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. 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 Content and structure

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

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

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

7. The image analysis task The task 1.In each gel image: Find and quantify the protein spots 2.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 NaCl1M NaCl

9. Pseudo-color superposition 2(3) OM NaCl1M 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 BeforeAfter - =

13. 2. Spot detection / image segmentation

14. 3. Spot quantification spot volume  protein quantity

15. 4. Spot pattern matching

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

17. Limitations 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”

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. 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 Content and structure – revisited

22. Comparison with microarrays 2-D gelsMicroarrays Labeling one channel*one or two-color Background subtr. yes Spot detection HARDeasy Spot quantitation can be difficultquite easy Spot matching HARDknown Identification MS or reference atlasknown *) recently also two-color

23. Variability normal1M NaCl normal1M NaCl biological replications growth condition

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

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. 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 Summary

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 Reference image Image warping Original imageAligned image

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