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Extracting quantitative information from proteomic 2-D gels

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. Proteomics lectures: starting points. Anders’ starting point this Monday:

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Extracting quantitative information from proteomic 2-D gels

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  1. 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

  2. Proteomics lectures:starting points • Anders’ starting point this Monday: • Let’s say that we want to study life at the protein level – what technologies do we have at hand? • Today’s lecture: • How can we get (large-scale) quantitative measurements of protein amounts? So that we can do statistics and bioinformatics

  3. 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

  4. DNA mRNA 2-D gels Modification Production Degradation P Co-factors Localisation Interaction TDP ACTIVITY Proteomics

  5. molecular charge acidic alkaline large ”protein soup” molecular size small spot volume  protein quantity 2-D gel electrophoresis: Protein separation and quantification

  6. biological experiment control treatment protein extracts 2-D gel electrophoresis 2-D gel images image analysis matrix with spot volume data rows: proteins (many) columns: gels (few) quantified data statistical analysis conclusions A typical 2-D gel experiment experimental design Example:

  7. 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

  8. Pseudo-color superposition 1(3) 0M NaCl 1M NaCl

  9. Pseudo-color superposition 2(3) OM NaCl 1M NaCl

  10. Pseudo-color superposition 3(3) (red: 0M NaCl, blue: 1M NaCl)

  11. 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

  12. 1. Background subtraction Before After - =

  13. 2. Spot detection / image segmentation

  14. spot volume  protein quantity 3. Spot quantification

  15. 4. Spot pattern matching

  16. biological experiment control treatment protein extracts 2-D gel electrophoresis 2-D gel images image analysis matrix with spot volume data rows: proteins (many) columns: gels (few) quantified data statistical analysis conclusions The typical 2-D gel experiment experimental design Example:

  17. Technological hydrofobic proteins don’t 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” Limitations

  18. Limited global matching efficiency Voss and Haberl (2000)

  19. Incomplete spot detection: Faint spots Detected Not detected

  20. Incomplete spot detection:Close spots

  21. 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

  22. Comparison with microarrays *) recently also two-color

  23. growth condition normal 1M NaCl biological replications normal 1M NaCl Variability

  24. Variance versus mean dependence • A dot in the plot: • the measurement of one protein • The quadratic dependence indicates a multiplicative error structure slope=2  variance  mean2 (2x5 gel set; normal growth condition)

  25. 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

  26. Logarithmic data transformation • Stabilized variance versus mean dependence after a logarithmic data transformation (2x5 gel set; normal growth condition)

  27. 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)

  28. 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

  29. 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

  30. Gel matching at the pixel level Original image Aligned image Reference image Image warping

  31. Future alternatives to quantitative 2-D gels? • Quantitative masspectrometry • Protein arrays

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