1
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Bhushan V, Nita-Lazar A. Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology. J Proteome Res 2024; 23:2700-2722. [PMID: 38451675 PMCID: PMC11296931 DOI: 10.1021/acs.jproteome.3c00839] [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] [Indexed: 03/08/2024]
Abstract
The mammalian cell is a complex entity, with membrane-bound and membrane-less organelles playing vital roles in regulating cellular homeostasis. Organellar protein niches drive discrete biological processes and cell functions, thus maintaining cell equilibrium. Cellular processes such as signaling, growth, proliferation, motility, and programmed cell death require dynamic protein movements between cell compartments. Aberrant protein localization is associated with a wide range of diseases. Therefore, analyzing the subcellular proteome of the cell can provide a comprehensive overview of cellular biology. With recent advancements in mass spectrometry, imaging technology, computational tools, and deep machine learning algorithms, studies pertaining to subcellular protein localization and their dynamic distributions are gaining momentum. These studies reveal changing interaction networks because of "moonlighting proteins" and serve as a discovery tool for disease network mechanisms. Consequently, this review aims to provide a comprehensive repository for recent advancements in subcellular proteomics subcontexting methods, challenges, and future perspectives for method developers. In summary, subcellular proteomics is crucial to the understanding of the fundamental cellular mechanisms and the associated diseases.
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Affiliation(s)
- Vanya Bhushan
- Functional Cellular Networks Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Aleksandra Nita-Lazar
- Functional Cellular Networks Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, United States
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2
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Schessner JP, Albrecht V, Davies AK, Sinitcyn P, Borner GHH. Deep and fast label-free Dynamic Organellar Mapping. Nat Commun 2023; 14:5252. [PMID: 37644046 PMCID: PMC10465578 DOI: 10.1038/s41467-023-41000-7] [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: 06/16/2022] [Accepted: 08/16/2023] [Indexed: 08/31/2023] Open
Abstract
The Dynamic Organellar Maps (DOMs) approach combines cell fractionation and shotgun-proteomics for global profiling analysis of protein subcellular localization. Here, we enhance the performance of DOMs through data-independent acquisition (DIA) mass spectrometry. DIA-DOMs achieve twice the depth of our previous workflow in the same mass spectrometry runtime, and substantially improve profiling precision and reproducibility. We leverage this gain to establish flexible map formats scaling from high-throughput analyses to extra-deep coverage. Furthermore, we introduce DOM-ABC, a powerful and user-friendly open-source software tool for analyzing profiling data. We apply DIA-DOMs to capture subcellular localization changes in response to starvation and disruption of lysosomal pH in HeLa cells, which identifies a subset of Golgi proteins that cycle through endosomes. An imaging time-course reveals different cycling patterns and confirms the quantitative predictive power of our translocation analysis. DIA-DOMs offer a superior workflow for label-free spatial proteomics as a systematic phenotype discovery tool.
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Affiliation(s)
- Julia P Schessner
- Department of Proteomics and Signal Transduction, Systems Biology of Membrane Trafficking Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
| | - Vincent Albrecht
- Department of Proteomics and Signal Transduction, Systems Biology of Membrane Trafficking Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
| | - Alexandra K Davies
- Department of Proteomics and Signal Transduction, Systems Biology of Membrane Trafficking Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
- School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | - Pavel Sinitcyn
- Computational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
| | - Georg H H Borner
- Department of Proteomics and Signal Transduction, Systems Biology of Membrane Trafficking Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany.
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3
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Mou M, Pan Z, Lu M, Sun H, Wang Y, Luo Y, Zhu F. Application of Machine Learning in Spatial Proteomics. J Chem Inf Model 2022; 62:5875-5895. [PMID: 36378082 DOI: 10.1021/acs.jcim.2c01161] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Spatial proteomics is an interdisciplinary field that investigates the localization and dynamics of proteins, and it has gained extensive attention in recent years, especially the subcellular proteomics. Numerous evidence indicate that the subcellular localization of proteins is associated with various cellular processes and disease progression. Mass spectrometry (MS)-based and imaging-based experimental approaches have been developed to acquire large-scale spatial proteomic data. To allow the reliable analysis of increasingly complex spatial proteomics data, machine learning (ML) methods have been widely used in both MS-based and imaging-based spatial proteomic data analysis pipelines. Here, we comprehensively survey the applications of ML in spatial proteomics from following aspects: (1) data resources for spatial proteome are comprehensively introduced; (2) the roles of different ML algorithms in data analysis pipelines are elaborated; (3) successful applications of spatial proteomics and several analytical tools integrating ML methods are presented; (4) challenges existing in modern ML-based spatial proteomics studies are discussed. This review provides guidelines for researchers seeking to apply ML methods to analyze spatial proteomic data and can facilitate insightful understanding of cell biology as well as the future research in medical and drug discovery communities.
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Affiliation(s)
- Minjie Mou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ziqi Pan
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Mingkun Lu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Huaicheng Sun
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
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4
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Arslan T, Pan Y, Mermelekas G, Vesterlund M, Orre LM, Lehtiö J. SubCellBarCode: integrated workflow for robust spatial proteomics by mass spectrometry. Nat Protoc 2022; 17:1832-1867. [PMID: 35732783 DOI: 10.1038/s41596-022-00699-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 03/18/2022] [Indexed: 11/09/2022]
Abstract
The molecular functions of a protein are defined by its inherent properties in relation to its environment and interaction network. Within a cell, this environment and network are defined by the subcellular location of the protein. Consequently, it is crucial to know the localization of a protein to fully understand its functions. Recently, we have developed a mass spectrometry- (MS) and bioinformatics-based pipeline to generate a proteome-wide resource for protein subcellular localization across multiple human cancer cell lines ( www.subcellbarcode.org ). Here, we present a detailed wet-lab protocol spanning from subcellular fractionation to MS-sample preparation and analysis. A key feature of this protocol is that it includes all generated cell fractions without discarding any material during the fractionation process. We also describe the subsequent quantitative MS-data analysis, machine learning-based classification, differential localization analysis and visualization of the output. For broad applicability, we evaluated the pipeline by using MS data generated by two different peptide pre-fractionation approaches, namely high-resolution isoelectric focusing and high-pH reverse-phase fractionation, as well as direct analysis without pre-fractionation by using long-gradient liquid chromatography-MS. Moreover, an R package covering the dry-lab part of the method was developed and made available through Bioconductor. The method is straightforward and robust, and the entire protocol, from cell harvest to classification output, can be performed within 1-2 weeks. The protocol enables accurate classification of proteins to 15 compartments and 4 neighborhoods, visualization of the output data and differential localization analysis including treatment-induced protein relocalization, condition-dependent localization or cell type-specific localization. The SubCellBarCode package is freely available at https://bioconductor.org/packages/devel/bioc/html/SubCellBarCode.html .
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Affiliation(s)
- Taner Arslan
- Department of Oncology and Pathology, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| | - Yanbo Pan
- Department of Oncology and Pathology, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| | - Georgios Mermelekas
- Department of Oncology and Pathology, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| | - Mattias Vesterlund
- Department of Oncology and Pathology, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| | - Lukas M Orre
- Department of Oncology and Pathology, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden.
| | - Janne Lehtiö
- Department of Oncology and Pathology, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden.
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5
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Moore DF, Sleat DE, Lobel P. A Method to Estimate the Distribution of Proteins across Multiple Compartments Using Data from Quantitative Proteomics Subcellular Fractionation Experiments. J Proteome Res 2022; 21:1371-1381. [PMID: 35522998 DOI: 10.1021/acs.jproteome.1c00781] [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] [Indexed: 11/28/2022]
Abstract
Knowledge of cellular location is key to understanding the biological function of proteins. One commonly used large-scale method to assign cellular locations is subcellular fractionation, followed by quantitative mass spectrometry to identify proteins and estimate their relative distribution among centrifugation fractions. In most of such subcellular proteomics studies, each protein is assigned to a single cellular location by comparing its distribution to those of a set of single-compartment reference proteins. However, in many cases, proteins reside in multiple compartments. To accurately determine the localization of such proteins, we previously introduced constrained proportional assignment (CPA), a method that assigns each protein a fractional residence over all reference compartments (Jadot Mol. Cell Proteomics 2017, 16(2), 194-212. 10.1074/mcp.M116.064527). In this Article, we describe the principles underlying CPA, as well as data transformations to improve accuracy of assignment of proteins and protein isoforms, and a suite of R-based programs to implement CPA and related procedures for analysis of subcellular proteomics data. We include a demonstration data set that used isobaric-labeling mass spectrometry to analyze rat liver fractions. In addition, we describe how these programs can be readily modified by users to accommodate a wide variety of experimental designs and methods for protein quantitation.
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Affiliation(s)
- Dirk F Moore
- Department of Biostatistics and Epidemiology, Rutgers School of Public Health and Rutgers Cancer Institute of New Jersey, 683 Hoes Lane West, Piscataway, New Jersey 08854, United States
| | - David E Sleat
- Center for Advanced Biotechnology and Medicine and Department of Biochemistry and Molecular Biology, Rutgers Biomedical and Health Sciences, 679 Hoes Lane West, Piscataway, New Jersey 08854, United States
| | - Peter Lobel
- Center for Advanced Biotechnology and Medicine and Department of Biochemistry and Molecular Biology, Rutgers Biomedical and Health Sciences, 679 Hoes Lane West, Piscataway, New Jersey 08854, United States
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6
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Ultrastructural and proteomic profiling of mitochondria-associated endoplasmic reticulum membranes reveal aging signatures in striated muscle. Cell Death Dis 2022; 13:296. [PMID: 35368021 PMCID: PMC8976840 DOI: 10.1038/s41419-022-04746-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 03/02/2022] [Accepted: 03/18/2022] [Indexed: 02/07/2023]
Abstract
Aging is a major risk for developing cardiac and skeletal muscle dysfunction, yet the underlying mechanism remains elusive. Here we demonstrated that the mitochondria-associated endoplasmic reticulum membranes (MAMs) in the rat heart and skeletal muscle were disrupted during aging. Using quantitative morphological analysis, we showed that the mitochondria-endoplasmic reticulum contacts (MERCs) were reduced by half over the lifespan with an early onset of accelerated thickening in the clefts. The ultrastructural changes were further validated by proteomic profiling of the MAM fractions. A combination of subcellular fractionation and quantitative mass spectrometry identified 1306 MAM-enriched proteins in both heart and skeletal muscle, with a catalog of proteins dysregulated with aging. Functional mapping of the MAM proteome suggested several aging signatures to be closely associated with the ER-mitochondria crosstalk, including local metabolic rewiring, calcium homeostasis imbalance, and impaired organelle dynamics and autophagy. Moreover, we identified a subset of highly interconnected proteins in an ER-mitochondria organization network, which were consistently down-regulated with aging. These decreased proteins, including VDAC1, SAMM50, MTX1 and MIC60, were considered as potential contributors to the age-related MAM dysfunction. This study highlights the perturbation in MAM integrity during the striated muscle aging process, and provides a framework for understanding aging biology from the perspective of organelle interactions.
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7
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Huang F, Tang X, Ye B, Wu S, Ding K. PSL-LCCL: a resource for subcellular protein localization in liver cancer cell line SK_HEP1. Database (Oxford) 2022; 2022:6521743. [PMID: 35134877 PMCID: PMC9248857 DOI: 10.1093/database/baab087] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 12/09/2021] [Accepted: 12/31/2021] [Indexed: 12/19/2022]
Abstract
The characterization of subcellular protein localization provides a basis for further
understanding cellular behaviors. A delineation of subcellular localization of proteins on
cytosolic membrane-bound organelles in human liver cancer cell lines (hLCCLs) has yet to
be performed. To obtain its proteome-wide view, we isolated and enriched six cytosolic
membrane-bound organelles in one of the hLCCLs (SK_HEP1) and quantified their proteins
using mass spectrometry. The vigorous selection of marker proteins and a
machine-learning-based algorithm were implemented to localize proteins at cluster and
neighborhood levels. We validated the performance of the proposed method by comparing the
predicted subcellular protein localization with publicly available resources. The profiles
enabled investigating the correlation of protein domains with their subcellular
localization and colocalization of protein complex members. A subcellular proteome
database for SK_HEP1, including (i) the subcellular protein localization and (ii) the
subcellular locations of protein complex members and their interactions, was constructed.
Our research provides resources for further research on hLCCLs proteomics. Database URL: http://www.igenetics.org.cn/project/PSL-LCCL/
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Affiliation(s)
| | | | - Bo Ye
- Department of Bioinformatics, School of Basic
Medicine, Chongqing Medical University, #1 Road Yixueyuan, Yuzhong
District, Chongqing 400016, People’s Republic of China
| | - Songfeng Wu
- *Correspondence may also be addressed to Songfeng Wu. Tel:
+8610-61777053; and Keyue Ding. Tel:
+86371-87160116;
| | - Keyue Ding
- *Correspondence may also be addressed to Songfeng Wu. Tel:
+8610-61777053; and Keyue Ding. Tel:
+86371-87160116;
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8
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Martinez-Val A, Bekker-Jensen DB, Steigerwald S, Koenig C, Østergaard O, Mehta A, Tran T, Sikorski K, Torres-Vega E, Kwasniewicz E, Brynjólfsdóttir SH, Frankel LB, Kjøbsted R, Krogh N, Lundby A, Bekker-Jensen S, Lund-Johansen F, Olsen JV. Spatial-proteomics reveals phospho-signaling dynamics at subcellular resolution. Nat Commun 2021; 12:7113. [PMID: 34876567 PMCID: PMC8651693 DOI: 10.1038/s41467-021-27398-y] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 11/12/2021] [Indexed: 12/12/2022] Open
Abstract
Dynamic change in subcellular localization of signaling proteins is a general concept that eukaryotic cells evolved for eliciting a coordinated response to stimuli. Mass spectrometry-based proteomics in combination with subcellular fractionation can provide comprehensive maps of spatio-temporal regulation of protein networks in cells, but involves laborious workflows that does not cover the phospho-proteome level. Here we present a high-throughput workflow based on sequential cell fractionation to profile the global proteome and phospho-proteome dynamics across six distinct subcellular fractions. We benchmark the workflow by studying spatio-temporal EGFR phospho-signaling dynamics in vitro in HeLa cells and in vivo in mouse tissues. Finally, we investigate the spatio-temporal stress signaling, revealing cellular relocation of ribosomal proteins in response to hypertonicity and muscle contraction. Proteomics data generated in this study can be explored through https://SpatialProteoDynamics.github.io .
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Affiliation(s)
- Ana Martinez-Val
- Novo Nordisk Foundation Center for Protein Research, Proteomics Program, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Dorte B Bekker-Jensen
- Novo Nordisk Foundation Center for Protein Research, Proteomics Program, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Evosep Systems, Odense, Denmark
| | - Sophia Steigerwald
- Novo Nordisk Foundation Center for Protein Research, Proteomics Program, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Max Planck Institute of Biochemistry, Department of Proteomics and Signal Transduction, Martinsried, Germany
| | - Claire Koenig
- Novo Nordisk Foundation Center for Protein Research, Proteomics Program, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ole Østergaard
- Novo Nordisk Foundation Center for Protein Research, Proteomics Program, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Adi Mehta
- Department of Immunology, Oslo University Hospital, Rikshospitalet, Postboks 4950, Nydalen, 0424, Oslo, Norway
| | - Trung Tran
- Department of Immunology, Oslo University Hospital, Rikshospitalet, Postboks 4950, Nydalen, 0424, Oslo, Norway
| | - Krzysztof Sikorski
- Department of Immunology, Oslo University Hospital, Rikshospitalet, Postboks 4950, Nydalen, 0424, Oslo, Norway
| | - Estefanía Torres-Vega
- Cardiac Proteomics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ewa Kwasniewicz
- Center for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark
| | | | - Lisa B Frankel
- Danish Cancer Society, Copenhagen, Denmark
- Danish Cancer Society Research Center, Copenhagen, Denmark
- Biotech Research and Innovation Centre, University of Copenhagen, Copenhagen, Denmark
| | - Rasmus Kjøbsted
- The August Krogh Section for Molecular Physiology, Department of Nutrition, Exercise and Sports, University of Copenhagen, Copenhagen, Denmark
| | - Nicolai Krogh
- Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Alicia Lundby
- Novo Nordisk Foundation Center for Protein Research, Proteomics Program, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Cardiac Proteomics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Simon Bekker-Jensen
- Center for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Fridtjof Lund-Johansen
- Department of Immunology, Oslo University Hospital, Rikshospitalet, Postboks 4950, Nydalen, 0424, Oslo, Norway.
| | - Jesper V Olsen
- Novo Nordisk Foundation Center for Protein Research, Proteomics Program, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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9
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Christopher JA, Stadler C, Martin CE, Morgenstern M, Pan Y, Betsinger CN, Rattray DG, Mahdessian D, Gingras AC, Warscheid B, Lehtiö J, Cristea IM, Foster LJ, Emili A, Lilley KS. Subcellular proteomics. NATURE REVIEWS. METHODS PRIMERS 2021; 1:32. [PMID: 34549195 PMCID: PMC8451152 DOI: 10.1038/s43586-021-00029-y] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/15/2021] [Indexed: 12/11/2022]
Abstract
The eukaryotic cell is compartmentalized into subcellular niches, including membrane-bound and membrane-less organelles. Proteins localize to these niches to fulfil their function, enabling discreet biological processes to occur in synchrony. Dynamic movement of proteins between niches is essential for cellular processes such as signalling, growth, proliferation, motility and programmed cell death, and mutations causing aberrant protein localization are associated with a wide range of diseases. Determining the location of proteins in different cell states and cell types and how proteins relocalize following perturbation is important for understanding their functions, related cellular processes and pathologies associated with their mislocalization. In this Primer, we cover the major spatial proteomics methods for determining the location, distribution and abundance of proteins within subcellular structures. These technologies include fluorescent imaging, protein proximity labelling, organelle purification and cell-wide biochemical fractionation. We describe their workflows, data outputs and applications in exploring different cell biological scenarios, and discuss their main limitations. Finally, we describe emerging technologies and identify areas that require technological innovation to allow better characterization of the spatial proteome.
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Affiliation(s)
- Josie A. Christopher
- Department of Biochemistry, University of Cambridge, Cambridge, UK
- Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, Cambridge, UK
| | - Charlotte Stadler
- Department of Protein Sciences, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| | - Claire E. Martin
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
| | - Marcel Morgenstern
- Institute of Biology II, Biochemistry and Functional Proteomics, Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Yanbo Pan
- Department of Oncology and Pathology, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| | - Cora N. Betsinger
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - David G. Rattray
- Department of Biochemistry & Molecular Biology, Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Diana Mahdessian
- Department of Protein Sciences, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| | - Anne-Claude Gingras
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Bettina Warscheid
- Institute of Biology II, Biochemistry and Functional Proteomics, Faculty of Biology, University of Freiburg, Freiburg, Germany
- BIOSS and CIBSS Signaling Research Centers, University of Freiburg, Freiburg, Germany
| | - Janne Lehtiö
- Department of Oncology and Pathology, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| | - Ileana M. Cristea
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Leonard J. Foster
- Department of Biochemistry & Molecular Biology, Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Andrew Emili
- Center for Network Systems Biology, Boston University, Boston, MA, USA
| | - Kathryn S. Lilley
- Department of Biochemistry, University of Cambridge, Cambridge, UK
- Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, Cambridge, UK
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10
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Borner GHH. Organellar Maps Through Proteomic Profiling - A Conceptual Guide. Mol Cell Proteomics 2020; 19:1076-1087. [PMID: 32345598 PMCID: PMC7338086 DOI: 10.1074/mcp.r120.001971] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 04/27/2020] [Indexed: 11/29/2022] Open
Abstract
Protein subcellular localization is an essential and highly regulated determinant of protein function. Major advances in mass spectrometry and imaging have allowed the development of powerful spatial proteomics approaches for determining protein localization at the whole cell scale. Here, a brief overview of current methods is presented, followed by a detailed discussion of organellar mapping through proteomic profiling. This relatively simple yet flexible approach is rapidly gaining popularity, because of its ability to capture the localizations of thousands of proteins in a single experiment. It can be used to generate high-resolution cell maps, and as a tool for monitoring protein localization dynamics. This review highlights the strengths and limitations of the approach and provides guidance to designing and interpreting profiling experiments.
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Affiliation(s)
- Georg H H Borner
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany.
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11
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Tannous A, Boonen M, Zheng H, Zhao C, Germain CJ, Moore DF, Sleat DE, Jadot M, Lobel P. Comparative Analysis of Quantitative Mass Spectrometric Methods for Subcellular Proteomics. J Proteome Res 2020; 19:1718-1730. [PMID: 32134668 DOI: 10.1021/acs.jproteome.9b00862] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Knowledge of intracellular location can provide important insights into the function of proteins and their respective organelles, and there is interest in combining classical subcellular fractionation with quantitative mass spectrometry to create global cellular maps. To evaluate mass spectrometric approaches specifically for this application, we analyzed rat liver differential centrifugation and Nycodenz density gradient subcellular fractions by tandem mass tag (TMT) isobaric labeling with reporter ion measurement at the MS2 and MS3 level and with two different label-free peak integration approaches, MS1 and data independent acquisition (DIA). TMT-MS2 provided the greatest proteome coverage, but ratio compression from contaminating background ions resulted in a narrower accurate dynamic range compared to TMT-MS3, MS1, and DIA, which were similar. Using a protein clustering approach to evaluate data quality by assignment of reference proteins to their correct compartments, all methods performed well, with isobaric labeling approaches providing the highest quality localization. Finally, TMT-MS2 gave the lowest percentage of missing quantifiable data when analyzing orthogonal fractionation methods containing overlapping proteomes. In summary, despite inaccuracies resulting from ratio compression, data obtained by TMT-MS2 assigned protein localization as well as other methods but achieved the highest proteome coverage with the lowest proportion of missing values.
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Affiliation(s)
- Abla Tannous
- Center for Advanced Biotechnology and Medicine, Piscataway, New Jersey 08854, United States
| | - Marielle Boonen
- URPhyM-Intracellular Trafficking Biology, NARILIS, University of Namur, 61 rue de Bruxelles, Namur 5000, Belgium
| | - Haiyan Zheng
- Center for Advanced Biotechnology and Medicine, Piscataway, New Jersey 08854, United States
| | - Caifeng Zhao
- Center for Advanced Biotechnology and Medicine, Piscataway, New Jersey 08854, United States
| | - Colin J Germain
- Center for Advanced Biotechnology and Medicine, Piscataway, New Jersey 08854, United States
| | - Dirk F Moore
- Department of Biostatistics, School of Public Health, Rutgers - The State University of New Jersey, Piscataway, New Jersey 08854, United States
| | - David E Sleat
- Center for Advanced Biotechnology and Medicine, Piscataway, New Jersey 08854, United States.,Department of Biochemistry and Molecular Biology, Robert-Wood Johnson Medical School, Rutgers Biomedical Health Sciences, Piscataway, New Jersey 08854, United States
| | - Michel Jadot
- URPhyM-Physiological Chemistry, NARILIS, University of Namur, 61 rue de Bruxelles, Namur 5000, Belgium
| | - Peter Lobel
- Center for Advanced Biotechnology and Medicine, Piscataway, New Jersey 08854, United States.,Department of Biochemistry and Molecular Biology, Robert-Wood Johnson Medical School, Rutgers Biomedical Health Sciences, Piscataway, New Jersey 08854, United States
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12
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Lundberg E, Borner GHH. Spatial proteomics: a powerful discovery tool for cell biology. Nat Rev Mol Cell Biol 2020; 20:285-302. [PMID: 30659282 DOI: 10.1038/s41580-018-0094-y] [Citation(s) in RCA: 278] [Impact Index Per Article: 69.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Protein subcellular localization is tightly controlled and intimately linked to protein function in health and disease. Capturing the spatial proteome - that is, the localizations of proteins and their dynamics at the subcellular level - is therefore essential for a complete understanding of cell biology. Owing to substantial advances in microscopy, mass spectrometry and machine learning applications for data analysis, the field is now mature for proteome-wide investigations of spatial cellular regulation. Studies of the human proteome have begun to reveal a complex architecture, including single-cell variations, dynamic protein translocations, changing interaction networks and proteins localizing to multiple compartments. Furthermore, several studies have successfully harnessed the power of comparative spatial proteomics as a discovery tool to unravel disease mechanisms. We are at the beginning of an era in which spatial proteomics finally integrates with cell biology and medical research, thereby paving the way for unbiased systems-level insights into cellular processes. Here, we discuss current methods for spatial proteomics using imaging or mass spectrometry and specifically highlight global comparative applications. The aim of this Review is to survey the state of the field and also to encourage more cell biologists to apply spatial proteomics approaches.
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Affiliation(s)
- Emma Lundberg
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, Sweden. .,Department of Genetics, Stanford University, Stanford, CA, USA. .,Chan Zuckerberg Biohub, San Francisco, CA, USA.
| | - Georg H H Borner
- Max Planck Institute of Biochemistry, Department of Proteomics and Signal Transduction, Martinsried, Germany.
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13
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Affiliation(s)
- Aldrin V Gomes
- Department of Neurobiology, Physiology, and Behavior (A.V.G.).,Department of Physiology and Membrane Biology (A.V.G.), University of California, Davis
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14
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Glazko G, Zybailov B, Emmert-Streib F, Baranova A, Rahmatallah Y. Proteome-transcriptome alignment of molecular portraits achieved by self-contained gene set analysis: Consensus colon cancer subtypes case study. PLoS One 2019; 14:e0221444. [PMID: 31437237 PMCID: PMC6705791 DOI: 10.1371/journal.pone.0221444] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 08/06/2019] [Indexed: 01/10/2023] Open
Abstract
Gene set analysis (GSA) has become the common methodology for analyzing transcriptomics data. However, self-contained GSA techniques are rarely, if ever, used for proteomics data analysis. Here we present a self-contained proteome level GSA of four consensus molecular subtypes (CMSs) previously established by transcriptome dissection of colon carcinoma specimens. Despite notable difference in structure of proteomics and transcriptomics data, many pathway-wide characteristic features of CMSs found at the mRNA level were reproduced at the protein level. In particular, CMS1 features show heavy involvement of immune system as well as the pathways related to mismatch repair, DNA replication and functioning of proteasome, while CMS4 tumors upregulate complement pathway and proteins participating in epithelial-to-mesenchymal transition (EMT). In addition, protein level GSA yielded a set of novel observations visible at the proteome, but not at the transcriptome level, including possible involvement of major histocompatibility complex II (MHC-II) antigens in the known immunogenicity of CMS1 and a connection between cholesterol trafficking and the regulation of Integrin-linked kinase (ILK) in CMS3. Overall, this study proves utility of self-contained GSA approaches as a critical tool for analyzing proteomics data in general and dissecting protein-level molecular portraits of human tumors in particular.
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Affiliation(s)
- Galina Glazko
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States of America
| | - Boris Zybailov
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, United States of America
| | - Frank Emmert-Streib
- Computational Medicine and Statistical Learning Laboratory, Tampere University of Technology, Korkeakoulunkatu, Tampere, Finland FI
| | - Ancha Baranova
- School of Systems Biology, George Mason University, Manassas VA, United States of America
- Research Center for Medical Genetics, Moscow, Russia
| | - Yasir Rahmatallah
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States of America
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15
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Gatto L, Breckels LM, Lilley KS. Assessing sub-cellular resolution in spatial proteomics experiments. Curr Opin Chem Biol 2019; 48:123-149. [PMID: 30711721 PMCID: PMC6391913 DOI: 10.1016/j.cbpa.2018.11.015] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Revised: 11/09/2018] [Accepted: 11/19/2018] [Indexed: 12/04/2022]
Abstract
The sub-cellular localisation of a protein is vital in defining its function, and a protein's mis-localisation is known to lead to adverse effect. As a result, numerous experimental techniques and datasets have been published, with the aim of deciphering the localisation of proteins at various scales and resolutions, including high profile mass spectrometry-based efforts. Here, we present a meta-analysis assessing and comparing the sub-cellular resolution of 29 such mass spectrometry-based spatial proteomics experiments using a newly developed tool termed QSep. Our goal is to provide a simple quantitative report of how well spatial proteomics resolve the sub-cellular niches they describe to inform and guide developers and users of such methods.
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Affiliation(s)
- Laurent Gatto
- Computational Proteomics Unit, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK; Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK; de Duve Institute, UCLouvain, Avenue Hippocrate 75, 1200 Brussels, Belgium.
| | - Lisa M Breckels
- Computational Proteomics Unit, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK; Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK
| | - Kathryn S Lilley
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK
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16
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Meng W, Wang XJ, Wang HCR. Targeting nuclear proteins for control of viral replication. Crit Rev Microbiol 2019; 45:495-513. [DOI: 10.1080/1040841x.2018.1553848] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Wen Meng
- Key Laboratory of Animal Epidemiology of the Ministry of Agriculture, College of Veterinary Medicine, China Agricultural University, Beijing, China
| | - Xiao-Jia Wang
- Key Laboratory of Animal Epidemiology of the Ministry of Agriculture, College of Veterinary Medicine, China Agricultural University, Beijing, China
| | - Hwa-Chain Robert Wang
- Department of Biomedical and Diagnostic Sciences, College of Veterinary Medicine, The University of Tennessee, Knoxville, USA
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17
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Geladaki A, Kočevar Britovšek N, Breckels LM, Smith TS, Vennard OL, Mulvey CM, Crook OM, Gatto L, Lilley KS. Combining LOPIT with differential ultracentrifugation for high-resolution spatial proteomics. Nat Commun 2019; 10:331. [PMID: 30659192 PMCID: PMC6338729 DOI: 10.1038/s41467-018-08191-w] [Citation(s) in RCA: 115] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Accepted: 12/18/2018] [Indexed: 01/09/2023] Open
Abstract
The study of protein localisation has greatly benefited from high-throughput methods utilising cellular fractionation and proteomic profiling. Hyperplexed Localisation of Organelle Proteins by Isotope Tagging (hyperLOPIT) is a well-established method in this area. It achieves high-resolution separation of organelles and subcellular compartments but is relatively time- and resource-intensive. As a simpler alternative, we here develop Localisation of Organelle Proteins by Isotope Tagging after Differential ultraCentrifugation (LOPIT-DC) and compare this method to the density gradient-based hyperLOPIT approach. We confirm that high-resolution maps can be obtained using differential centrifugation down to the suborganellar and protein complex level. HyperLOPIT and LOPIT-DC yield highly similar results, facilitating the identification of isoform-specific localisations and high-confidence localisation assignment for proteins in suborganellar structures, protein complexes and signalling pathways. By combining both approaches, we present a comprehensive high-resolution dataset of human protein localisations and deliver a flexible set of protocols for subcellular proteomics.
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Affiliation(s)
- Aikaterini Geladaki
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK
- Department of Genetics, University of Cambridge, 20 Downing Place, Cambridge, CB2 3EJ, UK
| | - Nina Kočevar Britovšek
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK
| | - Lisa M Breckels
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK
| | - Tom S Smith
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK
| | - Owen L Vennard
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK
| | - Claire M Mulvey
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK
| | - Oliver M Crook
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK
- MRC Biostatistics Unit, Cambridge Institute for Public Health, Forvie Site, Robinson Way, Cambridge, CB2 0SR, UK
| | - Laurent Gatto
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK
- de Duve Institute, UC Louvain, Avenue Hippocrate 75, Brussels, 1200, Belgium
| | - Kathryn S Lilley
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK.
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18
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SubCellBarCode: Proteome-wide Mapping of Protein Localization and Relocalization. Mol Cell 2019; 73:166-182.e7. [DOI: 10.1016/j.molcel.2018.11.035] [Citation(s) in RCA: 120] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2018] [Revised: 09/28/2018] [Accepted: 11/27/2018] [Indexed: 11/22/2022]
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19
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de la Rosa Carrillo D, Sikorski K, Khnykin D, Wu W, Lund-Johansen F. High-resolution antibody array analysis of proteins from primary human keratinocytes and leukocytes. PLoS One 2018; 13:e0209271. [PMID: 30589857 PMCID: PMC6307719 DOI: 10.1371/journal.pone.0209271] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 12/03/2018] [Indexed: 11/19/2022] Open
Abstract
Antibody array analysis of labeled proteomes has high throughput and is simple to perform, but validation remains challenging. Here, we used differential detergent fractionation and size exclusion chromatography in sequence for high-resolution separation of biotinylated proteins from human primary keratinocytes and leukocytes. Ninety-six sample fractions from each cell type were analyzed with microsphere-based antibody arrays and flow cytometry (microsphere affinity proteomics; MAP). Monomeric proteins and multi-molecular complexes in the cytosol, cytoplasmic organelles, membranes and nuclei were resolved as discrete peaks of antibody reactivity across the fractions. The fractionation also provided a two-dimensional matrix for assessment of specificity. Thus, antibody reactivity peaks were considered to represent specific binding if the position in the matrix was in agreement with published information about i) subcellular location, ii) size of the intended target, and iii) cell type-dependent variation in protein expression. Similarities in the reactivity patterns of either different antibodies to the same protein or antibodies to similar proteins were used as additional supporting evidence. This approach provided validation of several hundred proteins and identification of monomeric proteins and protein complexes. High-resolution MAP solves many of the problems associated with obtaining specificity with immobilized antibodies and a protein label. Thus, laboratories with access to chromatography and flow cytometry can perform large-scale protein analysis on a daily basis. This opens new possibilities for cell biology research in dermatology and validation of antibodies.
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Affiliation(s)
- Daniel de la Rosa Carrillo
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Dermatology, Oslo University Hospital, Oslo, Norway
- * E-mail:
| | - Krzysztof Sikorski
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- K.G. Jebsen Center for Cancer Immunotherapy, University of Oslo, Oslo, Norway
| | - Denis Khnykin
- Department of Pathology, Oslo University Hospital, Oslo, Norway
| | - Weiwei Wu
- Department of Immunology, Oslo University Hospital, Oslo, Norway
| | - Fridtjof Lund-Johansen
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- K.G. Jebsen Center for Cancer Immunotherapy, University of Oslo, Oslo, Norway
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20
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Williams EG, Wu Y, Wolski W, Kim JY, Lan J, Hasan M, Halter C, Jha P, Ryu D, Auwerx J, Aebersold R. Quantifying and Localizing the Mitochondrial Proteome Across Five Tissues in A Mouse Population. Mol Cell Proteomics 2018; 17:1766-1777. [PMID: 29945935 DOI: 10.1074/mcp.ra118.000554] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Revised: 04/17/2018] [Indexed: 12/12/2022] Open
Abstract
We have used SWATH mass spectrometry to quantify 3648 proteins across 76 proteomes collected from genetically diverse BXD mouse strains in two fractions (mitochondria and total cell) from five tissues: liver, quadriceps, heart, brain, and brown adipose (BAT). Across tissues, expression covariation between genes' proteins and transcripts-measured in the same individuals-broadly aligned. Covariation was however far stronger in certain subsets than others: only 8% of transcripts in the lowest expression and variance quintile covaried with their protein, in contrast to 65% of transcripts in the highest quintiles. Key functional differences among the 3648 genes were also observed across tissues, with electron transport chain (ETC) genes particularly investigated. ETC complex proteins covary and form strong gene networks according to tissue, but their equivalent transcripts do not. Certain physiological consequences, such as the depletion of ATP synthase in BAT, are thus obscured in transcript data. Lastly, we compared the quantitative proteomic measurements between the total cell and mitochondrial fractions for the five tissues. The resulting enrichment score highlighted several hundred proteins which were strongly enriched in mitochondria, which included several dozen proteins were not reported in literature to be mitochondrially localized. Four of these candidates were selected for biochemical validation, where we found MTAP, SOAT2, and IMPDH2 to be localized inside the mitochondria, whereas ABCC6 was in the mitochondria-associated membrane. These findings demonstrate the synergies of a multi-omics approach to study complex metabolic processes, and this provides a resource for further discovery and analysis of proteoforms, modified proteins, and protein localization.
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Affiliation(s)
- Evan G Williams
- From the ‡Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich CH-8093, Switzerland.,§Interfaculty Institute of Bioengineering, Laboratory of Integrative and Systems Physiology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland
| | - Yibo Wu
- From the ‡Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich CH-8093, Switzerland.,**Current address: RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho Tsurumi-ku Yokohama City, Kanagawa, 230-0045, Japan
| | - Witold Wolski
- ¶Functional Genomics Center Zurich, ETH Zurich, Zurich CH-8057, Switzerland
| | - Jun Yong Kim
- §Interfaculty Institute of Bioengineering, Laboratory of Integrative and Systems Physiology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland
| | - Jiayi Lan
- From the ‡Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich CH-8093, Switzerland
| | - Moaraj Hasan
- From the ‡Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich CH-8093, Switzerland
| | - Christian Halter
- From the ‡Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich CH-8093, Switzerland
| | - Pooja Jha
- §Interfaculty Institute of Bioengineering, Laboratory of Integrative and Systems Physiology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland
| | - Dongryeol Ryu
- §Interfaculty Institute of Bioengineering, Laboratory of Integrative and Systems Physiology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland.,‡‡Current address: Healthy Aging-Korean Medical Research Center, Pusan National University, School of Korean Medicine, Yangsan 50612, Republic of Korea
| | - Johan Auwerx
- §Interfaculty Institute of Bioengineering, Laboratory of Integrative and Systems Physiology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland;
| | - Ruedi Aebersold
- From the ‡Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich CH-8093, Switzerland; .,‖Faculty of Science, University of Zurich, Zurich CH-8057, Switzerland
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21
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Goodman SL. The path to VICTORy - a beginner's guide to success using commercial research antibodies. J Cell Sci 2018; 131:131/10/jcs216416. [PMID: 29764917 DOI: 10.1242/jcs.216416] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Commercial research antibodies are crucial tools in modern cell biology and biochemistry. In the USA some $2 billion a year are spent on them, but many are apparently not fit-for-purpose, and this may contribute to the 'reproducibility crisis' in biological sciences. Inadequate antibody validation and characterization, lack of user awareness, and occasional incompetence amongst suppliers have had immense scientific and personal costs. In this Opinion, I suggest some paths to make the use of these vital tools more successful. I have attempted to summarize and extend expert views from the literature to suggest that sustained routine efforts should made in: (1) the validation of antibodies, (2) their identification, (3) communication and controls, (4) the training of potential users, (5) the transparency of original equipment manufacturer (OEM) marketing agreements, and (5) in a more widespread use of recombinant antibodies (together denoted the 'VICTOR' approach).
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22
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The antibody horror show: an introductory guide for the perplexed. N Biotechnol 2018; 45:9-13. [PMID: 29355666 DOI: 10.1016/j.nbt.2018.01.006] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Revised: 01/03/2018] [Accepted: 01/16/2018] [Indexed: 01/21/2023]
Abstract
The biological literature reverberates with the inadequacies of commercial research-tool antibodies. The scientific community spends some $2 billion per year on such reagents. Excellent accessible scientific platforms exist for reliably making, validating and using antibodies, yet the laboratory end-user reality is somehow depressing - because they often "don't work". This experience is due to a bizarre and variegated spectrum of causes including: inadequately identified antibodies; inappropriate user and supplier validation; poor user training; and overloaded publishers. Colourful as this may appear, the outcomes for the community are uniformly grim, including badly damaged scientific careers, wasted public funding, and contaminated literature. As antibodies are amongst the most important of everyday reagents in cell biology and biochemistry, I have tried here to gently suggest a few possible solutions, including: a move towards using recombinant antibodies; obligatory unique identification of antibodies, their immunogens, and their producers; centralized international banking of standard antibodies and their ligands; routine, accessible open-source documentation of user experience with antibodies; and antibody-user certification.
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23
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Pandya NJ, Koopmans F, Slotman JA, Paliukhovich I, Houtsmuller AB, Smit AB, Li KW. Correlation profiling of brain sub-cellular proteomes reveals co-assembly of synaptic proteins and subcellular distribution. Sci Rep 2017; 7:12107. [PMID: 28935861 PMCID: PMC5608747 DOI: 10.1038/s41598-017-11690-3] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Accepted: 08/21/2017] [Indexed: 12/13/2022] Open
Abstract
Protein correlation profiling might assist in defining co-assembled proteins and subcellular distribution. Here, we quantified the proteomes of five biochemically isolated mouse brain cellular sub-fractions, with emphasis on synaptic compartments, from three brain regions, hippocampus, cortex and cerebellum. We demonstrated the expected co-fractionation of canonical synaptic proteins belonging to the same functional groups. The enrichment profiles also suggested the presence of many novel pre- and post-synaptic proteins. Using super-resolution microscopy on primary neuronal culture we confirmed the postsynaptic localization of PLEKHA5 and ADGRA1. We further detected profound brain region specific differences in the extent of enrichment for some functionally associated proteins. This is exemplified by different AMPA receptor subunits and substantial differences in sub-fraction distribution of their potential interactors, which implicated the differences of AMPA receptor complex compositions. This resource aids the identification of proteins partners and subcellular distribution of synaptic proteins.
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Affiliation(s)
- Nikhil J Pandya
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Frank Koopmans
- Department of Functional Genomics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Johan A Slotman
- Optical Imaging Center, Department of Pathology, Erasmus Medical Center, 3015 GE, Rotterdam, Netherlands
| | - Iryna Paliukhovich
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Adriaan B Houtsmuller
- Optical Imaging Center, Department of Pathology, Erasmus Medical Center, 3015 GE, Rotterdam, Netherlands
| | - August B Smit
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ka Wan Li
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
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24
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Wegler C, Gaugaz FZ, Andersson TB, Wiśniewski JR, Busch D, Gröer C, Oswald S, Norén A, Weiss F, Hammer HS, Joos TO, Poetz O, Achour B, Rostami-Hodjegan A, van de Steeg E, Wortelboer HM, Artursson P. Variability in Mass Spectrometry-based Quantification of Clinically Relevant Drug Transporters and Drug Metabolizing Enzymes. Mol Pharm 2017; 14:3142-3151. [DOI: 10.1021/acs.molpharmaceut.7b00364] [Citation(s) in RCA: 87] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Christine Wegler
- Department
of Pharmacy, Uppsala University, Uppsala 75123, Sweden
- Cardiovascular and Metabolic Diseases, Innovative Medicines and Early Development Biotech Unit, AstraZeneca R&D, Mölndal 431 50, Sweden
| | | | - Tommy B. Andersson
- Cardiovascular and Metabolic Diseases, Innovative Medicines and Early Development Biotech Unit, AstraZeneca R&D, Mölndal 431 50, Sweden
| | - Jacek R. Wiśniewski
- Biochemical
Proteomics Group, Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried 82152, Germany
| | - Diana Busch
- Center
of Drug Absorption and Transport, Department of Clinical Pharmacology, University Medicine of Greifswald, Greifswald 17489, Germany
| | - Christian Gröer
- Center
of Drug Absorption and Transport, Department of Clinical Pharmacology, University Medicine of Greifswald, Greifswald 17489, Germany
| | - Stefan Oswald
- Center
of Drug Absorption and Transport, Department of Clinical Pharmacology, University Medicine of Greifswald, Greifswald 17489, Germany
| | - Agneta Norén
- Department
of Surgical Sciences, Uppsala University, Uppsala 75185, Sweden
| | - Frederik Weiss
- NMI Natural
and Medical Sciences Institute, University of Tübingen, Reutlingen 72770, Germany
| | - Helen S. Hammer
- NMI Natural
and Medical Sciences Institute, University of Tübingen, Reutlingen 72770, Germany
| | - Thomas O. Joos
- NMI Natural
and Medical Sciences Institute, University of Tübingen, Reutlingen 72770, Germany
| | - Oliver Poetz
- NMI Natural
and Medical Sciences Institute, University of Tübingen, Reutlingen 72770, Germany
| | - Brahim Achour
- Centre
for Applied Pharmacokinetic Research, University of Manchester, Manchester M13 9PL, United Kingdom
| | - Amin Rostami-Hodjegan
- Centre
for Applied Pharmacokinetic Research, University of Manchester, Manchester M13 9PL, United Kingdom
| | - Evita van de Steeg
- TNO (Netherlands Organization for Applied Scientific Research), 3700 AJ Zeist, Netherlands
| | - Heleen M. Wortelboer
- TNO (Netherlands Organization for Applied Scientific Research), 3700 AJ Zeist, Netherlands
| | - Per Artursson
- Department
of Pharmacy, Uppsala University, Uppsala 75123, Sweden
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25
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Abstract
The organization of eukaryotic cells into distinct subcompartments is vital for all functional processes, and aberrant protein localization is a hallmark of many diseases. Microscopy methods, although powerful, are usually low-throughput and dependent on the availability of fluorescent fusion proteins or highly specific and sensitive antibodies. One method that provides a global picture of the cell is localization of organelle proteins by isotope tagging (LOPIT), which combines biochemical cell fractionation using density gradient ultracentrifugation with multiplexed quantitative proteomics mass spectrometry, allowing simultaneous determination of the steady-state distribution of hundreds of proteins within organelles. Proteins are assigned to organelles based on the similarity of their gradient distribution to those of well-annotated organelle marker proteins. We have substantially re-developed our original LOPIT protocol (published by Nature Protocols in 2006) to enable the subcellular localization of thousands of proteins per experiment (hyperLOPIT), including spatial resolution at the suborganelle and large protein complex level. This Protocol Extension article integrates all elements of the hyperLOPIT pipeline, including an additional enrichment strategy for chromatin, extended multiplexing capacity of isobaric mass tags, state-of-the-art mass spectrometry methods and multivariate machine-learning approaches for analysis of spatial proteomics data. We have also created an open-source infrastructure to support analysis of quantitative mass-spectrometry-based spatial proteomics data (http://bioconductor.org/packages/pRoloc) and an accompanying interactive visualization framework (http://www. bioconductor.org/packages/pRolocGUI). The procedure we outline here is applicable to any cell culture system and requires ∼1 week to complete sample preparation steps, ∼2 d for mass spectrometry data acquisition and 1-2 d for data analysis and downstream informatics.
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26
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A Golden Age for Working with Public Proteomics Data. Trends Biochem Sci 2017; 42:333-341. [PMID: 28118949 PMCID: PMC5414595 DOI: 10.1016/j.tibs.2017.01.001] [Citation(s) in RCA: 66] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2016] [Revised: 12/13/2016] [Accepted: 01/02/2017] [Indexed: 11/23/2022]
Abstract
Data sharing in mass spectrometry (MS)-based proteomics is becoming a common scientific practice, as is now common in the case of other, more mature ‘omics’ disciplines like genomics and transcriptomics. We want to highlight that this situation, unprecedented in the field, opens a plethora of opportunities for data scientists. First, we explain in some detail some of the work already achieved, such as systematic reanalysis efforts. We also explain existing applications of public proteomics data, such as proteogenomics and the creation of spectral libraries and spectral archives. Finally, we discuss the main existing challenges and mention the first attempts to combine public proteomics data with other types of omics data sets. The field of proteomics has matured and diversified substantially over the past 10 years. Proteomics data are increasingly shared through centralized, public repositories. Standardization efforts have ensured that a large proportion of these public data can be read and processed by any interested researcher. Because any proteomics data set is only partially understood, there is great opportunity for (orthogonal) reuse of public data. While public proteomics data has so far remained outside ethics and privacy discussions, recent work indicates that there is an inherent risk.
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