Review Article | Published:

Spatial proteomics: a powerful discovery tool for cell biology


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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E.L. acknowledges funding from the Knut and Alice Wallenberg foundation (KAW 2016.0204), the Swedish Research Council (2017-05327) and the Chan Zuckerberg Initiative (173965 (5022)). G.H.H.B. received funding from the German Research Foundation (DFG/Gottfried Wilhelm Leibniz Prize MA 1764/2-1) and the Max Planck Society for the Advancement of Science.

Reviewer information

Nature Reviews Molecular Cell Biology thanks G. W. Brown and other anonymous reviewer(s), for their contribution to the peer review of this work.

Author information

Both authors contributed equally to all aspects of preparing the article (researching data for the article, substantial contributions to the discussion of the content and writing, reviewing and editing of the manuscript before submission).

Competing interests

The authors declare no competing interests.

Correspondence to Emma Lundberg or Georg H. H. Borner.

Supplementary information

Supplementary information


Dynamic protein translocation

Translocation describes the movement of a protein between cellular compartments. Dynamic translocation refers to a constant change in translocation activity.

Multimodal organellar distribution

Refers to the distribution of proteins that simultaneously localize to multiple compartments within a cell.

Affinity reagents

Molecules, such as an antibody, protein, peptide or nucleic acid, that bind specifically to a target protein to enable the identification, visualization, capture or modulation of the target protein or its activity.

Tandem mass tag multiplexing

A strategy for quantitative proteomic analyses. Peptides from multiple samples are labelled with different mass tags, pooled and analysed as a single sample by mass spectrometry. The tags can be distinguished by their mass and thus enable the simultaneous, relative quantification of peptide and protein abundances across several samples.


Describes the process of partitioning a digital image into segments that represent, for example, a cell or a nucleus.

Citizen science

Public participation in scientific research.

Tagged yeast libraries

Genome-wide libraries of yeast cells, each expressing a protein fused to a fluorescent reporter protein (such as GFP).


Integration of proteomics, transcriptomics and genomics for the discovery and identification of peptides using mass spectrometry. Practically, DNA or RNA sequence information is used to provide an experiment-specific or cell-type-specific tailored database for proteomic protein identification rather than a generic organism-specific database.

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Fig. 1: Spatial proteomics by MS analysis of fractionated organelles.
Fig. 2: Spatial proteomics through interaction networks.
Fig. 3: Different approaches to imaging-based spatial proteomics.
Fig. 4: MS-based comparative spatial proteomics: example applications.
Fig. 5: Imaging-based comparative spatial proteomics: example applications.