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Kathryn Lilley

Cambridge Centre for Proteomics

 CCP NEWS:

SPG receive a TMT research award 2017

Kathryn Lilley receives the Juan Pablo Albar Proteome Pioneer Award 2017

Kathryn Lilley is BSPR Lecturer 2017-2017  - see http://www.bspr.org/news/bspr-lecturer

 

The Cambridge Centre for Proteomics (CCP) is comprised of four sections of which Kathryn Lilley is the director, a research group that specialises in quantitative proteomics, the spatial proteomics group (SPG) 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.

 

CCP Research

We develop 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. Protein location

Localisation of Organelle Proteins using Isotope Tagging (hyperLOPIT) (1,2), enables the assignment of proteins and protein complexes to sub-cellular locations, has been applied successfully to several biological systems (3,4). 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.  hyperLOPIT, is being applied in many collaborations including Martinez Arias (Genetics) and applied to various projects including the Oliver, Livesey, Ralser, Howe and Waller groups (Biochemistry) and Martinez Arias (Genetics), and Smith (UCL).

We are also developing to map the location of translation and how this is controlled - the Spatial Proteomics Group - in collaboration with Anne Willis (MRC Toxicology Unit, Leicester)

2. Multi protein complexes

i. Protein protein interaction methods. 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 Proximity Labeling using Tyramide (SPPLAT). Developed in collaboration with the Tony 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. Quantitative methods

Development of quantitative proteomics workflows involving label free methods (9, 10) and isobaric tagging  (11, 12)

4. Computational proteomics

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).

 

Applications of technologies:

1. DDIP - Drosophila developmental interactome project. We are working as part of a  large study across the Kathryn Lilley, Steve Russell (Genetics) andAlfonso Martinez Arias (Genetics) groups in Cambridge and colleagues at the University of Manchester (Simon Hubbard) and University College London (David Jones and Christine 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 (16). This study is funded by a BBSRC sLoLa.

2. Detox - CCP is part of a consortium with Gavin Thomas and Tony Larsen (York), Gill Stephens (Nottingham), Dave Kelly and Jeff Green (Sheffield)  Susan Molyneaux Hodgson (Exeter) and   Lucite, Green Biologics, Ingenza and the CPI. The project aims to provide the first ever systematic analysis of how chemicals poison bacterial cells. http://projectdetox.co.uk/. This work in funded through a BBSRC IB Catalyst award. (17)

3. The RNA binding proteome (with Chris Gehring formerly of KAUST) (18)

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:  Ania Andreyeva (Cor Lab.) Lisa Breckels (CPU), Konstantin Barylyuk (Waller lab), Marco Chiapello (Core facility and CPU), Josie Christopher, Ollie Crook (CPU), Michael Deery (Core lab manager), Mo Elzek (SPG), Renata Feret (Core facility), Laurent Gatto (CPU Director), Aikaterini Geladaki (SPG), Julie Howard (Core facility), Nina Kočevar Britovšek, Kathryn Lilley (Director of CCP), David-Paul Minde, Claire Mulvey (SPG), Daniel Nightingale, Harriet Parsons, Rayner Queiroz (SPG), Manasa Ramakrishna (SPG -MRC Toxicology Unit, Leicester), Lisa Breckels(CPU), Tom Smith (SPG) Owen Vennard, Eneko Villanueva (SPG), Hongtao Zhou (Rothamstead)

Visit Cambridge Proteomics Centre website

 

Key publications:

1. Dunkley TPJ, et al  (2006) Mapping the Arabisopsis organelle proteome.  Proc. Natl Acad. Sci.  103 (17): 6518-6523

2. Mulvey,CM et al (2017) Using hyperLOPIT to perform high resolution mapping of the spatial proteome. Nature Protoc. Jun;12(6):1110-1135. doi: 10.1038/nprot.2017.026

3. Thul P et al (2017) A subcellular map of the human proteome Science.  May 26;356(6340). pii: eaal3321. doi: 10.1126/science.aal3321

4. Christoforou et al (2016)  A draft map of the mouse pluripotent stem cell spatial proteome. Nature Communications  Jan 12;7:8992 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, et al. (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, et al  (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. Vowinckel, J et al (2016) The beauty of being (label)-free: sample preparation methods for SWATH-MS and next-generation targeted proteomics.doi: 10.12688/f1000research.2-272.v2. eCollection 2013.

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, et al (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

 16. Fabre, BF, et al (2016) Analysis of the Drosophila melanogaster proteome dynamics during the embryo early development by a combination of label-free proteomics approaches. Proteomics. 2016 Mar 31. doi: 10.1002/pmic.201500482.

17. Minde D-P, Dunker AK and Lilley KS (2017) Time, space, and disorder in the expanding proteome universe. Proteomics 17(7). doi: 10.1002/pmic.201600399

18. Marondedze, C et al, (2016) The RNA-binding protein repertoire of Arabidopsis thaliana.  Scientific Reports 11;6:29766. doi: 10.1038/srep29766

19. Mulvey, C et al, (2017) Using HyperLOPIT to perform high-resolution mapping of the spatial proteome  Nature Protoc. 12(6):1110-1135. doi: 10.1038/nprot.2017.026.

20. Thul, P. et al (2017) A subcellular map of the human proteome. Science. ;356(6340). pii: eaal3321. doi: 10.1126/science.aal3321