Quality standards in proteomics research facilities
2021; Springer Nature; Volume: 22; Issue: 6 Linguagem: Inglês
10.15252/embr.202152626
ISSN1469-3178
AutoresCristina Chiva, Teresa Mendes Maia, Christian Panse, Karel Stejskal, Thibaut Douché, Mariette Matondo, Damarys Loew, Dominic Helm, Mandy Rettel, Karl Mechtler, Francis Impens, Paolo Nanni, Anna Shevchenko, Eduard Sabidó,
Tópico(s)Molecular Biology Techniques and Applications
ResumoScience & Society19 May 2021Open Access Quality standards in proteomics research facilities Common standards and quality procedures are essential for proteomics facilities and their users Cristina Chiva Cristina Chiva orcid.org/0000-0001-8150-6203 Centre de Regulació Genòmica, Barcelona Institute of Science and Technology (BIST), Barcelona, Spain Univeristat Pompeu Fabra, Barcelona, Spain Search for more papers by this author Teresa Mendes Maia Teresa Mendes Maia orcid.org/0000-0003-0038-9629 VIB Proteomics Core, VIB, Ghent, Belgium VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium Department of Biomolecular Medicine, Ghent University, Ghent, Belgium Search for more papers by this author Christian Panse Christian Panse orcid.org/0000-0003-1975-3064 Functional Genomics Center Zurich, University/ETH Zurich, Zurich, Switzerland Swiss Institute of Bioinformatics, Lausanne, Switzerland Search for more papers by this author Karel Stejskal Karel Stejskal orcid.org/0000-0001-7835-8123 Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Vienna, Austria IMBA Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna Biocenter (VBC), Vienna, Austria Search for more papers by this author Thibaut Douché Thibaut Douché orcid.org/0000-0001-8627-6726 Institut Pasteur, Proteomics Platform, Mass Spectrometry for Biology Unit, USR CNRS 2000 MSBio Unit, CNRS, Paris, France Search for more papers by this author Mariette Matondo Mariette Matondo Institut Pasteur, Proteomics Platform, Mass Spectrometry for Biology Unit, USR CNRS 2000 MSBio Unit, CNRS, Paris, France Search for more papers by this author Damarys Loew Damarys Loew orcid.org/0000-0002-9111-8842 Institut Curie, Centre de Recherche, Laboratoire de Spectrométrie de Masse Protéomique, PSL Research University, Paris cedex 05, France Search for more papers by this author Dominic Helm Dominic Helm Proteomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany MS based Protein analysis Unit, Genomics Proteomics Core Facilities, DKFZ, Heidelberg, Germany Search for more papers by this author Mandy Rettel Mandy Rettel orcid.org/0000-0002-8304-3385 Proteomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Karl Mechtler Karl Mechtler orcid.org/0000-0002-3392-9946 Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Vienna, Austria IMBA Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna Biocenter (VBC), Vienna, Austria Gregor Mendel Institute (GMI), Austrian Academy of Sciences, Vienna BioCenter 7 (VBC), Vienna, Austria Search for more papers by this author Francis Impens Francis Impens orcid.org/0000-0003-2886-9616 VIB Proteomics Core, VIB, Ghent, Belgium VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium Department of Biomolecular Medicine, Ghent University, Ghent, Belgium Search for more papers by this author Paolo Nanni Corresponding Author Paolo Nanni [email protected] orcid.org/0000-0001-8429-3557 Functional Genomics Center Zurich, University/ETH Zurich, Zurich, Switzerland Search for more papers by this author Anna Shevchenko Corresponding Author Anna Shevchenko [email protected] Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany Search for more papers by this author Eduard Sabidó Corresponding Author Eduard Sabidó [email protected] orcid.org/0000-0001-6506-7714 Centre de Regulació Genòmica, Barcelona Institute of Science and Technology (BIST), Barcelona, Spain Univeristat Pompeu Fabra, Barcelona, Spain Search for more papers by this author Cristina Chiva Cristina Chiva orcid.org/0000-0001-8150-6203 Centre de Regulació Genòmica, Barcelona Institute of Science and Technology (BIST), Barcelona, Spain Univeristat Pompeu Fabra, Barcelona, Spain Search for more papers by this author Teresa Mendes Maia Teresa Mendes Maia orcid.org/0000-0003-0038-9629 VIB Proteomics Core, VIB, Ghent, Belgium VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium Department of Biomolecular Medicine, Ghent University, Ghent, Belgium Search for more papers by this author Christian Panse Christian Panse orcid.org/0000-0003-1975-3064 Functional Genomics Center Zurich, University/ETH Zurich, Zurich, Switzerland Swiss Institute of Bioinformatics, Lausanne, Switzerland Search for more papers by this author Karel Stejskal Karel Stejskal orcid.org/0000-0001-7835-8123 Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Vienna, Austria IMBA Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna Biocenter (VBC), Vienna, Austria Search for more papers by this author Thibaut Douché Thibaut Douché orcid.org/0000-0001-8627-6726 Institut Pasteur, Proteomics Platform, Mass Spectrometry for Biology Unit, USR CNRS 2000 MSBio Unit, CNRS, Paris, France Search for more papers by this author Mariette Matondo Mariette Matondo Institut Pasteur, Proteomics Platform, Mass Spectrometry for Biology Unit, USR CNRS 2000 MSBio Unit, CNRS, Paris, France Search for more papers by this author Damarys Loew Damarys Loew orcid.org/0000-0002-9111-8842 Institut Curie, Centre de Recherche, Laboratoire de Spectrométrie de Masse Protéomique, PSL Research University, Paris cedex 05, France Search for more papers by this author Dominic Helm Dominic Helm Proteomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany MS based Protein analysis Unit, Genomics Proteomics Core Facilities, DKFZ, Heidelberg, Germany Search for more papers by this author Mandy Rettel Mandy Rettel orcid.org/0000-0002-8304-3385 Proteomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Karl Mechtler Karl Mechtler orcid.org/0000-0002-3392-9946 Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Vienna, Austria IMBA Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna Biocenter (VBC), Vienna, Austria Gregor Mendel Institute (GMI), Austrian Academy of Sciences, Vienna BioCenter 7 (VBC), Vienna, Austria Search for more papers by this author Francis Impens Francis Impens orcid.org/0000-0003-2886-9616 VIB Proteomics Core, VIB, Ghent, Belgium VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium Department of Biomolecular Medicine, Ghent University, Ghent, Belgium Search for more papers by this author Paolo Nanni Corresponding Author Paolo Nanni [email protected] orcid.org/0000-0001-8429-3557 Functional Genomics Center Zurich, University/ETH Zurich, Zurich, Switzerland Search for more papers by this author Anna Shevchenko Corresponding Author Anna Shevchenko [email protected] Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany Search for more papers by this author Eduard Sabidó Corresponding Author Eduard Sabidó [email protected] orcid.org/0000-0001-6506-7714 Centre de Regulació Genòmica, Barcelona Institute of Science and Technology (BIST), Barcelona, Spain Univeristat Pompeu Fabra, Barcelona, Spain Search for more papers by this author Author Information Cristina Chiva1,2, Teresa Mendes Maia3,4,5, Christian Panse6,7, Karel Stejskal8,9, Thibaut Douché10, Mariette Matondo10, Damarys Loew11, Dominic Helm12,13, Mandy Rettel12, Karl Mechtler8,9,14, Francis Impens3,4,5, Paolo Nanni *,6, Anna Shevchenko *,15 and Eduard Sabidó *,1,2 1Centre de Regulació Genòmica, Barcelona Institute of Science and Technology (BIST), Barcelona, Spain 2Univeristat Pompeu Fabra, Barcelona, Spain 3VIB Proteomics Core, VIB, Ghent, Belgium 4VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium 5Department of Biomolecular Medicine, Ghent University, Ghent, Belgium 6Functional Genomics Center Zurich, University/ETH Zurich, Zurich, Switzerland 7Swiss Institute of Bioinformatics, Lausanne, Switzerland 8Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Vienna, Austria 9IMBA Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna Biocenter (VBC), Vienna, Austria 10Institut Pasteur, Proteomics Platform, Mass Spectrometry for Biology Unit, USR CNRS 2000 MSBio Unit, CNRS, Paris, France 11Institut Curie, Centre de Recherche, Laboratoire de Spectrométrie de Masse Protéomique, PSL Research University, Paris cedex 05, France 12Proteomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany 13MS based Protein analysis Unit, Genomics Proteomics Core Facilities, DKFZ, Heidelberg, Germany 14Gregor Mendel Institute (GMI), Austrian Academy of Sciences, Vienna BioCenter 7 (VBC), Vienna, Austria 15Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany *Corresponding author. E-mail: [email protected] *Corresponding author. E-mail: [email protected] *Corresponding author. E-mail: [email protected] EMBO Reports (2021)22:e52626https://doi.org/10.15252/embr.202152626 PDFDownload PDF of article text and main figures. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions Figures & Info Core facilities and research infrastructures have become an essential part of the scientific ecosystem. In the field of proteomics, national and international networks and research platforms have been established during the past decade that are supposed to set standards for high-quality services, promote an exchange of professional information, and enable access to cutting-edge, specialized proteomics technologies. Either centralized or distributed, these national and international proteomics infrastructures and technology platforms are generating massive amounts of data for the research community, and support a broad range of translational, computational and multi-omics initiatives and basic research projects. By delegating part of their work to these services, researchers expect that the core facility adjusts their analytical protocols appropriately for their project to acquire data conforming best research practice of the scientific community. The implementation of quality assessment measures and commonly accepted quality controls in data generation is therefore crucially important for proteomics research infrastructures and the scientists who rely on them. However, current quality control and quality assessment procedures in proteomics core facilities and research infrastructures are a motley collection of protocols, standards, reference compounds and software tools. Proteomics relies on a customized multi-step workflow typically consisting of sample preparation, data acquisition and data processing, and the implementation of each step differs among facilities. For example, sample preparation involves enzymatic digestion of the proteins, which can be performed in-solution, in-gel, or on-beads, with often different proteolytic enzymes, chemicals, and conditions among laboratories. Data acquisition protocols are often customized to the particular instrument set up, and the acquired spectra and chromatograms are processed by different software tools provided by equipment vendors, third parties or developed in-house. …current quality control and quality assessment procedures in proteomics core facilities and research infrastructures are a motley collection of protocols, standards, reference compounds and software tools. Moreover, core facilities implement their own guidelines to monitor the performance and quality of the entire workflow, typically utilizing different commercially available standards such as pre-digested cell lysates, recombinant proteins, protein mixtures, or isotopically labeled peptides. Currently, there is no clear consensus on if, when and how to perform quality control checks. There is even less quality control in walk-in facilities, where the staff is only responsible for correct usage of the instruments and users select and execute the analytical workflow themselves. It is not surprising therefore that instrument stability and robustness of the applied analytical approach are often unclear, which compromises analytical rigor. Establishing standardized practices Initiated by the HUPO Proteomics Standard Initiative (PSI) more than a decade ago, MIAPE guidelines (Minimal Information about Proteomics Experiment; Taylor et al, 2007) introduced common formats for sharing and reporting proteomics data, including unrestricted access to raw data at public repositories (Vizcaíno et al, 2014). Supported by journals' guidelines that request the deposition of raw data into such repositories as a condition for publication, these repositories have grown into a rich resource for data mining and multi-omics integration. However, MIAPE guidelines did not imply quality metrics and there is still no generic tool capable of independently ascertaining the technical quality of the deposited data. The importance of quality assessments for open-access proteomics was highlighted in the Amsterdam Principles more than 10 years ago (Rodriguez et al, 2009), but the development of quality threshold metrics was delegated to central repositories. A few years later, the Sydney workshop convened by the US National Cancer Institute made recommendations and formulated key principles for data quality metrics, and journal editors and reviewers were supposed to encourage or enforce their implementation in practice (Kinsinger et al, 2012). The corollary recognized "the need for formal comparison of methods on equal footing" thus alluding for the first time to a common quality control. More recently, recommendations for quality control metrics have indeed been included in publishing guidelines (Abbatiello et al, 2017). The need for common quality assessment protocols in scientific infrastructures has also been emphasized by international research associations. The Association of Biomolecular Research Facilities (ABRF), Core Technologies for Life Sciences (CTLS), Core for Life (C4L), and the Clinical Proteomic Tumor Analysis Consortium (CPTAC), initiated discussions and development of common quality procedures, and continuously promote sharing of best practices. A recent comprehensive survey among research facilities across Europe showed that the majority of core facilities do recognize the need and importance of quality controls (Kos-Braun et al, 2020). However, we believe that the issue of systematic quality procedures in proteomics infrastructures still has not received the public attention it deserves. Moreover, we maintain that community efforts toward quality control and quality assessment are not sufficiently organized to achieve systematic agreement, despite the availability of methods for the evaluation of analytical protocols, intra- and inter-laboratory comparison of reproducibility, and software tools for automated monitoring of instrument performance. A recent comprehensive survey among research facilities across Europe showed that the majority of core facilities do recognize the need and importance of quality controls. Common quality control procedures Common quality control procedures in proteomics core facilities ensure technical quality, reproducibility, comparability, and data integrity. A representative example of how these benefit the coordinated work of several proteomics units is the dissection and validation of a SARS protein interaction map (Gordon et al, 2020). Common quality controls foster reuse of resources, protect against bias in experimental design and improve daily routines (Fig 1). Systematic assessment of instrument performance, early recognition of poor-quality data, and monitoring carry-over and background signals enable long-term robustness and reproducibility of the proteomics workflow and leverage the impact of aging instruments or turnaround of the laboratory staff. Figure 1. Benefits of common quality procedures Main benefits of the implementation of common quality procedures in proteomics research infrastructures and core facilities for the users and customers, the scientific community, and the infrastructures themselves. Download figure Download PowerPoint Quality control procedures should be generic and flexible and support diverse workflows and instrumental platforms. Core facilities and research infrastructures are technology hubs and their operations are not, and should not, be limited to routine analytical measurements. Diversity of model organisms, scales, and research goals of the scientific community generate numerous project-specific protocols and great variability of workflows. Instrumentation platforms and analytical software will also remain diverse and heterogeneous in the foreseeable future. The choice of mass spectrometry equipment is often not only defined by scientific requirements but also influenced by the availability of funding and results in a collection of instruments of different generations, types, and vendors within the same facility. Core facilities and research infrastructures are technology hubs and their operations are not, and should not, be limited to routine analytical measurements. Quality procedures should be therefore organized into a framework that accommodates these diverse workflows and instrumental platforms. Such a framework should rely on common commercially available protein and peptide standards that—alone or spiked into the samples—are systematically analyzed for values relevant for quality control, such as the number identified of proteins, retention time and intensity of the peptide chromatographic peaks, ratio, or fold change of endogenous and isotopically labeled reference peptides. These repeated test runs would document the analytical performance of the entire workflow applied to an individual sample or sample batch. The information would be highly valuable for detecting random failures, monitoring instrument stability, and ensuring the reproducibility of repeated analyses, but also for continuous methods optimization and developing new methods. Moreover, common quality control parameters should be submitted to repositories along with the raw result data as required by MIAPE guidelines. … common quality control parameters should be submitted to repositories along with the raw result data as required by MIAPE guidelines. The frequent analysis of standard samples would also help to generate laboratory-based average references that monitor the performance drift of instruments and indicate whether the settings applied to analytical and computational workflow are optimal or would need readjustment. Such records could help to diagnose instrument malfunction and to test the performance of new instruments. In the future, software tools could be integrated with both instruments and reference data repositories to streamline the collection and management of quality control values. The aforementioned procedures could be a step towards ISO (International Organization for Standardization) or other quality certifications for core facilities and research infrastructures that require it. The role of funders and technology providers The development and implementation of common quality management schemes pose a challenge for the entire proteomics community and require support from technology providers and funding agencies. The proteomics community must therefore define a common set of quality parameters, standards, controlled vocabulary, and generic file formats to support collective testing and anonymized evaluation of the results. They should also work with technology providers to implement quality checks in vendors' software. Last but not least, the community should approach national and international funding bodies to raise awareness of the importance of common quality control procedure in order to secure their financial support. Research infrastructures and core facilities are in the position to drive initiatives that require extensive collaboration and concerted efforts. Within the Core for Life alliance (Meder et al, 2016; Lippens et al, 2019) (https://coreforlife.eu), our proteomics laboratories advocate for community policies for quality control procedures to ensure the high standards in proteomics services. Among other initiatives, we have developed and endorsed the QCloud tool as a cross-platform open-source quality control software for systematic monitoring of instrument performance (Chiva et al, 2018; Olivella et al, 2021). However, there is a further need in developing automated, user-friendly, and flexible routines suitable for inter-laboratory collection of quality data that satisfy data protection requirements and remain affordable for the broader research community. These and other practical measures require understanding and support of the entire proteomics community, funding, and publishing bodies. Acknowledgements The CRG/UPF Proteomics Unit is part of the Spanish Infrastructure for Omics Technologies (ICTS OmicsTech) and it is a member of the ProteoRed PRB3 consortium which is supported by grant PT17/0019 of the PE I + D+i 2013-2016 from the Instituto de Salud Carlos III (ISCIII) and ERDF. We acknowledge support from the Spanish Ministry of Science, Innovation and Universities, "Centro de Excelencia Severo Ochoa 2013-2017", SEV-2012- 0208, and "Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement de la Generalitat de Catalunya" (2017SGR595). The VIB Proteomics Core acknowledges support from the VIB Tech Watch Fund and FWO-SBO Project S006617N. Research in the Mechtler lab was supported by the ERA-CAPS I 3686 project of the Austrian Science Fund. We acknowledge support from the European Union's Horizon 2020 research and innovation program under grant agreement No 823839 (EPIC-XS). We are thankful to Andrej Shevchenko for fruitful discussions and critical reading of the manuscript and to the rest of the Core for Life alliance for setting up the right environment for fruitful discussions and stimulating exchange of scientific ideas. References Abbatiello S, Ackermann BL, Borchers C, Bradshaw RA, Carr SA, Chalkley R, Choi M, Deutsch E, Domon B, Hoofnagle AN et al (2017) New guidelines for publication of manuscripts describing development and application of targeted mass spectrometry measurements of peptides and proteins. Mol Cell Proteomics 16: 327–328CrossrefCASPubMedGoogle Scholar Chiva C, Olivella R, Borràs E, Espadas G, Pastor O, Solé A, Sabidó E (2018) QCloud: A cloud-based quality control system for mass spectrometry-based proteomics laboratories. 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Nat Biotechnol 32: 223–226CrossrefCASPubMedWeb of Science®Google Scholar Previous ArticleNext Article Read MoreAbout the coverClose modalView large imageVolume 22,Issue 6,04 June 2021This month's cover highlights the article PI3KC2β inactivation stabilizes VE‐cadherin junctions and preserves vascular integrity by Typhaine Anquetil, Marie‐Pierre Gratacap and colleagues showing that genetic inhibition of PI3KC2β reduces cerebral infarction in an ischemia/reperfusion mouse stroke model. The cover shows coronal brain sections of wild‐type (left) and PI3KC2β knock‐in mice (right) following ischemic stroke. TTC staining was used to detect the infarcted area in pink (pseudocolor). (Scientific image by Typhaine Anquetil and Marie‐Pierre Gratacap, Institute of Cardiovascular and Metabolic Diseases (I2MC), Inserm UMR‐1048, Toulouse, France.) Volume 22Issue 64 June 2021In this issue FiguresReferencesRelatedDetailsLoading ...
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