BMS 631 - LECTURE 10x Flow Cytometry: Theory J. Paul Robinson Professor of Immunopharmacology Professor of Biomedical Engineering Purdue University Multiparameter Data Analysis 3rd Ed. Shapiro p 207-214 Bindley Bioscience Center Purdue University Office: 494 0757 Fax 494 0517 email; robinson@flowcyt.cyto.purdue.edu WEB http://www.cyto.purdue.edu Page 1 © 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 Page 2 © 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 Page 3 © 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 Page 4 © 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 Page 5 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT log PE Using Gates Region 1 established Gated on Region 1 Page 6 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Quadrant Analysis (+ +) log PE ( - +) (- -) (+ -) Page 7 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Drawing Regions: Sample Preparation Sample Quality Data removed From analysis Spores Debris Spores Vegetative Debris B.subtilis spores B.subtilis veg. + spores Page 8 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Complex or Boolean Gating With two overlapping regions, several options are available: Page 9 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Boolean Gating Not Region 1: Page 10 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Boolean Gating Not Region 2: Page 11 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Boolean Gating Region 1 or Region 2: Page 12 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Boolean Gating Region 1 and Region 2: Page 13 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Boolean Gating Not (Region1 and Region 2): Page 14 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Light Scatter Gating 1000 600 800 Scatter Forward Side Scatter Projection Forward Scatter Projection Neutrophils Forward Scatter Projection 200 400 Monocytes 0 Lymphocytes 0 200 400 600 800 1000 90 Degree Scatter Human white blood cells Page 15 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT log PE Back-Gating Back gate Region 4 established Back-gating using Region 4 Page 16 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT 3 Parameter Data Display Isometric Display Page 17 © 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 Page 18 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT DNA Histogram Gating out bad data G2-M S A B C Counts # of Events G0-G1 Fluorescence Intensity Time Page 19 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Multi-color studies generate a lot of data ++ -- +- ++ -- +- ++ -- +- +- ++ -- +- ++ -- +- QUADSTATS Log Fluorescence QUADSTATS -+ -+ ++ -- +- ++ -- +- QUADSTATS Log Fluorescence QUADSTATS -+ -+ ++ -- +- -+ ++ -- +- QUADSTATS Log Fluorescence QUADSTATS -+ 10 Log Fluorescence -- QUADSTATS Log Fluorescence QUADSTATS -+ ++ Log Fluorescence +- -+ Log Fluorescence -- QUADSTATS Log Fluorescence QUADSTATS -+ ++ Log Fluorescence +- -+ Log Fluorescence -- QUADSTATS Log Fluorescence QUADSTATS -+ ++ 9 QUADSTATS -+ ++ -- +- -+ ++ -- +- Log Fluorescence QUADSTATS Log Fluorescence +- +- Log Fluorescence -+ 8 Log Fluorescence -- -- QUADSTATS 7 Log Fluorescence ++ ++ 6 Log Fluorescence -+ -+ 5 Log Fluorescence +- Log Fluorescence -- +- QUADSTATS -+ ++ -- +- Log Fluorescence Log Fluorescence QUADSTATS QUADSTATS QUADSTATS QUADSTATS QUADSTATS QUADSTATS QUADSTATS QUADSTATS QUADSTATS QUADSTATS -+ ++ -- +- Log Fluorescence -+ ++ -- +- Log Fluorescence -+ ++ -- +- Log Fluorescence -+ ++ -- +- Log Fluorescence -+ ++ -- +- Log Fluorescence -+ ++ -- +- Log Fluorescence -+ ++ -- +- Log Fluorescence -+ ++ -- +- Log Fluorescence -+ ++ -- +- Log Fluorescence Log Fluorescence Log Fluorescence Log Fluorescence Log Fluorescence Log Fluorescence Log Fluorescence Log Fluorescence Log Fluorescence Log Fluorescence Log Fluorescence Log Fluorescence Log Fluorescence Log Fluorescence Log Fluorescence Log Fluorescence Log Fluorescence Log Fluorescence Log Fluorescence Log Fluorescence ++ -- QUADSTATS Log Fluorescence QUADSTATS -+ ++ 4 Log Fluorescence +- Log Fluorescence -+ Log Fluorescence -- QUADSTATS Log Fluorescence ++ Log Fluorescence Log Fluorescence QUADSTATS 3 Log Fluorescence 2 Log Fluorescence 1 Log Fluorescence 3 color -+ 5 color 4 color -+ ++ -- +- Log Fluorescence Page 20 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Contour plots Dot plots Page 21 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Typical phenotypic analysis histograms • 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. Page 22 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Kinetic Analysis 50 ng PMA Stimulated 0 ng PMA Unstimulated 0 450 900 1350 1800 TIME (seconds) 0 450 900 1350 1800 TIME (seconds) 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. Page 23 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT 80 70 60 50 40 10 20 30 SSC-Height --> 90 100 120 Color Coded Dot Plots 10 20 30 40 50 60 70 80 90 100 120 10 4 FSC-Height --> 3 R4 2 10 10 1 FL2-Height --> 10 R3 R1 R2 10 1 10 2 10 3 10 4 FL4-Height --> 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. Page 24 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT 50 40 30 0 10 20 Forward Scatter 60 Contour Plot with Projection 0 10 20 30 60 90 Light Scatter 40 50 Page 25 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT 3 Color Combinations 4+4+4=12 PE Positives 4+4=8 FITC Negatives Page 26 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT 3 Color Combinations 10 100 1000 10 100 1000 1 10 1 Texas Red 10 .1 CD3 CD45 Negative 100 FITC 1000 CD20 IgG CD8 (dim) CD2 L .1 1 K CD4 Negative 10 CD5 100 1000 Phycoerytherin PE FITC CD7 CD8 CD20 .1 1 CD8 CD3 CD20 CD2 .1 .1 CD3 CD45 CD8 CD3 CD20 1 Texas Red 1 IgG 100 1000 1000 100 10 CD3 CD4 Negative TR-PE Pattern TR-FITC Pattern CD38 .1 4+4+4=12 Phycoerytherin PE-FITC Pattern FITC Page 27 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Page 28 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Lasers used for multicolor studies Page 29 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Page 30 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Page 31 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Innovative Data Analysis Page 32 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Page 33 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT Some advanced ways of showing data relationships 20 60 100 Classification Enrico Lugli et al, Università di Modena e Reggio Emilia Oral Presentation Immunology section 15.30-17.30 today © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT ? Page 34 Spectral analysis allows classification Page 35 © 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 • Page 36 © 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT