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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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).
The authors declare no competing interests.
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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/
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.
Antibody fragments consisting of a single monomeric variable antibody domain.
Small proteins that bind to target molecules with a similar specificity and affinity to antibodies.
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.
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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