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Subcellular proteomics

Nature Reviews Methods Primers volume 1, Article number: 32 (2021) Cite this article

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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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Fig. 1: Overview of spatial proteomics approaches.
Fig. 2: Proximity labelling proteomics.
Fig. 3: Generic data-dependent acquisition workflows in quantitative proteomics.
Fig. 4: Generic fluorescence immunocytochemistry proteomics workflow.
Fig. 5: Subtractive versus correlation profiling analysis.

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Acknowledgements

J.A.C. is funded through a BBSRC iCASE award with Astra Zeneca. D.M. is funded by the Knut and Alice Wallenberg Foundation (2016.0204) and the Swedish Research Council (2017-05327). C.S. is funded by Science for Life (SciLifeLab) national funding, the National Microscopy Infrastructure (VR-RFI 2019-00217), the European Proteomics Infrastructure Consortium EPIC-XS (project number 823839) and the EU Horizon 2020 programme. A.-C.G. is the Tier 1 Canada Research Chair in Functional Proteomics and is supported by the Canadian Institutes of Health Research (FDN143301). C.E.M. is supported by a KRESCENT Post-Doctoral Fellowship and Canadian Institutes of Health Research Fellowship. B.W. is supported by the Deutsche Forschungsgemeinschaft (Project IDs 403222702/SFB 1381, FOR 1905, FOR 2743), Germany’s Excellence Strategy (CIBSS — EXC-2189 — Project ID 390939984), European Research Council Consolidator Grant No. 648235 and the European Union Marie Curie Initial Training Networks program PerICo (Grant Agreement Number 812968). Work included in this study has also been performed in partial fulfilment of the requirements for the doctoral thesis of M.M. at the University of Freiburg. L.J.F. is supported by Genome Canada/Genome British Columbia (Project 264PRO). I.M.C. is funded by the National Institute of General Medical Sciences (GM114141), the National Institute of Child Health and Human Development (HD089275) and the Edward Mallinckrodt Jr. foundation. C.N.B. is funded by the National Institute of General Medical Sciences (T32GM007388). Y.P. is funded through the Swedish Cancer Society. J.L. is funded though the Erling-Persson Family Foundation, the Swedish Cancer Society, the Swedish Childhood Cancer Foundation, the Swedish Foundation for Strategic Research, the Swedish Research Council and the EU Horizon 2020 project (RESCUER and OncoBiome). A.E. acknowledges previous and ongoing grant support from the National Institutes of Health (NIH) (1UL1TR001430, R01AG064932, R01AG061706, R01DK110520).

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Authors and Affiliations

  1. Department of Biochemistry, University of Cambridge, Cambridge, UK

    Josie A. Christopher & Kathryn S. Lilley

  2. Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, Cambridge, UK

    Josie A. Christopher & Kathryn S. Lilley

  3. Department of Protein Sciences, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden

    Charlotte Stadler & Diana Mahdessian

  4. Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada

    Claire E. Martin & Anne-Claude Gingras

  5. Institute of Biology II, Biochemistry and Functional Proteomics, Faculty of Biology, University of Freiburg, Freiburg, Germany

    Marcel Morgenstern & Bettina Warscheid

  6. Department of Oncology and Pathology, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden

    Yanbo Pan & Janne Lehtiö

  7. Department of Molecular Biology, Princeton University, Princeton, NJ, USA

    Cora N. Betsinger & Ileana M. Cristea

  8. Department of Biochemistry & Molecular Biology, Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada

    David G. Rattray & Leonard J. Foster

  9. Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada

    Anne-Claude Gingras

  10. BIOSS and CIBSS Signaling Research Centers, University of Freiburg, Freiburg, Germany

    Bettina Warscheid

  11. Center for Network Systems Biology, Boston University, Boston, MA, USA

    Andrew Emili

Authors
  1. Josie A. Christopher
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  2. Charlotte Stadler
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Contributions

Introduction (K.S.L., J.A.C.); Experimentation (K.S.L., J.A.C., C.S., C.E.M., M.M., Y.P., C.N.B., D.G.R., D.M., A.-C.G., B.W., J.L., I.M.C., L.J.F.); Results (K.S.L., J.A.C., C.S., C.E.M., M.M., Y.P., C.N.B., D.G.R., D.M., A.-C.G., B.W., J.L., I.M.C., L.J.F.); Applications (K.S.L., J.A.C., C.S., C.E.M., M.M., Y.P., C.N.B., D.G.R., D.M., A.-C.G., B.W., J.L., I.M.C., L.J.F.); Reproducibility and data deposition (K.S.L., J.A.C., C.S., C.E.M., M.M., Y.P., C.N.B., D.G.R., D.M., A.-C.G., B.W., J.L., I.M.C., L.J.F.); Limitations and optimizations (K.S.L., J.A.C., C.S., C.E.M., M.M., Y.P., C.N.B., D.G.R., D.M., A.-C.G., B.W., J.L., I.M.C., L.J.F., A.E.); Outlook (K.S.L., J.L., C.S., A.E.); Overview of the Primer (K.S.L.).

Corresponding author

Correspondence to Kathryn S. Lilley.

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Nature Reviews Methods Primers thanks G. Borner and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

COMPARTMENTS: https://compartments.jensenlab.org/

Gene Ontology: http://geneontology.org/

Human Protein Atlas: https://www.proteinatlas.org/humanproteome/cell

Kaggle challenge for multi-label classification of cell organelles: https://www.kaggle.com/c/human-protein-atlas-image-classification

MIAPE guidelines: http://www.psidev.info/miape

Open Microscopy Environment: https://www.openmicroscopy.org/

Open-source Python tools for proteomics analysis: https://github.com/Roestlab/PythonProteomics

R programming packages: https://www.R-project.org/

UniProt: https://www.uniprot.org/

Glossary

Proteoforms

Different molecular forms in which the protein product of a single gene can be found.

Protein correlation profiling

Using distributions profiles of proteins unique to different organelles and protein complexes across subcellular biochemical fractions to determine the subcellular location or complex association of uncharacterized proteins.

de Duve’s principle

Comparing the distribution pattern across subcellular fractions of proteins known to be resident within a specific organelle of interest allows for inference of other proteins with similar distribution profiles that must also reside in the same compartment.

Nanobodies

Antibody fragments consisting of a single monomeric variable antibody domain.

Affimers

Small proteins that bind to target molecules with a similar specificity and affinity to antibodies.

Aptamers

Oligonucleotides or peptide molecules that bind to a specific target molecule.

Abbe’s law

The approximate diffraction limit of a microscope determined using the wavelength of light (λ), the refraction index of the medium the imaged object is in (n) and the numerical aperture (θ).

Posterior probabilities

In Bayesian statistics, the revised or updated probability of an event after incorporating prior knowledge with observed data.

Golgins

A family of proteins that selectively tether vesicles at the Golgi apparatus and mediate transport of vesicles as part of the secretory pathway.

Edman degradation

A cyclic peptide sequencing technique where amino-terminal amino acid groups are sequentially cleaved and identified using chromatography or electrophoresis.

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Christopher, J.A., Stadler, C., Martin, C.E. et al. Subcellular proteomics. Nat Rev Methods Primers 1, 32 (2021). https://doi.org/10.1038/s43586-021-00029-y

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