains are known. In this study, the mutant S. cerevisiae strain TMB 3400, which has good xylose fermentation properties, was compared with its parental strain to examine the factors behind the improved xylose utilization at protein level. The proteome of the parental and the mutant strains were characterized by difference in gel electrophoresis (DiGE) to quantitatively identify proteins that are expressed at altered levels in the mutant. The most significant changes detected by proteome analysis were the 6-10-fold increased levels of xylose reductase, xylitol dehydrogenase and transketolase (Tkl1) in the mutant, which is in accordance with previous knowledge about xylose metabolism in yeast. The level of acetaldehyde dehydrogenase (Ald6) was also significantly increased. In addition, several proteins homologous to proteins from yeast species other than S. cerevisiae were identified in both strains, demonstrating the genetic heterogeneity of industrial yeast strains. The results were also compared with a previously reported transcription analysis performed with identical experimental set-up; however, very little correlation between the two datasets was observed. The results of the proteome analysis were in good agreement with a parallel study in which rationally designed overexpression of XR, XDH and the non-oxidative pentose phosphate pathway resulted in similar improvement in xylose utilization, which demonstrates the usefulness of proteome analysis for the identification of target genes for further metabolic engineering strategies in industrial yeast strains.
Yeast 06/2009; 26(7):371-82. · 1.89 Impact Factor
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ABSTRACT: Proteome analysis involves many steps that generate large quantities of data in different formats. This creates a need for automatic data merging and extraction of important features from data. Furthermore, metadata need to be collected and reported to enable critical evaluation of results. Many data analysis tools are developed locally in research laboratories and are nontrivial to adapt for other laboratories, preventing optimal exploitation of generated data. The proteomics field would benefit from user-friendly analysis and data management platforms in which method developers can make their analysis tools available for the community. Here, we describe the Proteios Software Environment (ProSE) that is built around a Web-based local data repository for proteomics experiments. The application features sample tracking, project sharing between multiple users, and automated data merging and analysis. ProSE has built-in support for several quantitative proteomics workflows, and integrates searching in several search engines, automated combination of the search results with predetermined false discovery rates, annotation of proteins and submission of results to public repositories. ProSE also provides a programming interface to enable local extensions, as well as database access using Web services. ProSE provides an analysis platform for proteomics research and is targeted for multiuser projects with needs to share data, sample tracking, and analysis result. ProSE is open source software available at http://www.proteios.org .
Journal of Proteome Research 05/2009; 8(6):3037-43. · 5.11 Impact Factor
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ABSTRACT: Intestinal epithelial cells (IECs) play a key role in Crohn's disease, a chronic inflammatory bowel disease which requires invasive examinations to be diagnosed. The comparison of the cellular protein expression profiles of Crohn's disease patients and healthy subjects is fundamental for the identification of proteins clinically relevant as new biomarkers or as drug targets. For this purpose a differential label-free nano-LC ESI/QTOF mass spectrometry (MS) approach combined with targeted MS/MS analysis has been developed and applied to isolated IECs. We report here a study of the protein variations in IECs from healthy subjects (H) and Crohn's disease patients (CD). The method was previously validated using HT29 Cl.16E cell line, normal or treated with interferon-gamma as a model of inflammation. Subcellular fractions proteins were extracted from HT29 and IECs and for each fraction monodimensional gel-electrophoresis was performed and the proteins subjected to tryptic digestion. The resulting peptides were analysed by LC ESI/QTOF MS and the obtained chromatographic runs were aligned with msInspect software. The peptides differently expressed were statistically evaluated using the Proteios Software Environment (ProSE) and identified by LC ESI/QTOF MS/MS analysis and database search. The preliminary results obtained allowed the identification of many proteins involved in the inflammation processes.
Journal of proteomics 02/2009; 72(5):865-73. · 5.07 Impact Factor
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ABSTRACT: ProDaC (Proteomics Data Collection), a "Coordination Action" within the 6(th) EU framework programme, was created to support the collection, distribution and public availability of data from proteomics experiments. Within the consortium standards are created and maintained enabling an extensive data collection within the proteomics community. Important elements of ProDaC are workshops held twice a year to allow communication between the ProDaC partners and to report the ongoing progress. The most recent assembly was the 4(th) ProDaC workshop on August 15(th), 2008, in Amsterdam, The Netherlands. It took place directly before the 7(th) HUPO Annual World Congress (Human Proteome Organisation). Work package coordinators and partners presented the progress achieved since the last meeting. Additionally, an EU official presented funding opportunities for proteomics in the next EU framework programme and five external speakers presented talks about their work in relation to ProDaC.
Proteomics 01/2009; 9(2):218-22. · 4.43 Impact Factor
