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 and Ralser groups (Biochemistry).
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 (4).
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 (5).
3. DDIP - Drosophila developmental interactome project
We have recently embarked on a large study across the Lilley, Russell (Genetics) and Martinez Arias (Genetics) groups in Cambridge and colleagues at the University of Manchester (Hubbard and Bergman) 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. This study is funded by a BBSRC sLoLa.
4. Development of quantitative proteomics workflows involving label free methods (6) and isobaric tagging. (7)
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 (8). 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 (9).
Lab members: Andy Christoforou, Michael Deery (Core lab manager), Renata Feret, Laurent Gatto (director of CPU), Arnoud Groen, Adam Guterres, Julie Howard, Kathryn Lilley (Director of CCP), Claire Mulvey, Isabelle Nett, Daniel Nightingale, Nino Nikolovski, Konstanze Schott (Pathology) Lisa Simpson (CPU), Pavel Shliaha, 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. 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
5. Li XW, et al (2014) New insights into the DT40 B-Cell Receptor cluster using a proteomics proximity labelling assay. J. Biol. Chem. in press
6. Bond NJ, et al (2013) Improving qualitative and quantitative performance for MS(E)-based-label-free proteomics. J. Prot Res. 12(6):2340-53.
7. Shliaha PV, et al (2014) Additional precursor purification in isobaric mass tagging experiments by travelling wave ion mobility separation (TWIMS). J. Prot. Res. in press
8. Breckels LM, et al (2013) The effect of organelle discovery upon sub-cellular protein localization. J. Prot. 88:129-40
9. Gatto L, et al (2014) Mass-spectrometry based spatial proteomics data analysis using pRoloc and pRoloc data. Bioinformatics. 30 (9) 1322-4