A systems approach to sub-cellular localisation of proteins Kathryn S. Lilley Cambridge Centre for...
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Transcript of A systems approach to sub-cellular localisation of proteins Kathryn S. Lilley Cambridge Centre for...
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 2012Rouen
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.
Tagging of a fluorescent
fusion proteins
Immunofluorescence Mass spectrometry based methods
LC-MS/MS derived catalogue
Quantitative proteomics methods to show enrichment of proteins within different subcellular preparations
Prediction from sequence?LimitedStatic
Databases often carry contradictory assignmentsDifferent approaches can lead to conflicting data
....but can be highly complementary
FFP IF Pure Subtractive
Multiple locations ✔ ✔ x ✔
Sensitivity ✔ ✔ x x
Specificity ✔ variable ✔ ✔Hypothesis generating x x ✔ ✔
Hypothesis driven ✔ ✔ x xMultiplexing
limited limited x ✔No perturbation of
protein x possibly ✔ ✔
Throughput x x ✔ ✔Cost
x x ✔ ✔
high quality reagents ✔ ✔ x xUniversal approach
limited x ✔ ✔Isoform specificity
x ✔ ✔ ✔
Dynamic ✔ ✔ x ✔
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
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
Organelle Fractionation Western blot
LOPIT WorkflowDensity gradient centrifugation Differential Centrifugation
Reporter ion intensities mimic the peptide distribution profiles
050000
100000150000200000250000300000350000400000450000500000
1 2 3 4 5 6
Protein X
TMT126 TMT127 TMT128 TMT129 TMT130 TMT131 P
Gatto et al., 2010
Steady State Position
Mixed Locations
LOPIT in a whole organism
Drosophila embryos
Tan et al (2009) J. Prot Res 8(6):2667-2678
Arabidopsis thaliana root derived callus
Nino NikolovskiPaul DupreeDenis Rubstov
Increased coverage by combining experiments
membrane + membrane associated
2205 proteins identified
1826 quantified in all replicates after imputation
163 Golgi proteins
320 ER266 PM
Nikolovski 2012 in press
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
Plasma membrane
Endoplasmic reticulum
Golgi
Mitochondrion
Lysosome
Chicken DT40 cell line
Hall et al 2009
Tony JacksonStephanie HallMatthew Trotter
Combine with next slide
B C D
E F G
-4
-2
-8 -6 -4 -2 0
PC1
PC
2
ClathrinIgM
B-cell receptor and clathrin show average position away from plasma membrane cluster
IgM
IgM
clathrin
Rab4
B+C
E+F
-6
-4
-2
0
2
4
6
-8 -6 -4 -2 0 2 4 6 8
Hall et al 2009.
Dynamic system – in action
E14TG2a mouse embryonic stem cell line
Dppa5a
Sox2
Oct4Dppa4
Utf1
Mcl-1Tdgf-1
RiscFgf4
LIF receptor
Alkaline phosphataseE-ras
β-catenin
Erk-2
Andy Christoforou
HEK293T Human cell line
Andy Christoforou
Comparison with Human Protein Atlas
Andy Christoforou
Organelle Fractionation
Western blot
Trypsinization and labelling
Combine
MS/MS
MSnBASE ? ?
LOPIT pipeline
Machine learning methods to allow greater data mining
Dynamics changes in locationPredict multiple locations
Gatto and Lilley , Bioinformatics 2012
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
PC
2P
C2
PC
2
PC1
PC1
PC1
Tan et al. J .of Proteome Res. (2009) 8(6):2667-78
TGN
ABC transporters
Ribosomal (40S) cluster
Ribosomal (60S) cluster
ER membrane associated
PC1
PC
2P
C2
PC1
PC1
PC
2
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
FFP IF Pure Subtractive LOPIT
Multiple locations ✔ ✔ x limited ✔Sensitivity ✔ ✔ x x xSpecificity ✔ variable ✔ ✔ ✔Hypothesis generating x x ✔ ✔ ✔
Hypothesis driven ✔ ✔ x x xMultiplexing
limited limited x ✔ ✔No perturbation of
protein x possibly ✔ ✔ ✔
Throughput x x ✔ ✔ ✔Cost
x x ✔ ✔ ✔
high quality reagents ✔ ✔ x x xUniversal approach
limited x ✔ ✔ ✔Isoform specificity
x ✔ ✔ ✔ ✔
Dynamic ✔ ✔ x ✔ ✔
Summary
The ‘LOPITEERS’
Andy ChristoforouLaurent GattoArnoud GroenAdam GutteresClaire Mulvey
Dan NightingaleNino Nikolovski
Konstanze SchottPavel Shliaha Lisa Simpson
Matthew TrotterYuchong WangHoujiang Zhou
Cambridge Collaborators
Paul DupreeStephanie HallTony Jackson
Alfonso Martinez Arias
Ludovic Vallier
Dirk WaltherMatthias MannPeter James
Isaac Newton Trust