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Using hyperLOPIT to perform high-resolution mapping of the spatial proteome

Abstract

The organization of eukaryotic cells into distinct subcompartments is vital for all functional processes, and aberrant protein localization is a hallmark of many diseases. Microscopy methods, although powerful, are usually low-throughput and dependent on the availability of fluorescent fusion proteins or highly specific and sensitive antibodies. One method that provides a global picture of the cell is localization of organelle proteins by isotope tagging (LOPIT), which combines biochemical cell fractionation using density gradient ultracentrifugation with multiplexed quantitative proteomics mass spectrometry, allowing simultaneous determination of the steady-state distribution of hundreds of proteins within organelles. Proteins are assigned to organelles based on the similarity of their gradient distribution to those of well-annotated organelle marker proteins. We have substantially re-developed our original LOPIT protocol (published by Nature Protocols in 2006) to enable the subcellular localization of thousands of proteins per experiment (hyperLOPIT), including spatial resolution at the suborganelle and large protein complex level. This Protocol Extension article integrates all elements of the hyperLOPIT pipeline, including an additional enrichment strategy for chromatin, extended multiplexing capacity of isobaric mass tags, state-of-the-art mass spectrometry methods and multivariate machine-learning approaches for analysis of spatial proteomics data. We have also created an open-source infrastructure to support analysis of quantitative mass-spectrometry-based spatial proteomics data (http://bioconductor.org/packages/pRoloc) and an accompanying interactive visualization framework (http://www.bioconductor.org/packages/pRolocGUI). The procedure we outline here is applicable to any cell culture system and requires 1 week to complete sample preparation steps, 2 d for mass spectrometry data acquisition and 1–2 d for data analysis and downstream informatics.

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Figure 1: The hyperLOPIT workflow.
Figure 2
Figure 3: Biochemical subcellular fractionation using density gradient ultracentrifugation.
Figure 4: PCA plots of the first two principal components of data collected using the hyperLOPIT technology from embryonic mouse stem cells.
Figure 5: A screenshot of the (default) pRolocVis application in pRolocGUI.

References

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

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  2. Cody, N.A.L., Iampietro, C. & Lécuyer, E. The many functions of mRNA localization during normal development and disease: from pillar to post. Wiley Interdiscip. Rev.: Dev. Biol. 2, 781–796 (2013).

    CAS  Article  Google Scholar 

  3. De Matteis, M.A. & Luini, A. Mendelian disorders of membrane trafficking. N. Engl. J. Med. 365, 927–938 (2011).

    CAS  PubMed  Article  Google Scholar 

  4. Olkkonen, V.M. & Ikonen, E. When intracellular logistics fails - genetic defects in membrane trafficking. J. Cell Sci. 119, 5031 (2006).

    CAS  PubMed  Article  Google Scholar 

  5. Sadowski, P.G. et al. Quantitative proteomic approach to study subcellular localization of membrane proteins. Nat. Protoc. 1, 1778–1789 (2006).

    CAS  PubMed  Article  Google Scholar 

  6. Ting, L., Rad, R., Gygi, S.G. & Haas, W. MS3 eliminates ratio distortion in isobaric labeling multiplexed quantitative proteomics. Nat. Methods 8, 937–940 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. Christoforou, A. & Lilley, K.S. Taming the isobaric tagging elephant in the room in quantitative proteomics. Nat. Methods 8, 911–913 (2011).

    CAS  PubMed  Article  Google Scholar 

  8. Breckels, L.M., Mulvey, C.M., Lilley, K.S. & Gatto, L. A Bioconductor workflow for processing and analysing spatial proteomics data. F1000 Res. 5, 2926 (2016).

    Article  Google Scholar 

  9. Breckels, L.M. et al. The effect of organelle discovery upon sub-cellular protein localisation. J. Proteomics 88, 129–140 (2013).

    CAS  PubMed  Article  Google Scholar 

  10. Breckels, L.M. et al. Learning from heterogeneous data sources: an application in spatial proteomics. PLoS Comput. Biol. 12, e1004920 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  11. 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).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. Christoforou, A., Arias, A.M. & Lilley, K.S. in Methods in Molecular Biology (ed. Daniel Martins-de-Souza) 157–174 (Springer, 2014).

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. Christoforou, A. et al. A draft map of the mouse pluripotent stem cell spatial proteome. Nat. Commun. 7, 8992 (2016).

    PubMed  Article  CAS  Google Scholar 

  15. Breckels, L.M., Naake, T. & Gatto, L. pRolocGUI: interactive visualisation of spatial proteomics data. R Package Version 1.6.2. (2016).

  16. Gentleman, R. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004).

    PubMed  PubMed Central  Article  Google Scholar 

  17. Huber, W. et al. Orchestrating high-throughput genomic analysis with bioconductor. Nat. Methods 12, 115–121 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. Gatto, L. & Lilley, K.S. MSnbase-an R/bioconductor package for isobaric tagged mass spectrometry data visualization, processing and quantitation. Bioinformatics 28, 288–289 (2012).

    CAS  PubMed  Article  Google Scholar 

  19. Magin, T.M., McWhir, J. & Melton, D.W. A new mouse embryonic stem cell line with good germ line contribution and gene targeting frequency. Nucleic Acids Res. 20, 3795–3796 (1992).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  21. Groen, A.J. et al. Identification of trans-Golgi network proteins in Arabidopsis thaliana root tissue. J. Proteome Res. 13, 763–776 (2013).

    PubMed Central  Article  CAS  Google Scholar 

  22. 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 Proteomics 8, 1295–1305 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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

    CAS  PubMed  Article  Google Scholar 

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

    PubMed  PubMed Central  Article  CAS  Google Scholar 

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

    CAS  PubMed  Article  Google Scholar 

  26. Islinger, M., Eckerskorn, C. & Völkl, A. Free-flow electrophoresis in the proteomic era: a technique in flux. Electrophoresis 31, 1754–1763 (2010).

    CAS  PubMed  Article  Google Scholar 

  27. 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).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  28. de Michele, R. et al. Free-flow electrophoresis of plasma membrane vesicles enriched by two-phase partitioning enhances the quality of the proteome from Arabidopsis seedlings. J. Proteome Res. 15, 900–913 (2016).

    CAS  PubMed  Article  Google Scholar 

  29. Loh, K.H. et al. Proteomic analysis of unbounded cellular compartments: synaptic clefts. Cell 166, 1295–1307.e21 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. 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).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. Trost, M. et al. The phagosomal proteome in interferon-γ-activated macrophages. Immunity 30, 143–154 (2009).

    CAS  PubMed  Article  Google Scholar 

  32. Thimiri Govinda Raj, D.B. et al. A novel strategy for the comprehensive analysis of the biomolecular composition of isolated plasma membranes. Mol. Syst. Biol. 7, 541–541 (2011).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  33. Heard, W., Sklenář, J., Tomé, D.F.A., Robatzek, S. & Jones, A.M.E. Identification of regulatory and cargo proteins of endosomal and secretory pathways in Arabidopsis thaliana by proteomic dissection. Mol. Cell. Proteomics 14, 1796–1813 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. Bausch-Fluck, D. et al. A mass spectrometric-derived cell surface protein atlas. PLoS One 10, e0121314 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  35. Théry, C., Amigorena, S., Raposo, G. & Clayton, A. Isolation and characterization of exosomes from cell culture supernatants and biological fluids. Curr. Protoc. Cell Biol. Chap. 3: Unit 3.22 (2006).

    Google Scholar 

  36. Shah, A.D. et al. Integrative analysis of subcellular quantitative proteomics studies reveals functional cytoskeleton membrane-lipid raft interactions in cancer. J. Proteome Res. 15, 3451–3462 (2016).

    CAS  PubMed  Article  Google Scholar 

  37. Naba, A. et al. The extracellular matrix: tools and insights for the “omics” era. Matrix Biol. 49, 10–24 (2016).

    CAS  PubMed  Article  Google Scholar 

  38. Wysocka, J., Reilly, P.T. & Herr, W. Loss of HCF-1-chromatin association precedes temperature-induced growth arrest of tsBN67 cells. Mol. Cell. Biol. 21, 3820–3829 (2001).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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

    CAS  PubMed  Article  Google Scholar 

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. Rhee, H.-W. et al. Proteomic mapping of mitochondria in living cells via spatially-restricted enzymatic tagging. Science 339, 1328–1331 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. de Duve, C. Tissue fractionation. J. Cell Biol. 50, 20D–55D (1971).

    CAS  PubMed  Article  Google Scholar 

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

    CAS  PubMed  Article  Google Scholar 

  44. Jadot, M. et al. Accounting for protein subcellular localization: a compartmental map of the rat liver proteome. Mol. Cell. Proteomics 16, 194–212 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  45. 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.e366 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. Jakobsen, L. et al. Novel asymmetrically localizing components of human centrosomes identified by complementary proteomics methods. EMBO J. 30, 1520–1535 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. Rickwood, D.E. Iodinated Density Gradient Media — A Practical Approach (ed. Rickwood, D.) 147 (Oxford University Press, 1985).

  48. Pertl-Obermeyer, H. et al. Identification of cargo for adaptor protein (AP) complexes 3 and 4 by sucrose gradient profiling. Mol. Cell. Proteomics 15, 2877–2889 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. Graham, J. in Cell Biology Protocols 153–199 (Wiley, 2006).

  50. Lazar, C., Gatto, L., Ferro, M., Bruley, C. & Burger, T. Accounting for the multiple natures of missing values in label-free quantitative proteomics data sets to compare imputation strategies. J. Proteome Res. 15, 1116–1125 (2016).

    CAS  PubMed  Article  Google Scholar 

  51. Webb-Robertson, B.J. et al. Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics. J. Proteome Res. 14, 1993–2001 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. Braun, C.R. et al. Generation of multiple reporter ions from a single isobaric reagent increases multiplexing capacity for quantitative proteomics. Anal. Chem. 87, 9855–9863 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. Nikolovski, N., Shliaha, P.V., Gatto, L., Dupree, P. & Lilley, K.S. Label free protein quantification for plant Golgi protein localisation and abundance. Plant Physiol. 66, 1033–1043 (2014).

    Article  CAS  Google Scholar 

  54. Parsons, H.T. & Heazlewood, J.L. Beyond the Western front: targeted proteomics and organelle abundance profiling. Front. Plant Sci. 6, 301 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  55. Trotter, M.W.B., Sadowski, P.G., Dunkley, T.P.J., Groen, A.J. & Lilley, K.S. Improved sub-cellular resolution via simultaneous analysis of organelle proteomics data across varied experimental conditions. Proteomics 10, 4213–4219 (2010).

    CAS  PubMed  Article  Google Scholar 

  56. 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).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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

    CAS  PubMed  Article  Google Scholar 

  58. Tomizioli, M. et al. Deciphering thylakoid sub-compartments using a mass spectrometry-based approach. Mol. Cell. Proteom. 13, 2147–2167 (2014).

    CAS  Article  Google Scholar 

  59. Kalmar, T. et al. Regulated fluctuations in Nanog expression mediate cell fate decisions in embryonic stem cells. PLoS Biol. 7, e1000149 (2009).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  60. Wang, Y., Lilley, K.S. & Oliver, S.G. A protocol for the subcellular fractionation of Saccharomyces cerevisiae using nitrogen cavitation and density gradient centrifugation. Yeast 31, 127–135 (2014).

    CAS  PubMed  Article  Google Scholar 

  61. Baldwin, D.N. & Linial, M.L. Proteolytic activity, the carboxy terminus of Gag, and the primer binding site are not required for Pol incorporation into foamy virus particles. J. Virol. 73, 6387–6393 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. The UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res. 43, D204–D212 (2015).

  63. The Gene Ontology Consortium et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

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Acknowledgements

C.M.M. and L.M.B. were supported by a Wellcome Trust Technology Development Grant (grant no. 108467/Z/15/Z). L.G. was supported by the BBSRC Strategic Longer and Larger grant (grant no. BB/L002817/1). A.G. was funded through the Alexander S. Onassis Public Benefit Foundation, the Foundation for Education and European Culture (IPEP) and the Embiricos Trust Scholarship of Jesus College Cambridge. D.J.H.N. was supported by a BBSRC grant (grant no. BB/LOO2817). K.S.L. is a Wellcome Trust Joint Senior Investigator (grant no. 110170/Z/15/Z).

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C.M.M., L.M.B. and K.S.L. wrote the manuscript with contributions from M.J.D. and A.G. Figures were prepared by L.M.B., A.G. and C.M.M. L.G., D.J.H.N., N.K.B., M.E. and A.C. advised on the content and layout of the protocol.

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Correspondence to Kathryn S Lilley.

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Mulvey, C., Breckels, L., Geladaki, A. et al. Using hyperLOPIT to perform high-resolution mapping of the spatial proteome. Nat Protoc 12, 1110–1135 (2017). https://doi.org/10.1038/nprot.2017.026

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