The Cambridge Centre for Proteomics (CCP) is comprised of three sections, a research group that specialises in quantitative and spatial proteomics, and a core facility that provides facilities for the School of the Biological Sciences and for wider collaborators in Cambridge and the UK and the Computational Proteomics Unit (CPU). CCP offers a fee for service proteomics resource and is part of PrimeXS, a pan-European FP7 funded proteomics consortium involved in platform development and transnational access.
We are interested in the development of technologies which enable measurement of the dynamics of the proteome in a high throughput manner in space and time during cellular processes such as signalling and differentiation.
1. Localisation of Organelle Proteins using Isotope Tagging (LOPIT) (1), which allows the assignment of proteins and protein complexes to sub-cellular locations, has been applied successfully to several biological systems (2,3). The ability to assign individual proteins accurately to specific sub-cellular structures and monitor their movement within cells is of paramount importance to our understanding of cellular mechanisms. High resolution cellular mapping using hyperplexed quantitative proteomics methods, hyperLOPIT, are being developed in collaboration with Martinez Arias (Genetics) and applied to various projects including the Evan, Oliver, Livesey, Ralser and Waller groups (Biochemistry) (4).
2. Protein protein interaction methods. These include: i.Interactomes using Parallel Affinity Capture (iPAC) is a method developed in collaboration with the St Johnston (Gurdon Institute) and Russell (Genetics Dept.) groups to determine genuine residents of multi protein complexes (5). This method is being applied to a variety of different biological systems (6,7).
ii. Selective Proteomic ProximityLabeling using Tyramide (SPPLAT). Developed in collaboration with the A. Jackson (Biochemistry) and Sarah Perrett (University of Chinese Academy of Sciences), this method enables the identification of proteins in the immediate vicinity of a target membrane protein (8).
3. DDIP - Drosophila developmental interactome project. We are working as part of a large study across the Lilley, Russell (Genetics) and Martinez Arias (Genetics) groups in Cambridge and colleagues at the University of Manchester (Hubbard) and University College London (Jones and Orengo), to map the alternatively spliced proteome during the early stages of Drosophila development in a quantitative manner, and to look at its impact on protein protein interactions, sub cellular localization and protein structure, with emphasis on components of major signaling pathway proteins (9). This study is funded by a BBSRC sLoLa.
4. Development of quantitative proteomics workflows involving label free methods (10) and isobaric tagging (11) and their application (12)
5. Robust statistical and computational data analysis is of vital importance to the above techniques, and to proteomics in general, to ensure that data sets are efficiently mined and do not contain unacceptable levels of false discovery. CPU comprises a dedicated team of informaticians to develop bioinformatics and statistical tools, that utilize pattern recognition and machine learning methods to enable robust analysis of organelle proteomics and multi-protein complex data (13,14). The output of this research is manifested in the creation of open-source software solutions for quantitative data analysis that are applicable to the majority of quantitative proteomics applications (15).
Our work is funded by the BBSRC and the Wellcome Trust including a Joint Investigator Award between Kathryn Lilley and Anne Willis of the MRC Toxicology Unit, Leicester.
Lab members: May Alqurashi, Konstantin Barylyuk (Waller lab), Marco Chaipello (Core facility and CPU), Michael Deery (Core lab manager), Mo Elzek, Renata Feret (Core facility), Laurent Gatto (CPU), Aikaterini Geladaki, Julie Howard (Core facility), Rachael Huntly (Cook lab. Babraham Institute), Nina Kočevar Britovšek, Kathryn Lilley (Director of CCP), Claudius Marondedze, David-Paul Minde, Claire Mulvey, Daniel Nightingale, Harriet Parsons, Konstanze Schott (Pathology) Lisa Simpson (CPU), Eneko Villanueva, Houjiang Zhang, Hongtao Zhou (Rothamstead)
1. Dunkley TPJ, et al (2006) Mapping the Arabisopsis organelle proteome. Proc. Natl Acad. Sci. 103 (17): 6518-6523
2. Hall SL, et al (2009) The organelle proteome of the DT40 Lymphocyte cell line. Mol Cell Proteomics. 8(6):1295-305
3. Groen AJ et al, (2014) Identification of trans-golgi network proteins in Arabidopsis thaliana root tissue. J. Prot. Res 13(2):763-76
4. Christoforou et al (2016) A draft map of the mouse pluripotent stem cell spatial proteome. Nature Communications doi:10.1038/ncomms9992
5. Rees JS, et al (2011) In vivo analysis of proteomes and interactomes using parallel affinity capture (iPAC) coupled to mass spectrometry. Mol Cell Proteomics. 10 (6) M110--002386
6. Lowe N, Rees JS, Roote J, Ryder E, Armean IM, Johnson G, Drummond E, Spriggs H, Drummond J, Magbanua JP, Naylor H, Sanson B, Bastock R, Huelsmann S, Trovisco V, Landgraf M, Knowles-Barley S, Armstrong JD, White-Cooper H, Hansen C, Phillips RG; UK Drosophila Protein Trap Screening Consortium, Lilley KS, Russell S, St Johnston D. (2014) Analysis of the expression patterns, subcellular localisations and interaction partners of Drosophila proteins using a pigP protein trap library. Development 141(20):3994-4005. doi: 10.1242/dev.111054.
7. Chen C, Buhl, E, Xu, M, Croset, V, Rees, JS, Lilley KS, Benton, R, Hodge JL, Stanewsky, R. (2015) Drosophila Ionotropic Receptor 25a mediates circadian clock resetting by temperature Nature Nov 18. doi: 10.1038/nature16148
8. Li XW, et al (2014) New insights into the DT40 B-Cell Receptor cluster using a proteomics proximity labelling assay. J. Biol. Chem.;289(21):14434-47. doi: 10.1074/jbc.M113.529578
9. Fabre, BF, et al (2016) Analysis of the Drosophila melanogaster proteome dynamics during the embryo earlydevelopment by a combination of label-free proteomics approaches. Proteomics. 2016 Mar 31. doi: 10.1002/pmic.201500482.
10. Bond NJ, et al (2013) Improving qualitative and quantitative performance for MS(E)-based-label-free proteomics. J. Prot Res. 12(6):2340-53.
11. Shliaha PV, et al (2014) Additional precursor purification in isobaric mass tagging experiments by travelling wave ion mobility separation (TWIMS). J. Prot. Res. 13(7):3360-9. doi: 10.1021/pr500220g
12. Mulvey CM, Schröter C, Gatto L, Dikicioglu D, Fidaner IB, Christoforou A, Deery MJ, Cho LT, Niakan KK, Martinez-Arias A, Lilley KS. (2015) Dynamic Proteomic Profiling of Extra-Embryonic Endoderm Differentiation in Mouse Embryonic Stem Cells. Stem Cells. 2015 Sep;33(9):2712-25. doi: 10.1002/stem.2067. Epub 2015 Jun 23.
13. Breckels LM, et al (2013) The effect of organelle discovery upon sub-cellular protein localization. J. Prot. 88:129-40
14. Breckels, LM, et al (2016) Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics. PLoS Comput Biol. 2016 May 13;12(5):e1004920. doi: 10.1371/journal.pcbi.1004920.
15. Gatto L, et al (2014) Mass-spectrometry based spatial proteomics data analysis using pRoloc and pRoloc data. Bioinformatics. 30 (9) 1322-4