Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Subcellular proteomics

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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

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.

References

  1. 1.

    Gibson, T. J. Cell regulation: determined to signal discrete cooperation. Trends Biochem. Sci. 34, 471–482 (2009).

    Google Scholar 

  2. 2.

    Hung, M.-C. & Link, W. Protein localization in disease and therapy. J. Cell Sci. 124, 3381 (2011).

    Google Scholar 

  3. 3.

    Pankow, S., Martínez-Bartolomé, S., Bamberger, C. & Yates, J. R. Understanding molecular mechanisms of disease through spatial proteomics. Curr. Opin. Chem. Biol. 48, 19–25 (2019).

    Google Scholar 

  4. 4.

    Siljee, J. E. et al. Subcellular localization of MC4R with ADCY3 at neuronal primary cilia underlies a common pathway for genetic predisposition to obesity. Nat. Genet. 50, 180–185 (2018).

    Google Scholar 

  5. 5.

    Neel, D. S. et al. Differential subcellular localization regulates oncogenic signaling by ROS1 kinase fusion proteins. Cancer Res. 79, 546 (2019).

    Google Scholar 

  6. 6.

    Hübner, S., Eam, J. E., Hübner, A. & Jans, D. A. Laminopathy-inducing lamin A mutants can induce redistribution of lamin binding proteins into nuclear aggregates. Exp. Cell Res. 312, 171–183 (2006).

    Google Scholar 

  7. 7.

    Valastyan, J. S. & Lindquist, S. Mechanisms of protein-folding diseases at a glance. Dis. Model. Mech. 7, 9 (2014).

    Google Scholar 

  8. 8.

    Shin, S. J. et al. Unexpected gain of function for the scaffolding protein plectin due to mislocalization in pancreatic cancer. Proc. Natl Acad. Sci. USA 110, 19414–19419 (2013).

    ADS  Google Scholar 

  9. 9.

    Thelen, M. P. & Kye, M. J. The role of RNA binding proteins for local mRNA translation: implications in neurological disorders. Front. Mol. Biosci. https://doi.org/10.3389/fmolb.2019.00161 (2020).

    Article  Google Scholar 

  10. 10.

    Christoforou, A. et al. A draft map of the mouse pluripotent stem cell spatial proteome. Nat. Commun. 7, 1–12 (2016).

    Google Scholar 

  11. 11.

    Thul, P. J. et al. A subcellular map of the human proteome. Science https://doi.org/10.1126/science.aal3321 (2017). This ambitious work performs immunofluorescence and confocal microscopy to systematically assess the subcellular localization of more than 12,000 human proteins in several human cell lines, published in the HPA database.

    Article  Google Scholar 

  12. 12.

    Sullivan, D. P. et al. Deep learning is combined with massive-scale citizen science to improve large-scale image classification. Nat. Biotechnol. 36, 820–828 (2018).

    Google Scholar 

  13. 13.

    Hall, S. L., Hester, S., Griffin, J. L., Lilley, K. S. & Jackson, A. P. The organelle proteome of the DT40 lymphocyte cell line. Mol. Cell Proteom. 8, 1295–1305 (2009).

    Google Scholar 

  14. 14.

    Itzhak, D. N. et al. A mass spectrometry-based approach for mapping protein subcellular localization reveals the spatial proteome of mouse primary neurons. Cell Rep. 20, 2706–2718 (2017).

    Google Scholar 

  15. 15.

    Nightingale, D. J., Geladaki, A., Breckels, L. M., Oliver, S. G. & Lilley, K. S. The subcellular organisation of Saccharomyces cerevisiae. Curr. Opin. Chem. Biol. 48, 86–95 (2019).

    Google Scholar 

  16. 16.

    Tan, D. J. L. et al. Mapping organelle proteins and protein complexes in Drosophila melanogaster. J. Proteome Res. 8, 2667–2678 (2009).

    Google Scholar 

  17. 17.

    Barylyuk, K. et al. A subcellular atlas of Toxoplasma reveals the functional context of the proteome. Cell Host Microbe 28, 752–766.e9 (2020).

    Google Scholar 

  18. 18.

    Baers, L. L. et al. Proteome mapping of a cyanobacterium reveals distinct compartment organization and cell-dispersed metabolism. Plant. Physiol. 181, 1721–1738 (2019).

    ADS  Google Scholar 

  19. 19.

    Jeffery, C. J. Protein moonlighting: what is it, and why is it important? Philos. Trans. R. Soc. B: Biol. Sci. 373, 20160523 (2018).

    Google Scholar 

  20. 20.

    Gancedo, C., Flores, C.-L. & Gancedo, J. M. The expanding landscape of moonlighting proteins in yeasts. Microbiol. Mol. Biol. Rev. 80, 765 (2016).

    Google Scholar 

  21. 21.

    Lundberg, E. & Borner, G. H. H. Spatial proteomics: a powerful discovery tool for cell biology. Nat. Rev. Mol. Cell Biol. 20, 285–302 (2019).

    Google Scholar 

  22. 22.

    Pasquali, C., Fialka, I. & Huber, L. A. Subcellular fractionation, electromigration analysis and mapping of organelles. J. Chromatogr. B Biomed. Sci. Appl. 722, 89–102 (1999).

    Google Scholar 

  23. 23.

    Parsons, H. T. Preparation of highly enriched ER membranes using free-flow electrophoresis. Methods Mol. Biol. 1691, 103–115 (2018).

    Google Scholar 

  24. 24.

    Moon, M. H. Flow field-flow fractionation: recent applications for lipidomic and proteomic analysis. TrAC 118, 19–28 (2019).

    Google Scholar 

  25. 25.

    Oeyen, E. et al. Ultrafiltration and size exclusion chromatography combined with asymmetrical-flow field-flow fractionation for the isolation and characterisation of extracellular vesicles from urine. J. Extracell. Vesicles 7, 1490143 (2018).

    Google Scholar 

  26. 26.

    Chen, W. W., Freinkman, E. & Sabatini, D. M. Rapid immunopurification of mitochondria for metabolite profiling and absolute quantification of matrix metabolites. Nat. Protoc. 12, 2215–2231 (2017).

    Google Scholar 

  27. 27.

    Xiong, J. et al. Rapid affinity purification of intracellular organelles using twin strep tag. J. Cell Sci. 132, jcs235390 (2019).

    Google Scholar 

  28. 28.

    Ito, Y., Grison, M., Esnay, N., Fouillen, L. & Boutté, Y. in Plant Endosomes: Methods and Protocols (ed Otegui, M. S.) 119-141 (Springer, 2020).

  29. 29.

    Morgenstern, M. et al. Definition of a high-confidence mitochondrial proteome at quantitative scale. Cell Rep. 19, 2836–2852 (2017).

    Google Scholar 

  30. 30.

    Andersen, J. S. et al. Proteomic characterization of the human centrosome by protein correlation profiling. Nature 426, 570–574 (2003). This article presents the first protein correlation profiling experiment, which coupled de Duve’s principle with MS to characterize the human centrosome.

    ADS  Google Scholar 

  31. 31.

    Bouchnak, I., Brugire, S. & Moyet, L. A. Unraveling hidden components of the chloroplast envelope proteome: opportunities and limits of better MS sensitivity. Mol. Cell. Proteomics 18, 1285–1306 (2019).

    Google Scholar 

  32. 32.

    Chapel, A., Kieffer-Jaquinod, S. & Sagn. An extended proteome map of the lysosomal membrane reveals novel potential transporters. Mol. Cell. Proteomics 12, 1572–1588 (2013).

    Google Scholar 

  33. 33.

    Dengjel, J. et al. Identification of autophagosome-associated proteins and regulators by quantitative proteomic analysis and genetic screens. Mol. Cell. Proteomics https://doi.org/10.1074/mcp.M111.014035 (2012).

    Article  Google Scholar 

  34. 34.

    Krahmer, N. et al. Protein correlation profiles identify lipid droplet proteins with high confidence. Mol. Cell. Proteom. 12, 1115–1126 (2013).

    Google Scholar 

  35. 35.

    Niemann, M. et al. Mitochondrial outer membrane proteome of Trypanosoma brucei reveals novel factors required to maintain mitochondrial morphology. Mol. Cell. Proteomics 12, 515–528 (2013).

    Google Scholar 

  36. 36.

    Pagliarini, D. J. et al. A mitochondrial protein compendium elucidates complex I disease biology. Cell 134, 112–123 (2008).

    Google Scholar 

  37. 37.

    Tang, Y., Huang, A. & Gu, Y. Global profiling of plant nuclear membrane proteome in Arabidopsis. Nat. Plants 6, 838–847 (2020).

    Google Scholar 

  38. 38.

    Wiese, S. et al. Proteomics characterization of mouse kidney peroxisomes by tandem mass spectrometry and protein correlation profiling. Mol. Cell. Proteom. 6, 2045–2057 (2007).

    Google Scholar 

  39. 39.

    Schirmer, E. C., Florens, L., Guan, T., Yates, J. R. & Gerace, L. Nuclear membrane proteins with potential disease links found by subtractive proteomics. Science 301, 1380–1382 (2003).

    ADS  Google Scholar 

  40. 40.

    Foster, L. J. et al. A mammalian organelle map by protein correlation profiling. Cell 125, 187–199 (2006).

    Google Scholar 

  41. 41.

    Dunkley, T. P. J., Watson, R., Griffin, J. L., Dupree, P. & Lilley, K. S. Localization of organelle proteins by isotope tagging (LOPIT). Mol. Cell. Proteom. 3, 1128–1134 (2004). This article is the first published LOPIT experiment and multi-organellar mapping of protein endoplasmic reticulum and Golgi proteins in Arabidopsis using MS-based proteomics.

    Google Scholar 

  42. 42.

    Dunkley, T. P. J. et al. Mapping the Arabidopsis organelle proteome. Proc. Natl Acad. Sci. USA 103, 6518–6523 (2006).

    ADS  Google Scholar 

  43. 43.

    Geladaki, A. et al. Combining LOPIT with differential ultracentrifugation for high-resolution spatial proteomics. Nat. Commun. 10, 331 (2019).

    ADS  Google Scholar 

  44. 44.

    Itzhak, D. N., Tyanova, S., Cox, J. & Borner, G. H. H. Global, quantitative and dynamic mapping of protein subcellular localization. eLife 5, e16950 (2016).

    Google Scholar 

  45. 45.

    Jadot, M. et al. Accounting for protein subcellular localization: a compartmental map of the rat liver proteome. Mol. Cell. Proteom. 16, 194–212 (2017).

    Google Scholar 

  46. 46.

    Mardakheh, F. K. et al. Proteomics profiling of interactome dynamics by colocalisation analysis (COLA). Mol. Biosyst. 13, 92–105 (2016).

    Google Scholar 

  47. 47.

    Orre, L. M. et al. SubCellBarCode: proteome-wide mapping of protein localization and relocalization. Mol. Cell 73, 166–182 (2019).

    Google Scholar 

  48. 48.

    Nikolovski, N. et al. Putative glycosyltransferases and other plant Golgi apparatus proteins are revealed by LOPIT proteomics. Plant. Physiol. 160, 1037–1051 (2012).

    Google Scholar 

  49. 49.

    Tardif, M. et al. PredAlgo: a new subcellular localization prediction tool dedicated to green algae. Mol. Biol. Evol. 29, 3625–3639 (2012).

    Google Scholar 

  50. 50.

    Ohta, S. et al. The protein composition of mitotic chromosomes determined using multiclassifier combinatorial proteomics. Cell 142, 810–821 (2010).

    Google Scholar 

  51. 51.

    Groen, A. J. et al. Identification of trans-Golgi network proteins in Arabidopsis thaliana root tissue. J. Proteome Res. 13, 763–776 (2014).

    Google Scholar 

  52. 52.

    Crook, O. M., Mulvey, C. M., Kirk, P. D. W., Lilley, K. S. & Gatto, L. A Bayesian mixture modelling approach for spatial proteomics. PLoS Comput. Biol. 14, e1006516 (2018).

    ADS  Google Scholar 

  53. 53.

    Jean Beltran, P. M., Mathias, R. A. & Cristea, I. M. A portrait of the human organelle proteome in space and time during cytomegalovirus infection. Cell Syst. 3, 361–373 (2016).

    Google Scholar 

  54. 54.

    Kennedy, M. A., Hofstadter, W. A. & Cristea, I. M. TRANSPIRE: a computational pipeline to elucidate intracellular protein movements from spatial proteomics data sets. J. Am. Soc. Mass. Spectrom. 31, 1422–1439 (2020).

    Google Scholar 

  55. 55.

    Shin, J. J. H. et al. Spatial proteomics defines the content of trafficking vesicles captured by golgin tethers. Nat. Commun. 11, 5987 (2020). This article presents a general strategy for analysing intracellular sub-proteomes by combining acute cellular rewiring with high-resolution spatial proteomics.

    Google Scholar 

  56. 56.

    Jean Beltran, P. M., Cook, K. C. & Cristea, I. M. Exploring and exploiting proteome organization during viral infection. J. Virol. 91, e00268-17 (2017).

    Google Scholar 

  57. 57.

    de Duve, C., Pressman, B. C., Gianetto, R., Wattiaux, R. & Appelmans, F. Tissue fractionation studies. 6. Intracellular distribution patterns of enzymes in rat-liver tissue. Biochem. J. 60, 604–617 (1955). This study forms the basis for most biochemical fractionation strategies and demonstrates the importance of capturing quantitative data, as opposed to achieving ultra-pure organellar samples.

    Google Scholar 

  58. 58.

    Shehadul Islam, M., Aryasomayajula, A. & Selvaganapathy, P. R. A review on macroscale and microscale cell lysis methods. Micromachines 8, 83 (2017).

    Google Scholar 

  59. 59.

    Drissi, R., Dubois, M.-L. & Boisvert, F.-M. Proteomics methods for subcellular proteome analysis. FEBS J. 280, 5626–5634 (2013).

    Google Scholar 

  60. 60.

    Rhee, H. W. et al. Proteomic mapping of mitochondria in living cells via spatially restricted enzymatic tagging. Science 339, 1328–1331 (2013). This article is the first example of combining APEX with MS, capturing spatial and temporal information for the human mitochondria matrix proteome, including 31 proteins not previously associated with this compartment.

    ADS  Google Scholar 

  61. 61.

    Lam, S. S. et al. Directed evolution of APEX2 for electron microscopy and proximity labeling. Nat. Methods 12, 51–54 (2015).

    ADS  Google Scholar 

  62. 62.

    Kim, D. I. et al. Probing nuclear pore complex architecture with proximity-dependent biotinylation. Proc. Natl Acad. Sci. USA 111, 2453–2461 (2014).

    Google Scholar 

  63. 63.

    Roux, K. J., Kim, D. I., Raida, M. & Burke, B. A promiscuous biotin ligase fusion protein identifies proximal and interacting proteins in mammalian cells. J. Cell Biol. 196, 801–810 (2012). This article is the first description of BioID technology, identifying known and new components of the nuclear envelope using the well-characterized nuclear filament protein lamin A.

    Google Scholar 

  64. 64.

    Branon, T. C. et al. Efficient proximity labeling in living cells and organisms with TurboID. Nat. Biotechnol. 36, 880–887 (2018).

    Google Scholar 

  65. 65.

    Kim, D. I. et al. An improved smaller biotin ligase for BioID proximity labeling. Mol. Biol. Cell 27, 1188–1196 (2016).

    Google Scholar 

  66. 66.

    Ramanathan, M. et al. RNA–protein interaction detection in living cells. Nat. Methods 15, 207–212 (2018).

    Google Scholar 

  67. 67.

    Hung, V. et al. Proteomic mapping of the human mitochondrial intermembrane space in live cells via ratiometric APEX tagging. Mol. Cell 55, 332–341 (2014).

    Google Scholar 

  68. 68.

    Gupta, G. D. et al. A dynamic protein interaction landscape of the human centrosome–cilium interface. Cell 163, 1484–1499 (2015).

    Google Scholar 

  69. 69.

    Youn, J. Y. et al. High-density proximity mapping reveals the subcellular organization of mRNA-associated granules and bodies. Mol. Cell 69, 517–532 (2018). This article is an extensive BioID study using 119 baits to conduct prey–prey analysis of the proteomes of stress granules and processing bodies to investigate mRNA biology.

    Google Scholar 

  70. 70.

    Antonicka, H. et al. A high-density human mitochondrial proximity interaction network. Cell Metab. 32, 479–497 (2020).

    Google Scholar 

  71. 71.

    Gingras, A. C., Abe, K. T. & Raught, B. Getting to know the neighborhood: using proximity-dependent biotinylation to characterize protein complexes and map organelles. Curr. Opin. Chem. Biol. 48, 44–54 (2019).

    Google Scholar 

  72. 72.

    Qin, W., Cho, K. F., Cavanagh, P. E. & Ting, A. Y. Deciphering molecular interactions by proximity labeling. Nat. Methods 18, 133–143 (2021).

    Google Scholar 

  73. 73.

    Weston, L. A., Bauer, K. M. & Hummon, A. B. Comparison of bottom-up proteomic approaches for LC-MS analysis of complex proteomes. Anal. Methods 5, 4615–4621 (2013).

    Google Scholar 

  74. 74.

    Lambert, J.-P. et al. Interactome rewiring following pharmacological targeting of BET bromodomains. Mol. Cell 73, 621–638 (2019).

    Google Scholar 

  75. 75.

    Ludwig, C. et al. Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial. Mol. Syst. Biol. 14, e8126 (2018).

    Google Scholar 

  76. 76.

    Ong, S.-E. et al. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteom. 1, 376–386 (2002).

    Google Scholar 

  77. 77.

    Rauniyar, N. & Yates, J. R. Isobaric labeling-based relative quantification in shotgun proteomics. J. Proteome Res. 13, 5293–5309 (2014).

    Google Scholar 

  78. 78.

    Boersema, P. J., Raijmakers, R., Lemeer, S., Mohammed, S. & Heck, A. J. R. Multiplex peptide stable isotope dimethyl labeling for quantitative proteomics. Nat. Protoc. 4, 484–494 (2009).

    Google Scholar 

  79. 79.

    Ross, P. L. et al. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol. Cell. Proteom. 3, 1154–1169 (2004).

    Google Scholar 

  80. 80.

    Ankney, J. A., Muneer, A. & Chen, X. Relative and absolute quantitation in mass spectrometry–based proteomics. Annu. Rev. Anal. Chem. 11, 49–77 (2018).

    Google Scholar 

  81. 81.

    Fernández-Costa, C. et al. Impact of the identification strategy on the reproducibility of the DDA and DIA results. J. Proteome Res. 19, 3153–3161 (2020).

    Google Scholar 

  82. 82.

    Merrill, A. E. et al. NeuCode labels for relative protein quantification. Mol. Cell. Proteom. 13, 2503–2512 (2014).

    Google Scholar 

  83. 83.

    Erickson, B. K. et al. Evaluating multiplexed quantitative phosphopeptide analysis on a hybrid quadrupole mass filter/linear ion trap/orbitrap mass spectrometer. Anal. Chem. 87, 1241–1249 (2015).

    Google Scholar 

  84. 84.

    Altelaar, A. F. et al. Benchmarking stable isotope labeling based quantitative proteomics. J. Proteom. 88, 14–26 (2013).

    Google Scholar 

  85. 85.

    Thompson, A. et al. TMTpro: design, synthesis, and initial evaluation of a proline-based isobaric 16-plex tandem mass tag reagent set. Anal. Chem. 91, 15941–15950 (2019).

    Google Scholar 

  86. 86.

    McAlister, G. C. et al. MultiNotch MS3 enables accurate, sensitive, and multiplexed detection of differential expression across cancer cell line proteomes. Anal. Chem. 86, 7150–7158 (2014).

    Google Scholar 

  87. 87.

    Wang, Y. et al. Reversed-phase chromatography with multiple fraction concatenation strategy for proteome profiling of human MCF10A cells. Proteomics 11, 2019–2026 (2011).

    Google Scholar 

  88. 88.

    Gatto, L., Breckels, L. M. & Lilley, K. S. Assessing sub-cellular resolution in spatial proteomics experiments. Curr. Opin. Chem. Biol. 48, 123–149 (2019).

    Google Scholar 

  89. 89.

    Krahmer, N. et al. Organellar proteomics and phospho-proteomics reveal subcellular reorganization in diet-induced hepatic steatosis. Dev. Cell 47, 205–221 (2018).

    Google Scholar 

  90. 90.

    O’Rourke, M. B. et al. What is normalization? The strategies employed in top-down and bottom-up proteome analysis workflows. Proteomes 7, 29 (2019).

    Google Scholar 

  91. 91.

    Stertz, S. & Shaw, M. L. Uncovering the global host cell requirements for influenza virus replication via RNAi screening. Microbes Infect. 13, 516–525 (2011).

    Google Scholar 

  92. 92.

    de Groot, R., Lüthi, J., Lindsay, H., Holtackers, R. & Pelkmans, L. Large-scale image-based profiling of single-cell phenotypes in arrayed CRISPR–Cas9 gene perturbation screens. Mol. Syst. Biol. 14, e8064 (2018).

    Google Scholar 

  93. 93.

    Marx, V. Calling the next generation of affinity reagents. Nat. Methods 10, 829–833 (2013).

    Google Scholar 

  94. 94.

    Tiede, C. et al. Affimer proteins are versatile and renewable affinity reagents. eLife 6, e24903 (2017).

    Google Scholar 

  95. 95.

    Alamudi, S. H. & Chang, Y.-T. Advances in the design of cell-permeable fluorescent probes for applications in live cell imaging. Chem. Commun. 54, 13641–13653 (2018).

    Google Scholar 

  96. 96.

    Chazotte, B. Labeling mitochondria with mitotracker dyes. Cold Spring Harb. Protoc. 2011, 990–992 (2011).

    Google Scholar 

  97. 97.

    Giepmans, B. N., Adams, S. R., Ellisman, M. H. & Tsien, R. Y. The fluorescent toolbox for assessing protein location and function. Science 312, 217–224 (2006).

    ADS  Google Scholar 

  98. 98.

    Neumann, B. et al. Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes. Nature 464, 721–727 (2010). This study uses time-lapse microscopy and genome-wide small interfering RNA silencing of green fluorescent protein tagged cell lines to identify 592 essential genes for mitosis; the majority had previously not been annotated with cellular processes consistent with a function in mitosis.

    ADS  Google Scholar 

  99. 99.

    Leonetti, M. D., Sekine, S., Kamiyama, D., Weissman, J. S. & Huang, B. A scalable strategy for high-throughput GFP tagging of endogenous human proteins. Proc. Natl Acad. Sci. USA 113, E3501–E3508 (2016).

    Google Scholar 

  100. 100.

    Sarov, M. et al. A genome-scale resource for in vivo tag-based protein function exploration in C. elegans. Cell 150, 855–866 (2012).

    Google Scholar 

  101. 101.

    Chong, Y. T. et al. Yeast proteome dynamics from single cell imaging and automated analysis. Cell 161, 1413–1424 (2015).

    Google Scholar 

  102. 102.

    Stadler, C. et al. Immunofluorescence and fluorescent-protein tagging show high correlation for protein localization in mammalian cells. Nat. Methods 10, 315–323 (2013).

    Google Scholar 

  103. 103.

    Cheng, R. et al. Influence of fixation and permeabilization on the mass density of single cells: a surface plasmon resonance imaging study. Front. Chem. 7, 58 (2019).

    ADS  Google Scholar 

  104. 104.

    Amidzadeh, Z. et al. Assessment of different permeabilization methods of minimizing damage to the adherent cells for detection of intracellular RNA by flow cytometry. Avicenna J. Med. Biotechnol. 6, 38–46 (2014).

    Google Scholar 

  105. 105.

    Jamur, M. C. & Oliver, C. in Immunocytochemical Methods and Protocols (eds Oliver, C. & Jamur, M. C.) 63-66 (Humana, 2010).

  106. 106.

    Hobro, A. J. & Smith, N. I. An evaluation of fixation methods: spatial and compositional cellular changes observed by Raman imaging. Vib. Spectrosc. 91, 31–45 (2017).

    Google Scholar 

  107. 107.

    Stadler, C., Skogs, M., Brismar, H., Uhlen, M. & Lundberg, E. A single fixation protocol for proteome-wide immunofluorescence localization studies. J. Proteom. 73, 1067–1078 (2010).

    Google Scholar 

  108. 108.

    Nakagawa, T. et al. Optimum immunohistochemical procedures for analysis of macrophages in human and mouse formalin fixed paraffin-embedded tissue samples. J. Clin. Exp. Hematop. 57, 31–36 (2017).

    Google Scholar 

  109. 109.

    Syrbu, S. I. & Cohen, M. B. An enhanced antigen-retrieval protocol for immunohistochemical staining of formalin-fixed, paraffin-embedded tissues. Methods Mol. Biol. 717, 101–110 (2011).

    Google Scholar 

  110. 110.

    Cohen, M., Varki, N. M., Jankowski, M. D. & Gagneux, P. Using unfixed, frozen tissues to study natural mucin distribution. J. Vis. Exp. https://doi.org/10.3791/3928 (2012).

    Article  Google Scholar 

  111. 111.

    Scheffler, J. M., Schiefermeier, N. & Huber, L. A. Mild fixation and permeabilization protocol for preserving structures of endosomes, focal adhesions, and actin filaments during immunofluorescence analysis. Methods Enzymol. 535, 93–102 (2014).

    Google Scholar 

  112. 112.

    Micke, P. et al. Biobanking of fresh frozen tissue: RNA is stable in nonfixed surgical specimens. Lab. Invest. 86, 202–211 (2006).

    Google Scholar 

  113. 113.

    Magdeldin, S. & Yamamoto, T. Toward deciphering proteomes of formalin-fixed paraffin-embedded (FFPE) tissues. Proteomics 12, 1045–1058 (2012).

    Google Scholar 

  114. 114.

    Robertson, D., Savage, K., Reis-Filho, J. S. & Isacke, C. M. Multiple immunofluorescence labelling of formalin-fixed paraffin-embedded (FFPE) tissue. BMC Cell Biol. 9, 13 (2008).

    Google Scholar 

  115. 115.

    Pan, J., Thoeni, C., Muise, A., Yeger, H. & Cutz, E. Multilabel immunofluorescence and antigen reprobing on formalin-fixed paraffin-embedded sections: novel applications for precision pathology diagnosis. Mod. Pathol. 29, 557–569 (2016).

    Google Scholar 

  116. 116.

    Goltsev, Y. et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174, 968–981 (2018).

    Google Scholar 

  117. 117.

    Lin, J. R., Fallahi-Sichani, M. & Sorger, P. K. Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method. Nat. Commun. 6, 8390 (2015).

    ADS  Google Scholar 

  118. 118.

    Gut, G., Herrmann, M. D. & Pelkmans, L. Multiplexed protein maps link subcellular organization to cellular states. Science 361, eaar7042 (2018). This work describes a protocol that achieves 40-plex protein staining in the same biological sample using off-the-shelf antibodies for immunofluorescence in an iterative manner.

    Google Scholar 

  119. 119.

    Swinney, D. C. & Anthony, J. How were new medicines discovered? Nat. Rev. Drug Discov. 10, 507–519 (2011).

    Google Scholar 

  120. 120.

    Rathbun, L. I. et al. Cytokinetic bridge triggers de novo lumen formation in vivo. Nat. Commun. 11, 1269 (2020).

    ADS  Google Scholar 

  121. 121.

    Huang, B., Babcock, H. & Zhuang, X. Breaking the diffraction barrier: super-resolution imaging of cells. Cell 143, 1047–1058 (2010).

    Google Scholar 

  122. 122.

    Hell, S. W. & Wichmann, J. Breaking the diffraction resolution limit by stimulated emission: stimulated-emission-depletion fluorescence microscopy. Opt. Lett. 19, 780–782 (1994).

    ADS  Google Scholar 

  123. 123.

    Betzig, E. et al. Imaging intracellular fluorescent proteins at nanometer resolution. Science 313, 1642–1645 (2006).

    ADS  Google Scholar 

  124. 124.

    Rust, M. J., Bates, M. & Zhuang, X. Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM). Nat. Methods 3, 793–795 (2006).

    Google Scholar 

  125. 125.

    Cox, J. et al. Andromeda: a peptide search engine integrated into the MaxQuant environment. J. Proteome Res. 10, 1794–1805 (2011).

    ADS  Google Scholar 

  126. 126.

    Eng, J. K., McCormack, A. L. & Yates, J. R. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass. Spectrom. 5, 976–989 (1994).

    Google Scholar 

  127. 127.

    Perkins, D. N., Pappin, D. J. C., Creasy, D. M. & Cottrell, J. S. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20, 3551–3567 (1999).

    Google Scholar 

  128. 128.

    Fenyö, D. & Beavis, R. C. A method for assessing the statistical significance of mass spectrometry-based protein identifications using general scoring schemes. Anal. Chem. 75, 768–774 (2003).

    Google Scholar 

  129. 129.

    Moore, R. E., Young, M. K. & Lee, T. D. Qscore: an algorithm for evaluating SEQUEST database search results. J. Am. Soc. Mass. Spectrom. 13, 378–386 (2002).

    Google Scholar 

  130. 130.

    Colinge, J., Masselot, A., Giron, M., Dessingy, T. & Magnin, J. OLAV: towards high-throughput tandem mass spectrometry data identification. Proteomics 3, 1454–1463 (2003).

    Google Scholar 

  131. 131.

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Society B 57, 289–300 (1995).

    MathSciNet  MATH  Google Scholar 

  132. 132.

    Tyanova, S., Temu, T. & Cox, J. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat. Protoc. 11, 2301–2319 (2016).

    Google Scholar 

  133. 133.

    Röst, H. L. et al. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat. Biotechnol. 32, 219–223 (2014).

    Google Scholar 

  134. 134.

    MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010).

    Google Scholar 

  135. 135.

    Demichev, V., Messner, C. B., Vernardis, S. I., Lilley, K. S. & Ralser, M. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat. Methods 17, 41–44 (2020).

    Google Scholar 

  136. 136.

    Zhang, F., Ge, W., Ruan, G., Cai, X. & Guo, T. Data-independent acquisition mass spectrometry-based proteomics and software tools: a glimpse in 2020. Proteomics 20, 1900276 (2020).

    Google Scholar 

  137. 137.

    Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731–740 (2016).

    Google Scholar 

  138. 138.

    Malmström, L. Computational proteomics with Jupyter and Python. Methods Mol. Biol. 1977, 237–248 (2019).

    Google Scholar 

  139. 139.

    Levitsky, L. I., Klein, J. A., Ivanov, M. V. & Gorshkov, M. V. Pyteomics 4.0: five years of development of a python proteomics framework. J. Proteome Res. 18, 709–714 (2019).

    Google Scholar 

  140. 140.

    Mendik, P. et al. Translocatome: a novel resource for the analysis of protein translocation between cellular organelles. Nucleic Acids Res. 47, D495–D505 (2018).

    Google Scholar 

  141. 141.

    Ashburner, M. et al. Gene ontology: tool for the unification of biology. the gene ontology consortium. Nat. Genet. 25, 25–29 (2000).

    Google Scholar 

  142. 142.

    Chibucos, M. C., Siegele, D. A., Hu, J. C. & Giglio, M. The Evidence and Conclusion Ontology (ECO): supporting GO annotations. Methods Mol. Biol. 1446, 245–259 (2017).

    Google Scholar 

  143. 143.

    Binder, J. X. et al. COMPARTMENTS: unification and visualization of protein subcellular localization evidence. Database https://doi.org/10.1093/database/bau012 (2014).

    Article  Google Scholar 

  144. 144.

    UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res. 47, D506–D515 (2018).

  145. 145.

    Borner, G. H. H. Organellar maps through proteomic profiling — a conceptual guide. Mol. Cell. Proteom. 19, 1076–1087 (2020).

    Google Scholar 

  146. 146.

    Gatto, L. et al. A foundation for reliable spatial proteomics data analysis. Mol. Cell. Proteom. 13, 1937–1952 (2014).

    Google Scholar 

  147. 147.

    Gatto, L., Breckels, L. M., Naake, T. & Gibb, S. Visualization of proteomics data using R and Bioconductor. Proteomics 15, 1375–1389 (2015).

    Google Scholar 

  148. 148.

    Crook, O. M., Smith, T., Elzek, M. & Lilley, K. S. Moving profiling spatial proteomics beyond discrete classification. Proteomics 20, 1900392 (2020).

    Google Scholar 

  149. 149.

    Crook, O. M. et al. A semi-supervised Bayesian approach for simultaneous protein sub-cellular localisation assignment and novelty detection. PLoS Comput. Biol. 16, e1008288 (2020).

    Google Scholar 

  150. 150.

    Swan, A. L., Mobasheri, A., Allaway, D., Liddell, S. & Bacardit, J. Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology. OMICS 17, 595–610 (2013).

    Google Scholar 

  151. 151.

    MacQueen, J. in Proc. Fifth Berkeley Symp. Math. Stat. Prob., Vol. 1: Statistics 281-297 (Univ. of California Press, 1967).

  152. 152.

    Ester, M., Kriegel, H. P., Sander, J. & Xu, X. A density-based algorithm for discovering clusters in large spatial databases with noise. KDD 96, 226–231 (1996).

    Google Scholar 

  153. 153.

    Davies, A. K. et al. AP-4 vesicles contribute to spatial control of autophagy via RUSC-dependent peripheral delivery of ATG9A. Nat. Commun. 9, 3958 (2018).

    ADS  Google Scholar 

  154. 154.

    Hirst, J., Itzhak, D. N., Antrobus, R., Borner, G. H. H. & Robinson, M. S. Role of the AP-5 adaptor protein complex in late endosome-to-Golgi retrieval. PLoS Biol. 16, e2004411 (2018).

    Google Scholar 

  155. 155.

    Peikert, C. D. et al. Charting organellar importomes by quantitative mass spectrometry. Nat. Commun. 8, 1–14 (2017).

    Google Scholar 

  156. 156.

    Crook, O. M., Breckels, L. M., Lilley, K. S., Kirk, P. D. W. & Gatto, L. A Bioconductor workflow for the Bayesian analysis of spatial proteomics. F1000Research 8, 446 (2019).

    Google Scholar 

  157. 157.

    Crook, O. M., Davies, C. T. R., Gatto, L., Kirk, P. D. W. & Lilley, K. S. Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE. Preprint at https://www.biorxiv.org/content/10.1101/2021.01.04.425239v2 (2021).

  158. 158.

    Choi, H. et al. SAINT: probabilistic scoring of affinity purification-mass spectrometry data. Nat. Methods 8, 70–73 (2011).

    Google Scholar 

  159. 159.

    Hesketh, G. G. et al. The GATOR–Rag GTPase pathway inhibits mTORC1 activation by lysosome-derived amino acids. Science 370, 351–356 (2020).

    ADS  Google Scholar 

  160. 160.

    Go, C. D. et al. A proximity biotinylation map of a human cell. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/796391v1 (2019).

  161. 161.

    Knight, J. D. R. et al. ProHits-viz: a suite of web tools for visualizing interaction proteomics data. Nat. Methods 14, 645–646 (2017).

    Google Scholar 

  162. 162.

    Omasits, U., Ahrens, C. H., Müller, S. & Wollscheid, B. Protter: interactive protein feature visualization and integration with experimental proteomic data. Bioinformatics 30, 884–886 (2014).

    Google Scholar 

  163. 163.

    Maarten, L. V. D. & Hinton, G. E. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).

    MATH  Google Scholar 

  164. 164.

    Burry, R. W. Controls for immunocytochemistry: an update. J. Histochem. Cytochem. 59, 6–12 (2011).

    Google Scholar 

  165. 165.

    Uhlen, M. et al. A proposal for validation of antibodies. Nat. Methods 13, 823–827 (2016).

    Google Scholar 

  166. 166.

    Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).

    Google Scholar 

  167. 167.

    Lamprecht, M. R., Sabatini, D. M. & Carpenter, A. E. CellProfiler™: free, versatile software for automated biological image analysis. BioTechniques 42, 71–75 (2007).

    Google Scholar 

  168. 168.

    Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).

    ADS  Google Scholar 

  169. 169.

    Sommer, C., Straehle, C., Köthe, U. & Hamprecht, F. A. in 2011 IEEE Int. Symp. Biomed. Imaging: From Nano to Macro https://doi.org/10.1109/ISBI.2011.5872394 (IEEE, 2011).

  170. 170.

    Goldberg, I. G. et al. The Open Microscopy Environment (OME) data model and XML file: open tools for informatics and quantitative analysis in biological imaging. Genome Biol. 6, R47 (2005).

    Google Scholar 

  171. 171.

    Sigal, A. et al. Variability and memory of protein levels in human cells. Nature 444, 643–646 (2006).

    ADS  Google Scholar 

  172. 172.

    Breker, M., Gymrek, M. & Schuldiner, M. A novel single-cell screening platform reveals proteome plasticity during yeast stress responses. J. Cell Biol. 200, 839–850 (2013).

    Google Scholar 

  173. 173.

    Lu, A. X. et al. Integrating images from multiple microscopy screens reveals diverse patterns of change in the subcellular localization of proteins. eLife 7, e31892 (2018).

    Google Scholar 

  174. 174.

    LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    ADS  Google Scholar 

  175. 175.

    Lundervold, A. S. & Lundervold, A. An overview of deep learning in medical imaging focusing on MRI. Z. für Medizinische Phys. 29, 102–127 (2019).

    Google Scholar 

  176. 176.

    Caicedo, J. C. et al. Data-analysis strategies for image-based cell profiling. Nat. Methods 14, 849–863 (2017).

    Google Scholar 

  177. 177.

    Coelho, L. P. et al. Determining the subcellular location of new proteins from microscope images using local features. Bioinformatics 29, 2343–2349 (2013).

    Google Scholar 

  178. 178.

    Li, J., Newberg, J. Y., Uhlen, M., Lundberg, E. & Murphy, R. F. Automated analysis and reannotation of subcellular locations in confocal images from the Human Protein Atlas. PLoS ONE 7, e50514 (2012).

    ADS  Google Scholar 

  179. 179.

    Li, J., Xiong, L., Schneider, J. & Murphy, R. F. Protein subcellular location pattern classification in cellular images using latent discriminative models. Bioinformatics 28, i32–i39 (2012).

    Google Scholar 

  180. 180.

    Ouyang, W. et al. Analysis of the Human Protein Atlas image classification competition. Nat. Methods 16, 1254–1261 (2019).

    Google Scholar 

  181. 181.

    Kraus, O. Z., Ba, J. L. & Frey, B. J. Classifying and segmenting microscopy images with deep multiple instance learning. Bioinformatics 32, i52–i59 (2016).

    Google Scholar 

  182. 182.

    Witten, I. H., Frank, E., Hall, M. A. & Pal, C. J. Data Mining: Practical Machine Learning Tools and Techniques 4th edn (Morgan Kaufmann, 2016).

  183. 183.

    Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    MathSciNet  MATH  Google Scholar 

  184. 184.

    Bagshaw, R. D., Mahuran, D. J. & Callahan, J. W. A proteomic analysis of lysosomal integral membrane proteins reveals the diverse composition of the organelle. Mol. Cell. Proteom. 4, 133–143 (2005).

    Google Scholar 

  185. 185.

    Kikuchi, M. et al. Proteomic analysis of rat liver peroxisome: presence of peroxisome-specific isozyme of Lon protease. J. Biol. Chem. 279, 421–428 (2004).

    Google Scholar 

  186. 186.

    Kleffmann, T. & Russenberger, D. A. The Arabidopsis thaliana chloroplast proteome reveals pathway abundance and novel protein functions. Curr. Biol. 14, 354–362 (2004).

    Google Scholar 

  187. 187.

    Sickmann, A. et al. The proteome of Saccharomyces cerevisiae mitochondria. Proc. Natl. Acad. Sci. USA 100, 13207–13212 (2003).

    ADS  Google Scholar 

  188. 188.

    Taylor, S. W. et al. Characterization of the human heart mitochondrial proteome. Nat. Biotechnol. 21, 281–286 (2003).

    Google Scholar 

  189. 189.

    Zhang, L. et al. Proteomic analysis of mouse liver plasma membrane: use of differential extraction to enrich hydrophobic membrane proteins. Proteomics 5, 4510–4524 (2005).

    Google Scholar 

  190. 190.

    van den Berg, B. H., Harris, T., McCarthy, F. M., Lamont, S. J. & Burgess, S. C. Non-electrophoretic differential detergent fractionation proteomics using frozen whole organs. RCM 21, 3905–3909 (2007).

    ADS  Google Scholar 

  191. 191.

    McCarthy, F. M., Burgess, S. C., van den Berg, B. H. J., Koter, M. D. & Pharr, G. T. Differential detergent fractionation for non-electrophoretic eukaryote cell proteomics. J. Proteome Res. 4, 316–324 (2005).

    Google Scholar 

  192. 192.

    Schiller, H. B. et al. Time- and compartment-resolved proteome profiling of the extracellular niche in lung injury and repair. Mol. Syst. Biol. 11, 819 (2015).

    Google Scholar 

  193. 193.

    Guther, M. L. S., Urbaniak, M. D., Tavendale, A., Prescott, A. & Ferguson, M. A. J. High-confidence glycosome proteome for procyclic form Trypanosoma brucei by epitope-tag organelle enrichment and SILAC proteomics. J. Proteome Res. 13, 2796–2806 (2014).

    Google Scholar 

  194. 194.

    Islinger, M., Lers, G. H., Li, K. W., Loos, M. & Vlkl, A. Rat liver peroxisomes after fibrate treatment. A survey using quantitative mass spectrometry. J. Biol. Chem. 282, 23055–23069 (2007).

    Google Scholar 

  195. 195.

    Marelli, M. et al. Quantitative mass spectrometry reveals a role for the GTPase Rho1p in actin organization on the peroxisome membrane. J. Cell Biol. 167, 1099–1112 (2004).

    Google Scholar 

  196. 196.

    Ray, G. J. et al. A PEROXO-tag enables rapid isolation of peroxisomes from human cells. iScience 23, 101109 (2020).

    ADS  Google Scholar 

  197. 197.

    Goebel, T. et al. Proteaphagy in mammalian cells can function independent of ATG5/ATG7. Mol. Cell. Proteom. 19, 1120–1131 (2020).

    Google Scholar 

  198. 198.

    Schmidtke, C., Tiede, S., Thelen, M. & Kkel Lysosomal proteome analysis reveals that CLN3-defective cells have multiple enzyme deficiencies associated with changes in intracellular trafficking. J. Biol. Chem. 294, 9592–9604 (2019).

    Google Scholar 

  199. 199.

    Becker, A. C. & Gannag Influenza a virus induces autophagosomal targeting of ribosomal proteins. Mol. Cell. Proteom. 17, 1909–1921 (2018).

    Google Scholar 

  200. 200.

    Borner, G. H. H. et al. Fractionation profiling: a fast and versatile approach for mapping vesicle proteomes and protein–protein interactions. Mol. Biol. Cell 25, 3178–3194 (2014).

    Google Scholar 

  201. 201.

    Gronemeyer, T. et al. The proteome of human liver peroxisomes: identification of five new peroxisomal constituents by a label-free quantitative proteomics survey. PLoS ONE 8, e57395 (2013).

    ADS  Google Scholar 

  202. 202.

    Wühr, M. et al. The nuclear proteome of a vertebrate. Curr. Biol. 25, 2663–2671 (2015).

    Google Scholar 

  203. 203.

    Kislinger, T. et al. Global survey of organ and organelle protein expression in mouse: combined proteomic and transcriptomic profiling. Cell 125, 173–186 (2006).

    Google Scholar 

  204. 204.

    Loh, K. H. et al. Proteomic analysis of unbounded cellular compartments: synaptic clefts. Cell 166, 1295–1307 (2016).

    Google Scholar 

  205. 205.

    Xie, W. et al. A-type lamins form distinct filamentous networks with differential nuclear pore complex associations. Curr. Biol. 26, 2651–2658 (2016).

    Google Scholar 

  206. 206.

    Dong, J. M. et al. Proximity biotinylation provides insight into the molecular composition of focal adhesions at the nanometer scale. Sci. Signal. 9, rs4 (2016).

    Google Scholar 

  207. 207.

    Guo, Z. et al. E-cadherin interactome complexity and robustness resolved by quantitative proteomics. Sci. Signal. 7, rs7 (2014).

    Google Scholar 

  208. 208.

    Markmiller, S. et al. Context-dependent and disease-specific diversity in protein interactions within stress granules. Cell 172, 590–604 (2018).

    Google Scholar 

  209. 209.

    Firat-Karalar, E. N., Rauniyar, N., Yates, J. R. III & Stearns, T. Proximity interactions among centrosome components identify regulators of centriole duplication. Curr. Biol. 24, 664–670 (2014).

    Google Scholar 

  210. 210.

    Liu, X. et al. An AP-MS- and BioID-compatible MAC-tag enables comprehensive mapping of protein interactions and subcellular localizations. Nat. Commun. 9, 1188 (2018).

    ADS  Google Scholar 

  211. 211.

    Bersuker, K. et al. A proximity labeling strategy provides insights into the composition and dynamics of lipid droplet proteomes. Dev. Cell 44, 97–112 (2018).

    Google Scholar 

  212. 212.

    Chastney, M. R. et al. Topological features of integrin adhesion complexes revealed by multiplexed proximity biotinylation. J. Cell Biol. 219, e202003038 (2020).

    Google Scholar 

  213. 213.

    Stenström, L. et al. Mapping the nucleolar proteome reveals a spatiotemporal organization related to intrinsic protein disorder. Mol. Syst. Biol. 16, e9469–e9469 (2020).

    Google Scholar 

  214. 214.

    Carcamo, W. C. et al. Induction of cytoplasmic rods and rings structures by inhibition of the CTP and GTP synthetic pathway in mammalian cells. PLoS ONE 6, e29690 (2011).

    ADS  Google Scholar 

  215. 215.

    Havelaar, A. H. et al. World Health Organization global estimates and regional comparisons of the burden of foodborne disease in 2010. PLoS Med. 12, e1001923 (2015).

    Google Scholar 

  216. 216.

    Mulvey, C. M. et al. Subcellular proteomics reveals a role for nucleo-cytoplasmic trafficking at the DNA replication origin activation checkpoint. J. Proteome Res. 12, 1436–1453 (2013).

    Google Scholar 

  217. 217.

    Branca, R. M. M. et al. HiRIEF LC-MS enables deep proteome coverage and unbiased proteogenomics. Nat. Methods 11, 59–62 (2014).

    Google Scholar 

  218. 218.

    Snijder, B. & Pelkmans, L. Origins of regulated cell-to-cell variability. Nat. Rev. Mol. Cell Biol. 12, 119–125 (2011).

    Google Scholar 

  219. 219.

    Dueck, H., Eberwine, J. & Kim, J. Variation is function: are single cell differences functionally important?: testing the hypothesis that single cell variation is required for aggregate function. BioEssays 38, 172–180 (2016).

    Google Scholar 

  220. 220.

    [No authors listed]. The global challenge of cancer. Nature Cancer 1, 1–2 (2020). This paper emphasizes the importance of understanding cell to cell heterogeneity to understand disease development, resistance to therapy and disease recurrence.

    Google Scholar 

  221. 221.

    Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

    Google Scholar 

  222. 222.

    Mahdessian, D. et al. Spatiotemporal dissection of the cell cycle with single-cell proteogenomics. Nature 590, 649–654 (2021). This article presents a comprehensive spatio-temporal map of proteomics heterogeneity integrating immunofluorescence imaging with single-cell transcriptomics and precise measurements of the cell cycle in individual cells.

    Google Scholar 

  223. 223.

    Nagao, Y., Sakamoto, M., Chinen, T., Okada, Y. & Takao, D. Robust classification of cell cycle phase and biological feature extraction by image-based deep learning. Mol. Biol. Cell 31, 1346–1354 (2020).

    Google Scholar 

  224. 224.

    Vögtle, F. N. et al. Landscape of submitochondrial protein distribution. Nat. Commun. 8, 290 (2017).

    ADS  Google Scholar 

  225. 225.

    Vögtle, F. N. et al. Intermembrane space proteome of yeast mitochondria. Mol. Cell. Proteom. 11, 1840–1852 (2012).

    Google Scholar 

  226. 226.

    Parsons, H. T. et al. Separating golgi proteins from cis to trans reveals underlying properties of cisternal localization. Plant. Cell 31, 2010–2034 (2019).

    Google Scholar 

  227. 227.

    Parsons, H. T. et al. Isolation and proteomic characterization of the Arabidopsis Golgi defines functional and novel components involved in plant cell wall biosynthesis. Plant. Physiol. 159, 12–26 (2012).

    Google Scholar 

  228. 228.

    Willms, E. et al. Cells release subpopulations of exosomes with distinct molecular and biological properties. Sci. Rep. 6, 22519 (2016).

    ADS  Google Scholar 

  229. 229.

    Bobrie, A., Colombo, M., Krumeich, S., Raposo, G. & Théry, C. Diverse subpopulations of vesicles secreted by different intracellular mechanisms are present in exosome preparations obtained by differential ultracentrifugation. J. Extracell. Vesicles 1, 18397 (2012).

    Google Scholar 

  230. 230.

    Anderson, J. D. et al. Comprehensive proteomic analysis of mesenchymal stem cell exosomes reveals modulation of angiogenesis via nuclear factor-κB signaling. Stem Cells 34, 601–613 (2016).

    Google Scholar 

  231. 231.

    Bandu, R., Oh, J. W. & Kim, K. P. Mass spectrometry-based proteome profiling of extracellular vesicles and their roles in cancer biology. Exp. Mol. Med. 51, 1–10 (2019).

    Google Scholar 

  232. 232.

    Rontogianni, S. et al. Proteomic profiling of extracellular vesicles allows for human breast cancer subtyping. Commun. Biol. 2, 325 (2019).

    Google Scholar 

  233. 233.

    Li, J., He, X., Deng, Y. & Yang, C. An update on isolation methods for proteomic studies of extracellular vesicles in biofluids. Molecules 24, 3516 (2019).

    Google Scholar 

  234. 234.

    Gomkale, R. et al. Defining the substrate spectrum of the TIM22 complex identifies pyruvate carrier subunits as unconventional cargos. Curr. Biol. 30, 1119–1127 (2020).

    Google Scholar 

  235. 235.

    Nguyen, D. et al. Proteomics reveals signal peptide features determining the client specificity in human TRAP-dependent ER protein import. Nat. Commun. 9, 3765 (2018).

    ADS  Google Scholar 

  236. 236.

    Kozik, P. et al. Small molecule enhancers of endosome-to-cytosol import augment anti-tumor immunity. Cell Rep. 32, 107905 (2020).

    Google Scholar 

  237. 237.

    Weekes, M. P. et al. Quantitative temporal viromics: an approach to investigate host–pathogen interaction. Cell 157, 1460–1472 (2014).

    Google Scholar 

  238. 238.

    Cook, K. C. & Cristea, I. M. Location is everything: protein translocations as a viral infection strategy. Curr. Opin. Chem. Biol. 48, 34–43 (2019).

    Google Scholar 

  239. 239.

    Jean Beltran, P. M. et al. Infection-induced peroxisome biogenesis is a metabolic strategy for herpesvirus replication. Cell Host Microbe 24, 526–541 (2018).

    Google Scholar 

  240. 240.

    Federspiel, J. D. et al. Mitochondria and peroxisome remodeling across cytomegalovirus infection time viewed through the lens of inter-ViSTA. Cell Rep. 32, 107943 (2020).

    Google Scholar 

  241. 241.

    Horner, S. M., Wilkins, C., Badil, S., Iskarpatyoti, J. & Gale, M. Proteomic analysis of mitochondrial-associated ER membranes (MAM) during RNA virus infection reveals dynamic changes in protein and organelle trafficking. PLoS ONE 10, e0117963 (2015).

    Google Scholar 

  242. 242.

    Dehmelt, L. & Bastiaens, P. I. Spatial organization of intracellular communication: insights from imaging. Nat. Rev. Mol. Cell Biol. 11, 440–452 (2010). This review discusses how changes in subcellular localization and regulation of proteins can contribute to drastic consequences in the cell.

    Google Scholar 

  243. 243.

    Smith, Z. D., Nachman, I., Regev, A. & Meissner, A. Dynamic single-cell imaging of direct reprogramming reveals an early specifying event. Nat. Biotechnol. 28, 521–526 (2010).

    Google Scholar 

  244. 244.

    Bar-Even, A. et al. Noise in protein expression scales with natural protein abundance. Nat. Genet. 38, 636–643 (2006).

    Google Scholar 

  245. 245.

    Newman, J. R. et al. Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise. Nature 441, 840–846 (2006).

    ADS  Google Scholar 

  246. 246.

    Landhuis, E. Deep learning takes on tumours. Nature 580, 551–553 (2020). This study discusses how artificial intelligence methods combined with imaging tools for subcellular proteomics could be a useful advance for cancer research.

    ADS  Google Scholar 

  247. 247.

    Guardia, C. M., De Pace, R., Mattera, R. & Bonifacino, J. S. Neuronal functions of adaptor complexes involved in protein sorting. Curr. Opin. Neurobiol. 51, 103–110 (2018).

    Google Scholar 

  248. 248.

    Hanash, S. Disease proteomics. Nature 422, 226–232 (2003).

    ADS  Google Scholar 

  249. 249.

    Kavallaris, M. & Marshall, G. M. Proteomics and disease: opportunities and challenges. Med. J. Aust. 182, 575–579 (2005).

    Google Scholar 

  250. 250.

    Dénervaud, N. et al. A chemostat array enables the spatio-temporal analysis of the yeast proteome. Proc. Natl Acad. Sci. USA 110, 15842–15847 (2013).

    ADS  Google Scholar 

  251. 251.

    Tkach, J. M. et al. Dissecting DNA damage response pathways by analysing protein localization and abundance changes during DNA replication stress. Nat. Cell Biol. 14, 966–976 (2012).

    Google Scholar 

  252. 252.

    Samavarchi-Tehrani, P., Abdouni, H., Samson, R. & Gingras, A.-C. A versatile lentiviral delivery toolkit for proximity-dependent biotinylation in diverse cell types. Mol. Cell. Proteom. 17, 2256 (2018).

    Google Scholar 

  253. 253.

    Gatto, L., Breckels, L. M., Wieczorek, S., Burger, T. & Lilley, K. S. Mass-spectrometry-based spatial proteomics data analysis using pRoloc and pRolocdata. Bioinformatics 30, 1322–1324 (2014).

    Google Scholar 

  254. 254.

    Wilkinson, M. D. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 1–9 (2016).

    Google Scholar 

  255. 255.

    Berglund, L. et al. A genecentric Human Protein Atlas for expression profiles based on antibodies. Mol. Cell Proteom. 7, 2019–2027 (2008).

    Google Scholar 

  256. 256.

    Baker, M. Reproducibility crisis: blame it on the antibodies. Nature 521, 274–276 (2015).

    ADS  Google Scholar 

  257. 257.

    Linkert, M. et al. Metadata matters: access to image data in the real world. J. Cell Biol. 189, 777–782 (2010).

    Google Scholar 

  258. 258.

    Carpenter, A. E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).

    Google Scholar 

  259. 259.

    Vizcaíno, J. A. et al. ProteomeXchange provides globally coordinated proteomics data submission and dissemination. Nat. Biotechnol. 32, 223–226 (2014).

    Google Scholar 

  260. 260.

    Hahsler, M., Piekenbrock, M. & Doran, D. DBCSCAN: fast density-based clustering with R. J. Stat. Soft. https://doi.org/10.18637/jss.v091.i01 (2019).

    Article  Google Scholar 

  261. 261.

    Lund-Johansen, F. et al. MetaMass, a tool for meta-analysis of subcellular proteomics data. Nat. Methods 13, 837–840 (2016).

    Google Scholar 

  262. 262.

    Thompson, A. et al. Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal. Chem. 75, 1895–1904 (2003).

    Google Scholar 

  263. 263.

    Plubell, D. L. et al. Extended multiplexing of tandem mass tags (TMT) labeling reveals age and high fat diet specific proteome changes in mouse epididymal adipose tissue. Mol. Cell. Proteom. 16, 873–890 (2017).

    Google Scholar 

  264. 264.

    O’Brien, J. J. et al. The effects of nonignorable missing data on label-free mass spectrometry proteomics experiments. Ann. Appl. Stat. 12, 2075–2095 (2018).

    MathSciNet  MATH  Google Scholar 

  265. 265.

    Kurosawa, N. et al. Novel method for the high-throughput production of phosphorylation site-specific monoclonal antibodies. Sci. Rep. 6, 25174 (2016).

    ADS  Google Scholar 

  266. 266.

    Smith, T. C., Saul, R. G., Barton, E. R. & Luna, E. J. Generation and characterization of monoclonal antibodies that recognize human and murine supervillin protein isoforms. PLoS ONE 13, e0205910 (2018).

    Google Scholar 

  267. 267.

    Li, X.-S., Yuan, B.-F. & Feng, Y.-Q. Recent advances in phosphopeptide enrichment: strategies and techniques. Trends Anal. Chem. 78, 70–83 (2016).

    Google Scholar 

  268. 268.

    Svinkina, T. et al. Deep, quantitative coverage of the lysine acetylome using novel anti-acetyl-lysine antibodies and an optimized proteomic workflow. Mol. Cell Proteom. 14, 2429–2440 (2015).

    Google Scholar 

  269. 269.

    Weinert, B. T. et al. Time-resolved analysis reveals rapid dynamics and broad scope of the CBP/p300 acetylome. Cell 174, 231–244.e212 (2018).

    Google Scholar 

  270. 270.

    Bekker-Jensen, D. B. et al. Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition without the need for spectral libraries. Nat. Commun. 11, 787 (2020).

    ADS  Google Scholar 

  271. 271.

    Humphrey, S. J., Azimifar, S. B. & Mann, M. High-throughput phosphoproteomics reveals in vivo insulin signaling dynamics. Nat. Biotechnol. 33, 990–995 (2015).

    Google Scholar 

  272. 272.

    Masuda, T., Sugiyama, N., Tomita, M., Ohtsuki, S. & Ishihama, Y. Mass spectrometry-compatible subcellular fractionation for proteomics. J. Proteome Res. 19, 75–84 (2020).

    Google Scholar 

  273. 273.

    Murray, L. A., Sheng, X. & Cristea, I. M. Orchestration of protein acetylation as a toggle for cellular defense and virus replication. Nat. Commun. 9, 4967 (2018).

    ADS  Google Scholar 

  274. 274.

    Parker, C. E., Mocanu, V., Mocanu, M., Dicheva, N. & Warren, M. R. in Neuroproteomics Ch. 6 (CRC Press/Taylor & Francis, 2010).

  275. 275.

    Virág, D. et al. Current trends in the analysis of post-translational modifications. Chromatographia 83, 1–10 (2020).

    Google Scholar 

  276. 276.

    Lundberg, E. & Uhlén, M. Creation of an antibody-based subcellular protein atlas. Proteomics 10, 3984–3996 (2010).

    Google Scholar 

  277. 277.

    Jackson, H. W. et al. The single-cell pathology landscape of breast cancer. Nature 578, 615–620 (2020).

    ADS  Google Scholar 

  278. 278.

    Schürch, C. M. et al. Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front. Cell 182, 1341–1359.e19 (2020).

    Google Scholar 

  279. 279.

    Mund, A. et al. AI-driven deep visual proteomics defines cell identity and heterogeneity. Preprint at https://www.biorxiv.org/content/10.1101/2021.01.25.427969v1.abstract (2021).

  280. 280.

    Kwak, C. et al. Contact-ID, a tool for profiling organelle contact sites, reveals regulatory proteins of mitochondrial-associated membrane formation. Proc. Natl Acad. Sci. USA 117, 12109 (2020).

    Google Scholar 

  281. 281.

    Cho, K. F. et al. Split-TurboID enables contact-dependent proximity labeling in cells. Proc. Natl Acad. Sci. USA 117, 12143–12154 (2020).

    Google Scholar 

  282. 282.

    Ma, Y., McClatchy, D. B., Barkallah, S., Wood, W. W. & Yates, J. R. Quantitative analysis of newly synthesized proteins. Nat. Protoc. 13, 1744–1762 (2018).

    Google Scholar 

  283. 283.

    Fornasiero, E. F. et al. Precisely measured protein lifetimes in the mouse brain reveal differences across tissues and subcellular fractions. Nat. Commun. 9, 4230 (2018).

    ADS  Google Scholar 

  284. 284.

    Zecha, J. et al. Peptide level turnover measurements enable the study of proteoform dynamics. Mol. Cell. Proteom. 17, 974 (2018).

    Google Scholar 

  285. 285.

    Kleinpenning, F., Steigenberger, B., Wu, W. & Heck, A. J. R. Fishing for newly synthesized proteins with phosphonate-handles. Nat. Commun. 11, 3244 (2020).

    ADS  Google Scholar 

  286. 286.

    Bogenhagen, D. F. & Haley, J. D. Pulse-chase SILAC-based analyses reveal selective oversynthesis and rapid turnover of mitochondrial protein components of respiratory complexes. J. Biol. Chem. 295, 2544–2554 (2020).

    Google Scholar 

  287. 287.

    Duan, J. et al. Stochiometric quantification of the thiol redox proteome of macrophages reveals subcellular compartmentalization and susceptibility to oxidative perturbations. Redox Biol. 36, 101649 (2020).

    Google Scholar 

  288. 288.

    Klein, A. M. & Macosko, E. InDrops and Drop-seq technologies for single-cell sequencing. Lab. Chip 17, 2540–2541 (2017).

    Google Scholar 

  289. 289.

    Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).

    Google Scholar 

  290. 290.

    Lee, J. H. et al. Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360–1363 (2014).

    ADS  Google Scholar 

  291. 291.

    Mardakheh, F. K. et al. Global analysis of mRNA, translation, and protein localization: local translation is a key regulator of cell protrusions. Dev. Cell 35, 344–357 (2015).

    Google Scholar 

  292. 292.

    Adekunle, D. A. & Wang, E. T. Transcriptome-wide organization of subcellular microenvironments revealed by ATLAS-seq. Nucleic Acids Res. 48, 5859–5872 (2020).

    Google Scholar 

  293. 293.

    Lefebvre, F. A. et al. CeFra-seq: systematic mapping of RNA subcellular distribution properties through cell fractionation coupled to deep-sequencing. Methods 126, 138–148 (2017).

    Google Scholar 

  294. 294.

    Taniguchi, Y. et al. Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science 329, 533–538 (2010).

    ADS  Google Scholar 

  295. 295.

    Slavov, N. Unpicking the proteome in single cells. Science 367, 512–513 (2020).

    ADS  Google Scholar 

  296. 296.

    Swaminathan, J. et al. Highly parallel single-molecule identification of proteins in zeptomole-scale mixtures. Nat. Biotechnol. 36, 1076–1082 (2018).

    Google Scholar 

  297. 297.

    Budnik, B., Levy, E., Harmange, G. & Slavov, N. SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation. Genome Biol. 19, 161 (2018). This study reports a tandem mass tag carrier-based method to increase protein detection sensitivity sufficiently to allow for single-cell proteomics.

    Google Scholar 

  298. 298.

    Kelly, R. T. Single-cell proteomics: progress and prospects. Mol. Cell Proteom. 19, 1739–1748 (2020).

    Google Scholar 

  299. 299.

    Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017). This study reports the use of DNA-barcoded antibodies to convert the detection of surface proteins into a quantitative read-out jointly with RNA sequencing of single cells.

    Google Scholar 

  300. 300.

    Paul, I., White, C., Turcinovic, I. & Emili, A. Imaging the future: the emerging era of single-cell spatial proteomics. FEBS J. https://doi.org/10.1111/febs.15685 (2020).

    Article  Google Scholar 

  301. 301.

    Yao, Y., Docter, M., van Ginkel, J., de Ridder, D. & Joo, C. Single-molecule protein sequencing through fingerprinting: computational assessment. Phys. Biol. 12, 055003 (2015).

    ADS  Google Scholar 

  302. 302.

    Christiansen, E. M. et al. In silico labeling: predicting fluorescent labels in unlabeled images. Cell 173, 792–803 (2018).

    Google Scholar 

  303. 303.

    Bernocco, S. et al. Sequential detergent fractionation of primary neurons for proteomics studies. Proteomics 8, 930–938 (2008).

    Google Scholar 

  304. 304.

    Holden, P. & Horton, W. A. Crude subcellular fractionation of cultured mammalian cell lines. BMC Res. Notes 2, 243 (2009).

    Google Scholar 

  305. 305.

    Baghirova, S., Hughes, B. G., Hendzel, M. J. & Schulz, R. Sequential fractionation and isolation of subcellular proteins from tissue or cultured cells. MethodsX 2, 440–445 (2015).

    Google Scholar 

  306. 306.

    Ramsby, M. L., Makowski, G. S. & Khairallah, E. A. Differential detergent fractionation of isolated hepatocytes: biochemical, immunochemical and two-dimensional gel electrophoresis characterization of cytoskeletal and noncytoskeletal compartments. Electrophoresis 15, 265–277 (1994).

    Google Scholar 

  307. 307.

    Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2018).

    Google Scholar 

  308. 308.

    Deutsch, E. W., Lam, H. & Aebersold, R. PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows. EMBO Rep. 9, 429–434 (2008).

    Google Scholar 

  309. 309.

    Wang, M. et al. Assembling the community-scale discoverable human proteome. Cell Syst. 7, 412–421 (2018).

    Google Scholar 

  310. 310.

    Calvo, S. E., Clauser, K. R. & Mootha, V. K. MitoCarta2.0: an updated inventory of mammalian mitochondrial proteins. Nucleic Acids Res. 44, D1251–D1257 (2015).

    Google Scholar 

  311. 311.

    Schlüter, A., Real-Chicharro, A., Gabaldón, T., Sánchez-Jiménez, F. & Pujol, A. PeroxisomeDB 2.0: an integrative view of the global peroxisomal metabolome. Nucleic Acids Res. 38, D800–D805 (2009).

    Google Scholar 

  312. 312.

    Akhter, S., Kaur, H., Agrawal, P. & Raghava, G. P. S. RareLSD: a manually curated database of lysosomal enzymes associated with rare diseases. Database https://doi.org/10.1093/database/baz112 (2019).

    Article  Google Scholar 

  313. 313.

    Orloff, D. N., Iwasa, J. H., Martone, M. E., Ellisman, M. H. & Kane, C. M. The cell: an image library-CCDB: a curated repository of microscopy data. Nucleic Acids Res. 41, D1241–D1250, (2012).

    Google Scholar 

  314. 314.

    Williams, E. et al. Image Data Resource: a bioimage data integration and publication platform. Nat. Methods 14, 775–781 (2017).

    Google Scholar 

  315. 315.

    Forsberg, L. et al. Pre-fractionation of archival frozen tumours for proteomics applications. J. Biotechnol. 126, 582–586 (2006).

    Google Scholar 

  316. 316.

    Giesen, C. et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11, 417–422 (2014).

    Google Scholar 

  317. 317.

    Angelo, M. et al. Multiplexed ion beam imaging of human breast tumors. Nat. Med. 20, 436–442 (2014).

    Google Scholar 

  318. 318.

    Aichler, M. & Walch, A. MALDI imaging mass spectrometry: current frontiers and perspectives in pathology research and practice. Lab. Invest. 95, 422–431 (2015).

    Google Scholar 

  319. 319.

    Buchberger, A. R., DeLaney, K., Johnson, J. & Li, L. Mass spectrometry imaging: a review of emerging advancements and future insights. Anal. Chem. 90, 240–265 (2018).

    Google Scholar 

  320. 320.

    Bendall, S. C., Nolan, G. P., Roederer, M. & Chattopadhyay, P. K. A deep profiler’s guide to cytometry. Trends Immunol. 33, 323–332 (2012).

    Google Scholar 

  321. 321.

    Gorman, B. L. & Kraft, M. L. High-resolution secondary ion mass spectrometry analysis of cell membranes. Anal. Chem. 92, 1645–1652 (2020).

    Google Scholar 

  322. 322.

    Lever, J., Krzywinski, M. & Altman, N. Principal component analysis. Nat. Methods 14, 641–642 (2017).

    Google Scholar 

  323. 323.

    Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37, 38–44 (2019).

    Google Scholar 

  324. 324.

    Hansen, P. & Jaumard, B. Cluster analysis and mathematical programming. Math. Program. 79, 191–215 (1997).

    MathSciNet  MATH  Google Scholar 

  325. 325.

    Kristensen, A. R., Gsponer, J. & Foster, L. J. A high-throughput approach for measuring temporal changes in the interactome. Nat. Methods 9, 907–909 (2012).

    Google Scholar 

  326. 326.

    Havugimana, P. C. et al. A census of human soluble protein complexes. Cell 150, 1068–1081 (2012).

    Google Scholar 

  327. 327.

    Guerrero-Castillo, S. et al. The assembly pathway of mitochondrial respiratory chain complex I. Cell Metab. 25, 128–139 (2017).

    Google Scholar 

  328. 328.

    Tackett, A. J. et al. Proteomic and genomic characterization of chromatin complexes at a boundary. J. Cell Biol. 169, 35–47 (2005).

    Google Scholar 

  329. 329.

    Larance, M. et al. Global membrane protein interactome analysis using in vivo crosslinking and mass spectrometry-based protein correlation profiling. Mol. Cell. Proteom. 15, 2476 (2016).

    Google Scholar 

  330. 330.

    Kastritis, P. L. et al. Capturing protein communities by structural proteomics in a thermophilic eukaryote. Mol. Syst. Biol. 13, 936 (2017).

    Google Scholar 

  331. 331.

    Wessels, H. J. C. T. et al. LC-MS/MS as an alternative for SDS-PAGE in blue native analysis of protein complexes. Proteomics 9, 4221–4228 (2009).

    Google Scholar 

  332. 332.

    Savitski, M. M. et al. Tracking cancer drugs in living cells by thermal profiling of the proteome. Science 346, 1255784 (2014).

    Google Scholar 

  333. 333.

    Tan, C. S. H. et al. Thermal proximity coaggregation for system-wide profiling of protein complex dynamics in cells. Science 359, 1170–1177 (2018).

    ADS  Google Scholar 

  334. 334.

    Hashimoto, Y., Sheng, X., Murray-Nerger, L. A. & Cristea, I. M. Temporal dynamics of protein complex formation and dissociation during human cytomegalovirus infection. Nat. Commun. 11, 1–20 (2020).

    Google Scholar 

  335. 335.

    Taylor, C. F. et al. The Minimum Information About a Proteomics Experiment (MIAPE). Nat. Biotechnol. 25, 887–893 (2007).

    Google Scholar 

  336. 336.

    Taylor, C. F. et al. Guidelines for reporting the use of mass spectrometry in proteomics. Nat. Biotechnol. 26, 860–861 (2008).

    Google Scholar 

Download references

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).

Author information

Affiliations

Authors

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.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information

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

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing