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“Measuring Antigen Specific T-cells using Surface and Intracellular Staining Polychromatic Flow Cytometry” 4th Annual CFAR Flow Cytometry Wet-Workshop.

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Presentation on theme: "“Measuring Antigen Specific T-cells using Surface and Intracellular Staining Polychromatic Flow Cytometry” 4th Annual CFAR Flow Cytometry Wet-Workshop."— Presentation transcript:

1 “Measuring Antigen Specific T-cells using Surface and Intracellular Staining Polychromatic Flow Cytometry” 4th Annual CFAR Flow Cytometry Wet-Workshop October, Janet Staats Flow Cytometry Core Facility Center for AIDS Research Duke University Medical Center CYTOKINE FLOW CYTOMETRY: This presentation describes the work of a number of individuals at BD Biosciences / BD Immuno-cytometry Systems, as well as external collaborators, in optimizing and applying cytokine flow cytometry assays to the study of antigen-specific T cells.

2

3 Workshop Overview ICS Assay Instrument Qualification
Reagent Qualification Titration Spillover Panel Development / Assay Qualification Operator Qualification Training Proficiency Testing Data annotation Data Analysis Compensation Gating Back-gating Uniform/Standardized Gating Bioinformatics and Statistics

4 Workshop Overview ICS Assay Instrument Qualification
Reagent Qualification Titration Spillover Panel Development / Assay Qualification Operator Qualification Training Proficiency Testing Data annotation Data Analysis Compensation Gating Back-gating Uniform/Standardized Gating Bioinformatics and Statistics

5 Memory CD4 T Cell Response to Ag
IL-2 IL-4 Rantes Apoptosis EVENTS IN THE PATHWAY OF CD4 T CELL ACTIVATION: Measurement of cytokine production can be done within hours of T cell stimulation, before confounding effects of proliferation and/or apoptosis. This allows cytokine flow cytometry (CFC) assays to be highly quantitative. IFNg TNFa APC-T cell interactions Cytokine/Chemokine expression Proliferation/ Death From H. Maecker Duke University Medical Center

6 APC CD4+ T cell T, B, or APC CD8+ CTL
Ag APC CD4+ T cell Whole protein MHC II CD4 cytokines MHC I STIMULATING CD4 VERSUS CD8 CFC RESPONSES: This diagram illustrates that when whole proteins are used for stimulation in CFC assays, the preferred pathway of antigen presentation is via class II MHC. This allows prefential detection of CD4 T cell responses. Some CD8 responses can also be detected, via cross-presentation of soluble antigen on class I MHC. However, since cross-presentation is inefficient, the preferred method of detecting CD8 CFC responses is via use of peptides (or pools of peptides) that bypass the requirement for antigen presentation. peptide T, B, or APC CD8+ CTL Optimal peptide CD8 cytokines MHC I From H. Maecker Duke University Medical Center

7 Response to CMVpp65 Peptide Mix
pp65 protein peptide mix A2 peptide CMV lysate 0.27% 0.27% 0.04% 7.41% CD4 0.19% 2.03% 1.14% 0.87% CD8 From H. Maecker

8 Peptide Mixes 15 a.a. 11 a.a. CMV pp65: pool of 138 peptides
HIV p55: pool of 120 peptides Duke University Medical Center

9 Sampson Clinical Trial: 11-Color Maturation/Function Panel
Basic Subset Markers: CD3 (T-cells) CD4 (T-Helper Subset) CD8 (T-Suppressor Subset) Exclusion Markers: CD14 (Monocytes) CD19 (B-cells) vAmine (Dead cell marker) Maturational Markers: CD45RO – CD45RA CD27 – CD197 (CCR7) CD57 Functional Markers: CD107 IFN- TNF IL-2 Duke University Medical Center

10 Overview of 11-Color Assay
Thursday - Friday Monday Tuesday Wednesday 1. Thaw 2. Stimulate 3. Surface Stain 4. Lyse/Fix 5. Permeabilize 6. IC Stain 7. Acquisition 8. Analysis Brefeldin Monensin 6 h 7+g+M+ g+M+ M+ Rest 6 hrs Wash Wash Wash Wash CD107 cytokine lymphocyte erythrocyte CD8+ CM Response Costim SEB CMVpp65 Amine CD45RA CD197 CD3 CD4 CD8 IFN IL2 TNF CD107 Duke University Medical Center

11 Workshop Overview ICS Assay Instrument Qualification
Reagent Qualification Titration Spillover Panel Development / Assay Qualification Operator Qualification Training Proficiency Testing Data annotation Data Analysis Compensation Gating Back-gating Uniform/Standardized Gating Bioinformatics and Statistics

12 Instrument Performance: Gel smeared on flow cell

13 Instrument Performance: Low Staining Index

14 Instrument Performance: Variance in noise

15 Instrument Calibration:
Use of target channels

16 Instrument calibration vs biological variation
Note: instrument fluorescence detectors are set using established target channel values, allowable range <5% variance daily

17 Instrument calibration vs biological variation
Note: instrument fluorescence detectors are set using established target channel values, allowable range <5% variance daily 1Mar13: DP06_008 9Nov12: 009 & 010 14Nov12: DP06_013 20Nov12: DP06_014 & 017

18 Instrument calibration vs biological variation
3Jan13: DP06_018 6Nov12: DP06_012 16Feb13: DP06_020 23Jan13: DP06_019 1Mar13: DP06_008 9Nov12: DP06_009 14Nov12: DP06_013 20Nov12: DP06_014 9Nov12: DP06_010 20Nov12: DP06_017 Note: instrument fluorescence detectors are set using established target channel values, allowable range <5% variance daily

19 Batch Processing Error CD38 vs HLA-DR Staining on Ctrl 5L
28Feb08 5L CD8+ Lot 05262 04Marb08 5L CD8+ Lot 05262 06Mar08 5L CD8+ Lot 05262 11Mar08 5L CD8+ Lot 05262 Duke University Medical Center

20 Batch Processing Error CD38 vs HLA-DR Staining on Ctrl 5L
26Feb08 5L CD8+ Lot 05262 28Feb08 5L CD8+ Lot 05262 04Marb08 5L CD8+ Lot 05262 06Mar08 5L CD8+ Lot 05262 11Mar08 5L CD8+ Lot 05262 Duke University Medical Center

21 Workshop Overview ICS Assay Instrument Qualification
Reagent Qualification Titration Spillover Panel Development / Assay Qualification Operator Qualification Training Proficiency Testing Data annotation Data Analysis Compensation Gating Back-gating Uniform/Standardized Gating Bioinformatics and Statistics

22 Titration: objective results
MRA

23 Titration: subjective results
MRA

24 Optimization using Spillover Assessments: Using Titration Files to Assess Spreading Error
<Blue B-A> <Blue A-A> Blue Laser CD3AC (5ug/ml) Spillover assessment: After compensation CD3AC showed spilllover into Blue-B detector (FITC channel) <Violet H-A> Violet Laser <Red C-A> <Red B-A> Red A-A Red Laser <Green E-A> <Green D-A> <Green C-A> <Green B-A> <Green A-A> Green Laser Violet G- CD3 AmCyan Ottinger, et. al., Poster #28, 23rd Annual Clinical Cytometry Meeting (2008) Mahnke, et. al. Clin Lab Med September; 27(3): 469-v. Lamoreaux, et. al., Nature Protocols 1, (2006) on line 9 November 2006 Duke University Medical Center

25 Spillover Assessments: CD3 AmCyan (5µg/mL) Spillover into CD27 (0
Spillover Assessments: CD3 AmCyan (5µg/mL) Spillover into CD27 (0.32µg/mL) & CD57 FITC (1.8µg/mL) Spillover from CD3AC interferes with detection of dim CD27 pos cells Spillover from CD3AC does not interfere with detection of CD57 Spillover is acceptable if it does not interfere with proper classification of events mAb concentration may be varied to reduce spillover as long as frequency is unaffected 0.13 9.8e-4 Unstained Unstained 66.3 20.5 SSC CD27 FITC SSC CD57 FITC Conc may be varied to reduce spillover to acceptable limits … again, decreasing conc is ok as long as frequency is unaffected (classification remains accurate) …another way to say same thing : most important that spreading error does not interefere with classification, but ok to happen as long as no interference (certain amount acceptable) 4.58 0.047 CD3 AmCyan CD3 AmCyan Blue B Blue B Duke University Medical Center

26 Tandems Degrade! Ice Dark Fix Controls 6 hours Maecker, et. al.
Duke University Medical Center

27 Ship tandems at 4ºC

28 Panel Development: FS Test

29 Workshop Overview ICS Assay Instrument Qualification
Reagent Qualification Titration Spillover Panel Development / Assay Qualification Operator Qualification Training Proficiency Testing Data annotation Data Analysis Compensation Gating Back-gating Uniform/Standardized Gating Bioinformatics and Statistics

30 Why is Reproducibility Important?
CFSE Standardization Results (13 EXPERT IM Labs): Very high inter-laboratory variability. High background in some laboratories. Responses to Gag and Nef peptide pools were detected in HIV negative (control) donors! Example Gag stimulation HIV negative donor Example CMVpp65 stimulation CMV positive donor % CD8+ CFSE low Laboratory Duke University Medical Center

31 Intra-Operator Comparison

32 ICS Proficiency Testing Results: March 2007
Duke University Medical Center

33 History of Flow-based Proficiency/Standardization Efforts
22 pp Duke University Medical Center

34 Workshop Overview ICS Assay Instrument Qualification
Reagent Qualification Titration Spillover Panel Development / Assay Qualification Operator Qualification Training Proficiency Testing Data annotation Data Analysis Compensation Gating Back-gating Uniform/Standardized Gating Bioinformatics and Statistics

35 Improperly annotated data

36 Workshop Overview ICS Assay Instrument Qualification
Reagent Qualification Titration Spillover Panel Development / Assay Qualification Operator Qualification Training Proficiency Testing Data annotation Data Analysis Compensation Gating Back-gating Uniform/Standardized Gating Bioinformatics and Statistics

37 How would you gate? Markers: CD3 CD4 CD8 IL-2+IFNg (FSC) (SSC)
Duke University Medical Center

38 ICS Standardization Conclusions
ICS assays can be performed by multiple laboratories using a common protocol with good inter-laboratory precision (<20% C.V.), that improves as the frequency of responding cells increases. Gating is a significant source of variability, and can be reduced by centralized analysis and/or use of standardized gating. Cryopreserved PBMC may yield slightly more consistent results than shipped whole blood. Use of pre-aliquoted lyophilized reagents for stimulation and staining can reduce variability. Grouping: <0.1%, %, >0.5% BMC Immunology 2005, 6:13 Duke University Medical Center

39 Gating bias in proficiency panel results
IL2+IFN PE Unstim CEF CMV pp65 0.02% 0.01% 0.16% 0.03% 0.17% 0.21% CD4 FITC Duke University Medical Center

40 Compensation: False Positive CD4 Response to CEF Pepmix
mSA EOLm

41 Compensation: False Positive CD4 Response to CEF Pepmix

42 Backgating: Include CD3dim+ cells in gate

43 Backgating: Include CD8dim+ in gate

44 C (3.1%) B (3.4%) J (4.8%) A (6.8%) K (9.4%) D (10.2%) H (10.2%) F (10.5%) I (12.7%) E (13.4%) G (16.9%) 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.

45 C (3.1%) B (3.4%) J (4.8%) A (6.8%) K (9.4%) D (10.2%) H (10.2%) F (10.5%) I (12.7%) E (13.4%) G (16.9%) 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.

46 CIC ICS Gating Panel 110 labs participated and there were 110 different approaches to gating

47 CIC ICS Gating Panel McNeil et. a. Cytometry A Aug;83(8):728-38

48 Reproducible analysis allows us to measure an expansion of CD4+ CM cells post vaccination with some degree of confidence N CM EM TE E Pre-Vaccination 33% 21% 27% 2% 17% Post-Vaccination 8% 48% 25% Duke University Medical Center

49 Would you know a positive if you saw one?
2xSD? RCV? Outside Normal Range >0.05%? “Another aspect of analysis of ICS experiments that affects reproducibility across laboratories is the question of how to define a positive response. Criteria for determining a positive response can range from simply subtracting background to twofold, threefold, or even fourfold above background to more complex statistical analyses” Roederer. Cytometry Part A, 73A: (2008) Horton et. al. J Immuno Methods, 323:39-54 (2007) Maecker et. al. Cytometry Part A, 69A: (2006) Comin-Anduix et. al. Clin Cancer Res, 12(1): (2006) Duke University Medical Center

50 Workshop Overview ICS Assay Instrument Qualification
Reagent Qualification Titration Spillover Panel Development / Assay Qualification Operator Qualification Training Proficiency Testing Data annotation Data Analysis Compensation Gating Back-gating Uniform/Standardized Gating Bioinformatics and Statistics – Death-by-excel!

51 Assay Complexity Duke University Medical Center

52 Endpoints for 11-Color Maturation/Function Panel DEATH BY EXCEL ……..
Basic (3) Maturation (5) Function Boolean (16) CD4+ CD8- CD4+ CD8+ CD4- CD8+ Naïve Central Memory Effector Memory Effector Terminal Effector CD107 IFN- IL-2 TNF- Basic (3) Maturation (5) Boolean (16) 240/stim X X = X 3 Stimulations/Sample (CoStim, SEB, CMVpp65) = 720 Endpoints/Sample 720 Endpoints/Sample x 200 Samples (192 Participants + 8 Controls) = 144,000 Endpoints/Trial Note 1: Frequency of parent only, reporting units of #cells/µL doubles the total EP/trial Duke University Medical Center

53 Data Annotation - for all 143,280 data points!
Study ID Method Assay Name Batch # Operator Sample ID Visit ID Accession # % Viable (Flow) % Viable (Guava) Recovery CD4 count CD8 count Gate Name (Parameter Names) Tube Name File Name Error Code (1-11) Checking: X1 - for electronic data X3 - for manual entry Requires STRONG statistical support: Quickly exceeds limits of excel Format data for statistical analysis FJ: column (gates) vs row (file) CSV: column (identifiers) vs row (single value) Check data Manual check: 8sec/value x = 49 days!!! Duke University Medical Center

54 Specimen processing matters
N=60 samples, stained across 5 batches Duke University Medical Center

55 Acknowledgements VRC Steve Perfetto Laurie Lamoureaux Mario Roederer
EQAPOL Duke CFAR Kent Weinhold Jennifer Enzor Twan Weaver Cliburn Chan Scott White Duke Tisch Brain Tumor Center Gary Archer Duane Mitchell John Sampson CVC Sylvia Janetski Lisa McNeil Duke University Medical Center

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