Presentation on theme: "Dale and Betty Bumpers Vaccine Research Center National Institute of Allergy and Infectious Diseases National Institutes of Health Polychromatic Flow Cytometry."— Presentation transcript:
Dale and Betty Bumpers Vaccine Research Center National Institute of Allergy and Infectious Diseases National Institutes of Health Polychromatic Flow Cytometry Evaluation of Staining Panels Data Analysis Pratip K. Chattopadhyay, Ph.D.
Data Analysis Perspectives Data analysis is a generic term. Typically, thought of as no more than a means to report cell percentages, but there are data analysis tools, tips, and tricks to: Troubleshoot staining (evaluate staining panels) Check/prove the quality and validity of data Explore biological subsets
Evaluation of Staining Panels How do you test whether panel is working? Once preliminary gating is complete (i.e., excluding dead cells, identifying lymphocytes), examine every combination of markers. For example, if the panel consists of five reagents (A-E), plot A vs. B, A vs. C, A vs. D, A vs. E. Next: B vs. C, B vs. D, B vs. E. Then: C vs. D, C vs. E, and D vs. E. Can do this with N X N plots in FlowJo. Goal: Flag suspicious staining patterns.
N X N Plots Every marker combination in panel. A rapid means to identify problems. Over Compensation Under Compensation Over Compensation Transformation/Compensation
Flag Suspicious Patterns Very little Ki-67 HLA-DR biology? Retitrate Poor CD69 Leaner: Overcomp Poor CD38 Very little CD25
Develop Action Plan for Each Problem Very little expression: Examine different subset (not expressed in CD4, but what about CD8?) Try different sample, try stimulation. Healthy DonorHIV+ Donor
Develop an Action Plan for Each Problem Biologically questionable: Test simpler panel on same sample, compare against commercial reagent, examine other marker combinations to see if reason can be identified. Original problem: Unusually high HLA-DR expression on resting CD8 In healthy individual. HIV- Individual NXN plot shows that some CD3+ HLA-DR gating is imprecise. Some CD3- events are sneaking in. These are probably HLADR+ CD14+ cells. HLA-DR CD27
Other Type of Panel Problems Biologically impossible: Lots of cells double positive for markers that should rarely be co- expressed. (e.g., CD4+ CD8+) Fluorochrome aggregates: Not a problem if events are few/scattered, just gate out. When lots of agg, big reagent problem. Also, messes up transformation. Diagonal populations: Highly correlated expression is rare. Think through whether it is biologically possible, or compensation error. Leaners: Suggest compensation problems. Negative population too bright, or all cells positive: Re-titrate reagent. Too little expression, or poor separation: Compare to another reagent, re-do experiment (just to see if it repeats), simplify panel and build it again. All CD3+ CD127+? No!
Once Satisfied with Panel… Your focus will turn to the generation of reliable data.
Reliable Data = Consistent Instruments Try to avoid changing instruments during study… Instruments can be different! Can discriminate CD38+ and -. Cannot discriminate CD38+ and -, 5pe spread into APC. Instrument AInstrument B
Verifying Sample Validity Check the validity of the data generated. Plot Time vs. All Fl. parameters An experiment where sample introduction into instrument was uneven. In this case, HTS (high-throughput system) was used = uneven fluorescent signal collection. Solved by cleaning and calibrating HTS. Time Fluorescence Time Fluorescence
Other Tips for Reliable Polychro Data Avoid changing reagent lots (especially of in-house conjugates) during large study. (Bridging studies help in clinical settings, where new lot is compared to old.) Test a few times before undertaking large study. See if panel holds up when you stain multiple samples at once, or in a plate. Do a practice run of study conditions (without precious samples) before large studies. Have a means to check panel in every experiment. I keep a well-characterized control, or simply a sample of fresh healthy donor cells, to run each experiment day. Use movies (FlowJo) to rigorously check staining patterns and gating between samples and between experiment days. Do a rough analysis of data after every experiment to identify problems before next set of samples are thawed. Track a sample that is shared between experiments.
Data Analysis Tools So far, Ive shown that certain data analysis tools, tips, and tricks can be used to prove that: 1)The panel works, and that… 2)Data collection is being performed reliably. Next: What methods are available to explore biology in dataset? FlowJo SPICE Frequency Difference Gating
Experimental Setting: EBV and Burkitts Lymphoma EBV first discovered in tumor samples from Burkitts lymphomapatients. Disease described by surgeon (Burkitt) in equatorial Africa (1956). Remains most common malignancy of children there. B-cell lymphoma, involves the jaw or facial bone. Fast growing, aggressive tumor. Highly treatable (but access problematic). Over 50 years later, we dont know if EBV causes this disease. >95% people worldwide are EBV+… but endemic Burkitts is rare. EBV DNA is ubiquitous, found in normal tissue and tumor tissue. Some transformed cells expel EBV DNA. Does abnormal T-cell response to EBV increase risk of Burkitts? 14-color flow.
Data Analysis Challenges 1)60 person study 2)1-million events per participant X 2 tubes, 6 phenotypic markers to describe 7 antigen-specificities (7 epitopes of EBV) 3)How do you know when you dont have enough events to analyze? 4)Analyzing the entire dataset
Data Analysis Challenges 1)60 person study 2)1-million events per participant X 2 tubes, 6 phenotypic markers to describe 7 antigen- specificities (7 epitopes of EBV) Complexity of dataset. We deal with these challenges by batching the analysis. Analyzing all of the samples at once, setting gates for a single representative sample Then, copying these gates to the rest of samples. Advantages: less subjectivity in gating, saves time. Disadvantages: Requires stringent quality control, from instrument (setup and calibration), reagent (titration), reliable data collection
Data Analysis Challenges 1)60 person study 2)1-million events per participant X 2 tubes, 6 phenotypic markers to describe 7 antigen- specificities (7 epitopes of EBV) 3)How do you know if you dont have enough events to analyze? Establishing L.O.D.
Data Analysis Challenges 1)60 person study 2)1-million events per participant X 2 tubes, 6 phenotypic markers to describe 7 antigen- specificities (7 epitopes of EBV) 3)How do you know when you dont have enough events to analyze? 4)Analyzing the entire dataset We dont know what combination of markers defines the important cell type in disease. The relevant population may be defined by + or - expression of a given marker. Or, expression of a given marker may not matter at all in defining the relevant cell type. Thus, to analyze complete dataset, need to compare 3 6 (729) populations across the groups.
Simplified Presentation of Incredibly Complex Experiments SPICE allows you to define categorical variables (groups to compare across), plots the proportion of each cell population (for any combination of markers) for each participant, and calculates the p-values for difference across groups.
p = 0.009 p = 0.008p = 0.003 EBV-GLC Specific T-cells : Holoendemicvs. Sporadic With SPICE, you can easily examine all possible phenotypes, and record significant ones. * Less differentiated = RO+ 127+ 57-
p = 0.009 p = 0.008p = 0.003 EBV-GLC Specific T-cells : Holoendemicvs. Sporadic 4 of the 6 phenotypic markers were hidden (treated as neutral) in these analyses. * Less differentiated = RO+ 127+ 57-
p = 0.009 p = 0.008p = 0.003 EBV-GLC Specific T-cells : Holoendemic vs. Sporadic SPICE does statistics that compare the frequencies between groups (+ = t test; # = rank). * Less differentiated = RO+ 127+ 57-
Malaria Affects Only Differentiation of EBV-Specific T-cells RO+ 127+ RO+ 57- RO+ R7- 57- 127+ RO+ 127- RO+ R7- RO+ R7- 127- PD1+ RO- 127- 27- 57+ 127- RO-27+ 127- Less Differentiated (127+, 57-)More Differentiated (CD127-, 57+, PD1+) CLG0.0150.0870.016 GLC0.0030.0090.0080.003 LLD0.010 YLL0.031 YVL0.013 CMV0.810.150.230.570.620.790.120.300.650.99 CD80.150.840.250.520.710.260.450.680.610.19 CMV-specific and bulk CD8+ T-cells are unaltered by malaria exposure. Does malaria modulate only EBV-specific T-cells, or all CD8+ T-cells? Chart of sig pheno.
Thus… Holoendemic Region High Malaria Prevalence More Mature EBV-specific T-cells (CD127-, 57+) Increased Burkitts Prevalence Sporadic Region No Malaria Less Mature EBV-specific T-cells (CD127+ 57-) No Burkitts And… malaria is affecting only EBV-specific T-cells, providing more evidence that this is an EBV-associated disease.
However… Differences only in certain EBV-specific T-cell populations, and only for certain combinations of markers. Problems: Too few EBV-specific events per individual Relies heavily on subjective gates, based on discrete clusters of cells Alternate analysis: a bioinformatics-based approach Frequency Difference Gating (FDG, FlowJo) Concatenates data from each group; more events to analyze No human gating Algorithm finds regions across all parameters where two groups differ most
EBV Latency-Specific T Cells Elevated in holoendemic malaria (H>S) Elevated in sporadic malaria (S>H) GroupFDG (H>S)%FDG (S>H)% Holoendemic32.26.8 Sporadic7.635.2 FDG identifies cell populations, across all markers studied, which differ the most between study groups.
Phenotypes with Greatest Differences CD45RO CCR7 CD27 CD127 Naïve-like, stem memory cells?Central MemoryOther / Effector Elevated in Holoendemic Elevated in Sporadic Frequency among T-cells Specific for EBV Latency Antigens No malaria exposure > more central memory EBV-sp cells > low Burkitts risk High malaria > more effectors, rapid recruitment of naives? >Burkitts risk
Conclusions of EBV Study Picture that is emerging? The typical response to this common virus: The uncommon response to this common virus: EBV infection or reactivation Maintenance of central memory EBV specific T-cells EBV infection or reactivation Differentiation of EBV specific T-cells (Malaria) VAX? viral? Response? Relapse?
Summary How you employ data analysis techniques to: 1)Evaluate panels 2)Ensure data collection is reliable during experiment 3)Make sure analysis is consistent 4)Analyze the entirety of the dataset 5)Query subtle differences between groups.
Tomorrow : Talk 3 Going even further… … the limitations of these approaches, new automated tools for analysis, new single cell technologies…
Washington Monument (Data analysis can be a monumental effort.) Questions?
Please Note Comments? Questions? Please e-mail : firstname.lastname@example.org@mail.nih.gov This material is provided as a service to the flow cytometry community. Please do not re-package elements of this presentation or copy slides without prior consent and proper attribution.
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