1. Page 1 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT BMS 631 - LECTURE 10x Flow Cytometry: Theory Bindley Bioscience Center Purdue University Office: 494 0757 Fax 494 0517 email; robinson@flowcyt.cyto.purdue.edu WEB http://www.cyto.purdue.edu Multiparameter Data Analysis 3 rd Ed. Shapiro p 207-214 J. Paul Robinson Professor of Immunopharmacology Professor of Biomedical Engineering Purdue University

2. Page 2 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Data Analysis Gating Data displays –histogram –dot plot –isometric display –contour plot –chromatic (color) plots –3 D projection

3. Page 3 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Gating Real-time gating vs. software gating Establishing regions Gating strategies Quadrant analysis Complex or Boolean gates Back gating

4. Page 4 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Real-Time vs. Software Gating Real-time or live gating: -restrict the data that will be accepted by a computer (some characteristic must be met before data is stored) Software or analysis gating: -excludes certain stored data from a particular analysis procedure

5. Page 5 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Establishing Regions Establishing regions: -objective or subjective? -training/skill/practice Possible shapes: -rectangles -ellipses -free-hand -quadrants Statistics

6. Page 6 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Region 1 established Gated on Region 1 Using Gates log PE

7. Page 7 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Quadrant Analysis log PE (+ +)( - +) (+ -) (- -)

8. Page 8 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Drawing Regions: Sample Preparation Sample Quality B.subtilis sporesB.subtilis veg. + spores Debris Spores Debris Vegetative Data removed From analysis

9. Page 9 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Complex or Boolean Gating With two overlapping regions, several options are available:

10. Page 10 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Boolean Gating Not Region 1:

11. Page 11 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Boolean Gating Not Region 2:

12. Page 12 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Boolean Gating Region 1 or Region 2:

13. Page 13 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Boolean Gating Region 1 and Region 2:

14. Page 14 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Not (Region1 and Region 2): Boolean Gating

15. Page 15 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT 0 200 400 600 8001000 Side Scatter Projection Light Scatter Gating Forward Scatter Projection 90 Degree Scatter Neutrophils Lymphocytes Monocytes Forward Scatter Human white blood cells

16. Page 16 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Back-Gating Region 4 established Back-gating using Region 4 log PE Back gate Back gate

17. Page 17 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT 3 Parameter Data Display Isometric Display

18. Page 18 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Methods that can change results: 1. Doublet discrimination 2. Time as a quality control parameter Example: DNA content -need to eliminate debris & clumps -need to gate out doublets -maintain constant flow rate

19. Page 19 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT DNA Histogram G 0 -G 1 S G 2 -M Fluorescence Intensity # of Events Time Counts A BC Gating out bad data

20. Page 20 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Multi-color studies generate a lot of data 1 2 3 4 5 6 7 8 9 10 3 color 4 color 5 color

21. Page 21 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Contour plots Dot plots

22. Page 22 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT This figure shows two examples of simultaneous 2 color immunophenotyping. In figure 3 (a) the directly labeled MABs used were CD4-PE / CD8-FITC. In this example approximately 50% of the cells were positive for CD4 and 23% positive for CD8. These percentages were calculated based upon the settings of the negative control for 2% positivity. Right figure shows a similar situation for CD2-PE / CD19-FITC. Typical phenotypic analysis histograms

23. Page 23 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Kinetic Analysis 50 ng PMA Stimulated Fluorescence 0 ng PMA Unstimulated TIME (seconds) 018004509001350 TIME (seconds) 018004509001350 Figure: This figure shows an example of stimulation of neutrophils by PMA (50 nm/ml). On the left the unstimulated cells show no increase in DCF fluorescence. On the right, activated cells increase the green DCF fluorescence at least 10 times the initial fluorescence.

24. Page 24 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Color Coded Dot Plots Key to understanding this figure, is the notion that different populations can be identified by colors and the relationship of these populations to one another can be monitored.

25. Page 25 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Contour Plot with Projection 90 Light Scatter 60 50 40 30 20 100 Forward Scatter 60 50 40 30 20 10 0

26. Page 26 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT 3 Color Combinations Negatives Positives 4+4=8 FITC PE APC 4+4+4=12

27. Page 27 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT 3 Color Combinations FITC PE APC 4+4+4=12

28. Page 28 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT

29. Page 29 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Lasers used for multicolor studies

30. Page 30 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT

31. Page 31 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT

32. Page 32 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Innovative Data Analysis

33. Page 33 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT

34. Page 34 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Some advanced ways of showing data relationships 20 60 100 Enrico Lugli et al, Università di Modena e Reggio Emilia Oral Presentation Immunology section 15.30-17.30 today Classification ?

35. Page 35 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Spectral analysis allows classification

36. Page 36 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Conclusions The more parameters you have, the more complex the analysis will be But…when you have more parameters (variables) you have more opportunities for population discrimination Display of data in histogram and dotplot formats assists the analysis process Displays in 3D are nice but not particularly useful for analysis. Multiple parameter displays such as PCA or LDA are more useful for high content data sets