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Animal cell 10-30mm Plant cell 10-100mm Yeast cell – 5mm
- E. coli – 2mm
Giraffe neck nerve 3m Size matters
The spatial sub-cellular proteome Methods and considerations - - PowerPoint PPT Presentation
The spatial sub-cellular proteome Methods and considerations - - PowerPoint PPT Presentation
The spatial sub-cellular proteome Methods and considerations Kathryn Lilley Cambridge Centre for Proteomics Size matters Animal cell 10-30 m m Plant cell 10-100 m m Yeast cell 5 m m E. coli 2 m m Giraffe neck nerve 3m The crowded cell
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Courtesy of David S. Goodsell, The Scripps Research Institute
The crowded cell
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Subcellular spatial proteomics
Proteins reside within discreet subcellular niches where they carry out their function. Some proteins reside at multiple locations and fulfil different roles in a context specific manner. Differential subcellular distribution is controlled by: post transcriptional and post translational modification processing differential binding partners on location abundance changes Changes in subcellular dynamics are as important as changes in abundance, post translational status and interacting partners. Many proteomics protocols are not compatible with maintaining organelle integrity
We want to know….. Where a protein is localized If it is present in multiple locations Its interacting partners at these locations How this varies for different protein isoforms How this various for different cell types Dynamic changes in the above …and on a proteome-wide scale ……and on a cell wide-scale ……….and with sub-organelle resolution
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Different protein products that do the same job in more than one location
MDH
Proteins that are ‘inert’ in one location, but active in another
Transcription factors
Proteins that have different jobs in different locations
Beta catenin, LDH
Proteins as double agents
Proteins that do the same job in more than one location
Importins/exportins
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Where is a protein located?
Fluorescence microscopy
Proteinatlas.org
Challenges:
Automated assignment to subcellular niches Link-out to databases GO Cellular Compartment Antibodies Fusion protein without perturbation
CyTof-Imaging Giesen, 2014
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B
H R P
+ fluorescein/biotin- conjugated arylazide/H2O2
H R P H R P H R P
+ biotin- tyramide/H2O2
BirA *
+ biotin
A
bait
Biotin ligase biotinoyl-5’AMP (Lysine) Footprint – 10nm – tethered, more for freely diffusing baits (10 um) or half a cell Conjugated horse radish peroxidase Exposed tyrosine Footprint - 40-200nm (shorter half lives than above)
Rees et al Mol. Cell Prot. (2015 – in revision)
Proximity labelling scale per experiment:
- Protein complexes
- Membrane environments
- Organelle contents
What proteins are nearby? (complexes, lipid rafts, sub organelle etc….)
Challenges:
Correct for contamination Fusions protein perturbation Reproducibility Link-out to databases Interaction networks GO CC
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Organelle catalogues
Proteinatlas.org
Purification of subcellular niche Challenges:
Experimental design sympathetic of contamination – quantitation No or limited steady state information Link-out to databases GO CC
D
- uter
membrane mitochondrial matrix inter membrane space (IMS)
APEX APEX APEX APEX APEX APEX
+ biotin- phenol/H2O2
inter membrane space (IMS) mitochondrial matrix inner mitochondrial membrane (IMM)
Ascorbate peroxidase Smaller than BirA* Fusion protein directed to subcellular niche
APEX
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Correlation of protein distributions
Whole cell maps per experiment?
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HyperLOPIT
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Christoforou – in revision
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LDH HXK1 HXK2 PFK PFK PFK ALDOA TPIS GAPDH PGK PGM ENO PKM
HyperLOPIT – Glycolysis (mouse Embryonic stem cells E14TGA)
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Challenges:
Fractionation Mappability to
- ther datasets
Data visualization Pattern recognition and classification Deconvolution of mixed location Interpretation of dynamic data Link-out to databases Interaction networks GO CC
As a scaffold for other spatial datasets
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pRolocGUI
https://lgatto.shinyapps.io/christoforou2014/
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SOX2 Interacting partners
Sox2 SSBP DLGP5 ATX2 USP9X GNA1 IMB1 SYEP SYDC XPO1 XPO4 IF2B1 XRCC5 RAGP1 TYDP1 RFA2 RFA3RFA1 WDR18 NOL9 PK1IP TEX10 LYAR RBBP7
Black circles correspond to proteins listed as Sox2 interactors by Z. Gao et al (doi: 10.1074/jbc.M111.320143)
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Sox2, Oct4 and Nanog and their common binding partners CHD4,MTA2,SALL4,SALL1,ERR2,P66B,MTA1
Oct4 Sox2 Nanog
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Transfer learning algorithm which allows integration of heterogeneous data types
Spatial proteomics HPA GO CC Output of 3rd party software , e.g. Yloc Etc…… Primary and auxillary data Data improvement Have to combine data without compromising biological relevance of the primary data
Data Fusion
Breckels – in prep.
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Common challenges within spatial ‘omics
Inter-relationship - improving metabolic models and vice versa Linking the biological scales
- rganism-tissue-cell type –single cell-subcellular compartments