Compensation: It’s not just for pretty pictures. Fluorescence Spillover Compensation Simple in...
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Transcript of Compensation: It’s not just for pretty pictures. Fluorescence Spillover Compensation Simple in...
Compensation: Compensation:
It’s not just for It’s not just for pretty picturespretty pictures
Fluorescence Spillover Compensation
• Simple in concept…Correct spillover into different parameters
• Straightforward in execution…Proper settings based on controls
• …But a lifetime to understand?Profound impact on visualizationNonintuitive aspectsSubtle interactions that can be hard to
diagnose
FITC Single Stain Control
450 500 550 600
Argon Laser FL1
FL2
FITC PE
FL2
PE
FL1
FITC
Total signal detected in
FL1
Unwanted signal
detected in FL2
= roughly 15%
True PE = Total FL2 – 15% FL1
FITC Single Stain Control
FITC Compensation Control
PE
- n
o s
tainUnwanted
signal detected in
FL2
roughly 15%
Total signal detected in
FL1
FITC CD3
FL2-15%FL1
Uncompensated Compensated
FITC Compensation Control
FITC CD3FITC CD3
PE
- n
o st
ain
PE
- n
o st
ain
Compensation in 2 colors:Mostly aesthetic
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104
100 101 102 103 104100
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104
CD3
CD4
CD3
CompensatedUncompensated
Accurate identification and enumeration of subsets is still easy in two color experiments
Compensation:Mostly aesthetic
• Accurate discrimination of subsets is possible with uncompensated data
• However, this is true only when the expression of all antigens is uniform on each subset (e.g., CD45 / CD3 / CD4 / CD8)
• Otherwise, it may not be possible to gate on subsets (with current tools)
Compensation for more colors:It’s not just pretty pictures
• Spillover from unviewed measurement channel can alter event positions– without obvious visual evidence (no diagnostic diagonals!)
• Thus, gate positions may depend on unviewed measurement channels and be different for various tubes in a panel
• Separation of populations may require multi-dimensional surfaces (rather than lines of a polygon).
Is this data properly compensated?
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103
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103
Yes
No
Can’t Tell
Impact of Compensation on Visualization and Analysis of Data
• “Visualization artifacts” lead to:– Manual overcompensation– Incorrect gate settings
• Specific staining controls become essential
What causes this apparent visualization artifact (data spreading)?
Spreading due to Measurement Error
Why do these populations look funny?
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PE-A: CD8
Cy7
PE
-A:
CD
20
Lymphocytes
Uncompensated Compensated
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103
104
105
<PE-A>: CD8
<C
y7P
E-A
>:
CD
20
Lymphocytes
Multicolor Compensation
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103
104
105
<PE-A>: CD8
<C
y7P
E-A
>:
CD
20
Lymphocytes
The Actual Spread
Imperfect Measurement Leads to Apparent Spread in Compensation
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104
SpilloverFluorescence
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103
Primary Fluorescence
Uncompensated
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103
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103
Compensated
7001100
200
(-200)
Why is there a 400-unit spread? Photon counting statistics.
Log Transformation of Data Display Leads to Manual Overcompensation
SpilloverFluorescence
1 10 100 103 104
1
0.1
10
100
103
104
105
A B C D
Primary Fluorescence1 10 100 103 104
0
-200
-400
400
200
A B C D
Primary Fluorescence
Events in channel 0(out of 2446 total):
A: 30B: 475C: 933D: 1190
10.1 10 100 103 104 105
Spillover Fluorescence-100 0 100
Spillover Fluorescence
Overcompensation Cannot Correct Error-induced Spread
1 10 100 103 104
0
-200
-400
400
200
A B C D
Primary Fluorescence1 10 100 103 104
1
0.1
10
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103
104
105
A B C D
Primary Fluorescence
1 10 100 103 104
0
-400
-800
1 10 100 103 104
1
0.1
10
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103
104
105
Primary FluorescencePrimary Fluorescence
SpilloverFluorescence
SpilloverFluorescence
Compensation Does NOT Introduce or Increase Error:
Compensation Only Reveals It!
• All measurements have errors; often the error is proportional to the square-root of the measurement.
• The measurement error is present before compensation. Compensation does not increase this error, it does not change it, it does not introduce any more error.
• Compensation simply makes the error more apparent by shifting it to the low end of the log-scale.
Spread of Compensated Data
• Properly compensated data may not appear rectilinear (“rectangular”), because of measurement errors.
• This effect on compensated data is unavoidable, and it cannot be “corrected”.
• It is important to distinguish between incorrect compensation and the effects of measurement errors.
How can we identify undercompensated data?
Diagonals in the data indicate poorly compensated data: but ONLY AT 45˚! Other slopes indicate
nonlinear correlations that have nothing to do with compensation. Visual estimation is very difficult.
Proportional to intensity (spillover)
Proportional to square root of
intensity
Controls
Experimental controls fall into three categories:
Instrument setup and validation (compensation, brightness)
Staining/gating controls (Viability, FMO / ABO)
Biological
Experimental controls are necessary to properly analyze and interpret data.
Instrument Setup Controls
Typically, fluorescent beads… with a range of fluorescences from “negative” to very bright.
Use these to validate:•Laser stability & focusing•Filter performance•PMT sensitivity (voltage)•Fluidics performance•Daily variability
Consider setting target fluorescence values for beads during alignment: this allows for greatest consistency in analysis (gating) between experiments.
Compensation Controls
Single-stained samples…must be at least as bright as the reagent you are using in the experiment!
Can use any “carrier”, as long as the positive & negative populations for each control have the same fluorescence when unstained:
Cells (mix stained & unstained)Subpopulations (CD8 within total T)Beads (antibody-capture)
One compensation for every color… and one for each unique lot of a tandem (Cy5PE, Cy7PE, Cy7APC, TRPE)
Compensation Controls
Note: you can mix bead controls on some colors and cell controls on other colors.
However – the negative control for each type must match the positive control: do not use a “universal negative” unstained beads (or cells) for all controls!
Collect sufficient events to precisely estimate fluorescence medians:
Beads: At least 5-10,000Cells: At least 20-50,000
Using Beads to Compensate• Exact reagent used in experiment
• 100% positive (not ever rare)
• Small CV, very bright (precise fluorescence measurement)
• Sonicate weekly to reduce aggregates
• Some reagents may not work (Ig, non mouse, too dim, EMA/PI) – for these, use cell-based compensations
Why Bright Comp Controls?
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FITC-A
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103
104
105
Cy7PE-A
Autofluorescence
FITC spillover into Cy7PE (1%)
Unstained cells
Bright cells
Dimmer cells
Estimating a low spillover fluorescence accurately is impossible (autofluorescence).
Therefore, compensation is generally only valid for samples that are duller than the compensation control.
Insufficiently-Bright Comp Control Is …. Bad!
Note that either under- or over-compensation can result from using comp controls that are too dim!
Compensation of Multicolor FACS Data
• It is impossible to set proper compensation using visual guides (dot plots, histograms).– Use statistics (medians of gated cells)– Use automated compensation tools
• Antibody-capture beads are an excellent way to set compensation!
• Compensation controls must be matched to your experiment and at least as bright as any of your reagents.
Some Examples of Problems
• The following four examples illustrate some types of problems that can be occur related to compensation.
• In each case, compensation itself is not the problem: there is an underlying reagent, instrumentation, or analysis problem.
• However, the manifestation of this problem is an apparent incorrect compensation!
Good Instrument Alignment Is Critical!
Day 1 Day 2
Uncompensated
PE
TR-PE
While the amount of compensation did not differ, the
measurement error (correlation) decreased leading
to much better visualization of the
population!
Compensated
CFSE: A Special CaseCells stained with CFSE-DA, FITC-CD8, or unstained were collected uncompensated.
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10 1
10 2
10 3
10 4
10 5
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10 1
10 2
10 3
10 4
10 5
PE Cy5PE
FITC FITC
CFSE has a slightly redder spectrum than FITC… must use CFSE as a comp control!
Note that this exacerbates the higher “IL4+” gate required for CD8 cells.
The undercompensation would not have been detected except by looking at the APC vs. Cy7APC graphic…
Fix/Perm Changes Cy7APC Compensation Requirement
The longer Cy7APC is in fixative, the more it “falls apart”, leading to
more APC signal
Different lots of tandems can require different compensation!
TR-PE reagent 1Median = 21,100
TR-PE reagent 2Median = 8,720
PEMedian = 484
PEMedian = 698
Compensation Required(∆PE / ∆TRPE)
2.3%
8.0%
Wrong TR-PE comp control
Compensating with the wrong TRPE
Right TR-PE comp control
Staining Controls• Staining controls are necessary to identify
cells which do or do not express a given antigen.
• The threshold for positivity may depend on the amount of fluorescence in other channels!
Staining Controls• Unstained cells or complete isotype control
stains are improper controls for determining positive vs. negative expression in multi-color experiments.
• The best control is to stain cells with all reagents except the one of interest.
FMO Control“Fluorescence Minus One”
Also known as “ABO” (all-but-one)
Identifying CD4 cells with 4 colors
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Unstained Control FMO Control Fully Stained
PE
FITC
FITCPE
Cy5PECy7PE
––––
CD3–
CD8CD45RO
CD3CD4CD8
CD45RO
Isotype BoundsFMO Bounds
PBMC were stained as shown in a 4-color experiment. Compensation was properly set for all spillovers
Complex Interactions in Compensation
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104
100 101 102 103 104 100 101 102 103 104
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101
102
103
104
10Unstained Control FMO Control Fully Stained
PE
FITC
Correct Compensation
Incorrect Compensation
The same data is shown with correct or wrong Cy5PE->Cy7PE comp setting. Note that neither of these channels is shown here!
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PE
FITC
Unstained Control FMO Control Fully StainedFITC
PECy5PECy7PE
––––
CD3–
CD8CD45RO
CD3CD4CD8
CD45RO
Isotype Bounds
FMO Bounds
FMO controls aid even when compensation is improper
Incorrect Cy5PE into Cy7PE compensation
FMO Controls
• FMO controls are a much better way to identify positive vs. negative cells
• FMO controls can also help identify problems in compensation that are not immediately visible
• FMO controls should be used whenever accurate discrimination is essential or when antigen expression is relatively low
Quad-Gates of the Future
Log Fluorescence #1
DoublePositive
SinglePositive
SinglePositive
DoublePositive
SinglePositive
SinglePositive
LogFluor.
#2
Present
Compensation & Data Visualization
These “new” distributions are much more frequently seen nowadays, with the use of red dyes (Cy7PE, Cy7APC) and with more precise instruments.
Some users have questioned the correctness of these distributions, leading some manufacturers to try to provide “corrections”.
However, this cannot be “corrected”–what is needed is education!
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103
Spread ofpositives
Events piling up on axes
Compensation Offset???• At least two FACS data analysis software
programs offer a “feature” to turn on a “compensation offset”–to try to make data look more like what we have expected.
• The term “Compensation Offset” simply means that random noise is added to the data to hide the “spread” in compensated data.
• This “feature” reduces sensitivity!
Compensation Offset???
• Artificially increase error does make distributions rectangular again--but at a significant expense!– Low level antigen expression will be lost– In >4 color experiments, problems multiply!
Compensation Offset???• An even more insidious problem is that overcompensation
becomes impossible to detect:
•Turn OFF this “feature” in your software10 -1 100 101 102 103
10-1
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103
0 100 101 102 103
0
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103
0 100 101 102 103
0
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10 -1 100 101 102 10310 -1
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10 -1 100 101 102 10310
-1
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103
10 -1 100 101 102 10310 -1
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Correct
5% over-compensated
Standard Transformed Offset
Is There A Solution?• The spread in compensated data is
unavoidable (basic physics)
• Can we visualize data so that the distributions are more intuitive?
• Nearly all immunophenotyping data is shown on a logarithmic scale… why?– Dynamic range of expression (4 logs)
– Often, distributions are in fact log-normal
Alternatives to a Log Scale
• Compensation reveals a linear-domain spreading in the distribution.
• This is most obvious at the low end of fluorescence, because the measurement error is small compared to bright cells.
• Can we re-scale the low end of the fluorescence scale to effect a different compression in this domain?
• What about negative values?– Remember, this is just a fluorescence from which we
subtract an estimated value with measurement error
“Bi-exponential” Scaling
SpilloverFluorescence
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1
0.1
10
100
103
104
105
A B C D
Primary Fluorescence1 10 100 103 104
0
-200
-400
400
200
A B C D
Primary Fluorescence
S p i l l o v e r
F l u o r e s c e n c e
1 1 0 1 0 0 1 03
1 04
1 0
1 0 0
- 1 03
P r i m a r y F l u o r e s c e n c e
1 04
1 05
- 1 0
- 1 0 0
1 03 Positive Log
Negative Log
Linear
Wayne MooreDave Parks
“Bi-exponential” Transformation Makes Compensated Data More Intuitive
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CD8
CD4
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104
CCR5
CD103
0 103 104
0
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104
CD103
CCR5
0 103 104
0
103
104
CD8
CD4
Negative Log Scale:
Events with measured
fluorescence < 0
Linear Scale: Compression of the amount of visual space
devoted to this range
Gated for CD3+ Lymphocytes;
Stained for CD3, CD4, CD8, CCR5, CD103, and 7 other
reagents
Question: What is the expression of CCR5 and
CD103 on CD4 Lymphocytes?How many events are on
the axis?
LOTS!
Transformed Distributions
Result:
No events hidden on the axes
Populations are visually
identifiable
“Bi-exponential” Transformation Makes Compensated Data More Intuitive
Only changes the visualization of data
• Does not affect gating or statistics• Cannot change the overlap (or lack thereof) of two
populations.
Supports the basic goal of graphing data: showing it in an intuitive, aesthetic manner
Note: the transformation is complex: it is different for each measurement channel and compensation matrix, and depends on the autofluorescence distribution. However, these parameters can be automatically selected by the software.
0 103 104 105
0
10 3
10 4
10 5
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10 1
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10 5
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10 1
10 2
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10 5
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0
10 3
10 4
10 5
CD4
CD8
Median
Transformed
Transformation Confirms Compensation
SummaryCompensation is straightforward…. But:• Interpretation of compensated data is complicated by
“spillover spreading” arising from measurement errors• Proper compensation requires careful attention to
compensation controls: bright, matched reagents & staining conditions
• Proper analysis of compensated data requires careful attention to staining controls
• Biexponential transformation of displays is critical for analysis and interpretation of compensated data
• Bagwell & Adams: Ann NYAS 677:167 (1993)
Software compensation
• Stewart & Stewart: Cytometry 38:162 (1999)
4-color compensation, pitfalls
• Roederer: Current Protocols in Cytometry
Protocol, explanation, discussion
• Roederer: Cytometry 45:194 (2001)
Visualization artifacts, FMO controls
• http://www.drmr.com/compensation/
Compensation Resources