1. A systems approach to sub-cellular localisation of proteins Kathryn S. Lilley Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, United Kingdom, CB2 1QR SPEAF 2012 Rouen

2. Organelles of the cell http://media.web.britannica.com/eb-media Eucaryote cells have many different types of sub-cellular compartments (some specific to a cell type) Many proteins reside in multiple locations Within these locations many form functional units Dynamic changes in these locations (and binding partners) reflect biological processes in which a protein functions Most proteomics protocols start with addition of detergents which destroy delicate sub-cellular structures Changes in sub-cellular dynamics are as important as changes in abundance, post translational status and interacting partners.

3. Tagging of a fluorescent fusion proteins ImmunofluorescenceMass spectrometry based methods LC-MS/MS derived catalogue Quantitative proteomics methods to show enrichment of proteins within different subcellular preparations Prediction from sequence? Limited Static

4. Databases often carry contradictory assignments Different approaches can lead to conflicting data....but can be highly complementary

5. FFPIFPureSubtractive Multiple locations ✔✔ x ✔ Sensitivity ✔✔ xx Specificity ✔ variable ✔✔ Hypothesis generating xx ✔✔ Hypothesis driven ✔✔ xx Multiplexing limited x ✔ No perturbation of protein xpossibly ✔✔ Throughput xx ✔✔ Cost xx ✔✔ high quality reagents ✔✔ xx Universal approach limitedx ✔✔ Isoform specificity x ✔✔✔ Dynamic ✔✔ x ✔

6. Organelle “enrichment” rather than organelle “purification” Purification strategies can result in contamination by other organelles and false-positive hits Dynamic proteome: proteins in transit (cargo proteins) may be just passing through! Purification of an organelle gives no information about steady state location of proteins with multiple localisations

7. Sampling the cell as a whole? de Duve’s Principle (adapted) Based on the principle that during analytical centrifugation, organelle structures will migrate until they reach their buoyant densities Proteins from the same organelle will have identical distribution profiles through the gradient Novel organelle residents can be assigned by matching their profiles to the distribution of known marker proteins …….. Winner of the 1974 Nobel Prize in physiology or medicine for his discovery of the lysosome and the peroxisome. PCP and LOPIT or in fact any method that gives differential enrichment Equilibrium density centrifugation

8. Organelle Fractionation Western blot LOPIT Workflow Density gradient centrifugationDifferential Centrifugation

9. Reporter ion intensities mimic the peptide distribution profiles TMT 126 TMT 127 TMT 128 TMT 129 TMT 130 TMT 131

10. Gatto et al., 2010 Steady State Position Mixed Locations

11. LOPIT in a whole organism Drosophila embryos Tan et al (2009) J. Prot Res 8(6):2667-2678

12. Arabidopsis thaliana root derived callus Nino Nikolovski Paul Dupree Denis Rubstov

13. Increased coverage by combining experiments membrane + membrane associated 2205 proteins identified 1826 quantified in all replicates after imputation 163 Golgi proteins 320 ER 266 PM Nikolovski 2012 in press

14. Saccharomyces cerevisiae Y. Wang and S. Oliver - unpublished Comparison with GFP dataset (Huh et al, 2003) revealed good overlap for some organelles, but not for PM or Golgi >1500 proteins

15. Plasma membrane Endoplasmic reticulum Golgi Mitochondrion Lysosome Chicken DT40 cell line Hall et al 2009 Tony Jackson Stephanie Hall Matthew Trotter Combine with next slide

16. BC D E F G PC1 PC2 Clathrin IgM B-cell receptor and clathrin show average position away from plasma membrane cluster IgM clathrin Rab4 B+C E+F Hall et al 2009. Dynamic system – in action

17. E14TG2a mouse embryonic stem cell line Dppa5a Sox2 Oct4 Dppa4 Utf1 Mcl-1 Tdgf-1 Risc Fgf4 LIF receptor Alkaline phosphatase E-ras β-catenin Erk-2 Andy Christoforou

18. HEK293T Human cell line Andy Christoforou

19. Comparison with Human Protein Atlas Andy Christoforou

20. Organelle Fractionation Western blot Trypsinization and labelling Combine MS/MS MSnBASE ? ? LOPIT pipeline Machine learning methods to allow greater data mining Dynamics changes in location Predict multiple locations Gatto and Lilley, Bioinformatics 2012

21. Peroxisome Proteasome Ribosomal (60S) cluster Nucleus Ribosomal (40S) cluster Cytoplasm Original Dataset Protein- organelle prediction with supervised KNN Identification and assignment of proteins to organelles with phenoDisco Drosophila embryos PC2 PC1 Tan et al. J.of Proteome Res. (2009) 8(6):2667-78

22. TGN ABC transporters Ribosomal (40S) cluster Ribosomal (60S) cluster ER membrane associated PC1 PC2 PC1 PC2 LOPIT on Arabidopsis Original Dataset Protein- organelle prediction with supervised KNN Identification and assignment of proteins to organelles with phenoDisco Dunkley et al. PNAS (2006) 103(17):6518-23 Chloroplast envelope

23. FFPIFPureSubtractiveLOPIT Multiple locations ✔✔ xlimited ✔ Sensitivity ✔✔ xxx Specificity ✔ variable ✔✔✔ Hypothesis generating xx ✔✔✔ Hypothesis driven ✔✔ xxx Multiplexing limited x ✔✔ No perturbation of protein xpossibly ✔✔✔ Throughput xx ✔✔✔ Cost xx ✔✔✔ high quality reagents ✔✔ xxx Universal approach limitedx ✔✔✔ Isoform specificity x ✔✔✔✔ Dynamic ✔✔ x ✔✔ Summary

24. The ‘LOPITEERS’ Andy Christoforou Laurent Gatto Arnoud Groen Adam Gutteres Claire Mulvey Dan Nightingale Nino Nikolovski Konstanze Schott Pavel Shliaha Lisa Simpson Matthew Trotter Yuchong Wang Houjiang Zhou Cambridge Collaborators Paul Dupree Stephanie Hall Tony Jackson Alfonso Martinez Arias Ludovic Vallier Cambridge Collaborators Paul Dupree Stephanie Hall Tony Jackson Alfonso Martinez Arias Ludovic Vallier Dirk Walther Matthias Mann Peter James Isaac Newton Trust