Dale and Betty Bumpers

Vaccine Research CenterNational Institute of Allergy and Infectious DiseasesNational Institutes of Health

Polychromatic Flow CytometryEvaluation 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 CompensationUnder CompensationOver CompensationTransformation/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 Donor HIV+ 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 CD8In healthy individual.

HIV- Individual

NXN plot showsthat 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 A Instrument B

Verifying Sample ValidityCheck 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

Fluo

resc

ence

Time

Fluo

resc

ence

Other Tips for Reliable Polychro DataAvoid 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, I’ve 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 Burkitt’s Lymphoma

EBV first discovered in tumor samples from Burkitt’s 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 don’t know if EBV causes this disease.

• >95% people worldwide are EBV+… but endemic Burkitt’s 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 Burkitt’s? 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 don’t 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 andcalibration), 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 don’t 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 don’t have enough events to analyze?

4) Analyzing the entire dataset

We don’t 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 36 (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 = 0.009p = 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 = 0.009p = 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 = 0.009p = 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+)

CLG 0.015 0.087 0.016

GLC 0.003 0.009 0.008 0.003

LLD 0.010

YLL 0.031

YVL 0.013

CMV 0.81 0.15 0.23 0.57 0.62 0.79 0.12 0.30 0.65 0.99

CD8 0.15 0.84 0.25 0.52 0.71 0.26 0.45 0.68 0.61 0.19

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 Burkitt’s Prevalence

Sporadic Region

No Malaria

Less Mature EBV-specific T-cells(CD127+ 57-)

No Burkitt’s

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 individualRelies 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 analyzeNo human gatingAlgorithm 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)

Group FDG (H>S)% FDG (S>H)%

Holoendemic 32.2 6.8

Sporadic 7.6 35.2

FDG identifies cell populations, across all markers studied, which differ the most between study groups.

Phenotypes with Greatest Differences

CD45ROCCR7CD27

CD127

Naïve-like, stem memory cells? Central MemoryOther /Effector

Elevated in HoloendemicElevated in Sporadic

Freq

uenc

y am

ong

T-ce

llsSp

ecifi

c fo

rEB

V La

tenc

y A

ntige

ns

No malaria exposure > more central memory EBV-sp cells > low Burkitt’s risk

High malaria > more effectors, rapid recruitment of naives? >Burkitt’s risk

Conclusions of EBV Study

Picture that is emerging?

The typical response to this common virus:

The uncommon response to this common virus:

EBVinfection

or reactivation

Maintenanceof central memory EBV specific T-cells

EBVinfection

or reactivation

Differentiation ofEBV specific

T-cells(Malaria)

VAX?

viral?

Response?

Relapse?

Summary

How you employ data analysis techniques to:

1) Evaluate panels2) Ensure data collection is reliable during

experiment3) Make sure analysis is consistent4) Analyze the entirety of the dataset5) 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 : pchattop@mail.nih.gov

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