Multicolor Flow Cytometry Workshop

(1)

Multicolor Flow Cytometry Workshop

Holden Maecker, PhD

(2)

Learning Objectives

•

Explain the critical aspects of digital and multicolor flow cytometry that make it different from traditional analog flow cytometry with 1–4 colors

•

Describe the role of instrument configuration in the performance of multicolor flow cytometry

•

Perform instrument QC using BD

^TM

Cytometer Setup and Tracking beads, and understand the use of baseline and application settings in BD FACSDiva

^TM

software

•

Design a robust multicolor reagent panel, understanding the role of spillover, tandem dyes, and antibody titration

•

Create appropriate controls for a multicolor experiment, and be able

to find and correct potential problems in multicolor data

(3)

Schedule: Day 1

9:00–10:30 I. Introduction, review of basic concepts 10:30–10:45 Break

10:45–11:45 II. Digital and multicolor flow cytometry Exercise 1: Adjusting biexponential displays

12:00–1:00 Lunch

1:00–2:00 III. Instrument setup, optimization, and QC Exercise 2: Determining stain index and spill index 2:00–4:00 Acquisition of data:

• Instrument characterization using CS&T

• Baseline and application settings determination

• Compensation using BD^™ CompBeads

• 8-color stained PBMCs

(4)

Schedule: Day 2

9:00–10:00 IV. Design and optimization of multicolor panels:

• Selection of fluorochromes

• Matching fluorochromes with antibody specificities

• Determining application-specific settings

Demonstration: Visualizing data on a virtual cytometer 10:00–12:00 Data analysis in BD FACSDiva software

12:00–1:00 Lunch

1:00–2:30 V. Controls and Data QC

Exercise 3: Finding and correcting a spillover problem 2:30–2:45 Break

2:45–4:00 Review and summary, discussion of participant issues

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Multicolor Flow Cytometry Workshop:

I. Review of Flow Cytometry Basics

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Outline

• Definitions, what can be measured by flow cytometry

• Fluidics: Sheath and sample streams, flow cells, sorting

• Optics: Lasers, filters

• Electronics: PMTs, signal processing

• Fluorochromes: Spectra, spillover

• Data analysis: FCS files, gating, statistics

(7)

Definitions

• Flow cytometry: The study of cells as they move in fluid suspension, allowing multiple

measurements to be made for each cell.

• FACS

^TM

: Fluorescence-activated cell sorting

(8)

What Measurements Can Be Made?

• Forward scatter (FSC): Proportional to cell size

• Side scatter (SSC): Proportional to cell granularity

• Fluorescence:

- Binding of fluorescent-labeled antibodies - Ca

⁺⁺

-sensitive dyes within cells

- Fluorescent proteins expressed by cells

- Binding of DNA dyes

(9)

Scatter Profile of Lysed Whole Blood

Side Scatter

Forward Scatter

0 200 400 600 800 1000 02004006008001000

Largest and most granular population

Smallest and least

granular population Lymphocytes

Monocytes Granulocytes

(10)

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Major Components of a Flow Cytometer

• Sample injection port

• Sheath and waste reservoirs

• Flow cell

• Laser(s)

• Optical filters

• Photomultiplier tubes ( PMTs ) or photodiodes

• Signal processor

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Cytometer Fluidics Create Laminar Flow

Sample stream

Sheath stream

Cell Laser beam

Flow cell

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Cell Sorting

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Multicolor Experiment Cytometer Configuration

PMT

Longpass filter Bandpass

filter

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Background and Autofluorescence

All cells have a certain level of background fluorescence due to:

• Autofluorescence from pigments and fluorescent moieties on cellular proteins

• Non-specifically bound antibodies and free antibody in the sample stream

The level of autofluorescence varies with the wavelength of excitation and collection:

• Highest in FITC, PE detectors

• Lowest in far red (APC, Cy™7) detectors

(16)

Fluorescence Sensitivity

Detection Efficiency (Q): The number of photoelectrons generated per molecule of fluorophore

• Dependent upon fluorophore, laser, filters, PMT sensitivity, voltage gain setting, etc.

Background (B): Non-specific signal intrinsic to the system

• Dependent upon autofluorescence, unbound fluorophore, ambient

light, etc.

(17)

Common Fluorophores for Ab Conjugation

FLUOROCHROME Type of molecule Typical excitation laser Approximate emission peak

Fluorescein

isothyocyanate (FITC) Small organic 488 nm 518 nm

Alexa Fluor® 488 Small organic 488 nm 518 nm

Phycoerythrin (PE) Protein 488 or 532 nm 574 nm

PE-Texas Red® Protein tandem 488 or 532 nm 615 nm

PE-Cy™5 Protein tandem 488 or 532 nm 665 nm

Peridinin chlorophyll

protein (PerCP) Protein 488 or 532 nm 676 nm

PerCP-Cy™ 5.5 Protein tandem 488 or 532 nm 695 nm

PE-Cy™ 7 Protein tandem 488 or 532 nm 776 nm

Allophycocyanin (APC) Protein 633 nm 659 nm

APC-Cy™ 7 or APC-H7 Protein tandem 633 nm 784 nm

Pacific Blue^TM Small organic 405 nm 454 nm

AmCyan Protein 405 nm 487 nm

(18)

Fluorescence Spillover

Emission of FITC in the PE channel

(19)

Compensating for Spillover

Uncompensated Compensated

FITC mean fluorescence PE mean fluorescence --- --- Negative Positive Negative Positive --- --- --- ---

Uncompensated 125 3,540 185 1,650

Compensated 125 3,560 135 135

1,650 – 185 3,540 – 125

% Spillover = X 100

(20)

FCS Files

• FCS 2.0 and FCS 3.0 conventions

• Often referred to as list-mode files

• Contain all of the measurements (FSC-H, FSC-A,

SSC-H, SSC-A, FL1-H…) for each individual cell

processed in a given sample

(21)

Hierarchical Gating

(22)

Web Reference Tools

• BD Spectrum Viewer:

www.bdbiosciences.com/spectra

• Maecker lab weblog:

http://maeckerlab.typepad.com

(protocols, manuscripts, literature updates)

(23)

Multicolor Flow Cytometry Workshop:

II. Digital & Multicolor Flow Cytometry

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Differences From Analog Instruments

• Optics: Fiber optics and octagons/trigons

• Fluidics: Optimized for high flow rates

• Electronics: Digital signal processing

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PMT Octagon and Trigon

PMT

Longpass filter

Bandpass

filter

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Filter Nomenclature Conventions

Longpass (LP) filter: Allows light above a certain wavelength to pass, reflects shorter wavelengths

• Example: 505 LP = 505 nm longpass

Bandpass (BP) filter: Allows light within a certain range of wavelengths to pass (above and below a specified

midpoint)

• Example: 530/30 BP = 515–545 nm bandpass

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Effect of Flow Rate

• Higher flow rates mean a broader sample stream ( less precise focusing)

• Less precise focusing means less accurate fluorescence measurement of dim populations ( population spreading)

• Higher flow rates also increase the frequency of

coincident events (can be gated out based on FSC area vs height)

• In practice, flow rates of 8,000–12,000 events per second are acceptable on the BD™ LSR II (vs 2,000–3,000

events per second on a BD FACSCalibur™ flow

cytometer)

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Digital Signal Processing

Generates high resolution fluorescence values that can include negative numbers

• No compression of populations at the low end of the fluorescence scale

• More accurate representation of dim populations

Allows compensation to be performed in the software at any time

• Uncompensated data and any associated compensation matrix are both stored

• Compensation can be changed at any time

Peak area and peak height can both be recorded for all

parameters

(29)

Biexponential Display of Digital Data

0 10³ 10⁴ 10⁵

10¹ 10² 10³ 10⁴ 10⁵

10¹ 10² 10³ 10⁴ 10⁵ 10¹

10² 10³ 10⁴ 10⁵

Uncompensated Compensated

Log

Biexp

0 10³ 10⁴ 10⁵

A B

C D

APC Area

APC-Cy7 Area

Antibody capture beads stained with 3 levels of an APC reagent

The transformed display shows aligned populations In the APC-Cy7 dimension

All populations align correctly

(30)

Spillover Affects Resolution Sensitivity

Without CD45 AmCyan With CD45 AmCyan

CD19 FITC

(31)

Conclusions

• Optical platforms using octagons and trigons result in more efficient light collection and flexibility in the use of detectors and filters

• BD LSR II fluidics allow running at higher flow rates with minimal compromise to the data

• Digital signal processing provides more accurate

representation of dim populations, and more accurate and flexible compensation—but logarithmic data display may not be appropriate

• More colors mean more spillover issues, with loss of

resolution sensitivity in affected detectors

(32)

Exercise 1

Adjusting biexponential displays:

1. Open the FCS file “exercise1.fcs”

2. Gate on small lymphocytes, then on double-positive events for CD45 AmCyan vs CD3 Pacific Blue

3. From this gate, create a plot of CD4 FITC vs CD8 APC-H7 4. Turn on biexponential scaling for the x- and y-axes, and

note the changes to the plots

5. Turn on manual biexponential scaling and experiment with various scaling factors for FITC and APC-H7, noting how the plots change

Questions:

1. Would gating be affected by biexponential scaling?

2. Is it important to use the same scaling for all samples in an

experiment?

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Multicolor Flow Cytometry Workshop:

III. Instrument Setup and QC

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Outline

1. Configure your instrument

2. Characterize your instrument 3. Design your panel

4. Optimize settings for your panel 5. Run appropriate controls

6. QC your data

(35)

Outline

1. Configure your instrument

• Number and type of lasers

• Number of PMTs per laser

• Choice of filters and dichroic mirrors

These choices will determine:

• What fluorochromes you can use effectively

• How well certain fluorochrome combinations will perform

(36)

How Do We Measure Performance?

W2

W1

D

Where D = difference between positive and negative peak medians W = 2 x rSD (robust standard deviation)

Stain Index = D / W

Resolution Sensitivity

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CD127 PE

300 400 500 600 700 0

5 10 15 20 25 30 35 40

25 mW green laser (532 nm) 100 mW blue laser (488 nm) 25 mW blue laser (488 nm)

PMT voltage

Stain index

An Example: Green vs Blue Lasers

• Green laser is more efficient for PE and PE tandems

• Blue laser is more efficient for FITC, PerCP, and GFP

(38)

Second Example: Filters and Spillover

(39)

Outline

2. Characterize your instrument

• Obtain minimum baseline PMT settings

• Track performance over time

This allows you to:

• Run the instrument where it is most sensitive

• Be alert to changes in the instrument that might affect

performance

(40)

SD_EN= 20

Baseline PMTV is set by placing the dim bead MFI to equal 10X SD Baseline PMTV is set by placing the dim bead MFI to equal 10X SD_EN_EN

460 V

Automated Baseline PMT Voltage Determination Using BD CS&T

MFI= 200

(41)

Performance Tracking

A variety of parameters can be tracked:

• Linearity, CVs, laser alignment

• PMT voltages must hit target values

Data can be visualized in Levey-Jennings plots:

400 425 450 475 500 525 550

10/22/04 11/11/04 12/01/04 12/21/04 01/10/05 01/30/05 02/19/05 03/11/05

Time

PMT Voltage

FITC Channel (Blue laser) FITC Channel (Blue laser)

(42)

Exercise 2

Calculating stain index and spill index:

1. Open the FCS file “exercise2.fcs” (AmCyan Compbeads) 2. Calculate the stain index in the primary detector (AmCyan)

by determining:

[Median (positive peak)] - [Median (neg peak)]

2 x rSD (neg peak)

3. Calculate the spill index in FITC by determining the FITC stain index as above, then calculating:

[Stain index (FITC) / Stain index (AmCyan)]

Questions:

1. What is an acceptable stain index?

2. How high can the spill index be before it is problematic?

(43)

Stain Index for Various Fluorochromes

Reagent Clone Filter Stain Index

PE RPA-T4 585/40 356.3

Alexa Fluor^®647 RPA-T4 660/20 313.1

APC RPA-T4 660/20 279.2

PE-Cy7 RPA-T4 780/60 278.5

PE-Cy5 RPA-T4 695/40 222.1

PerCP-Cy5.5 Leu-3a 695/40 92.7

PE-Alexa Fluor^® 610 RPA-T4 610/20 80.4 Alexa Fluor^® 488 RPA-T4 530/30 75.4

FITC RPA-T4 530/30 68.9

PerCP Leu-3a 695/40 64.4

APC-Cy7 RPA-T4 780/60 42.2

Alexa Fluor^® 700 RPA-T4 720/45 39.9

Pacific Blue™ RPA-T4 440/40 22.5

AmCyan RPA-T4 525/50 20.2

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Antibody Cocktail for Data Acquisition

• CD4 FITC

• CD127 PE

• HLA-DR PerCP-Cy™5.5

• CD45RA PE-Cy7

• CD25 APC

• CD8 APC-H7

• CD3 V450

• CD45 AmCyan

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Schedule: Day 2

9:00–10:00 IV. Design and optimization of multicolor panels:

• Selection of fluorochromes

• Matching fluorochromes with antibody specificities

• Determining application-specific settings

Demonstration: Visualizing data on a virtual cytometer 10:00–12:00 Data analysis in BD FACSDiva software

12:00–1:00 Lunch

1:00–2:30 V. Controls and Data QC

Exercise 3: Finding and correcting a spillover problem 2:30–2:45 Break

2:45–4:00 Review and summary, discussion of participant issues

(46)

Multicolor Flow Cytometry Workshop:

IV. Panel Design & Application Settings

(47)

Outline

3. Design your panel

• Reserve the brightest fluorochromes for the dimmest markers and vice versa

• Avoid spillover from bright populations into detectors requiring high sensitivity

• Beware of tandem dye issues

• Titrate antibodies for best separation

This allows you to:

• Maintain resolution sensitivity where you need it most

• Avoid artifacts of tandem dye degradation

(48)

Various Fluorochromes—Stain Index

Reagent Clone Filter Stain Index

PE RPA-T4 585/40 356.3

Alexa Fluor^®647 RPA-T4 660/20 313.1

APC RPA-T4 660/20 279.2

PE-Cy7 RPA-T4 780/60 278.5

PE-Cy5 RPA-T4 695/40 222.1

PerCP-Cy5.5 Leu-3a 695/40 92.7

PE-Alexa Fluor^® 610 RPA-T4 610/20 80.4 Alexa Fluor^® 488 RPA-T4 530/30 75.4

FITC RPA-T4 530/30 68.9

PerCP Leu-3a 695/40 64.4

APC-Cy7 RPA-T4 780/60 42.2

Alexa Fluor^® 700 RPA-T4 720/45 39.9

Pacific Blue™ RPA-T4 440/40 22.5

AmCyan RPA-T4 525/50 20.2

(49)

Spillover Affects Resolution Sensitivity

Without CD45 AmCyan With CD45 AmCyan

CD19 FITC

Note that this is only an issue when the two markers (CD45

and CD19) are co-expressed on the same cell population.

(50)

Special Requirements for Tandem Dyes

Compensation requirements for tandem dye conjugates can vary, even between two experiments with the same antibody

• Degrade with exposure to light, temperature, and fixation

• Stained cells are most vulnerable

Solutions:

• Minimize exposure to above agents

• Use BD stabilizing fixative if a final fix is necessary

• Run label-specific compensation

(51)

False Positives Due to Tandem Degradation

A.

False positives in APC channel reduced in absence of APC-Cy7

False positives in PE channel remain

Gating scheme CD8 APC-Cy7+ cells CD4 PE-Cy7+ cells

B.

With CD8 APC-Cy7 and CD4 PE-Cy7:

Without CD8 APC-Cy7:

(52)

(53)

Antibody Titration Basics

For most purposes, the main objective is to maximize the signal-to-noise ratio (pos/neg separation)

• This may occur at less than saturating antibody concentrations

• This may or may not be the manufacturer’s recommended titer, depending on the application

Titer is affected by:

• Staining volume (eg, 100 L)

• Number of cells (not critical up to ~5 x 10

⁶

)

• Staining time and temperature (eg, 30 min at RT)

• Type of sample (whole blood, PBMCs, etc)

(54)

Antibody Titration Example

1 10

100 10001

10 100 1000

10000

signal

noise S:N

ng antibody

(55)

Outline

4. Optimize settings for your panel

• Derive experiment-specific PMT settings

• Run compensation controls for each experiment

This allows you to:

• Use the most appropriate settings for your panel

• Avoid gross errors of compensation

(56)

Application Settings for a New Panel (I)

Balancing detectors and checking spillover:

1. Start with the current baseline CS&T settings

2. Run single-stained BD

^TM

CompBeads to see if all populations are on scale

• Decrease voltage if positives are off-scale

• Increase voltage if the negative mean is below zero

3. Verify that each positive bead is at least 2x brighter in its primary detector vs other detectors (use the unstained control worksheet)

• If not, increase voltage in the primary detector

• Spill indexes for all combinations should be <0.8

(57)

Application Settings for a New Panel (II)

Optimizing voltages for cells of interest:

1. Run fully-stained cells and:

• Decrease voltages for any detectors where events are off- scale

• Increase voltages for any detectors where low-end resolution is poor (SD

_{NEG PEAK}

should be 5–10x SD

_EN

)

2. Save application settings

3. Run single-stained BD CompBeads and calculate compensation

4. Run samples

(58)

Application Settings for an Existing Panel

1. Start with the current CS&T settings

2. Apply previously saved application settings

3. Run single-stained BD CompBeads and calculate compensation

4. Run samples

(59)

Demonstration

Visualizing data using a virtual cytometer:

1. Demonstration of data display as PMT voltages change

2. Note the percentage of variance due to electronic noise at different voltages

Questions:

1. What percentage of the variance contributed by electronic noise is acceptable?

2. Do you need to calculate this for all detectors and all panels?

3. Is there such a thing as too high a voltage?

(60)

Multicolor Flow Cytometry Workshop:

V. Controls and Data QC

(61)

Outline

5. Run appropriate controls

• Instrument setup controls (eg, voltage and compensation determination)

• Gating controls (eg, FMO)

• Biological controls (eg, unstimulated samples, healthy donors)

This allows you to:

• Obtain consistent setup and compensation

• Gate problem markers reproducibly

• Make appropriate biological comparisons and conclusions

(62)

BD CompBeads as Single-Color Controls

BD CompBeads provide a

convenient way to create single- color compensation controls:

• Using the same antibodies as in the experimental samples

• Creating a (usually) bright and uniform positive fluorescent peak

• Without using additional cells

(63)

Frequent Compensation Questions

Do I need to use the same antibody for compensation as I use in the experiment?

• Yes, for certain tandem dyes (eg, PE-Cy7, APC-Cy7) Are capture beads better than cells for compensation?

• Usually, as long as the antibody binds to the bead and is as bright or brighter than stained cells

Should compensation controls be treated the same as experimental samples (eg, fixed and permeabilized)?

• Yes, although with optimal fix/perm protocols this may

not make much difference

(64)

Gating Controls

Isotype control: Non-specific antibody of same isotype as the test antibody. For example :

• IgG

₁

FITC + IgG

_2a

PE + IgG

₁

APC

Fluorescence-minus-one (FMO) control: All test antibodies except the one of interest. For example :

• CD3 FITC + CD4 APC (no PE)

Combined control: All test antibodies except the one of interest, which is replaced by an isotype control. For example :

• CD3 FITC + IgG

_2a

PE + CD4 PE

Biological controls can sometimes be used as gating controls.

(65)

Gating Controls (continued)

• Isotype controls don’t take spillover into account

• FMO controls don’t take background staining into account

• Combined controls take both into account, but still may

not accurately represent the background staining of the

test antibody

(66)

Comparison of Gating Controls

(67)

Consider Using Lyophilized Reagents

• Lyophilization provides increased stability, even at room temperature or 37°C

• One batch of reagents can be used for an entire longitudinal study

• Pre-configured plates (BD Lyoplate™ plates) can avoid errors of reagent addition

• Complex experiments (multiple stimuli, multiple polychromatic staining cocktails) become easier

• Lyophilized cell controls can provide run-to-run

standardization

(68)

Outline

6. QC your data

• Visually inspect compensation

• Visually inspect gating

• Set sample acceptance criteria

This allows you to:

• Avoid classification errors and false conclusions due to

improper compensation and/or gating, or sample artifacts

(69)

Visually Inspect Compensation

• Create a template containing dot plots of each color combination in your experiment, then examine a fully stained sample for possible compensation problems

• Yikes!

(70)

Compensation Problems Can Have Cascading Effects

Compensation at 110% Compensation at 15%

(71)

Visually Inspect Gating

• Check gating across all samples in the experiment.

• Gates may need to be adjusted across donors and/or experimental runs. Dynamic (eg, snap-to) gates may help in some cases.

IL-2 PE

IFN FITC

(72)

Types of Sample Acceptance Criteria

• Minimum viability and recovery for cryopreserved PBMCs

• Minimum number of events collected in an appropriate gate (eg, lymphocytes)

• Minimum number of events within a region of interest, to

calculate an accurate percentage

(73)

Exercise 3

Finding and correcting a spillover problem:

1. Open the FCS file “exercise3.fcs”

2. Gate on small lymphcytes, then use the provided worksheet to look at all color combinations

3. Using biexponential display, change compensation for FITC - % AmCyan and note the changes in the plots

4. Find the compensation that aligns the FITC means of the AmCyan positive and negative populations

Questions:

1. What could cause the discrepancy between calculated compensation by AutoComp and visually appropriate compensation on cells?

2. How might this problem, if uncorrected, affect your

results?

(74)

Outline: Review

1. Configure your instrument

2. Characterize your instrument 3. Design your panel

4. Optimize settings for your panel 5. Run appropriate controls

6. QC your data

(75)

A Question for You to Answer

How many colors can you combine and still have robust results? This depends on:

-The experimental question -The instrument used

-The markers to be combined

(76)