1
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Kardell O, von Toerne C, Merl-Pham J, König AC, Blindert M, Barth TK, Mergner J, Ludwig C, Tüshaus J, Eckert S, Müller SA, Breimann S, Giesbertz P, Bernhardt AM, Schweizer L, Albrecht V, Teupser D, Imhof A, Kuster B, Lichtenthaler SF, Mann M, Cox J, Hauck SM. Multicenter Collaborative Study to Optimize Mass Spectrometry Workflows of Clinical Specimens. J Proteome Res 2024; 23:117-129. [PMID: 38015820 PMCID: PMC10775142 DOI: 10.1021/acs.jproteome.3c00473] [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: 07/31/2023] [Revised: 11/02/2023] [Accepted: 11/14/2023] [Indexed: 11/30/2023]
Abstract
The foundation for integrating mass spectrometry (MS)-based proteomics into systems medicine is the development of standardized start-to-finish and fit-for-purpose workflows for clinical specimens. An essential step in this pursuit is to highlight the common ground in a diverse landscape of different sample preparation techniques and liquid chromatography-mass spectrometry (LC-MS) setups. With the aim to benchmark and improve the current best practices among the proteomics MS laboratories of the CLINSPECT-M consortium, we performed two consecutive round-robin studies with full freedom to operate in terms of sample preparation and MS measurements. The six study partners were provided with two clinically relevant sample matrices: plasma and cerebrospinal fluid (CSF). In the first round, each laboratory applied their current best practice protocol for the respective matrix. Based on the achieved results and following a transparent exchange of all lab-specific protocols within the consortium, each laboratory could advance their methods before measuring the same samples in the second acquisition round. Both time points are compared with respect to identifications (IDs), data completeness, and precision, as well as reproducibility. As a result, the individual performances of participating study centers were improved in the second measurement, emphasizing the effect and importance of the expert-driven exchange of best practices for direct practical improvements.
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Affiliation(s)
- Oliver Kardell
- Metabolomics
and Proteomics Core (MPC), Helmholtz Zentrum
München,German Research Center for Environmental Health (GmbH), Munich 80939, Germany
| | - Christine von Toerne
- Metabolomics
and Proteomics Core (MPC), Helmholtz Zentrum
München,German Research Center for Environmental Health (GmbH), Munich 80939, Germany
| | - Juliane Merl-Pham
- Metabolomics
and Proteomics Core (MPC), Helmholtz Zentrum
München,German Research Center for Environmental Health (GmbH), Munich 80939, Germany
| | - Ann-Christine König
- Metabolomics
and Proteomics Core (MPC), Helmholtz Zentrum
München,German Research Center for Environmental Health (GmbH), Munich 80939, Germany
| | - Marcel Blindert
- Metabolomics
and Proteomics Core (MPC), Helmholtz Zentrum
München,German Research Center for Environmental Health (GmbH), Munich 80939, Germany
| | - Teresa K. Barth
- Clinical
Protein Analysis Unit (ClinZfP), Biomedical Center (BMC), Faculty
of Medicine, Ludwig-Maximilians-University
(LMU) Munich, Großhaderner Straße 9, Martinsried 82152, Germany
| | - Julia Mergner
- Bavarian
Center for Biomolecular Mass Spectrometry at Klinikum Rechts der Isar
(BayBioMS@MRI), Technical University of
Munich, Munich 80333, Germany
| | - Christina Ludwig
- Bavarian
Center for Biomolecular Mass Spectrometry (BayBioMS), TUM School of
Life Sciences, Technical University of Munich, Freising 85354, Germany
| | - Johanna Tüshaus
- Chair
of Proteomics and Bioanalytics, Technical
University of Munich, Freising 85354, Germany
| | - Stephan Eckert
- Chair
of Proteomics and Bioanalytics, Technical
University of Munich, Freising 85354, Germany
| | - Stephan A. Müller
- German
Center
for Neurodegenerative Diseases (DZNE) Munich, DZNE, Munich 81377, Germany
- Neuroproteomics,
School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich 81675, Germany
| | - Stephan Breimann
- German
Center
for Neurodegenerative Diseases (DZNE) Munich, DZNE, Munich 81377, Germany
- Neuroproteomics,
School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich 81675, Germany
| | - Pieter Giesbertz
- German
Center
for Neurodegenerative Diseases (DZNE) Munich, DZNE, Munich 81377, Germany
- Neuroproteomics,
School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich 81675, Germany
| | - Alexander M. Bernhardt
- German
Center
for Neurodegenerative Diseases (DZNE) Munich, DZNE, Munich 81377, Germany
- Department
of Neurology, Ludwig-Maximilians-Universität
München, Munich 80539, Germany
| | - Lisa Schweizer
- Department
of Proteomics and Signal Transduction, Max-Planck
Institute of Biochemistry, Martinsried 82152, Germany
| | - Vincent Albrecht
- Department
of Proteomics and Signal Transduction, Max-Planck
Institute of Biochemistry, Martinsried 82152, Germany
| | - Daniel Teupser
- Institute
of Laboratory Medicine, University Hospital,
LMU Munich, Munich 81377, Germany
| | - Axel Imhof
- Clinical
Protein Analysis Unit (ClinZfP), Biomedical Center (BMC), Faculty
of Medicine, Ludwig-Maximilians-University
(LMU) Munich, Großhaderner Straße 9, Martinsried 82152, Germany
| | - Bernhard Kuster
- Bavarian
Center for Biomolecular Mass Spectrometry (BayBioMS), TUM School of
Life Sciences, Technical University of Munich, Freising 85354, Germany
- Chair
of Proteomics and Bioanalytics, Technical
University of Munich, Freising 85354, Germany
| | - Stefan F. Lichtenthaler
- German
Center
for Neurodegenerative Diseases (DZNE) Munich, DZNE, Munich 81377, Germany
- Neuroproteomics,
School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich 81675, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich 81377, Germany
| | - Matthias Mann
- Department
of Proteomics and Signal Transduction, Max-Planck
Institute of Biochemistry, Martinsried 82152, Germany
| | - Jürgen Cox
- Computational Systems
Biochemistry Research Group, Max-Planck
Institute of Biochemistry, Martinsried 82152, Germany
| | - Stefanie M. Hauck
- Metabolomics
and Proteomics Core (MPC), Helmholtz Zentrum
München,German Research Center for Environmental Health (GmbH), Munich 80939, Germany
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2
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Tian S, Zhan D, Yu Y, Wang Y, Liu M, Tan S, Li Y, Song L, Qin Z, Li X, Liu Y, Li Y, Ji S, Wang S, Zheng Y, He F, Qin J, Ding C. Quartet protein reference materials and datasets for multi-platform assessment of label-free proteomics. Genome Biol 2023; 24:202. [PMID: 37674236 PMCID: PMC10483797 DOI: 10.1186/s13059-023-03048-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 08/23/2023] [Indexed: 09/08/2023] Open
Abstract
BACKGROUND Quantitative proteomics is an indispensable tool in life science research. However, there is a lack of reference materials for evaluating the reproducibility of label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based measurements among different instruments and laboratories. RESULTS Here, we develop the Quartet standard as a proteome reference material with built-in truths, and distribute the same aliquots to 15 laboratories with nine conventional LC-MS/MS platforms across six cities in China. Relative abundance of over 12,000 proteins on 816 mass spectrometry files are obtained and compared for reproducibility among the instruments and laboratories to ultimately generate proteomics benchmark datasets. There is a wide dynamic range of proteomes spanning about 7 orders of magnitude, and the injection order has marked effects on quantitative instead of qualitative characteristics. CONCLUSION Overall, the Quartet offers valuable standard materials and data resources for improving the quality control of proteomic analyses as well as the reproducibility and reliability of research findings.
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Affiliation(s)
- Sha Tian
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Dongdong Zhan
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Ying Yu
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Yunzhi Wang
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Mingwei Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Subei Tan
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Yan Li
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Lei Song
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Zhaoyu Qin
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Xianju Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Yang Liu
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Yao Li
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Shuhui Ji
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Shanshan Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Yuanting Zheng
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China.
| | - Fuchu He
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China.
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China.
| | - Jun Qin
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China.
| | - Chen Ding
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China.
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3
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Colomé N, Abian J, Aloria K, Arizmendi JM, Barceló-Batllori S, Braga-Lagache S, Burlet-Schiltz O, Carrascal M, Casal JI, Chicano-Gálvez E, Chiva C, Clemente LF, Elortza F, Estanyol JM, Fernandez-Irigoyen J, Fernández-Puente P, Fidalgo MJ, Froment C, Fuentes M, Fuentes-Almagro C, Gay M, Hainard A, Heller M, Hernández ML, Ibarrola N, Iloro I, Kieselbach T, Lario A, Locard-Paulet M, Marina-Ramírez A, Martín L, Morato-López E, Muñoz J, Navajas R, Odena MA, Odriozola L, de Oliveira E, Paradela A, Pasquarello C, de Los Rios V, Ruiz-Romero C, Sabidó E, Sánchez Del Pino M, Sancho J, Santamaría E, Schaeffer-Reiss C, Schneider J, de la Torre C, Valero ML, Vilaseca M, Wu S, Wu L, Ximénez de Embún P, Canals F, Corrales FJ. Multi-laboratory experiment PME11 for the standardization of phosphoproteome analysis. J Proteomics 2022; 251:104409. [PMID: 34758407 DOI: 10.1016/j.jprot.2021.104409] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 10/12/2021] [Accepted: 10/27/2021] [Indexed: 11/16/2022]
Abstract
Global analysis of protein phosphorylation by mass spectrometry proteomic techniques has emerged in the last decades as a powerful tool in biological and biomedical research. However, there are several factors that make the global study of the phosphoproteome more challenging than measuring non-modified proteins. The low stoichiometry of the phosphorylated species and the need to retrieve residue specific information require particular attention on sample preparation, data acquisition and processing to ensure reproducibility, qualitative and quantitative robustness and ample phosphoproteome coverage in phosphoproteomic workflows. Aiming to investigate the effect of different variables in the performance of proteome wide phosphoprotein analysis protocols, ProteoRed-ISCIII and EuPA launched the Proteomics Multicentric Experiment 11 (PME11). A reference sample consisting of a yeast protein extract spiked in with different amounts of a phosphomix standard (Sigma/Merck) was distributed to 31 laboratories around the globe. Thirty-six datasets from 23 laboratories were analyzed. Our results indicate the suitability of the PME11 reference sample to benchmark and optimize phosphoproteomics strategies, weighing the influence of different factors, as well as to rank intra and inter laboratory performance.
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Affiliation(s)
- Núria Colomé
- ProteoRed-ISCIII, Vall d'Hebron Institute of Oncology (VHIO), Barcelona 08035, Spain
| | - Joaquín Abian
- ProteoRed-ISCIII, Instituto de Investigaciones Biomédicas de Barcelona, IIBB-CSIC/IDIBAPS, 08036 Barcelona, Spain
| | - Kerman Aloria
- ProteoRed-ISCIII, Proteomics Core Facility-SGIKER, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Jesús M Arizmendi
- Department of Biochemistry and Molecular Biology, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Leioa, Spain
| | | | - Sophie Braga-Lagache
- Department for BioMedical Research (DBMR), Proteomics and Mass Spectrometry Core Facility, University of Bern, CH-3010 Bern, Switzerland
| | - Odile Burlet-Schiltz
- Proteomics and Mass Spectrometry of Biomolecules, Proteomics Infrastructure of Toulouse, Proteomics French Infrastructure, ProFI. Institut de Pharmacologie et Biologie Structurale (IPBS), Université de Toulouse, UPS, CNRS, Toulouse, France
| | - Montse Carrascal
- ProteoRed-ISCIII, Instituto de Investigaciones Biomédicas de Barcelona, IIBB-CSIC/IDIBAPS, 08036 Barcelona, Spain
| | - J Ignacio Casal
- ProteoRed-ISCIII, Centro de Investigaciones Biológicas-CSIC, Madrid 28040, Spain
| | - Eduard Chicano-Gálvez
- ProteoRed-ISCIII, Proteomics Unit, IMIBIC/UCO/HURS, IMIBIC Building Fl.3, 14004 Córdoba, Spain
| | - Cristina Chiva
- Proteomics Unit, Center for Genomics Regulation, Barcelona Institute of Science and Technology (BIST), Barcelona, Spain; ProteoRed ISCIII, Proteomics Unit, Universitat Pompeu Fabra, Barcelona, Spain
| | | | - Felix Elortza
- ProteoRed-ISCIII, CIC bioGUNE, Proteomics Platform, Basque Research & Technology Alliance (BRTA), CIBERehd,Bizkaia Science and Technology Park, 48160 Derio, Spain
| | - Josep M Estanyol
- ProteoRed-ISCIII, Scientific and Technological Centers (CCiTUB), University of Barcelona, 08036 Barcelona, Spain
| | - Joaquín Fernandez-Irigoyen
- Proteored-ISCIII. Proteomics Unit, Clinical Neuroproteomics Group, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, 31008 Pamplona, Spain
| | - Patricia Fernández-Puente
- Grupo de Investigación de Reumatología (GIR), Agrupación CICA-INIBIC, Universidad de A Coruña, A Coruña, Spain
| | - María José Fidalgo
- ProteoRed-ISCIII, Scientific and Technological Centers (CCiTUB), University of Barcelona, 08036 Barcelona, Spain
| | - Carine Froment
- Proteomics and Mass Spectrometry of Biomolecules, Proteomics Infrastructure of Toulouse, Proteomics French Infrastructure, ProFI. Institut de Pharmacologie et Biologie Structurale (IPBS), Université de Toulouse, UPS, CNRS, Toulouse, France
| | - Manuel Fuentes
- Department of Medicine and General Cytometry Service-Nucleus, Proteomics Unit, CIBERONC, Cancer Research Center (IBMCC/CSIC/USAL/IBSAL), Universidad de Salamanca, Spain
| | - Carlos Fuentes-Almagro
- Proteomics Unit, SCAI, University of Córdoba, Ramón y Cajal Building, Rabanales Campus, 14071, Córdoba, Spain
| | - Marina Gay
- ProteoRed-ISCIII, Institute for Research in Biomedicine (IRB Barcelona), BIST (The Barcelona Institute of Science and Technology), Baldiri i Reixac 10, 08028 Barcelona, Spain
| | | | - Manfred Heller
- Department for BioMedical Research (DBMR), Proteomics and Mass Spectrometry Core Facility, University of Bern, CH-3010 Bern, Switzerland
| | | | - Nieves Ibarrola
- ProteoRed-ISCIII, Proteomics Unit. Cancer Research Center (IBMCC/CSIC/USAL/IBSAL), Universidad de Salamanca-CSIC, Salamanca, Spain
| | - Ibon Iloro
- ProteoRed-ISCIII, CIC bioGUNE, Proteomics Platform, Basque Research & Technology Alliance (BRTA), CIBERehd,Bizkaia Science and Technology Park, 48160 Derio, Spain
| | | | | | - Marie Locard-Paulet
- Proteomics and Mass Spectrometry of Biomolecules, Proteomics Infrastructure of Toulouse, Proteomics French Infrastructure, ProFI. Institut de Pharmacologie et Biologie Structurale (IPBS), Université de Toulouse, UPS, CNRS, Toulouse, France
| | | | - Luna Martín
- ProteoRed-ISCIII, Vall d'Hebron Institute of Oncology (VHIO), Barcelona 08035, Spain
| | | | - Javier Muñoz
- ProteoRed-ISCIII, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
| | - Rosana Navajas
- ProteoRed-ISCIII, Centro Nacional de Biotecnologia (CSIC), 28049, Madrid, Spain
| | - M Antonia Odena
- ProteoRed-ISCIII, Proteomics Platform, Barcelona Science Park, 08028, Barcelona, Spain
| | - Leticia Odriozola
- ProteoRed-ISCIII, CIMA, University of Navarra, 31008, Pamplona, Spain
| | - Eliandre de Oliveira
- ProteoRed-ISCIII, Proteomics Platform, Barcelona Science Park, 08028, Barcelona, Spain
| | - Alberto Paradela
- ProteoRed-ISCIII, Centro Nacional de Biotecnologia (CSIC), 28049, Madrid, Spain
| | | | - Vivian de Los Rios
- ProteoRed-ISCIII, Centro de Investigaciones Biológicas-CSIC, Madrid 28040, Spain
| | - Cristina Ruiz-Romero
- Grupo de Investigación de Reumatología (GIR) - ProteoRed-ISCIII, Unidad de Proteómica, INIBIC-Complejo Hospitalario Universitario de A Coruña, SERGAS, A Coruña, Spain
| | - Eduard Sabidó
- Proteomics Unit, Center for Genomics Regulation, Barcelona Institute of Science and Technology (BIST), Barcelona, Spain; ProteoRed ISCIII, Proteomics Unit, Universitat Pompeu Fabra, Barcelona, Spain
| | - Manuel Sánchez Del Pino
- Biotechnology and Biomedicine Interdisciplinary Research Unit (ERI BIOTECMED), University of Valencia, 46100 Burjassot, Spain
| | - Jaime Sancho
- ProteoRed-ISCIII, IPBLN -CSIC, 18016 Granada, Spain
| | - Enrique Santamaría
- Proteored-ISCIII. Proteomics Unit, Clinical Neuroproteomics Group, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, 31008 Pamplona, Spain
| | - Christine Schaeffer-Reiss
- Laboratoire de Spectrométrie de Masse BioOrganique, Université de Strasbourg, CNRS, IPHC UMR 7178, 67000, Strasbourg, France
| | - Justine Schneider
- Laboratoire de Spectrométrie de Masse BioOrganique, Université de Strasbourg, CNRS, IPHC UMR 7178, 67000, Strasbourg, France
| | - Carolina de la Torre
- ProteoRed-ISCIII, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
| | - M Luz Valero
- ProteoRed-ISCIII, Proteomics Unit, Central Service for Experimental Research (SCSIE), University of Valencia, 46100, Burjassot, Spain
| | - Marta Vilaseca
- ProteoRed-ISCIII, Institute for Research in Biomedicine (IRB Barcelona), BIST (The Barcelona Institute of Science and Technology), Baldiri i Reixac 10, 08028 Barcelona, Spain
| | - Shuai Wu
- Agilent Technologies, Inc., Santa Clara, CA 95051, USA
| | - Linfeng Wu
- Agilent Technologies, Inc., Santa Clara, CA 95051, USA
| | | | - Francesc Canals
- ProteoRed-ISCIII, Vall d'Hebron Institute of Oncology (VHIO), Barcelona 08035, Spain.
| | - Fernando J Corrales
- ProteoRed-ISCIII, Centro Nacional de Biotecnologia (CSIC), 28049, Madrid, Spain; ProteoRed-ISCIII, CIMA, University of Navarra, 31008, Pamplona, Spain.
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- ProteoRed-ISCIII, Centro Nacional de Biotecnologia (CSIC), 28049, Madrid, Spain; ProteoRed-ISCIII-PRB3, Spanish Proteomics Networked Platform, Centro Nacional de Biotecnología (CSIC), 28049, Madrid, Spain
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- ProteoRed-ISCIII, Centro Nacional de Biotecnologia (CSIC), 28049, Madrid, Spain; European Proteomics Association, Standardization Initiative, , Centro Nacional de Biotecnología (CSIC), 28049, Madrid, Spain
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4
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Ucal Y, Coskun A, Ozpinar A. Quality will determine the future of mass spectrometry imaging in clinical laboratories: the need for standardization. Expert Rev Proteomics 2019; 16:521-532. [DOI: 10.1080/14789450.2019.1624165] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Affiliation(s)
- Yasemin Ucal
- School of Medicine, Department of Medical Biochemistry, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
| | - Abdurrahman Coskun
- School of Medicine, Department of Medical Biochemistry, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
| | - Aysel Ozpinar
- School of Medicine, Department of Medical Biochemistry, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
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5
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Bittremieux W, Tabb DL, Impens F, Staes A, Timmerman E, Martens L, Laukens K. Quality control in mass spectrometry-based proteomics. MASS SPECTROMETRY REVIEWS 2018; 37:697-711. [PMID: 28802010 DOI: 10.1002/mas.21544] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2017] [Revised: 07/24/2017] [Accepted: 07/24/2017] [Indexed: 05/21/2023]
Abstract
Mass spectrometry is a highly complex analytical technique and mass spectrometry-based proteomics experiments can be subject to a large variability, which forms an obstacle to obtaining accurate and reproducible results. Therefore, a comprehensive and systematic approach to quality control is an essential requirement to inspire confidence in the generated results. A typical mass spectrometry experiment consists of multiple different phases including the sample preparation, liquid chromatography, mass spectrometry, and bioinformatics stages. We review potential sources of variability that can impact the results of a mass spectrometry experiment occurring in all of these steps, and we discuss how to monitor and remedy the negative influences on the experimental results. Furthermore, we describe how specialized quality control samples of varying sample complexity can be incorporated into the experimental workflow and how they can be used to rigorously assess detailed aspects of the instrument performance.
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Affiliation(s)
- Wout Bittremieux
- Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium
- Biomedical Informatics Research Center Antwerp (Biomina), University of Antwerp/Antwerp University Hospital, Edegem, Belgium
| | - David L Tabb
- Division of Molecular Biology and Human Genetics, Stellenbosch University Faculty of Medicine and Health Sciences, Tygerberg Hospital, Cape Town, South Africa
| | - Francis Impens
- VIB Proteomics Core, Ghent, Belgium
- VIB-UGent Center for Medical Biotechnology, Ghent, Belgium
- Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent, Belgium
| | - An Staes
- VIB Proteomics Core, Ghent, Belgium
- VIB-UGent Center for Medical Biotechnology, Ghent, Belgium
- Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent, Belgium
| | - Evy Timmerman
- VIB Proteomics Core, Ghent, Belgium
- VIB-UGent Center for Medical Biotechnology, Ghent, Belgium
- Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent, Belgium
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, Ghent, Belgium
- Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent, Belgium
- Bioinformatics Institute Ghent, Ghent University, Zwijnaarde, Belgium
| | - Kris Laukens
- Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium
- Biomedical Informatics Research Center Antwerp (Biomina), University of Antwerp/Antwerp University Hospital, Edegem, Belgium
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6
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Buck A, Heijs B, Beine B, Schepers J, Cassese A, Heeren RMA, McDonnell LA, Henkel C, Walch A, Balluff B. Round robin study of formalin-fixed paraffin-embedded tissues in mass spectrometry imaging. Anal Bioanal Chem 2018; 410:5969-5980. [PMID: 29968108 PMCID: PMC6096706 DOI: 10.1007/s00216-018-1216-2] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 05/14/2018] [Accepted: 06/21/2018] [Indexed: 12/12/2022]
Abstract
Mass spectrometry imaging (MSI) has provided many results with translational character, which still have to be proven robust in large patient cohorts and across different centers. Although formalin-fixed paraffin-embedded (FFPE) specimens are most common in clinical practice, no MSI multicenter study has been reported for FFPE samples. Here, we report the results of the first round robin MSI study on FFPE tissues with the goal to investigate the consequences of inter- and intracenter technical variation on masking biological effects. A total of four centers were involved with similar MSI instrumentation and sample preparation equipment. A FFPE multi-organ tissue microarray containing eight different types of tissue was analyzed on a peptide and metabolite level, which enabled investigating different molecular and biological differences. Statistical analyses revealed that peptide intercenter variation was significantly lower and metabolite intercenter variation was significantly higher than the respective intracenter variations. When looking at relative univariate effects of mass signals with statistical discriminatory power, the metabolite data was more reproducible across centers compared to the peptide data. With respect to absolute effects (cross-center common intensity scale), multivariate classifiers were able to reach on average > 90% accuracy for peptides and > 80% for metabolites if trained with sufficient amount of cross-center data. Overall, our study showed that MSI data from FFPE samples could be reproduced to a high degree across centers. While metabolite data exhibited more reproducibility with respect to relative effects, peptide data-based classifiers were more directly transferable between centers and therefore more robust than expected. Graphical abstract ᅟ.
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Affiliation(s)
- Achim Buck
- Research Unit Analytical Pathology, Helmholtz Zentrum München, 85764, Oberschleißheim, Germany
| | - Bram Heijs
- Center for Proteomics and Metabolomics, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Birte Beine
- Medizinisches Proteom-Center, Ruhr-Universität Bochum, 44801, Bochum, Germany
- Leibniz-Institut für Analytische Wissenschaften - ISAS-e.V, 44139, Dortmund, Germany
| | - Jan Schepers
- Department of Methodology and Statistics, Faculty of Psychology and Neuroscience, Maastricht University, 6200 MD, Maastricht, The Netherlands
| | - Alberto Cassese
- Department of Methodology and Statistics, Faculty of Psychology and Neuroscience, Maastricht University, 6200 MD, Maastricht, The Netherlands
| | - Ron M A Heeren
- The Maastricht MultiModal Molecular Imaging Institute (M4I), Maastricht University, Universiteitssingel 50, Pigeon Hole 57, P.O. Box 616, 6200 MD, Maastricht, The Netherlands
| | - Liam A McDonnell
- Center for Proteomics and Metabolomics, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
- Fondazione Pisana per la Scienza ONLUS, 56017, Pisa, Italy
| | - Corinna Henkel
- Medizinisches Proteom-Center, Ruhr-Universität Bochum, 44801, Bochum, Germany
- Bruker Daltonik, Bremen, Germany
| | - Axel Walch
- Research Unit Analytical Pathology, Helmholtz Zentrum München, 85764, Oberschleißheim, Germany
| | - Benjamin Balluff
- The Maastricht MultiModal Molecular Imaging Institute (M4I), Maastricht University, Universiteitssingel 50, Pigeon Hole 57, P.O. Box 616, 6200 MD, Maastricht, The Netherlands.
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7
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Martínez-Bartolomé S, Medina-Aunon JA, López-García MÁ, González-Tejedo C, Prieto G, Navajas R, Salazar-Donate E, Fernández-Costa C, Yates JR, Albar JP. PACOM: A Versatile Tool for Integrating, Filtering, Visualizing, and Comparing Multiple Large Mass Spectrometry Proteomics Data Sets. J Proteome Res 2018; 17:1547-1558. [DOI: 10.1021/acs.jproteome.7b00858] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Affiliation(s)
- Salvador Martínez-Bartolomé
- Proteomics Laboratory, National Center for Biotechnology, CSIC, Madrid 28049, Spain
- Department of Chemical Physiology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | | | | | | | - Gorka Prieto
- Department of Communications Engineering, University of the Basque Country (UPV/EHU), Bilbao 48013, Spain
| | - Rosana Navajas
- Proteomics Laboratory, National Center for Biotechnology, CSIC, Madrid 28049, Spain
| | | | - Carolina Fernández-Costa
- Department of Chemical Physiology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
- Immunology, Centro de Investigaciones Biomédicas (CINBIO), Centro singular de Investigación de Galicia: Instituto de Investigación Sanitaria Galicia Sur (IIS-GS), University of Vigo, Campus Universitario, s/n, Vigo 36310, Spain
| | - John R. Yates
- Department of Chemical Physiology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | - Juan Pablo Albar
- Proteomics Laboratory, National Center for Biotechnology, CSIC, Madrid 28049, Spain
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8
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Chiva C, Olivella R, Borràs E, Espadas G, Pastor O, Solé A, Sabidó E. QCloud: A cloud-based quality control system for mass spectrometry-based proteomics laboratories. PLoS One 2018; 13:e0189209. [PMID: 29324744 PMCID: PMC5764250 DOI: 10.1371/journal.pone.0189209] [Citation(s) in RCA: 96] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Accepted: 11/21/2017] [Indexed: 01/03/2023] Open
Abstract
The increasing number of biomedical and translational applications in mass spectrometry-based proteomics poses new analytical challenges and raises the need for automated quality control systems. Despite previous efforts to set standard file formats, data processing workflows and key evaluation parameters for quality control, automated quality control systems are not yet widespread among proteomics laboratories, which limits the acquisition of high-quality results, inter-laboratory comparisons and the assessment of variability of instrumental platforms. Here we present QCloud, a cloud-based system to support proteomics laboratories in daily quality assessment using a user-friendly interface, easy setup, automated data processing and archiving, and unbiased instrument evaluation. QCloud supports the most common targeted and untargeted proteomics workflows, it accepts data formats from different vendors and it enables the annotation of acquired data and reporting incidences. A complete version of the QCloud system has successfully been developed and it is now open to the proteomics community (http://qcloud.crg.eu). QCloud system is an open source project, publicly available under a Creative Commons License Attribution-ShareAlike 4.0.
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Affiliation(s)
- Cristina Chiva
- Proteomics Unit, Centre de Regulació Genòmica (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Barcelona
- Universitat Pompeu Fabra (UPF), Barcelona, Barcelona
| | - Roger Olivella
- Proteomics Unit, Centre de Regulació Genòmica (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Barcelona
- Universitat Pompeu Fabra (UPF), Barcelona, Barcelona
| | - Eva Borràs
- Proteomics Unit, Centre de Regulació Genòmica (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Barcelona
- Universitat Pompeu Fabra (UPF), Barcelona, Barcelona
| | - Guadalupe Espadas
- Proteomics Unit, Centre de Regulació Genòmica (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Barcelona
- Universitat Pompeu Fabra (UPF), Barcelona, Barcelona
| | - Olga Pastor
- Proteomics Unit, Centre de Regulació Genòmica (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Barcelona
- Universitat Pompeu Fabra (UPF), Barcelona, Barcelona
| | - Amanda Solé
- Proteomics Unit, Centre de Regulació Genòmica (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Barcelona
- Universitat Pompeu Fabra (UPF), Barcelona, Barcelona
| | - Eduard Sabidó
- Proteomics Unit, Centre de Regulació Genòmica (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Barcelona
- Universitat Pompeu Fabra (UPF), Barcelona, Barcelona
- * E-mail:
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9
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Li H, Han J, Pan J, Liu T, Parker CE, Borchers CH. Current trends in quantitative proteomics - an update. JOURNAL OF MASS SPECTROMETRY : JMS 2017; 52:319-341. [PMID: 28418607 DOI: 10.1002/jms.3932] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2017] [Revised: 03/28/2017] [Accepted: 04/06/2017] [Indexed: 05/11/2023]
Abstract
Proteins can provide insights into biological processes at the functional level, so they are very promising biomarker candidates. The quantification of proteins in biological samples has been routinely used for the diagnosis of diseases and monitoring the treatment. Although large-scale protein quantification in complex samples is still a challenging task, a great amount of effort has been made to advance the technologies that enable quantitative proteomics. Seven years ago, in 2009, we wrote an article about the current trends in quantitative proteomics. In writing this current paper, we realized that, today, we have an even wider selection of potential tools for quantitative proteomics. These tools include new derivatization reagents, novel sampling formats, new types of analyzers and scanning techniques, and recently developed software to assist in assay development and data analysis. In this review article, we will discuss these innovative methods, and their current and potential applications in proteomics. Copyright © 2017 John Wiley & Sons, Ltd.
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Affiliation(s)
- H Li
- University of Victoria - Genome British Columbia Proteomics Centre, University of Victoria, Victoria, BC, V8Z 7X8, Canada
| | - J Han
- University of Victoria - Genome British Columbia Proteomics Centre, University of Victoria, Victoria, BC, V8Z 7X8, Canada
| | - J Pan
- University of Victoria - Genome British Columbia Proteomics Centre, University of Victoria, Victoria, BC, V8Z 7X8, Canada
| | - T Liu
- University of Victoria - Genome British Columbia Proteomics Centre, University of Victoria, Victoria, BC, V8Z 7X8, Canada
| | - C E Parker
- University of Victoria - Genome British Columbia Proteomics Centre, University of Victoria, Victoria, BC, V8Z 7X8, Canada
| | - C H Borchers
- University of Victoria - Genome British Columbia Proteomics Centre, University of Victoria, Victoria, BC, V8Z 7X8, Canada
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC, V8P 5C2, Canada
- Proteomics Centre, Segal Cancer Centre, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, H3T 1E2, Canada
- Gerald Bronfman Department of Oncology, Jewish General Hospital, Montreal, Quebec, H3T 1E2, Canada
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10
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Bittremieux W, Walzer M, Tenzer S, Zhu W, Salek RM, Eisenacher M, Tabb DL. The Human Proteome Organization-Proteomics Standards Initiative Quality Control Working Group: Making Quality Control More Accessible for Biological Mass Spectrometry. Anal Chem 2017; 89:4474-4479. [PMID: 28318237 DOI: 10.1021/acs.analchem.6b04310] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
To have confidence in results acquired during biological mass spectrometry experiments, a systematic approach to quality control is of vital importance. Nonetheless, until now, only scattered initiatives have been undertaken to this end, and these individual efforts have often not been complementary. To address this issue, the Human Proteome Organization-Proteomics Standards Initiative has established a new working group on quality control at its meeting in the spring of 2016. The goal of this working group is to provide a unifying framework for quality control data. The initial focus will be on providing a community-driven standardized file format for quality control. For this purpose, the previously proposed qcML format will be adapted to support a variety of use cases for both proteomics and metabolomics applications, and it will be established as an official PSI format. An important consideration is to avoid enforcing restrictive requirements on quality control but instead provide the basic technical necessities required to support extensive quality control for any type of mass spectrometry-based workflow. We want to emphasize that this is an open community effort, and we seek participation from all scientists with an interest in this field.
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Affiliation(s)
- Wout Bittremieux
- Department of Mathematics and Computer Science, University of Antwerp , Middelheimlaan 1, 2020 Antwerp, Belgium.,Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp/Antwerp University Hospital , Wilrijkstraat 10, 2650 Edegem, Belgium
| | - Mathias Walzer
- Department of Computer Science, University of Tübingen , Tübingen 72076, Germany.,Center for Bioinformatics, University of Tübingen , Tübingen 72074, Germany
| | - Stefan Tenzer
- Institute for Immunology, University Medical Center of the Johannes-Gutenberg University Mainz D 55131, Germany
| | - Weimin Zhu
- National Center for Protein Science , No. 38, Science Park Road, Changping District, Beijing 102206, China
| | - Reza M Salek
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI) , Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Martin Eisenacher
- Medical Bioinformatics, Medizinisches Proteom-Center, Ruhr-University Bochum , Bochum 44801, Germany
| | - David L Tabb
- Division of Molecular Biology and Human Genetics, Stellenbosch University Faculty of Medicine and Health Sciences , Tygerberg Hospital, Francie Van Zijl Drive, Cape Town 7505, South Africa
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11
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Vialas V, Colomé-Calls N, Abian J, Aloria K, Alvarez-Llamas G, Antúnez O, Arizmendi JM, Azkargorta M, Barceló-Batllori S, Barderas MG, Blanco F, Casal JI, Casas V, de la Torre C, Chicano-Gálvez E, Elortza F, Espadas G, Estanyol JM, Fernandez-Irigoyen J, Fernandez-Puente P, Fidalgo MJ, Fuentes M, Gay M, Gil C, Hainard A, Hernaez ML, Ibarrola N, Kopylov AT, Lario A, Lopez JA, López-Lucendo M, Marcilla M, Marina-Ramírez A, Marko-Varga G, Martín L, Mora MI, Morato-López E, Muñoz J, Odena MA, de Oliveira E, Orera I, Ortea I, Pasquarello C, Ray KB, Rezeli M, Ruppen I, Sabidó E, Del Pino MMS, Sancho J, Santamaría E, Vazquez J, Vilaseca M, Vivanco F, Walters JJ, Zgoda VG, Corrales FJ, Canals F, Paradela A. A multicentric study to evaluate the use of relative retention times in targeted proteomics. J Proteomics 2016; 152:138-149. [PMID: 27989941 DOI: 10.1016/j.jprot.2016.10.014] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2016] [Revised: 09/27/2016] [Accepted: 10/24/2016] [Indexed: 12/19/2022]
Abstract
Despite the maturity reached by targeted proteomic strategies, reliable and standardized protocols are urgently needed to enhance reproducibility among different laboratories and analytical platforms, facilitating a more widespread use in biomedical research. To achieve this goal, the use of dimensionless relative retention times (iRT), defined on the basis of peptide standard retention times (RT), has lately emerged as a powerful tool. The robustness, reproducibility and utility of this strategy were examined for the first time in a multicentric setting, involving 28 laboratories that included 24 of the Spanish network of proteomics laboratories (ProteoRed-ISCIII). According to the results obtained in this study, dimensionless retention time values (iRTs) demonstrated to be a useful tool for transferring and sharing peptide retention times across different chromatographic set-ups both intra- and inter-laboratories. iRT values also showed very low variability over long time periods. Furthermore, parallel quantitative analyses showed a high reproducibility despite the variety of experimental strategies used, either MRM (multiple reaction monitoring) or pseudoMRM, and the diversity of analytical platforms employed. BIOLOGICAL SIGNIFICANCE From the very beginning of proteomics as an analytical science there has been a growing interest in developing standardized methods and experimental procedures in order to ensure the highest quality and reproducibility of the results. In this regard, the recent (2012) introduction of the dimensionless retention time concept has been a significant advance. In our multicentric (28 laboratories) study we explore the usefulness of this concept in the context of a targeted proteomics experiment, demonstrating that dimensionless retention time values is a useful tool for transferring and sharing peptide retention times across different chromatographic set-ups.
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Affiliation(s)
- Vital Vialas
- ProteoRed-ISCIII, Universidad Complutense de Madrid, Madrid 28040, Spain
| | - Núria Colomé-Calls
- ProteoRed-ISCIII, Vall d'Hebron Institute of Oncology (VHIO), Barcelona 08035, Spain
| | - Joaquín Abian
- ProteoRed-ISCIII, Instituto de Investigaciones Biomédicas de Barcelona, IIBB-CSIC/IDIBAPS, Barcelona 08036, Spain
| | - Kerman Aloria
- Department of Biochemistry and Molecular Biology, University of the Basque Country-UPV/EHU, Leioa 48940, Spain
| | | | - Oreto Antúnez
- ProteoRed-ISCIII, SCSIE Universitat de Valencia, Burjassot 46100, Spain
| | - Jesus M Arizmendi
- ProteoRed-ISCIII, University of the Basque Country-UPV/EHU, Leioa 48940, Spain
| | - Mikel Azkargorta
- ProteoRed-ISCIII, CIC bioGUNE, Science and Technology Park of Bizkaia, Derio, Spain
| | | | - María G Barderas
- ProteoRed-ISCIII, Hospital Nacional de Parapléjicos-SESCAM, Toledo, Spain
| | | | - J Ignacio Casal
- ProteoRed-ISCIII, Centro de Investigaciones Biológicas-CSIC, Madrid 28040, Spain
| | - Vanessa Casas
- ProteoRed-ISCIII, Instituto de Investigaciones Biomédicas de Barcelona, IIBB-CSIC/IDIBAPS, Barcelona 08036, Spain
| | - Carolina de la Torre
- ProteoRed-ISCIII, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
| | - Eduardo Chicano-Gálvez
- ProteoRed-ISCIII, Maimonides Institute for Biomedical Research and Universidad de Córdoba, Córdoba 14004, Spain
| | - Felix Elortza
- ProteoRed-ISCIII, CIC bioGUNE, Science and Technology Park of Bizkaia, Derio, Spain
| | - Guadalupe Espadas
- ProteoRed-ISCIII, Centre de Regulació Genòmica, Universitat Pompeu Fabra, Barcelona 08003, Spain
| | - Josep M Estanyol
- ProteoRed-ISCIII, Scientific and Technological Centers (CCiTUB), University of Barcelona, Barcelona 08036, Spain
| | | | | | - María José Fidalgo
- ProteoRed-ISCIII, Scientific and Technological Centers (CCiTUB), University of Barcelona, Barcelona 08036, Spain
| | - Manuel Fuentes
- ProteoRed-ISCIII, Cancer Research Center, University of Salamanca-CSIC, IBSAL, Salamanca 37007, Spain
| | - Marina Gay
- ProteoRed-ISCIII, Institute for Research in Biomedicine (IRB Barcelona), Barcelona 08028, Spain
| | - Concha Gil
- ProteoRed-ISCIII, Universidad Complutense de Madrid, Madrid 28040, Spain
| | - Alexandre Hainard
- Proteomics Core Facility CMU, University of Geneva, Geneva, Switzerland
| | | | - Nieves Ibarrola
- ProteoRed-ISCIII, Cancer Research Center, University of Salamanca-CSIC, IBSAL, Salamanca 37007, Spain
| | - Arthur T Kopylov
- Orekhovich Institute of Biomedical Chemistry RAMS, Moscow 119121, Russian Federation
| | - Antonio Lario
- ProteoRed-ISCIII, IPBLN (CSIC), Armilla, Granada, Spain
| | - Juan Antonio Lopez
- ProteoRed-ISCIII, Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid 28029, Spain
| | - María López-Lucendo
- ProteoRed-ISCIII, Centro de Investigaciones Biológicas-CSIC, Madrid 28040, Spain
| | - Miguel Marcilla
- ProteoRed-ISCIII, Centro Nacional de Biotecnologia (CSIC), Madrid 28049, Spain
| | | | - Gyorgy Marko-Varga
- Centre of Excellence in Biological and Medical Mass spectrometry, Lund University, Lund, Sweden
| | - Luna Martín
- ProteoRed-ISCIII, Vall d'Hebron Institute of Oncology (VHIO), Barcelona 08035, Spain
| | - Maria I Mora
- ProteoRed-ISCIII, CIMA, University of Navarra, Pamplona 31008, Spain
| | | | - Javier Muñoz
- ProteoRed-ISCIII, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
| | | | | | - Irene Orera
- ProteoRed-ISCIII, Instituto Aragonés de Ciencias de la Salud, Zaragoza 50009, Spain
| | - Ignacio Ortea
- ProteoRed-ISCIII, Maimonides Institute for Biomedical Research and Universidad de Córdoba, Córdoba 14004, Spain
| | - Carla Pasquarello
- Proteomics Core Facility CMU, University of Geneva, Geneva, Switzerland
| | | | - Melinda Rezeli
- Centre of Excellence in Biological and Medical Mass spectrometry, Lund University, Lund, Sweden
| | - Isabel Ruppen
- ProteoRed-ISCIII, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
| | - Eduard Sabidó
- ProteoRed-ISCIII, Centre de Regulació Genòmica, Universitat Pompeu Fabra, Barcelona 08003, Spain
| | | | - Jaime Sancho
- ProteoRed-ISCIII, IPBLN (CSIC), Armilla, Granada, Spain
| | - Enrique Santamaría
- ProteoRed-ISCIII, Navarrabiomed Biomedical Research Center-IdiSNa, Pamplona, Spain
| | - Jesus Vazquez
- ProteoRed-ISCIII, Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid 28029, Spain
| | - Marta Vilaseca
- ProteoRed-ISCIII, Institute for Research in Biomedicine (IRB Barcelona), Barcelona 08028, Spain
| | | | | | - Victor G Zgoda
- Orekhovich Institute of Biomedical Chemistry RAMS, Moscow 119121, Russian Federation
| | | | - Francesc Canals
- ProteoRed-ISCIII, Vall d'Hebron Institute of Oncology (VHIO), Barcelona 08035, Spain.
| | - Alberto Paradela
- ProteoRed-ISCIII, Centro Nacional de Biotecnologia (CSIC), Madrid 28049, Spain.
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12
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Qin W, Liang YZ, Qin BY, Zhang JL, Xia N. The Clinical Significance of Glycoprotein Phospholipase D Levels in Distinguishing Early Stage Latent Autoimmune Diabetes in Adults and Type 2 Diabetes. PLoS One 2016; 11:e0156959. [PMID: 27351175 PMCID: PMC4925120 DOI: 10.1371/journal.pone.0156959] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2016] [Accepted: 05/22/2016] [Indexed: 01/15/2023] Open
Abstract
Autoantibodies have been widely used as markers of latent autoimmune diabetes in adults (LADA); however, the specificity and sensitivity of autoantibodies as markers of LADA are weak compared with those found in type 1 diabetes (T1DM). In this study, we aimed to identify other plasma proteins as potential candidates that can be used effectively to determine early stage LADA and type 2 diabetes (T2DM) to facilitate early diagnosis and treatment. These issues were addressed by studying new-onset ‘classic’ T1DM (n = 156), LADA (n = 174), T2DM (n = 195) and healthy cohorts (n = 166). Plasma samples were obtained from the four cohorts. We employed isobaric tag for relative and absolute quantitation (iTRAQ) together with liquid chromatography tandem mass spectrometry (LC-MS) to identify plasma proteins with significant changes in LADA. The changes were validated by Western blot and ELISA analyses. Among the four cohorts, 311 unique proteins were identified in three iTRAQ runs, with 157 present across the three data sets. Among them, 49/311 (16.0%) proteins had significant changes in LADA compared with normal controls, including glycoprotein phospholipase D (GPLD1), which was upregulated in LADA. Western blot and ELISA analyses showed that GPLD1 levels were higher in both LADA and T1DM cohorts than in both T2DM and healthy cohorts, while there were no significant differences in the plasma concentrations of GPLD1 between the LADA and T1DM cohorts. GPLD1 is implicated as a potential candidate plasma protein for determining early stage LADA and T2DM.
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Affiliation(s)
- Wen Qin
- Department of pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Province, China
| | - Yu-Zhen Liang
- Department of Endocrinology and Metabolism, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Province, China
| | - Bao-Yu Qin
- Department of Elderly Endocrinology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Province, China
| | - Jia-Li Zhang
- Department of Endocrinology and Metabolism, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Province, China
| | - Ning Xia
- Department of Endocrinology and Metabolism, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Province, China
- * E-mail:
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13
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Bittremieux W, Meysman P, Martens L, Valkenborg D, Laukens K. Unsupervised Quality Assessment of Mass Spectrometry Proteomics Experiments by Multivariate Quality Control Metrics. J Proteome Res 2016; 15:1300-7. [PMID: 26974716 DOI: 10.1021/acs.jproteome.6b00028] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Despite many technological and computational advances, the results of a mass spectrometry proteomics experiment are still subject to a large variability. For the understanding and evaluation of how technical variability affects the results of an experiment, several computationally derived quality control metrics have been introduced. However, despite the availability of these metrics, a systematic approach to quality control is often still lacking because the metrics are not fully understood and are hard to interpret. Here, we present a toolkit of powerful techniques to analyze and interpret multivariate quality control metrics to assess the quality of mass spectrometry proteomics experiments. We show how unsupervised techniques applied to these quality control metrics can provide an initial discrimination between low-quality experiments and high-quality experiments prior to manual investigation. Furthermore, we provide a technique to obtain detailed information on the quality control metrics that are related to the decreased performance, which can be used as actionable information to improve the experimental setup. Our toolkit is released as open-source and can be downloaded from https://bitbucket.org/proteinspector/qc_analysis/ .
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Affiliation(s)
- Wout Bittremieux
- Department of Mathematics and Computer Science, University of Antwerp , 2020 Antwerp, Belgium.,Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp/Antwerp University Hospital , 2650 Edegem, Belgium
| | - Pieter Meysman
- Department of Mathematics and Computer Science, University of Antwerp , 2020 Antwerp, Belgium.,Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp/Antwerp University Hospital , 2650 Edegem, Belgium
| | - Lennart Martens
- Department of Medical Protein Research, VIB , 9000 Ghent, Belgium.,Department of Biochemistry, Faculty of Medicine and Health Sciences, Ghent University , 9000 Ghent, Belgium.,Bioinformatics Institute Ghent, Ghent University , 9000 Ghent, Belgium
| | - Dirk Valkenborg
- Flemish Institute for Technological Research (VITO) , 2400 Mol, Belgium.,CFP, University of Antwerp , 2020 Antwerp, Belgium.,I-BioStat, Hasselt University , 3590 Diepenbeek, Belgium
| | - Kris Laukens
- Department of Mathematics and Computer Science, University of Antwerp , 2020 Antwerp, Belgium.,Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp/Antwerp University Hospital , 2650 Edegem, Belgium
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14
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Bennett KL, Wang X, Bystrom CE, Chambers MC, Andacht TM, Dangott LJ, Elortza F, Leszyk J, Molina H, Moritz RL, Phinney BS, Thompson JW, Bunger MK, Tabb DL. The 2012/2013 ABRF Proteomic Research Group Study: Assessing Longitudinal Intralaboratory Variability in Routine Peptide Liquid Chromatography Tandem Mass Spectrometry Analyses. Mol Cell Proteomics 2015; 14:3299-309. [PMID: 26435129 DOI: 10.1074/mcp.o115.051888] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2015] [Indexed: 11/06/2022] Open
Abstract
Questions concerning longitudinal data quality and reproducibility of proteomic laboratories spurred the Protein Research Group of the Association of Biomolecular Resource Facilities (ABRF-PRG) to design a study to systematically assess the reproducibility of proteomic laboratories over an extended period of time. Developed as an open study, initially 64 participants were recruited from the broader mass spectrometry community to analyze provided aliquots of a six bovine protein tryptic digest mixture every month for a period of nine months. Data were uploaded to a central repository, and the operators answered an accompanying survey. Ultimately, 45 laboratories submitted a minimum of eight LC-MSMS raw data files collected in data-dependent acquisition (DDA) mode. No standard operating procedures were enforced; rather the participants were encouraged to analyze the samples according to usual practices in the laboratory. Unlike previous studies, this investigation was not designed to compare laboratories or instrument configuration, but rather to assess the temporal intralaboratory reproducibility. The outcome of the study was reassuring with 80% of the participating laboratories performing analyses at a medium to high level of reproducibility and quality over the 9-month period. For the groups that had one or more outlying experiments, the major contributing factor that correlated to the survey data was the performance of preventative maintenance prior to the LC-MSMS analyses. Thus, the Protein Research Group of the Association of Biomolecular Resource Facilities recommends that laboratories closely scrutinize the quality control data following such events. Additionally, improved quality control recording is imperative. This longitudinal study provides evidence that mass spectrometry-based proteomics is reproducible. When quality control measures are strictly adhered to, such reproducibility is comparable among many disparate groups. Data from the study are available via ProteomeXchange under the accession code PXD002114.
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Affiliation(s)
- Keiryn L Bennett
- From the ‡CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria;
| | - Xia Wang
- §University of Cincinnati, Department of Mathematical Sciences, University of Cincinnati, Cincinnati, Ohio, 45221-0025
| | - Cory E Bystrom
- ¶Cleveland HeartLab, Inc., Research and Development, Cleveland HeartLab, Inc., Cleveland, Ohio, 44103
| | - Matthew C Chambers
- ‖Vanderbilt University, Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, 37232
| | - Tracy M Andacht
- **Centers for Disease Control and Prevention, Emergency Response Branch, Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, Georgia, 30341
| | - Larry J Dangott
- ‡‡Texas A&M University, Department of Biochemistry & Biophysics, Texas A&M University, College Station, Texas, 77843
| | - Félix Elortza
- §§CIC bioGUNE, Centro de Investigacion Cooperativa en Biociencias, ProteoRed-ISCIII, Bilbao, Spain
| | - John Leszyk
- ¶¶University of Massachusetts, Department of Biochemistry and Molecular Pharmacology Proteomics and Mass Spectrometry Facility, University of Massachusetts Medical School, Shrewsbury, Massachusetts, 01545
| | - Henrik Molina
- ‖‖The Rockefeller University, Proteomics Resource Center, The Rockefeller University, New York, New York, 10065
| | | | - Brett S Phinney
- University of California, Davis, Proteomics Core, University of California-Davis Genome Center, Davis, California, 95616
| | - J Will Thompson
- Duke University, Proteomics and Metabolomics Core Facility, Duke University Medical Center, Durham, North Carolina, 27708
| | | | - David L Tabb
- ‖Vanderbilt University, Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, 37232;
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