Extracting quantitative information from proteomic 2D gels

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Extracting quantitative information from
proteomic 2-D gels

• Lecture in the bioinformatics course Gene
  expression and cell models
• April 20, 2005
• John Gustafsson
• Mathematical Statistics
• Chalmers

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Proteomics lecturesstarting points

• Anders starting point this Monday
• Lets say that we want to study life at the
  protein level what technologies do we have at
  hand?
• Todays lecture
• How can we get (large-scale) quantitative
  measurements of protein amounts? So that we can
  do statistics and bioinformatics

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Content and structure

• Proteomics
• The 2-D gel technology
• Extracting quantitative information
• Image analysis of 2-D gels
• Comparison with microarrays
• Statistic analysis of quantitative 2-D gel data

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Proteomics
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2-D gel electrophoresis Protein separation and
quantification
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A typical 2-D gel experiment
experimental design
Example
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The image analysis task

• The task
• In each gel image Find and quantify the protein
  spots
• In the group of gel images Match protein spots
  in different images that correspond to the same
  protein
• Issues
• automation
• time

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Pseudo-color superposition 1(3)
0M NaCl
1M NaCl
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Pseudo-color superposition 2(3)
OM NaCl
1M NaCl
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Pseudo-color superposition 3(3)
(red 0M NaCl, blue 1M NaCl)
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The standard solution workflow

• In each gel image
• 1. Background subtraction
• 2. Spot detection
• 3. Spot quantification
• In the group of gel images
• 4. Spot pattern matching

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1. Background subtraction
Before
After
-

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2. Spot detection / image segmentation
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3. Spot quantification
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4. Spot pattern matching
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The typical 2-D gel experiment
experimental design
Example
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Limitations

• Technological
• hydrofobic proteins dont dissolve
• limited pI/size coverage
• limited labeling/staining

• Image analytical
• Limited global matching efficiency of automatic
  algorithms
• Need for time consuming manual guidance
• The image analysis bottle-neck

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Limited global matching efficiency
Voss and Haberl (2000)
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Incomplete spot detection Faint spots
Detected
Not detected
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Incomplete spot detectionClose spots
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Content and structure revisited

• Proteomics
• The 2-D gel technology
• Extracting quantitative information
• Image analysis of 2-D gels
• Comparison with microarrays
• Statistic analysis of quantitative 2-D gel data

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Comparison with microarrays
) recently also two-color
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Variability
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Variance versus mean dependence

• A dot in the plot
• the measurement of one protein
• The quadratic dependence indicates a
  multiplicative error structure

slope2 ? variance ? mean2
(2x5 gel set normal growth condition)
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Why transform the data?

• A mathematical data transformation can be used to
  
• Make errors more normally distributed
• Stabilize variance versus mean dependence
• Then the model on transformed scale is more
  simple than on original scale
• Simplifies the subsequent analysis

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Logarithmic data transformation

• Stabilized variance versus mean dependence after
  a logarithmic data transformation

(2x5 gel set normal growth condition)
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Statistical analysis of quantitative 2-D gel data

• Examples
• Test of differential expression
• Cluster analysis
• cluster proteins
• cluster cell/tissue samples
• Classification
• classify tissue samples (i.e. tumor classes)

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Summary

• Proteomics
• The 2-D gel technology
• Extracting quantitative information
• Image analysis of 2-D gels
• Comparison with microarrays
• Statistic analysis of quantitative 2-D gel data

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An alternative approach to the matching problem

• The standard solution
• First spot detection
• Then matching of point patterns
• An alternative, recent approach
• Matching at the pixel level
• Computationally heavy

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Gel matching at the pixel level
Reference image
Original image
Aligned image
Image warping
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Future alternatives to quantitative 2-D gels?

• Quantitative masspectrometry
• Protein arrays