Transcript Lecture 6 and 7

Lecture 6
Comparative analysis
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General workflow for proteomic analysis
Sample
Sample preparation
Protein mixture
Sample separation and visualisation
Comparative analysis
Digestion
Peptides
Mass spectrometry
MS data
Database search
Protein identification
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Sequence of events for
comparative analysis
Scanning of image
Image processing
Spot Detection
Gel Matching
Data analysis
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Scanning of image
Convert ‘analog’ spots on gel
into digital data
High resolution images on
densitometers/imaging systems
For wet or dried gels that have
been stained, X-ray films and
blots
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(Biorad, Biosurplus.com, Institute of Arctic Biology)
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Densitometry
(UIC)
(Proteomic Identification of 14-3-3ζ as an Adapter for IGF-1 and
Akt/GSK-3β Signaling and Survival of Renal Mesangial Cells, Singh et
al., Int J Biol Sci 2007; 3:27-39 )
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Densitometry
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Image Processing
Digital data converted into Gaussian curves.
Algorithms used to smoothen curve, removing statistical
noise
Contrast enhancement to see better spots
Background subtraction to remove meaningless changes
in the background of the gel
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Smoothing Gaussian curves
Raw data
curve #1
curve #2
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(BARS,Statlib)
(CBU Imaging Wiki)
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Contrast enhancement
(brneurosci.org)
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Background subtraction
(NIH Image)
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Image Processing
(Olympus)
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Contrast enhancement
(Olympus)
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Smoothing
(Olympus)
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Background subtraction
(Olympus)
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Spot detection
Automatic detection
aided by manual input
Need to adjust
sensitivity
Too little sensitivity =
missed spots
Too much sensitivity =
false positives
(Biorad)
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Spot detection
Streaks
Overlapping spots
(Biorad)
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Gel Matching
Compare identical spots on
different gels
Matching is seldom 100%
due to variations in
experimental techniques
(staining, gel preparation)
Use of landmarks to
improve matching
Most time-consuming step
(Proteomics – from protein sequence to function, Pennington &
Dunn [editors])
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Gel Matching
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(Biorad)
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Manual spot matching
Matched
Unmatched
(Biorad)
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Data Analysis
After matching, data are
arranged into a table
Subjected to normalisation to
account for inconsistencies in
staining and gel preparation
Normalise by:
•Total gel intensity
•Total intensity of subset of
spots
(Proteomics – from protein sequence to function, Pennington & Dunn [editors])
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Data Analysis
(Biorad)
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e.g. with CyDye (GE Bioscience)
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Internal standard to make sure that abundance is normalised and variation
Is due to biological variation rather than gel-to-gel variation
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Lecture 7
In-gel digestion
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General workflow for proteomic analysis
Sample
Sample preparation
Protein mixture
Sample separation and visualisation
Comparative analysis
Digestion
Peptides
Mass spectrometry
MS data
Database search
Protein identification
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Rationale for digestion of proteins
Error is proportional to mass of the protein
PTMs further complicate assignments based on mass
Sensitivity of MS measurement increases with the use of
smaller peptides (6-20 amino acids)
Proteases are able to cut at specific amino acid residues
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Trypsin
Arginine or Lysine
(ExPasy PeptideCutter)
Proline
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Chymotrypsin
Leucine, Methionine and
Histidine (minor)
Tryptophan, Tyrosine and
Phenylalanine (major)
(ExPasy PeptideCutter)
Proline
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Peptide masses from tryptic digest
Peptide Cutter
(Mass Spectrometric Sequencing of Proteins from Silver-Stained Polyacrylamide Gels,
Shevchenko et al., Anal. Chem. 1996, 68, 850-858)
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Typical protocol for in-gel digestion
•Excision of Commassie stained spot(s) from gel(s)
•Destaining with NH4HCO3 /acetonitrile
•Reduction with DTT
•Alkylation with IAA
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Overview of in-gel digestion
•Absorption of minimal amount of trypsin into gel (on
ice)
•Overnight incubation of trypsin at 37ºC
•Extraction of peptides from gel with 5% formic acid in
NH4HCO3 /acetonitrile, or trifluoroacetic acid
•Clean up by ZipTips (removes ionic salts)
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