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2013 Duke CFAR Flow Cytometry Workshop

2013 Duke CFAR Flow Cytometry Workshop. Data Analysis. Results from Pre-Workshop Analysis Comp Profile. Results from Pre-Workshop Analysis. Results from Pre-Workshop Analysis. Results from Pre-Workshop Analysis. Results from Pre-Workshop Analysis. Elements of Data Analysis.

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2013 Duke CFAR Flow Cytometry Workshop

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  1. 2013 Duke CFAR Flow Cytometry Workshop Data Analysis

  2. Results from Pre-Workshop AnalysisComp Profile

  3. Results from Pre-Workshop Analysis

  4. Results from Pre-Workshop Analysis

  5. Results from Pre-Workshop Analysis

  6. Results from Pre-Workshop Analysis

  7. Elements of Data Analysis • Compensation – electronic adjustment for spectral overlap • When to compensate • Acquisition – if gating on #CD3+, requires compensation • Off-line • Spillover • Biexponential Transformation • Gates • Analysis Regions • Backgating – used to tweak gates and analysis regions so as to optimize response (maximize positive and minimize negative responses) • Training

  8. Elements of Data Analysis • Compensation – electronic adjustment for spectral overlap • When to compensate • Acquisition – if gating on #CD3+, requires compensation • Off-line • Spillover • Biexponential Transformation • Gates • Analysis Regions • Backgating – used to tweak gates and analysis regions so as to optimize response (maximize positive and minimize negative responses) • Training

  9. C (3.1%) B (3.4%) J (4.8%) A (6.8%) K (9.4%) D (10.2%) H (10.2%) F (10.5%) G (16.9%) I (12.7%) E (13.4%) • Here labs are listed in order of their total TNFa response. It is visually apparent that, while all labs had overcompensation, it is worst in labs with the lowest cytokine responses.

  10. JO Analysis Modified comp FlowJo A700-PCPCy5.5 = 6 & PCPCy5.5-PEA610 = 235 JO Analysis Autocomp FlowJo A700-PCPCy5.5 = 29.32 JO Analysis Modified comp FlowJo A700-PCPCy5.5 = 6 Inaccurate Automated Compensation:Requirement for Manual Adjustment HM analysis Diva (Lab J) CD28 PCP-Cy5.5 CD3 A700 Note: Green laser excitation for both PerCPCy5.5 & PEA610

  11. Compensation: Inspect and Manually Correct as Needed Auto Manually adjusted PE-PEA610 = 12.87 PE-PEA610 = 11

  12. EQAPOL: example of compensation affecting cytokine results

  13. Comp Profile

  14. “Corrected” Matrix (Auto-comp w/ Manual tweaking) Original vs Manually-tweaked FlowJo Compensation Values Original Matrix (Auto-comp) Note 1: Compensation pairs discussed during the call are marked with pink arrows. Red arrows indicate other compensation pairs I felt could benefit from manually tweaking compensation values. Note 2: flowjo automatically flags manual edits using red text; all other differences are flowjo doing weird rounding/display stuff (ex. for PEA610-PE “590” is really “59.36;” the value has not been modified… this drives me NUTS!

  15. Compensation Cannot Correct Spreading Error

  16. Elements of Data Analysis • Compensation – electronic adjustment for spectral overlap • When to compensate • Acquisition – if gating on #CD3+, requires compensation • Off-line • Spillover • Biexponential Transformation • Gates • Analysis Regions • Backgating – used to tweak gates and analysis regions so as to optimize response (maximize positive and minimize negative responses) • Training

  17. 5 10 4 10 20.6 3 10 <G710-A>: CD4 CY55PE 2 10 0 2 3 4 5 0 10 10 10 10 <B515-A>: IFNg FITC 5 10 4 10 3 10 2 10 41 0 2 3 4 5 0 10 10 10 10 <G710-A>: CD4 CY55PE <B515-A>: IFNg FITC No Biexponential Transformation:Off-scale Negative Affects Gate Placement Original gate Revised gate CD4 PE-Cy5.5 IFNFITC

  18. FlowJo v8.3.3 (Rm 120 G5): BiExponential Transformation of Specimen 1 Tube 1 (Unstim) CD4+ Gate

  19. Elements of Data Analysis • Compensation – electronic adjustment for spectral overlap • When to compensate • Acquisition – if gating on #CD3+, requires compensation • Off-line • Spillover • Biexponential Transformation • Gates • Analysis Regions • Backgating – used to tweak gates and analysis regions so as to optimize response (maximize positive and minimize negative responses) • Training

  20. CIC Gating Panel: Gating Recommendations

  21. CIC Gating Panel: Gating Recommendations (examples of adequate analysis)

  22. CIC Gating Panel: Gating Recommendations (examples of inadequate analysis)

  23. Elements of Data Analysis • Compensation – electronic adjustment for spectral overlap • When to compensate • Acquisition – if gating on #CD3+, requires compensation • Off-line • Spillover • Biexponential Transformation • Gates • Analysis Regions • Backgating – used to tweak gates and analysis regions so as to optimize response (maximize positive and minimize negative responses) • Training

  24. EQAPOL: example of backgates showing CD3 dim+ excluded from gate

  25. A Before Backgate IFNg Backgate After Backgate Exclusion CD3 AmCyan B CD4 Gated CD8 Gated 5.23 0.27 Before Backgate CD8 APC-Cy7 0.38 5.74 CD4 PerCP-Cy5.5 After Backgate IFNg PE-Cy7 BACKGATING: purity & recovery Duke University Medical Center

  26. Elements of Data Analysis • Compensation – electronic adjustment for spectral overlap • When to compensate • Acquisition – if gating on #CD3+, requires compensation • Off-line • Spillover • Biexponential Transformation • Gates • Analysis Regions • Backgating – used to tweak gates and analysis regions so as to optimize response (maximize positive and minimize negative responses) • Training

  27. Intra-Operator Comparison: Original Analysis N=5 FTE analyzing 8 stims 12 colors

  28. Intra-Operator Comparison: Original Analysis N=5 FTE analyzing 8 stims 12 colors

  29. 5 10 4 10 20.6 3 10 <G710-A>: CD4 CY55PE 2 10 0 2 3 4 5 0 10 10 10 10 <B515-A>: IFNg FITC 5 10 4 10 3 10 2 10 41 0 2 3 4 5 0 10 10 10 10 <G710-A>: CD4 CY55PE <B515-A>: IFNg FITC Intra-Operator Analysis:12 Color ICS NM Analysis - CD3+ Lymphocytes Gated Original gate Revised gate CD4 PE-Cy5.5 IFNFITC

  30. original Intra-Operator AnalysisBefore & After Correcting CD4- & CD8- Gates final

  31. Intra-Operator Analysis: Same data file created in different FlowJo versions but pasted from the exact same FlowJo File (preferences identical) Created in V6.4.2 Opened & copied in V6.4.6 -looks correct Created in V6.4.6 Opened & copied in V6.4.6 -looks bad

  32. Intra-Operator AnalysisBefore & After FlowJoManual Transformation

  33. Intra-Operator Comparison:Functional Values

  34. Gating Strategy

  35. Gating Strategy for 11-Color Maturation/Function Panel: 1 of 3 57.8 88.3 <G710-A>: CD4 CY55PE 0.79 FSC-H SSC-A <Violet H-A>: vAmine CD14PB CD19 PB 99.3 41.4 36.3 FSC-W <V705-A>: CD8 Q705 FSC-A <Violet G-A>: CD3 Amcyan Basic Gates: - 3 total Ungated Singlets CD3+ Exclusion- SSC-A FSC-H Exclusion (Violet H) FSC-W CD3 AmCyan FSC-A Scatter CD4+CD8- CD4+CD8+ CD4 PerCP-Cy5.5 CD8+CD4- CD8 Alexa700 Duke University Medical Center

  36. Gating Strategy for Sampson 11-Color Maturation/Function Panel: 2 of 3 54.1 28.6 56.4 43 <G660-A>: CD27 CY5PE <G660-A>: CD27 CY5PE 2.58 8.46 0.33 6.55 62.5 22 22.9 3.98 <G660-A>: CD27 CY5PE <V545-A>: CD57 Q545 1.07 13.2 5.67 0.12 <V545-A>: CD57 Q545 <V545-A>: CD57 Q545 3.98 11.7 21.5 51.7 55.9 24.2 42.9 56.9 Maturational Gates: - 5 per basic subset CD4+CD8- CD4+CD8+ N CM TE E CD8+CD4- CD27 APC-Alexa750 N CM TE E CD57 FITC N CM TE E CD57 FITC CD27 APC-Alexa750 EM CD57 FITC CD27 APC-Alexa750 EM CD45RO ECD EM CD45RO ECD CD45RO ECD Central Memory EffectorMemory Terminal Effector Naive Effector Central Memory EffectorMemory Terminal Effector Naive Effector Central Memory EffectorMemory Terminal Effector Naive Effector Duke University Medical Center

  37. Gating Strategy for Sampson 11-Color Maturation/Function Panel: 3 of 3 4.19 1.14 2.59 <R710-A>: CD107a AX680 0.31 Functional & Boolean Gates: - 4 functional gates per maturational subset CM: CD8+CD4- CD107 IFN- Backgate! IL-2 TNF- Duke University Medical Center

  38. Gating Strategy for Sampson 11-Color Maturation/Function Panel: 3 of 3 4.19 1.14 2.59 <R710-A>: CD107a AX680 0.31 Functional & Boolean Gates: Polyfunctional (1: ++++) - 4 functional gates per maturational subset - 16 boolean gates per maturational subset Polyfunctional (4: +++) CM: CD8+CD4- CD107 Bifunctional (6: ++) IFN- Boolean Gates Key: 7 = CD107 g = IFN- 2 = IL-2 T = TNF- IL-2 Monofunctional (4: +) TNF- Nonfunctional (1: ----) Duke University Medical Center

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