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Forensic Flow Cytometry:
Troubleshooting Flow Cytometry Data
Pratip K. Chattopadhyay, Ph.D.
ISAC Scholar
ImmunoTechnology Section
Vaccine Research Center, NIH
Jennifer Wilshire, Ph.D.
Assistant Manager
Flow Cytometry Core Facility
Memorial Sloan-Kettering Cancer Center
These slides were presented at the CYTO2015 Conference.
They are distributed here for the benefit of the flow cytometry community.
Please do not:
1.  Take any material from these presentations (copied or adapted) without
obtaining the express consent of Pratip Chattopadhyay,
pchattop@mail.nih.gov.
2.  Modify this material in any way or distribute it outside of this source.
Please do:
1.  Send your flow cytometry troubleshooting examples. I’d love to create a
publicly available database of examples like the ones presented here.
2.  Send me your questions and comments!
3.  Learn and Enjoy!	
  
Disclaimer : “Forensic” Flow Cytometry	
  
If you are here to learn about how flow
cytometry is used to solve real crimes
(e.g., murder, robbery, etc.) …
Sorry, we’ll be talking about
other heinous acts:
Poor experimental
Planning,
Execution,
and Analysis.
Today	
  
Recognize Problematic Staining Patterns
(See how easy it is to fall prey to criminal cytometry)
Troubleshoot Experiments and Analysis
(Become a crime-stopper)
Guidelines to Improve Cytometry Experiments.
(Learn how to live a virtuous life)
Guidelines to Improve Cytometry Experiments	
  
Experiment Planning:
“Don’t run the red light.
Spend time planning your experiments properly.”
Start with a robust system for instrument setup & monitoring.
Guidelines to Improve Cytometry Experiments	
  
Experiment Planning:
“Don’t run the red light.
Spend time planning your experiments properly.”
Start with a robust system for instrument setup & monitoring.
Why bother?
Reliability!
Set Panel
Instruments
Panel
Panel
Panel
PanelSingle	
  panel	
  performs	
  reliably	
  
in	
  mul2-­‐instrument	
  (center)	
  study.	
  
Single	
  instrument	
  
works	
  for	
  most	
  panels	
  
Instrument Setup and Monitoring: Voltages	
  
How to set voltages?
Option 1: Just set all unstained signals to 102.
What’s the crime here? (Audience?)
Instrument Setup and Monitoring: Voltages	
  
How to set voltages?
Option 1: Just set all unstained signals to 102.
Criminal neglect.
This method ignores:
- dynamic range of PMT,
- linearity of PMT,
- the tenet that signal from dye
should be highest in the detector
designed to read that dye.
“Neglecting” Dynamic Range and Linearity of PMTs	
  
Hard to know relative
performance of different
PMTs in your instrument.
CVs of fluorescent signals
may not be optimized,
leading to issues with
resolution and gating.
“Neglecting” Dynamic Range and Linearity of PMTs	
  
Hard to know relative
performance of different
PMTs in your instrument.
CVs may not be optimized,
leading to issues with
resolution and gating.
Quality Control
Panel Design
Data Analysis
This complicates…
“Neglecting” Dynamic Range and Linearity of PMTs	
  
Hard to know relative
performance of different
PMTs in your instrument.
CVs may not be optimized,
leading to issues with
resolution and gating.
Quality Control
Panel Design
Data Analysis
This complicates…
“I will never know if my PMTs
are working properly.”
“I never resolve dim markers
on the 1st detector off green
laser, so I don’t use Cy7PE.”
“My percent CD4+ never
matches any other site in
multicenter study.”
Leading to criminal confessions:
“Neglecting” Dynamic Range and Linearity of PMTs	
  
Is this always a problem?
No… sometimes this will be a victimless crime.
But troubleshooting experiments depends on your
confidence in instrument setup!
Poor instrument setup makes forensic flow harder!
Guideline: Consider linearity and dynamic range
when choosing PMT voltage.
Another Rule for Choosing Voltages	
  
Signal from a dye should be strongest in channel set up to detect that dye.
So, signal from Alexa 680 (or R700APC or Cy55APC) should be brightest
when measured off of the red laser detector with 710nm filters in it.
	
  
Another Rule for Setting Voltages	
  
Signal from a dye should be strongest on channel set up to detect that dye.
So, signal from Alexa 680 (or R700APC or Cy55APC) should be brightest
when measured off of the red laser detector with 710nm filters in it, even in systems
also measuring Cy55PE. Ensure this by optimizing detector voltages.
	
  
If voltages across two detectors aren’t optimized…	
  
You may detect more secondary
(spillover) signal from Cy55PE in your
RedLaser/710nm channel, than
primary (Alexa 680).
What happens as a consequence? (Audience?)
If voltages across two detectors aren’t optimized…	
  
You may be detect more secondary
(spillover) signal from Cy5PE in your
RedLaser/710nm channel, than
primary (Alexa 680).
What happens as a consequence? (Audience?)
0 10
2
10
3
10
4
10
5
Cy5PE (Secondary)
0
102
103
104
10
5
Alexa680(Primary)
Hint
If voltages across two detectors aren’t optimized…	
  
You may be detect more secondary
(spillover) signal from Cy5PE in your
RedLaser/710nm channel, than
primary (Alexa 680).
What happens as a consequence? (Audience?)
0 10
2
10
3
10
4
10
5
Cy5PE (Secondary)
0
102
103
104
10
5
Alexa680(Primary)
Hint Compensation values > 100%.
Will this sink your panels?
Not always…
a multicolor panel can work just fine with
compensations > 100% in some color combinations.
But… troubleshooting becomes harder.
And panel development is more complex.
Instrument Setup and Monitoring: Voltages	
  
How to do a better job setting voltages?
Option 1: Just set all unstained signals to 102.
Option 2: Bead-based protocols.
Good practice. Perfetto (Nature Protocols), CS&T (BD)
Provide quantitative measure of instrument performance,
in the form of metrics like Q (sensitivity) and B (resolution).
Beads have limitations, though:
- high intrinsic CV
- loaded with broad-spectrum dyes, not our fluors
- beads and dyes vary by lot.
So, the metrics from these methods are a bit inaccurate.
Instrument Setup and Monitoring: Voltages	
  
How to do an even better job setting voltages?
Option 1: Just set all unstained signals to 102.
Option 2: Bead-based protocols.
Option 3: LED Pulser
New device (Jim Wood, Wake) that emits consistent signals to PMT
for accurate measurement of sensitivity and resolution (Q & B).
Reliable method to compare detectors (track them for
QC), or compare instruments.
Can use Q and B values to 1) set voltages properly, 2) predict how a
panel will stain on different instruments, and 3) predict whether a
putative staining panel will work.
More info: CYTO U webinar, Steve Perfetto’s Oral Presentation
Review:
Guidelines to Improve Cytometry Experiments	
  
Experiment Planning:
Spend time planning your experiments properly.
1)  Set up instrument with bead-based protocols
or LED Pulser.
Guidelines to Improve Cytometry Experiments	
  
Experiment Planning:
“Spend time planning your experiments properly.”
1)  Set up instrument with bead-based protocols
or LED Pulser.
2)  Put some effort into panel design!
Panel Design: Step 1… Titrate Every Reagent	
  
Titration is fundamental to successful flow and good data.
Don’t rely on the manufacturer’s titer…
… they can’t test all experimental conditions,
and suggested titers may be significantly higher
than what will work just fine in your system.
Titration Guidelines	
  
Choose a wide range of concentrations that captures:
Saturation
(where signal does not increase with
more antibody)
Loss of positive staining
(or at least diminished staining).
O/n rt
0 5 10 15 20
Dilution
0
102
10
3
104
105
B515-A:CD8FITCV1002
O/n 37C
0 5 10 15 20
Dilution
O/n 4C
0 5 10 15 20
Dilution
Example of a good titration experiment
(courtesy Margaret Beddall)
Titration Guidelines	
  
1) Choose a wide range of concentrations.
2) Perform titrations under the same conditions as
experiment.
If you titrate antibody at room temperature, but need
to stain cells at 37C for one experiment,
Redo the titration at 37C first!
Example of this in case studies later today.
Titration Guidelines	
  
1) Choose a wide range of concentrations.
2) Perform titrations under the same conditions as
experiment.
3) Analyze titrations on same cell type as in your study.
Some markers differ in expression level by tissue,
or are expressed across different cell types
at different levels.
Need to account for this by using right sample type, or
including other markers in titration.
How Do Analyze Titration Data?
10
-3
10
-2
10
-1
10
0
10
1
Dilution
0
10
2
10
3
10
4
10
5
B515-A:CD3Ax488
10
-3
10
-2
10
-1
10
0
10
1
Dilution
0
10
2
10
3
10
4
10
5
B515-A:CD3Ax488
Saturating Titer:
The concentration at which
positive signal plateaus, more
antibody does not increase
positive signal.
Use this concentration when
quantifying receptor density,
working across changing
experimental conditions.
How Do Analyze Titration Data?
10
-3
10
-2
10
-1
10
0
10
1
Dilution
0
10
2
10
3
10
4
10
5
B515-A:CD3Ax488
Saturating Titer:
Going over saturation increases non-
specific binding.
How Do Analyze Titration Data?
10
-3
10
-2
10
-1
10
0
10
1
Dilution
0
10
2
10
3
10
4
10
5
B515-A:CD3Ax488
Separating Titer:
Subjective, lower concentration where
staining still resolves well.
Use this concentration in most
experiments, where staining time/temp
are constant.
Saves antibody.
Helps reduce spreading error in other
channels when working with complex
panels.
How Do Analyze Titration Data?
10
-3
10
-2
10
-1
10
0
10
1
Dilution
0
10
2
10
3
10
4
10
5
B515-A:CD3Ax488
Blasphemy!
How can you work below saturation?
How Do Analyze Titration Data?
10
-3
10
-2
10
-1
10
0
10
1
Dilution
0
10
2
10
3
10
4
10
5
B515-A:CD3Ax488
Blasphemy!
How can you work below saturation?
You can… because we are still at
antibody excess.
This is, in fact, why we needn’t be
terribly precise in our antibody volumes.
There is often at least a 4x range of
concentrations that are just fine.
100x	
  
50x	
  
25x	
  
How Do Analyze Titration Data?
10
-3
10
-2
10
-1
10
0
10
1
Dilution
0
10
2
10
3
10
4
10
5
B515-A:CD3Ax488
But then you will underestimate %+!
No… Rarely happens.
50	
  
60	
  
70	
  
80	
  
90	
  
100	
  
0	
   2	
   4	
   6	
   8	
   10	
  
100x	
  
50x	
  
25x	
  
%	
  CD3+	
  
How Do Analyze Titration Data?
Guidelines to Improve Cytometry Experiments	
  
Experiment Planning:
“Spend time planning your experiments properly.”
1)  Set up instrument with bead-based protocols or
LED Pulser.
2)  Put some effort into panel design!
+ Titration – range of concentrations that works.
Guidelines to Improve Cytometry Experiments	
  
Experiment Planning:
“Spend time planning your experiments properly.”
1)  Set up instrument with bead-based protocols or
LED Pulser.
2)  Put some effort into panel design!
+ Titration – range of concentrations that works.
+ Panel Design – Choosing which reagents will
work together, and at what concentrations.
We do this by considering spectral overlap and
spreading error.
Review of Panel Design*
www.isac-net.org
Don’t randomly combine antibodies and fluorochromes.
Quick & Easy (Spectral Overlap)
Pick fluorochromes that
don’t overlap.
Qdots and Brilliant Violet
fluorochromes
are particularly
useful for this.
Pair highly overlapping
fluorochromes with markers
that aren’t co-expressed.
Choose bright dyes for dim markers.
The Better Way (Spreading Error)
*See ISAC’s Polychromatic Flow Cytometry Course (2011) and my Tutorials (2010, 2013).
Review of Panel Design*
www.isac-net.org
Don’t randomly combine antibodies and fluorochromes.
Quick & Easy (Spectral Overlap)
Pick fluorochromes that
don’t overlap.
Qdots and Brilliant Violet
fluorochromes
are particularly
useful for this.
Pair highly overlapping
fluorochromes with markers
that aren’t co-expressed.
Choose bright dyes for dim markers.
Limitations
*See ISAC’s Polychromatic Flow Cytometry Course (2011) and my Tutorials (2010, 2013).
Multicolor flow will always use dyes that
overlap (and that overlap is no big deal!).
Cells are so heterogeneous that there is
usually some co-expression of markers in
any (interesting) phenotyping experiment.
Reagent brightness doesn’t depend only
on the dye used for conjugation. Antibody
clones differ in signal strength, too.
So, charts and guides ranking dye
brightness may be of limited utility.
Review of Panel Design*
www.isac-net.org
Don’t randomly combine antibodies and fluorochromes.
Quick & Easy (Spectral Overlap)
Pick fluorochromes that
don’t overlap.
Qdots and Brilliant Violet
fluorochromes
are particularly
useful for this.
Pair highly overlapping
fluorochromes with markers
that aren’t co-expressed.
Choose bright dyes for dim markers.
The Better Way (Spreading Error)
Characterize spreading
error for the fluorochromes
and instruments you use.
Place dim markers on channels
with low spreading error.
Iteratively add markers to panel to
characterize effect of various
combinations on other channels.
*See ISAC’s Polychromatic Flow Cytometry Course (2011) and my Tutorials (2010, 2013).
Why Worry About Spreading Error?
When fluorescence is low, error associated with
photon detection is easily observed.
Particularly high photon counting errors occur
for fluorochromes that give off very few photons (e.g., Cy7APC).
The result: the negative population spreads out and masks dim
populations in neighboring channels.
How do you calculate spreading error
www.isac-net.org
(See PC Previous Tutorials for Manual Example.)
How do you calculate spreading error
www.isac-net.org
(See PC Previous Tutorials for Manual Example.)
How do you calculate spreading error
www.isac-net.org
(See PC Previous Tutorials for Manual Example.)
How do use spreading error in panel design?
Save the resulting table.
You can even copy table to spreadsheet and make a heat map.
Dye for Plot vs. Detector for Plot
DyeforPlot
A-BUV395
B-BUV490
C-BUV550
D-BUV680
E-BUV737
F-BUV815
G-BV421
H-BV480
I-BV570
J-BV605
K-BV650
L-BV711
M-BV745
N-BV786
O-BB515
P-BB630
Q-BB667
R-BB700
S-BB796
T-PE
U-CF594PE
V-CY5PE
W-CY55PE
X-CY7PE
Y-APC
Z-R700APC
ZZ-H7APC
A-BUV395
B-BUV490
C-BUV550
D-BUV680
E-BUV737
F-BUV815
G-BV421
H-BV480
I-BV570
J-BV605
K-BV650
L-BV711
M-BV745
N-BV786
O-BB515
P-BB630
Q-BB667
R-BB700
S-BB796
T-PE
U-CF594PE
V-CY5PE
W-CY55PE
X-CY7PE
Y-APC
Z-R700APC
ZZ-H7APC
Detector for Plot
Spread
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
Dye
Detector
Dye for Plot vs. Detector for Plot
DyeforPlot
A-BUV395
B-BUV490
C-BUV550
D-BUV680
E-BUV737
F-BUV815
G-BV421
H-BV480
I-BV570
J-BV605
K-BV650
L-BV711
M-BV745
N-BV786
O-BB515
P-BB630
Q-BB667
R-BB700
S-BB796
T-PE
U-CF594PE
V-CY5PE
W-CY55PE
X-CY7PE
Y-APC
Z-R700APC
ZZ-H7APC
A-BUV395
B-BUV490
C-BUV550
D-BUV680
E-BUV737
F-BUV815
G-BV421
H-BV480
I-BV570
J-BV605
K-BV650
L-BV711
M-BV745
N-BV786
O-BB515
P-BB630
Q-BB667
R-BB700
S-BB796
T-PE
U-CF594PE
V-CY5PE
W-CY55PE
X-CY7PE
Y-APC
Z-R700APC
ZZ-H7APC
Detector for Plot
Spread
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
Dye
Detector
Now you can see (FOR ONE PARTICULAR INSTRUMENT) how including Cy5PE
in the panel, induces spreading error into a number of other
detectors off of the green laser. Don’t put dim markers into those detectors.	
  
How do use spreading error in panel design?
Dye for Plot vs. Detector for Plot
DyeforPlot
A-BUV395
B-BUV490
C-BUV550
D-BUV680
E-BUV737
F-BUV815
G-BV421
H-BV480
I-BV570
J-BV605
K-BV650
L-BV711
M-BV745
N-BV786
O-BB515
P-BB630
Q-BB667
R-BB700
S-BB796
T-PE
U-CF594PE
V-CY5PE
W-CY55PE
X-CY7PE
Y-APC
Z-R700APC
ZZ-H7APC
A-BUV395
B-BUV490
C-BUV550
D-BUV680
E-BUV737
F-BUV815
G-BV421
H-BV480
I-BV570
J-BV605
K-BV650
L-BV711
M-BV745
N-BV786
O-BB515
P-BB630
Q-BB667
R-BB700
S-BB796
T-PE
U-CF594PE
V-CY5PE
W-CY55PE
X-CY7PE
Y-APC
Z-R700APC
ZZ-H7APC
Detector for Plot
Spread
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
Dye
Detector
You can also see how low the spreading error is off violet laser detectors.
Consider putting dim markers on those channels.
	
  
How do use spreading error in panel design?
Dye for Plot vs. Detector for Plot
DyeforPlot
A-BUV395
B-BUV490
C-BUV550
D-BUV680
E-BUV737
F-BUV815
G-BV421
H-BV480
I-BV570
J-BV605
K-BV650
L-BV711
M-BV745
N-BV786
O-BB515
P-BB630
Q-BB667
R-BB700
S-BB796
T-PE
U-CF594PE
V-CY5PE
W-CY55PE
X-CY7PE
Y-APC
Z-R700APC
ZZ-H7APC
A-BUV395
B-BUV490
C-BUV550
D-BUV680
E-BUV737
F-BUV815
G-BV421
H-BV480
I-BV570
J-BV605
K-BV650
L-BV711
M-BV745
N-BV786
O-BB515
P-BB630
Q-BB667
R-BB700
S-BB796
T-PE
U-CF594PE
V-CY5PE
W-CY55PE
X-CY7PE
Y-APC
Z-R700APC
ZZ-H7APC
Detector for Plot
Spread
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
Dye
Detector
This analysis is now EASY to do (using FlowJo),
and can be done with any of the panels you run on your instrument!	
  
How do use spreading error in panel design?
Guidelines to Improve Cytometry Experiments	
  
Experiment Planning:
“Spend time planning your experiments properly.”
1)  Set up instrument with bead-based protocols or
LED Pulser.
2)  Put some effort into panel design!
+ Titration – range of concentrations that works.
+ Panel Design –
Quick and Dirty Spectral Overlap Method
Better Spreading Error Method
Guidelines to Improve Cytometry Experiments	
  
Experiment Execution:
Proceed cautiously & follow protocols when staining.
Two examples of vendor protocols I didn’t follow.
(Things you may not be aware of.)
Pratip’s Criminal Past (1st Offense)
Ignored manufacturer’s recommendation to stain on ice when
using fix/perm kits for intra-cellular cytokine staining with
surface Qdot antibodies.
Pratip’s Criminal Past (1st Offense)
Ignored manufacturer’s recommendation to stain on ice when
using fix/perm kits for intra-cellular cytokine staining.
Pratip’s Criminal Past (2nd Offense)
Stained a multicolor panel with many BD and BioLegend
“Brilliant” dyes. BD recommends use of Brilliant Stain Buffer, to
minimize non-specific interactions between reagents.
Non-specific interactions are worst in whole blood lysed
protocols, but can appear when staining ficolled PBMC.
I would see these interactions essentially as slightly
undercompensated staining patterns.
Pratip’s Criminal Past (2nd Offense)
Example from BD:
CD8
CD19
Withour Brilliant Stain
Buffer.
Pratip’s Criminal Past (2nd Offense)
Example from BD:
CD8
CD19
Without Brilliant Stain
Buffer.
Pratip’s Criminal Past (2nd Offense)
Example from BD:
CD8
CD19
Without Brilliant Stain
Buffer.
With Brilliant Stain
Buffer.
Guidelines to Improve Cytometry Experiments	
  
Experiment Execution:
Proceed cautiously & follow protocols when staining.
Follow new vendor protocols exactly.
There’s a reason for their recommended conditions.
Guidelines to Improve Cytometry Experiments	
  
Analysis of Data:
Use tools that help check data quality.
1) NXN Plot.
N X N PlotsFITC	
  
PE	
   TRPE	
   Cy5PE	
   Cy5.5PE	
  
PE	
  
TRPE	
   Cy5PE	
   Cy5.5PE	
  
TRPE	
  
Cy5PE	
   Cy5.5PE	
  
Cy5PE	
  
Cy5.5PE	
  
And so on…
For every marker combination in panel.
N X N Plot for Troubleshooting
A rapid means to identify problems.
Over Compensation
Under Compensation
Over Compensation
Transformation/Compensation
N X N Plot for Troubleshooting
Let’s look more closely at
Transformation/Compensation
example…
Negative and Super-Negative
www.isac-­‐net.org	
  
ALer	
  bi-­‐exponen2al	
  transforma2on,	
  nega2ve	
  popula2ons	
  should	
  have	
  a	
  
uniform	
  distribu2on.	
  
	
  
“Super-­‐nega2ve”	
  =	
  “really	
  pregnant”	
  or	
  “extremely	
  dead.”	
  
	
  
Why	
  does	
  this	
  happen?	
  
0 103
104
105
<R710-A>: CD69
0
10
3
104
10
5
<G780-A>:CD4
CD4-­‐Nega2ve	
  
	
  
CD4	
  “Super”	
  Nega2ve	
  
Negative and Super-Negative
www.isac-net.org
After bi-exponential transformation, negative populations should have a
uniform distribution.
“Super-negative” = “really pregnant” or “extremely dead.”
Why does this happen? Bad compensation in another dimension.
0 103
104
105
<R710-A>: CD69
0
10
3
104
10
5
<G780-A>:CD4
0 102
103
104
105
<V800-A>: CD57
0
10
3
104
105
<G780-A>:CD4
Negative and Super-Negative
www.isac-net.org
As soon as we fix comps, problem is solved.
0 10
3
10
4
10
5
<R710-A>: CD69
0
10
3
10
4
10
5
<G780-A>:CD4
0 10
2
10
3
10
4
10
5
<V800-A>: CD57
0
10
3
10
4
10
5
<G780-A>:CD4
Sort of. There are still a few super-negative events. Why?
Fluorochrome Aggregates
www.isac-net.org
What are they?
High-order complexes of fluorochromes that
non-specifically bind cells.
How do you recognize them?
Random, punctate staining. Brighter than the
larger population of cells that is specifically
staining for the marker.
Why worry about aggregates?
Affect accuracy of subset identification, don’t
phenotype junk!
Effect on transformation and data visualization.
0 10
3
10
4
10
5
<G610-A>: `HLA-DR
0
10
2
10
3
10
4
10
5
<V800-A>:CD57
Fluorochrome Aggregates : Solutions
www.isac-net.org
Staining Time
Make staining cocktail and spin at full speed in
microfuge for a few minutes.
Mark cap to indicate where pellet would be,
and transfer cocktail from other side to new
tube.
Analysis Time
Make gates that exclude the aggregates.
Perform transformation after excluding
aggregates.
0 10
3
10
4
10
5
<G610-A>: `HLA-DR
0
10
2
10
3
10
4
10
5
<V800-A>:CD57
Guidelines to Improve Cytometry Experiments	
  
Analysis of Data:
Use tools that help check data quality.
1)  NXN Plot.
2)  FlowClean: gates out problems
in HTS data.
FlowClean	
  
As number of parameters increases,
we need to study more samples to make statistically robust conclusions.
High-throughput systems (HTS) for cytometers will help with this.
Quality control of HTS data is challenging.
Cytometer pressure is often disrupted by bubbles, debris, or clogs.
This can alter fluorescent signal, resulting in abnormal populations of cells.
We developed FlowClean because existing methods for finding HTS
problems were subjective, slow, or confusing to use.
Cha$opadhyay	
  and	
  Fletez-­‐Brant	
  
FlowClean	
  Concept	
  
Most	
  flow	
  experiments	
  aim	
  to	
  count	
  cell	
  popula2ons	
  based	
  
on	
  marker	
  expression.	
  	
  
	
  
Since	
  the	
  sample	
  tube	
  contains	
  cells	
  mixed	
  together,	
  the	
  
frequency	
  of	
  each	
  popula2on	
  should	
  be	
  stable	
  over	
  the	
  
collec2on	
  period.	
  
	
  
When	
  it’s	
  not	
  stable	
  (i.e.,	
  new	
  popula2ons	
  appear	
  and	
  exis2ng	
  
ones	
  are	
  lost)	
  collec2on/pressure	
  problems	
  are	
  happening.	
  	
  
	
  
FlowClean	
  is	
  an	
  automated	
  tool	
  to	
  detect	
  these.	
  
FlowClean	
  Workflow	
  
FCS File Partition Distributions Generate Cellular Address
#Cells
50%50%
#Cells
50%50%
<V705>
<G560>
0
0
1
1
Partition
Cell <V705> <G560> <B515>
1 0 1 0
2 0 1 0
3 1 1 0
4 0 0 1
Define "Populations"
by matching addresses:!
Population 1!
Population 2!
Population 3
Track Populations Over Time
Transform Population!
Frequencies into CLR
Flag Anamolous Events
Amend FCS File
FlowClean	
  on	
  Real	
  Experimental	
  Data	
  
0
102
10
3
10
4
10
5
0
10
2
10
3
10
4
105
10
4
10
5
<R710-A>:CD38
LSG
RV152 212341-0200_B cel.fcsEvent Count: 164602
Time
03006009001200
Time
101
10
2
103
10
4
Good_vs_Bad
LSG
RV152 212341-0200_B cel.fcs
Event Count: 164602
2 3 4 5
0
102
10
3
10
4
10
5
<R710-A>:CD38
LSG
RV152 212341-0200_B cel.fcsEvent Count: 164602
03006009001200
Time
0
10
2
10
3
10
4
10
5
<R710-A>:CD38
03006009001200
Time
10
1
10
2
10
3
10
4
Good_vs_Bad
LSG
RV152 212341-0200_B cel.fcs
Event Count: 164602
bad
010 2
103
104
105
SSC-A
0
102
10
3
104
105
<R710-A>:CD38
good
RV152 212341-0200_B cel.fcs
010
2
10
3
10
4
10
5
SSC-A
0
102
10
3
10
4
10
5
<R710-A>:CD38
A
B CBad
Good
Time
<R710>CD38
Time
ChangePointMeans
Good_Bad
Time
<R710>CD38
Time
SSC-A
<R710>CD38
•  679	
  files	
  from	
  large	
  study	
  
(RV152)	
  
•  12-­‐14	
  colors,	
  HTS	
  
•  Each	
  file	
  =	
  seconds,	
  each	
  
plate	
  <	
  5min	
  
	
  
FlowClean	
  on	
  Real	
  Experimental	
  Data	
  
12%
88%
Files	
  
Flagged	
  
No	
  
Problems	
  
1 2 3 4 5 6 7 8 9 10 11 12
A 1 111 1 1 111 11 1 111 11 111 1
B 11 11 11 111 11 1 1 1 1 11 1 1
C 1 1 1 11 11 1 1
D
E
F 1 1 1
G 11 1 1 1 1
H 1 1
A
Are	
  there	
  any	
  posi2ons	
  in	
  plate	
  where	
  problems	
  are	
  most	
  common?	
  
Perhaps	
  at	
  early	
  wells,	
  sugges2ng	
  need	
  for	
  beder	
  cleaning	
  of	
  HTS	
  equipment.	
  
FlowClean	
  Summary	
  
•  Developed	
  a	
  computa2onal	
  tool	
  that	
  makes	
  flow	
  data	
  more	
  
powerful,	
  by	
  elimina2ng	
  ar2facts	
  and	
  noise.	
  
•  We	
  proved	
  it	
  worked	
  in	
  a	
  large	
  dataset,	
  not	
  on	
  a	
  “toy”	
  case,	
  
as	
  is	
  oLen	
  done	
  for	
  new	
  algorithms.	
  
•  We	
  showed	
  that	
  it’s	
  unique	
  features	
  are	
  necessary,	
  classical/
simple	
  methods	
  don’t	
  work.	
  
	
  
•  We	
  compared	
  it	
  to	
  exis2ng	
  algorithms,	
  and	
  objec2vely	
  
showed	
  that	
  it	
  was	
  beder.	
  
Available	
  on	
  BioConductor	
  and	
  GenePadern	
  (J.	
  Spidlin).	
  	
  
Guidelines to Improve Cytometry Experiments	
  
Analysis of Data:
Use tools that help check data quality.
1)  NXN Plot.
2)  FlowClean: gates out problems
in HTS data.
3) Concatenation of all study files.
Concatenation for QC
When performing a large study, with many samples,
covering many weeks of experiments, you need good ways
to detect experimental or instrument issues and flag them.
One method:
Add a parameter to each FCS file that indicates sample
number. (Numbers should be consecutive: 1, 2, 3…)
Export a randomly selected subset events from each fcs file
into a single “concatenated” file.
0 50 100 150 200
Sequence
0
10
2
103
104
10
5
<B515-A>:CD45RA-FITC
0 50 100 150 200
Sequence
0
10
2
10
3
104
10
5
<G560-A>:KI67-PE
0 50 100 150 200
Sequence
0
102
103
104
10
5
<G610-A>:CD4-TRPE
0 50 100 150 200
Sequence
0
102
103
104
10
5
<V800-A>:CD8-BV780
0 50 100 150 200
Sequence
0
10
2
10
3
104
10
5
<G780-A>:CCR5-CY7PE
0 50 100 150 200
Sequence
0
10
2
10
3
104
10
5
<R660-A>:CD95-APC
0 50 100 150 200
Sequence
0
10
2
10
3
104
10
5
<R710-A>:CCR7-A680
0 50 100 150 200
Sequence
0
10
2
10
3
104
10
5
<V450-A>:HLA-DR-CB
0 50 100 150 200
Sequence
0
10
2
10
3
104
10
5
<G660-A>:CD27-CY5PE
0 50 100 150 200
Sequence
0
10
2
10
3
104
10
5
<V655-A>:CD38-QD655
0 50 100 150 200
Sequence
0
10
2
10
3
104
10
5
<V705-A>:CD57-QD705
Concatenation for QC
From	
  this,	
  you	
  can	
  pick	
  out	
  the	
  
experiment	
  days	
  and	
  markers	
  that	
  are	
  
problema2c.	
  
	
  
It	
  can	
  also	
  help	
  iden2fy	
  universal	
  gates	
  
for	
  analysis	
  of	
  the	
  complete	
  data	
  set.	
  
Guidelines to Improve Cytometry Experiments	
  
Planning:	
  Stop	
  to	
  Do	
  Setup	
  and	
  Design	
  
	
  
	
  
ExecuEon:	
  CauEon	
  with	
  Reagent	
  Protocols	
  
	
  
	
  
Analysis:	
  Use	
  Tools	
  that	
  Drive	
  Data	
  Quality	
  
Case studies
Crimes Against Flow Cytometry
Flow Cytometry Unit
Punk E.
CytometriskiFLOW CYTOMETRY UNIT
(FCU)
Suspect	
  DescripEon	
  	
  Talented	
  graduate	
  student,	
  with	
  some	
  flow	
  cytometry	
  experience.	
  
	
  
Indicted	
  on	
  five	
  counts,	
  described	
  herein.	
  
	
  
Count 1: Filing False Police Report
Suspect	
  visited	
  FCU	
  claiming	
  that	
  CD25	
  staining	
  “totally	
  
different”	
  across	
  two	
  panels.	
  
	
  
Even	
  though	
  he	
  was	
  “100%	
  sure”	
  that	
  an2body	
  was	
  
added	
  to	
  both	
  tubes	
  at	
  same	
  concentra2on,	
  and	
  that	
  
same	
  reagent	
  was	
  used.	
  
	
  
	
  
Count 1: Filing False Police Report
Panel	
  1	
   Panel	
  2	
  
Count 1: Filing False Police Report
www.isac-­‐net.org	
  
Panel	
  1	
   Panel	
  2	
  
Bright	
  
Dim	
  
Nega2ve	
  
“The	
  padern	
  
is	
  totally	
  
different,”	
  
said	
  suspect.	
  
Upon	
  interroga2on,	
  suspect	
  claimed	
  there	
  were	
  no	
  differences	
  in	
  
staining	
  or	
  analysis.	
  	
  “I	
  don’t	
  know	
  what	
  the	
  #$@#%	
  is	
  happening!”	
  he	
  
exclaimed.	
  
How Do These Plots Differ?
Panel	
  1	
   Panel	
  2	
  
How Do These Plots Differ?
Panel	
  1	
   Panel	
  2	
  
The	
  axes	
  differ…	
  
Compensa2on	
  and/or	
  transforma2on	
  must	
  be	
  different.	
  
How Do These Plots Differ?
Panel	
  1	
   Panel	
  2	
  
Lower	
  resolu2on	
  plot	
  (bigger	
  dot/per	
  event)	
  shows	
  lots	
  of	
  “junk”	
  
in	
  Panel	
  2.	
  	
  Fluorochrome	
  aggregates.	
  
Panel	
  1	
   Panel	
  2	
  
Count 1: Filing False Police Report
When	
  fluorochrome	
  aggregates	
  are	
  gated	
  out,	
  and	
  then	
  the	
  data	
  is	
  
transformed…	
  paderns	
  are	
  very	
  similar.	
  
Bright	
  
Dim	
  
Nega2ve	
  
Count 2: Unauthorized Entry
Searching	
  the	
  suspect’s	
  FCS	
  files,	
  FCU	
  
inves2gators	
  discover	
  unusual	
  
popula2ons.	
  
	
  
We	
  oLen	
  see	
  three	
  popula2ons	
  
(bright,	
  dim,	
  and	
  nega2ve)	
  in	
  flow	
  
cytometry	
  staining,	
  but	
  four	
  is	
  
unusual.	
  
	
  
Also,	
  highly	
  correlated	
  expression	
  
(diagonals)	
  are	
  unusual.	
  
Count 2: Unauthorized Entry
0 10
2
10
3
10
4
10
5
SSC-A
0
50K
100K
150K
200K
250K
FSC-A
Original	
  lymphocyte	
  gate	
  
Diagonal	
  popula2on	
  overlaid	
  
Inves2ga2ve	
  process	
  called	
  “backga2ng.”	
  
The	
  diagonal	
  popula2on	
  is	
  not	
  distributed	
  throughout	
  lymphocyte	
  gate.	
  	
  Cells	
  
entering	
  lymphocyte	
  gate	
  from	
  border,	
  where	
  SSC	
  is	
  larger.	
  	
  	
  
Count 2: Unauthorized Entry
0 10
2
10
3
10
4
10
5
SSC-A
0
50K
100K
150K
200K
250K
FSC-A
Solu2on:	
  	
  A	
  2ghter	
  lymphocyte	
  gate	
  that	
  excludes	
  border	
  dwellers.	
  	
  	
  
New	
  lymph	
  gate	
  overlaid	
  on	
  old	
  gate.	
  
	
  
Diagonal	
  popula2on	
  from	
  old	
  gate.	
  
New,	
  2ghter	
  lymph	
  gate.	
  
Count 2: Unauthorized Entry
Suspect’s	
  gate	
   FCU	
  Inves2ga2ve	
  Task	
  Force	
  
Count 3: T.W.I.
T	
  .	
  W	
  .	
  I. 	
   	
   	
  “Titra2on	
  Without	
  Intracellular	
  (staining).”	
  	
  
1:20	
  dilu2on	
  of	
  an2body	
  
1:20	
  dilu2on	
  of	
  an2body	
  
Suspect	
  asks,	
  “why	
  don’t	
  they	
  look	
  the	
  same?”	
  
Count 3: T.W.I.
T	
  .	
  W	
  .	
  I. 	
   	
   	
  “Titra2on	
  Without	
  Intracellular	
  (staining).”	
  	
  
1:20	
  dilu2on	
  of	
  an2body	
  
Intracellular	
  
1:20	
  dilu2on	
  of	
  an2body	
  
Surface	
  
Suspect	
  admits	
  one	
  added	
  before	
  fix/perm,	
  another	
  aLer.	
  
Titrate	
  under	
  same	
  condi2ons	
  as	
  experiment.	
  
Count 4: Felony Failure to Wash
FCU	
  inves2gators	
  found	
  staining	
  that	
  
didn’t	
  make	
  biological	
  sense.	
  
	
  
Chadopadhyay,	
  et	
  al.	
  	
  
Perforin	
  always	
  expressed	
  with	
  Granzyme	
  
B,	
  but	
  Granzyme	
  B	
  can	
  be	
  expressed	
  
without	
  Perforin.	
  
	
  
What	
  had	
  the	
  suspect	
  done	
  to	
  the	
  
Granzyme	
  B+	
  Perforin-­‐	
  cells?	
  
Count 4: Felony Failure to Wash
During	
  a	
  vigorous	
  interroga2on,	
  suspect	
  broke	
  
down	
  and	
  admided	
  that	
  he	
  had	
  forgoden	
  to	
  
add	
  GrzB	
  an2body	
  during	
  staining.	
  
	
  
Tried	
  to	
  cover	
  up	
  crime	
  by	
  adding	
  an2body	
  at	
  
the	
  cytometer,	
  but	
  forgot	
  to	
  wash	
  it	
  out.	
  
	
  
Moral:	
  Inves2gate	
  paderns	
  that	
  aren’t	
  
consistent	
  with	
  biology.	
  Technical	
  mistakes	
  
can	
  lead	
  to	
  non-­‐specific	
  binding.	
  
Count 5:
Misrepresentation without Titration
At	
  trial,	
  defense	
  adorney	
  submided	
  evidence	
  claiming	
  
that	
  “Pra2p’s	
  an2body	
  conjugates	
  never	
  work	
  reliably,”	
  
hoping	
  to	
  undermine	
  the	
  case	
  against	
  his	
  client.	
  
	
  
	
  
	
  
	
  
Count 5:
Misrepresentation without Titration
Witnesses	
  report,	
  however,	
  that	
  defendant	
  “is	
  always	
  
rushing,”	
  and	
  “seems	
  to	
  finish	
  staining	
  way	
  before	
  
anyone	
  else	
  in	
  the	
  lab.”	
  
	
  
Surveillance	
  footage	
  revealed	
  that	
  suspect	
  stained	
  at	
  
4C	
  for	
  5	
  minutes.	
  
	
  
FCU	
  2tra2ons	
  showed	
  that	
  reagent	
  was	
  fine.	
  
	
  
Titra2on	
  under	
  same	
  condiEons	
  as	
  experiment	
  shows	
  
that	
  staining	
  will	
  work	
  even	
  at	
  4C,	
  but	
  you	
  need	
  much	
  
more	
  an2body.	
  	
  
	
  
	
  
	
  
0 5 10 15 20
Dilution
0
102
103
104
105
B515-A:CD8FITC
0
102
103
104
105
B515-A:CD8FITC
2' 37C
2' 4C
0 5
5' 4C
5' 37C
15 20
tion 15' rt
0 5 10 15 20
Dilution5' rt
0 5 10 15 20
Dilution 2hrs rt
0 5 10 15 20
Dilution 6hrs rt
0 5
5	
  minutes	
  
4C	
  
15	
  minutes	
  
RT	
  
Dilu6on	
  
Summary
Resolving Problems in Flow Cytometry Data
•  is easier when instrument, reagents, and staining
procedures are optimized.
•  requires examination of ALL parameters (hidden
problems can exist with certain marker combinations that
aren’t present in gating tree).
•  is based on pattern recognition, with some patterns
indicating a very easy to diagnose problem.
www.isac-net.org
Welcome to FCU, Now Here Are Your Cases
“Report of missing CCR7+ central memory cells.”
CD45RA	
  FITC	
  
CCR7	
  A700	
  
Mul2color	
  Panel	
  
Percentage of CD45RA- CCR7+ cells is too
low in multicolor panel.
CD45RA	
  FITC	
  
CCR7	
  PE	
  
2-­‐color	
  Test	
   What happened?
Welcome to FCU, Now Here Are Your Cases
“Report of missing CCR7+ central memory cells.”
CD45RA	
  FITC	
  
CCR7	
  A700	
  
Mul2color	
  Panel	
  
Percentage of CD45RA- CCR7+ cells is too
low in multicolor panel.
CD45RA	
  FITC	
  
CCR7	
  PE	
  
2-­‐color	
  Test	
  
What happened?
1)  Titration of A700 reagent?
2)  Spreading error from
another marker in panel?
3)  Poor reagent choice for
CCR7?
Welcome to FCU, Now Here Are Your Cases
“Domestic dispute, disagreement about whether new antibody works.”
10
0
10
1
10
2
10
3
10
0
10
1
10
2
10
3
Compensated
New	
  AnEbody	
  
CD4	
  Cy5PE	
  
Who’s right?
*	
  Compensated	
  data	
  
Welcome to FCU, Now Here Are Your Cases
“Domestic dispute, disagreement about whether new antibody works.”
10
0
10
1
10
2
10
3
10
0
10
1
10
2
10
3
Compensated
New	
  AnEbody	
  
CD4	
  Cy5PE	
  
Who’s right?
*	
  Compensated	
  data	
  
Events piling up
on axes
Spreading of
negative
distribution
“Holes” in distribution:
appear to separate
uniform populations
Welcome to FCU, Now Here Are Your Cases
“Domestic dispute, disagreement about whether new antibody works.”
10
0
10
1
10
2
10
3
10
0
10
1
10
2
10
3
Compensated
New	
  AnEbody	
  
CD4	
  Cy5PE	
  
Who’s right?
*	
  Compensated	
  data	
  
Events piling up
on axes
Spreading of
negative
distribution
“Holes” in distribution:
appear to separate
uniform populations
Welcome to FCU, Now Here Are Your Cases
“Fraud: Vendor sold me a kit for making cell lines from single sorted cells.
Most of the cells didn’t grown into lines. Vendor claims user error.”
What could have gone wrong?
Full Panel
Welcome to FCU, Now Here Are Your Cases
“Fraud: Vendor sold me a kit for making cell lines from single sorted cells.
Most of the cells didn’t grown into lines. Vendor claims user error.”
What could have gone wrong?
1)  Not really CMV+, just
fluorochrome aggregates.
2)  No viability marker, all/most cells
are dead.
Full Panel
Welcome to FCU, Now Here Are Your Cases
“Fraud: Vendor sold me a kit for making cell lines from single sorted cells.
Most of the cells didn’t grown into lines. Vendor claims user error.”
What could have gone wrong?
1)  Not really CMV+, just
fluorochrome aggregates.
2)  No viability marker, all/most cells
are dead.
Full Panel
Viability	
  
Gate	
  
Welcome to FCU, Now Here Are Your Cases
“Serial criminal on the loose.
Find the crimes…”
Experimental	
  
Stain	
  
CD4 A488
CD3 PE
CD8 Cy7PE
DAPI
Lymphs	
  
(Stained	
  today)	
  
Beads	
  
No stain
1.  Set	
  voltages	
  
Beads	
  
CD3 FITC
Lymphs	
  
CD3 PE
DAPI
Beads	
  
CD4 Cy7PE
Beads	
  
DAPI
Single	
  	
  
Colors	
  
Universal	
  	
  
Nega2ve	
  
Beads	
  
No stain
(Stained	
  last	
  week	
  and	
  fixed)	
  2.	
  Compensa2on	
  
Lymphs	
  
No stain
3.	
  Ga2ng	
  
Welcome to FCU, Now Here Are Your Cases
“Serial criminal on the loose.
Find the crimes…”
Experimental	
  
Stain	
  
CD4 A488
CD3 PE
CD8 Cy7PE
DAPI
Lymphs	
  
(Stained	
  today)	
  
Beads	
  
No stain
1.  Set	
  voltages	
  
Beads	
  
CD3 FITC
Lymphs	
  
CD3 PE
DAPI
Beads	
  
CD4 Cy7PE
Beads	
  
DAPI
Single	
  	
  
Colors	
  
Universal	
  	
  
Nega2ve	
  
Beads	
  
No stain
(Stained	
  last	
  week	
  and	
  fixed)	
  2.	
  Compensa2on	
  
Lymphs	
  
No stain
3.	
  Ga2ng	
  
Review	
  
Recognize Problematic Staining Patterns
Troubleshoot Experiments and Analysis
Characteristic patterns, tools to identify trouble,
strategies to troubleshoot
Guidelines to Improve Cytometry Experiments.
Stop and Setup, Cautiously Experiment, Drive New Tools for QC
 
Thank	
  you!	
  
	
  
Help	
  us	
  compile	
  more	
  troubleshoo2ng	
  examples!	
  
	
  
	
  
	
  
	
  
If	
  you	
  have	
  good	
  troubleshoo2ng	
  examples,	
  please	
  email:	
  
pchadop@mail.nih.gov	
  
	
  
	
  
Perhaps	
  we	
  can	
  have	
  a	
  public,	
  anonymous	
  repository	
  for	
  
such	
  data	
  through	
  FlowRepository	
  or	
  CytoU.	
  

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Cyto 2015 Forensic Flow Cytometry Tutorial

  • 1. Forensic Flow Cytometry: Troubleshooting Flow Cytometry Data Pratip K. Chattopadhyay, Ph.D. ISAC Scholar ImmunoTechnology Section Vaccine Research Center, NIH Jennifer Wilshire, Ph.D. Assistant Manager Flow Cytometry Core Facility Memorial Sloan-Kettering Cancer Center
  • 2. These slides were presented at the CYTO2015 Conference. They are distributed here for the benefit of the flow cytometry community. Please do not: 1.  Take any material from these presentations (copied or adapted) without obtaining the express consent of Pratip Chattopadhyay, pchattop@mail.nih.gov. 2.  Modify this material in any way or distribute it outside of this source. Please do: 1.  Send your flow cytometry troubleshooting examples. I’d love to create a publicly available database of examples like the ones presented here. 2.  Send me your questions and comments! 3.  Learn and Enjoy!  
  • 3. Disclaimer : “Forensic” Flow Cytometry   If you are here to learn about how flow cytometry is used to solve real crimes (e.g., murder, robbery, etc.) … Sorry, we’ll be talking about other heinous acts: Poor experimental Planning, Execution, and Analysis.
  • 4. Today   Recognize Problematic Staining Patterns (See how easy it is to fall prey to criminal cytometry) Troubleshoot Experiments and Analysis (Become a crime-stopper) Guidelines to Improve Cytometry Experiments. (Learn how to live a virtuous life)
  • 5. Guidelines to Improve Cytometry Experiments   Experiment Planning: “Don’t run the red light. Spend time planning your experiments properly.” Start with a robust system for instrument setup & monitoring.
  • 6. Guidelines to Improve Cytometry Experiments   Experiment Planning: “Don’t run the red light. Spend time planning your experiments properly.” Start with a robust system for instrument setup & monitoring. Why bother? Reliability! Set Panel Instruments Panel Panel Panel PanelSingle  panel  performs  reliably   in  mul2-­‐instrument  (center)  study.   Single  instrument   works  for  most  panels  
  • 7. Instrument Setup and Monitoring: Voltages   How to set voltages? Option 1: Just set all unstained signals to 102. What’s the crime here? (Audience?)
  • 8. Instrument Setup and Monitoring: Voltages   How to set voltages? Option 1: Just set all unstained signals to 102. Criminal neglect. This method ignores: - dynamic range of PMT, - linearity of PMT, - the tenet that signal from dye should be highest in the detector designed to read that dye.
  • 9. “Neglecting” Dynamic Range and Linearity of PMTs   Hard to know relative performance of different PMTs in your instrument. CVs of fluorescent signals may not be optimized, leading to issues with resolution and gating.
  • 10. “Neglecting” Dynamic Range and Linearity of PMTs   Hard to know relative performance of different PMTs in your instrument. CVs may not be optimized, leading to issues with resolution and gating. Quality Control Panel Design Data Analysis This complicates…
  • 11. “Neglecting” Dynamic Range and Linearity of PMTs   Hard to know relative performance of different PMTs in your instrument. CVs may not be optimized, leading to issues with resolution and gating. Quality Control Panel Design Data Analysis This complicates… “I will never know if my PMTs are working properly.” “I never resolve dim markers on the 1st detector off green laser, so I don’t use Cy7PE.” “My percent CD4+ never matches any other site in multicenter study.” Leading to criminal confessions:
  • 12. “Neglecting” Dynamic Range and Linearity of PMTs   Is this always a problem? No… sometimes this will be a victimless crime. But troubleshooting experiments depends on your confidence in instrument setup! Poor instrument setup makes forensic flow harder! Guideline: Consider linearity and dynamic range when choosing PMT voltage.
  • 13. Another Rule for Choosing Voltages   Signal from a dye should be strongest in channel set up to detect that dye. So, signal from Alexa 680 (or R700APC or Cy55APC) should be brightest when measured off of the red laser detector with 710nm filters in it.  
  • 14. Another Rule for Setting Voltages   Signal from a dye should be strongest on channel set up to detect that dye. So, signal from Alexa 680 (or R700APC or Cy55APC) should be brightest when measured off of the red laser detector with 710nm filters in it, even in systems also measuring Cy55PE. Ensure this by optimizing detector voltages.  
  • 15. If voltages across two detectors aren’t optimized…   You may detect more secondary (spillover) signal from Cy55PE in your RedLaser/710nm channel, than primary (Alexa 680). What happens as a consequence? (Audience?)
  • 16. If voltages across two detectors aren’t optimized…   You may be detect more secondary (spillover) signal from Cy5PE in your RedLaser/710nm channel, than primary (Alexa 680). What happens as a consequence? (Audience?) 0 10 2 10 3 10 4 10 5 Cy5PE (Secondary) 0 102 103 104 10 5 Alexa680(Primary) Hint
  • 17. If voltages across two detectors aren’t optimized…   You may be detect more secondary (spillover) signal from Cy5PE in your RedLaser/710nm channel, than primary (Alexa 680). What happens as a consequence? (Audience?) 0 10 2 10 3 10 4 10 5 Cy5PE (Secondary) 0 102 103 104 10 5 Alexa680(Primary) Hint Compensation values > 100%. Will this sink your panels? Not always… a multicolor panel can work just fine with compensations > 100% in some color combinations. But… troubleshooting becomes harder. And panel development is more complex.
  • 18. Instrument Setup and Monitoring: Voltages   How to do a better job setting voltages? Option 1: Just set all unstained signals to 102. Option 2: Bead-based protocols. Good practice. Perfetto (Nature Protocols), CS&T (BD) Provide quantitative measure of instrument performance, in the form of metrics like Q (sensitivity) and B (resolution). Beads have limitations, though: - high intrinsic CV - loaded with broad-spectrum dyes, not our fluors - beads and dyes vary by lot. So, the metrics from these methods are a bit inaccurate.
  • 19. Instrument Setup and Monitoring: Voltages   How to do an even better job setting voltages? Option 1: Just set all unstained signals to 102. Option 2: Bead-based protocols. Option 3: LED Pulser New device (Jim Wood, Wake) that emits consistent signals to PMT for accurate measurement of sensitivity and resolution (Q & B). Reliable method to compare detectors (track them for QC), or compare instruments. Can use Q and B values to 1) set voltages properly, 2) predict how a panel will stain on different instruments, and 3) predict whether a putative staining panel will work. More info: CYTO U webinar, Steve Perfetto’s Oral Presentation
  • 20. Review: Guidelines to Improve Cytometry Experiments   Experiment Planning: Spend time planning your experiments properly. 1)  Set up instrument with bead-based protocols or LED Pulser.
  • 21. Guidelines to Improve Cytometry Experiments   Experiment Planning: “Spend time planning your experiments properly.” 1)  Set up instrument with bead-based protocols or LED Pulser. 2)  Put some effort into panel design!
  • 22. Panel Design: Step 1… Titrate Every Reagent   Titration is fundamental to successful flow and good data. Don’t rely on the manufacturer’s titer… … they can’t test all experimental conditions, and suggested titers may be significantly higher than what will work just fine in your system.
  • 23. Titration Guidelines   Choose a wide range of concentrations that captures: Saturation (where signal does not increase with more antibody) Loss of positive staining (or at least diminished staining). O/n rt 0 5 10 15 20 Dilution 0 102 10 3 104 105 B515-A:CD8FITCV1002 O/n 37C 0 5 10 15 20 Dilution O/n 4C 0 5 10 15 20 Dilution Example of a good titration experiment (courtesy Margaret Beddall)
  • 24. Titration Guidelines   1) Choose a wide range of concentrations. 2) Perform titrations under the same conditions as experiment. If you titrate antibody at room temperature, but need to stain cells at 37C for one experiment, Redo the titration at 37C first! Example of this in case studies later today.
  • 25. Titration Guidelines   1) Choose a wide range of concentrations. 2) Perform titrations under the same conditions as experiment. 3) Analyze titrations on same cell type as in your study. Some markers differ in expression level by tissue, or are expressed across different cell types at different levels. Need to account for this by using right sample type, or including other markers in titration.
  • 26. How Do Analyze Titration Data? 10 -3 10 -2 10 -1 10 0 10 1 Dilution 0 10 2 10 3 10 4 10 5 B515-A:CD3Ax488
  • 27. 10 -3 10 -2 10 -1 10 0 10 1 Dilution 0 10 2 10 3 10 4 10 5 B515-A:CD3Ax488 Saturating Titer: The concentration at which positive signal plateaus, more antibody does not increase positive signal. Use this concentration when quantifying receptor density, working across changing experimental conditions. How Do Analyze Titration Data?
  • 28. 10 -3 10 -2 10 -1 10 0 10 1 Dilution 0 10 2 10 3 10 4 10 5 B515-A:CD3Ax488 Saturating Titer: Going over saturation increases non- specific binding. How Do Analyze Titration Data?
  • 29. 10 -3 10 -2 10 -1 10 0 10 1 Dilution 0 10 2 10 3 10 4 10 5 B515-A:CD3Ax488 Separating Titer: Subjective, lower concentration where staining still resolves well. Use this concentration in most experiments, where staining time/temp are constant. Saves antibody. Helps reduce spreading error in other channels when working with complex panels. How Do Analyze Titration Data?
  • 31. 10 -3 10 -2 10 -1 10 0 10 1 Dilution 0 10 2 10 3 10 4 10 5 B515-A:CD3Ax488 Blasphemy! How can you work below saturation? You can… because we are still at antibody excess. This is, in fact, why we needn’t be terribly precise in our antibody volumes. There is often at least a 4x range of concentrations that are just fine. 100x   50x   25x   How Do Analyze Titration Data?
  • 32. 10 -3 10 -2 10 -1 10 0 10 1 Dilution 0 10 2 10 3 10 4 10 5 B515-A:CD3Ax488 But then you will underestimate %+! No… Rarely happens. 50   60   70   80   90   100   0   2   4   6   8   10   100x   50x   25x   %  CD3+   How Do Analyze Titration Data?
  • 33. Guidelines to Improve Cytometry Experiments   Experiment Planning: “Spend time planning your experiments properly.” 1)  Set up instrument with bead-based protocols or LED Pulser. 2)  Put some effort into panel design! + Titration – range of concentrations that works.
  • 34. Guidelines to Improve Cytometry Experiments   Experiment Planning: “Spend time planning your experiments properly.” 1)  Set up instrument with bead-based protocols or LED Pulser. 2)  Put some effort into panel design! + Titration – range of concentrations that works. + Panel Design – Choosing which reagents will work together, and at what concentrations. We do this by considering spectral overlap and spreading error.
  • 35. Review of Panel Design* www.isac-net.org Don’t randomly combine antibodies and fluorochromes. Quick & Easy (Spectral Overlap) Pick fluorochromes that don’t overlap. Qdots and Brilliant Violet fluorochromes are particularly useful for this. Pair highly overlapping fluorochromes with markers that aren’t co-expressed. Choose bright dyes for dim markers. The Better Way (Spreading Error) *See ISAC’s Polychromatic Flow Cytometry Course (2011) and my Tutorials (2010, 2013).
  • 36. Review of Panel Design* www.isac-net.org Don’t randomly combine antibodies and fluorochromes. Quick & Easy (Spectral Overlap) Pick fluorochromes that don’t overlap. Qdots and Brilliant Violet fluorochromes are particularly useful for this. Pair highly overlapping fluorochromes with markers that aren’t co-expressed. Choose bright dyes for dim markers. Limitations *See ISAC’s Polychromatic Flow Cytometry Course (2011) and my Tutorials (2010, 2013). Multicolor flow will always use dyes that overlap (and that overlap is no big deal!). Cells are so heterogeneous that there is usually some co-expression of markers in any (interesting) phenotyping experiment. Reagent brightness doesn’t depend only on the dye used for conjugation. Antibody clones differ in signal strength, too. So, charts and guides ranking dye brightness may be of limited utility.
  • 37. Review of Panel Design* www.isac-net.org Don’t randomly combine antibodies and fluorochromes. Quick & Easy (Spectral Overlap) Pick fluorochromes that don’t overlap. Qdots and Brilliant Violet fluorochromes are particularly useful for this. Pair highly overlapping fluorochromes with markers that aren’t co-expressed. Choose bright dyes for dim markers. The Better Way (Spreading Error) Characterize spreading error for the fluorochromes and instruments you use. Place dim markers on channels with low spreading error. Iteratively add markers to panel to characterize effect of various combinations on other channels. *See ISAC’s Polychromatic Flow Cytometry Course (2011) and my Tutorials (2010, 2013).
  • 38. Why Worry About Spreading Error? When fluorescence is low, error associated with photon detection is easily observed. Particularly high photon counting errors occur for fluorochromes that give off very few photons (e.g., Cy7APC). The result: the negative population spreads out and masks dim populations in neighboring channels.
  • 39. How do you calculate spreading error www.isac-net.org (See PC Previous Tutorials for Manual Example.)
  • 40. How do you calculate spreading error www.isac-net.org (See PC Previous Tutorials for Manual Example.)
  • 41. How do you calculate spreading error www.isac-net.org (See PC Previous Tutorials for Manual Example.)
  • 42. How do use spreading error in panel design? Save the resulting table. You can even copy table to spreadsheet and make a heat map. Dye for Plot vs. Detector for Plot DyeforPlot A-BUV395 B-BUV490 C-BUV550 D-BUV680 E-BUV737 F-BUV815 G-BV421 H-BV480 I-BV570 J-BV605 K-BV650 L-BV711 M-BV745 N-BV786 O-BB515 P-BB630 Q-BB667 R-BB700 S-BB796 T-PE U-CF594PE V-CY5PE W-CY55PE X-CY7PE Y-APC Z-R700APC ZZ-H7APC A-BUV395 B-BUV490 C-BUV550 D-BUV680 E-BUV737 F-BUV815 G-BV421 H-BV480 I-BV570 J-BV605 K-BV650 L-BV711 M-BV745 N-BV786 O-BB515 P-BB630 Q-BB667 R-BB700 S-BB796 T-PE U-CF594PE V-CY5PE W-CY55PE X-CY7PE Y-APC Z-R700APC ZZ-H7APC Detector for Plot Spread 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Dye Detector
  • 43. Dye for Plot vs. Detector for Plot DyeforPlot A-BUV395 B-BUV490 C-BUV550 D-BUV680 E-BUV737 F-BUV815 G-BV421 H-BV480 I-BV570 J-BV605 K-BV650 L-BV711 M-BV745 N-BV786 O-BB515 P-BB630 Q-BB667 R-BB700 S-BB796 T-PE U-CF594PE V-CY5PE W-CY55PE X-CY7PE Y-APC Z-R700APC ZZ-H7APC A-BUV395 B-BUV490 C-BUV550 D-BUV680 E-BUV737 F-BUV815 G-BV421 H-BV480 I-BV570 J-BV605 K-BV650 L-BV711 M-BV745 N-BV786 O-BB515 P-BB630 Q-BB667 R-BB700 S-BB796 T-PE U-CF594PE V-CY5PE W-CY55PE X-CY7PE Y-APC Z-R700APC ZZ-H7APC Detector for Plot Spread 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Dye Detector Now you can see (FOR ONE PARTICULAR INSTRUMENT) how including Cy5PE in the panel, induces spreading error into a number of other detectors off of the green laser. Don’t put dim markers into those detectors.   How do use spreading error in panel design?
  • 44. Dye for Plot vs. Detector for Plot DyeforPlot A-BUV395 B-BUV490 C-BUV550 D-BUV680 E-BUV737 F-BUV815 G-BV421 H-BV480 I-BV570 J-BV605 K-BV650 L-BV711 M-BV745 N-BV786 O-BB515 P-BB630 Q-BB667 R-BB700 S-BB796 T-PE U-CF594PE V-CY5PE W-CY55PE X-CY7PE Y-APC Z-R700APC ZZ-H7APC A-BUV395 B-BUV490 C-BUV550 D-BUV680 E-BUV737 F-BUV815 G-BV421 H-BV480 I-BV570 J-BV605 K-BV650 L-BV711 M-BV745 N-BV786 O-BB515 P-BB630 Q-BB667 R-BB700 S-BB796 T-PE U-CF594PE V-CY5PE W-CY55PE X-CY7PE Y-APC Z-R700APC ZZ-H7APC Detector for Plot Spread 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Dye Detector You can also see how low the spreading error is off violet laser detectors. Consider putting dim markers on those channels.   How do use spreading error in panel design?
  • 45. Dye for Plot vs. Detector for Plot DyeforPlot A-BUV395 B-BUV490 C-BUV550 D-BUV680 E-BUV737 F-BUV815 G-BV421 H-BV480 I-BV570 J-BV605 K-BV650 L-BV711 M-BV745 N-BV786 O-BB515 P-BB630 Q-BB667 R-BB700 S-BB796 T-PE U-CF594PE V-CY5PE W-CY55PE X-CY7PE Y-APC Z-R700APC ZZ-H7APC A-BUV395 B-BUV490 C-BUV550 D-BUV680 E-BUV737 F-BUV815 G-BV421 H-BV480 I-BV570 J-BV605 K-BV650 L-BV711 M-BV745 N-BV786 O-BB515 P-BB630 Q-BB667 R-BB700 S-BB796 T-PE U-CF594PE V-CY5PE W-CY55PE X-CY7PE Y-APC Z-R700APC ZZ-H7APC Detector for Plot Spread 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Dye Detector This analysis is now EASY to do (using FlowJo), and can be done with any of the panels you run on your instrument!   How do use spreading error in panel design?
  • 46. Guidelines to Improve Cytometry Experiments   Experiment Planning: “Spend time planning your experiments properly.” 1)  Set up instrument with bead-based protocols or LED Pulser. 2)  Put some effort into panel design! + Titration – range of concentrations that works. + Panel Design – Quick and Dirty Spectral Overlap Method Better Spreading Error Method
  • 47. Guidelines to Improve Cytometry Experiments   Experiment Execution: Proceed cautiously & follow protocols when staining. Two examples of vendor protocols I didn’t follow. (Things you may not be aware of.)
  • 48. Pratip’s Criminal Past (1st Offense) Ignored manufacturer’s recommendation to stain on ice when using fix/perm kits for intra-cellular cytokine staining with surface Qdot antibodies.
  • 49. Pratip’s Criminal Past (1st Offense) Ignored manufacturer’s recommendation to stain on ice when using fix/perm kits for intra-cellular cytokine staining.
  • 50. Pratip’s Criminal Past (2nd Offense) Stained a multicolor panel with many BD and BioLegend “Brilliant” dyes. BD recommends use of Brilliant Stain Buffer, to minimize non-specific interactions between reagents. Non-specific interactions are worst in whole blood lysed protocols, but can appear when staining ficolled PBMC. I would see these interactions essentially as slightly undercompensated staining patterns.
  • 51. Pratip’s Criminal Past (2nd Offense) Example from BD: CD8 CD19 Withour Brilliant Stain Buffer.
  • 52. Pratip’s Criminal Past (2nd Offense) Example from BD: CD8 CD19 Without Brilliant Stain Buffer.
  • 53. Pratip’s Criminal Past (2nd Offense) Example from BD: CD8 CD19 Without Brilliant Stain Buffer. With Brilliant Stain Buffer.
  • 54. Guidelines to Improve Cytometry Experiments   Experiment Execution: Proceed cautiously & follow protocols when staining. Follow new vendor protocols exactly. There’s a reason for their recommended conditions.
  • 55. Guidelines to Improve Cytometry Experiments   Analysis of Data: Use tools that help check data quality. 1) NXN Plot.
  • 56. N X N PlotsFITC   PE   TRPE   Cy5PE   Cy5.5PE   PE   TRPE   Cy5PE   Cy5.5PE   TRPE   Cy5PE   Cy5.5PE   Cy5PE   Cy5.5PE   And so on… For every marker combination in panel.
  • 57. N X N Plot for Troubleshooting A rapid means to identify problems. Over Compensation Under Compensation Over Compensation Transformation/Compensation
  • 58. N X N Plot for Troubleshooting Let’s look more closely at Transformation/Compensation example…
  • 59. Negative and Super-Negative www.isac-­‐net.org   ALer  bi-­‐exponen2al  transforma2on,  nega2ve  popula2ons  should  have  a   uniform  distribu2on.     “Super-­‐nega2ve”  =  “really  pregnant”  or  “extremely  dead.”     Why  does  this  happen?   0 103 104 105 <R710-A>: CD69 0 10 3 104 10 5 <G780-A>:CD4 CD4-­‐Nega2ve     CD4  “Super”  Nega2ve  
  • 60. Negative and Super-Negative www.isac-net.org After bi-exponential transformation, negative populations should have a uniform distribution. “Super-negative” = “really pregnant” or “extremely dead.” Why does this happen? Bad compensation in another dimension. 0 103 104 105 <R710-A>: CD69 0 10 3 104 10 5 <G780-A>:CD4 0 102 103 104 105 <V800-A>: CD57 0 10 3 104 105 <G780-A>:CD4
  • 61. Negative and Super-Negative www.isac-net.org As soon as we fix comps, problem is solved. 0 10 3 10 4 10 5 <R710-A>: CD69 0 10 3 10 4 10 5 <G780-A>:CD4 0 10 2 10 3 10 4 10 5 <V800-A>: CD57 0 10 3 10 4 10 5 <G780-A>:CD4 Sort of. There are still a few super-negative events. Why?
  • 62. Fluorochrome Aggregates www.isac-net.org What are they? High-order complexes of fluorochromes that non-specifically bind cells. How do you recognize them? Random, punctate staining. Brighter than the larger population of cells that is specifically staining for the marker. Why worry about aggregates? Affect accuracy of subset identification, don’t phenotype junk! Effect on transformation and data visualization. 0 10 3 10 4 10 5 <G610-A>: `HLA-DR 0 10 2 10 3 10 4 10 5 <V800-A>:CD57
  • 63. Fluorochrome Aggregates : Solutions www.isac-net.org Staining Time Make staining cocktail and spin at full speed in microfuge for a few minutes. Mark cap to indicate where pellet would be, and transfer cocktail from other side to new tube. Analysis Time Make gates that exclude the aggregates. Perform transformation after excluding aggregates. 0 10 3 10 4 10 5 <G610-A>: `HLA-DR 0 10 2 10 3 10 4 10 5 <V800-A>:CD57
  • 64. Guidelines to Improve Cytometry Experiments   Analysis of Data: Use tools that help check data quality. 1)  NXN Plot. 2)  FlowClean: gates out problems in HTS data.
  • 65. FlowClean   As number of parameters increases, we need to study more samples to make statistically robust conclusions. High-throughput systems (HTS) for cytometers will help with this. Quality control of HTS data is challenging. Cytometer pressure is often disrupted by bubbles, debris, or clogs. This can alter fluorescent signal, resulting in abnormal populations of cells. We developed FlowClean because existing methods for finding HTS problems were subjective, slow, or confusing to use. Cha$opadhyay  and  Fletez-­‐Brant  
  • 66. FlowClean  Concept   Most  flow  experiments  aim  to  count  cell  popula2ons  based   on  marker  expression.       Since  the  sample  tube  contains  cells  mixed  together,  the   frequency  of  each  popula2on  should  be  stable  over  the   collec2on  period.     When  it’s  not  stable  (i.e.,  new  popula2ons  appear  and  exis2ng   ones  are  lost)  collec2on/pressure  problems  are  happening.       FlowClean  is  an  automated  tool  to  detect  these.  
  • 67. FlowClean  Workflow   FCS File Partition Distributions Generate Cellular Address #Cells 50%50% #Cells 50%50% <V705> <G560> 0 0 1 1 Partition Cell <V705> <G560> <B515> 1 0 1 0 2 0 1 0 3 1 1 0 4 0 0 1 Define "Populations" by matching addresses:! Population 1! Population 2! Population 3 Track Populations Over Time Transform Population! Frequencies into CLR Flag Anamolous Events Amend FCS File
  • 68. FlowClean  on  Real  Experimental  Data   0 102 10 3 10 4 10 5 0 10 2 10 3 10 4 105 10 4 10 5 <R710-A>:CD38 LSG RV152 212341-0200_B cel.fcsEvent Count: 164602 Time 03006009001200 Time 101 10 2 103 10 4 Good_vs_Bad LSG RV152 212341-0200_B cel.fcs Event Count: 164602 2 3 4 5 0 102 10 3 10 4 10 5 <R710-A>:CD38 LSG RV152 212341-0200_B cel.fcsEvent Count: 164602 03006009001200 Time 0 10 2 10 3 10 4 10 5 <R710-A>:CD38 03006009001200 Time 10 1 10 2 10 3 10 4 Good_vs_Bad LSG RV152 212341-0200_B cel.fcs Event Count: 164602 bad 010 2 103 104 105 SSC-A 0 102 10 3 104 105 <R710-A>:CD38 good RV152 212341-0200_B cel.fcs 010 2 10 3 10 4 10 5 SSC-A 0 102 10 3 10 4 10 5 <R710-A>:CD38 A B CBad Good Time <R710>CD38 Time ChangePointMeans Good_Bad Time <R710>CD38 Time SSC-A <R710>CD38 •  679  files  from  large  study   (RV152)   •  12-­‐14  colors,  HTS   •  Each  file  =  seconds,  each   plate  <  5min    
  • 69. FlowClean  on  Real  Experimental  Data   12% 88% Files   Flagged   No   Problems   1 2 3 4 5 6 7 8 9 10 11 12 A 1 111 1 1 111 11 1 111 11 111 1 B 11 11 11 111 11 1 1 1 1 11 1 1 C 1 1 1 11 11 1 1 D E F 1 1 1 G 11 1 1 1 1 H 1 1 A Are  there  any  posi2ons  in  plate  where  problems  are  most  common?   Perhaps  at  early  wells,  sugges2ng  need  for  beder  cleaning  of  HTS  equipment.  
  • 70. FlowClean  Summary   •  Developed  a  computa2onal  tool  that  makes  flow  data  more   powerful,  by  elimina2ng  ar2facts  and  noise.   •  We  proved  it  worked  in  a  large  dataset,  not  on  a  “toy”  case,   as  is  oLen  done  for  new  algorithms.   •  We  showed  that  it’s  unique  features  are  necessary,  classical/ simple  methods  don’t  work.     •  We  compared  it  to  exis2ng  algorithms,  and  objec2vely   showed  that  it  was  beder.   Available  on  BioConductor  and  GenePadern  (J.  Spidlin).    
  • 71. Guidelines to Improve Cytometry Experiments   Analysis of Data: Use tools that help check data quality. 1)  NXN Plot. 2)  FlowClean: gates out problems in HTS data. 3) Concatenation of all study files.
  • 72. Concatenation for QC When performing a large study, with many samples, covering many weeks of experiments, you need good ways to detect experimental or instrument issues and flag them. One method: Add a parameter to each FCS file that indicates sample number. (Numbers should be consecutive: 1, 2, 3…) Export a randomly selected subset events from each fcs file into a single “concatenated” file.
  • 73. 0 50 100 150 200 Sequence 0 10 2 103 104 10 5 <B515-A>:CD45RA-FITC 0 50 100 150 200 Sequence 0 10 2 10 3 104 10 5 <G560-A>:KI67-PE 0 50 100 150 200 Sequence 0 102 103 104 10 5 <G610-A>:CD4-TRPE 0 50 100 150 200 Sequence 0 102 103 104 10 5 <V800-A>:CD8-BV780 0 50 100 150 200 Sequence 0 10 2 10 3 104 10 5 <G780-A>:CCR5-CY7PE 0 50 100 150 200 Sequence 0 10 2 10 3 104 10 5 <R660-A>:CD95-APC 0 50 100 150 200 Sequence 0 10 2 10 3 104 10 5 <R710-A>:CCR7-A680 0 50 100 150 200 Sequence 0 10 2 10 3 104 10 5 <V450-A>:HLA-DR-CB 0 50 100 150 200 Sequence 0 10 2 10 3 104 10 5 <G660-A>:CD27-CY5PE 0 50 100 150 200 Sequence 0 10 2 10 3 104 10 5 <V655-A>:CD38-QD655 0 50 100 150 200 Sequence 0 10 2 10 3 104 10 5 <V705-A>:CD57-QD705 Concatenation for QC From  this,  you  can  pick  out  the   experiment  days  and  markers  that  are   problema2c.     It  can  also  help  iden2fy  universal  gates   for  analysis  of  the  complete  data  set.  
  • 74. Guidelines to Improve Cytometry Experiments   Planning:  Stop  to  Do  Setup  and  Design       ExecuEon:  CauEon  with  Reagent  Protocols       Analysis:  Use  Tools  that  Drive  Data  Quality  
  • 76. Crimes Against Flow Cytometry Flow Cytometry Unit Punk E. CytometriskiFLOW CYTOMETRY UNIT (FCU) Suspect  DescripEon    Talented  graduate  student,  with  some  flow  cytometry  experience.     Indicted  on  five  counts,  described  herein.    
  • 77. Count 1: Filing False Police Report Suspect  visited  FCU  claiming  that  CD25  staining  “totally   different”  across  two  panels.     Even  though  he  was  “100%  sure”  that  an2body  was   added  to  both  tubes  at  same  concentra2on,  and  that   same  reagent  was  used.      
  • 78. Count 1: Filing False Police Report Panel  1   Panel  2  
  • 79. Count 1: Filing False Police Report www.isac-­‐net.org   Panel  1   Panel  2   Bright   Dim   Nega2ve   “The  padern   is  totally   different,”   said  suspect.   Upon  interroga2on,  suspect  claimed  there  were  no  differences  in   staining  or  analysis.    “I  don’t  know  what  the  #$@#%  is  happening!”  he   exclaimed.  
  • 80. How Do These Plots Differ? Panel  1   Panel  2  
  • 81. How Do These Plots Differ? Panel  1   Panel  2   The  axes  differ…   Compensa2on  and/or  transforma2on  must  be  different.  
  • 82. How Do These Plots Differ? Panel  1   Panel  2   Lower  resolu2on  plot  (bigger  dot/per  event)  shows  lots  of  “junk”   in  Panel  2.    Fluorochrome  aggregates.  
  • 83. Panel  1   Panel  2   Count 1: Filing False Police Report When  fluorochrome  aggregates  are  gated  out,  and  then  the  data  is   transformed…  paderns  are  very  similar.   Bright   Dim   Nega2ve  
  • 84. Count 2: Unauthorized Entry Searching  the  suspect’s  FCS  files,  FCU   inves2gators  discover  unusual   popula2ons.     We  oLen  see  three  popula2ons   (bright,  dim,  and  nega2ve)  in  flow   cytometry  staining,  but  four  is   unusual.     Also,  highly  correlated  expression   (diagonals)  are  unusual.  
  • 85. Count 2: Unauthorized Entry 0 10 2 10 3 10 4 10 5 SSC-A 0 50K 100K 150K 200K 250K FSC-A Original  lymphocyte  gate   Diagonal  popula2on  overlaid   Inves2ga2ve  process  called  “backga2ng.”   The  diagonal  popula2on  is  not  distributed  throughout  lymphocyte  gate.    Cells   entering  lymphocyte  gate  from  border,  where  SSC  is  larger.      
  • 86. Count 2: Unauthorized Entry 0 10 2 10 3 10 4 10 5 SSC-A 0 50K 100K 150K 200K 250K FSC-A Solu2on:    A  2ghter  lymphocyte  gate  that  excludes  border  dwellers.       New  lymph  gate  overlaid  on  old  gate.     Diagonal  popula2on  from  old  gate.   New,  2ghter  lymph  gate.  
  • 87. Count 2: Unauthorized Entry Suspect’s  gate   FCU  Inves2ga2ve  Task  Force  
  • 88. Count 3: T.W.I. T  .  W  .  I.      “Titra2on  Without  Intracellular  (staining).”     1:20  dilu2on  of  an2body   1:20  dilu2on  of  an2body   Suspect  asks,  “why  don’t  they  look  the  same?”  
  • 89. Count 3: T.W.I. T  .  W  .  I.      “Titra2on  Without  Intracellular  (staining).”     1:20  dilu2on  of  an2body   Intracellular   1:20  dilu2on  of  an2body   Surface   Suspect  admits  one  added  before  fix/perm,  another  aLer.   Titrate  under  same  condi2ons  as  experiment.  
  • 90. Count 4: Felony Failure to Wash FCU  inves2gators  found  staining  that   didn’t  make  biological  sense.     Chadopadhyay,  et  al.     Perforin  always  expressed  with  Granzyme   B,  but  Granzyme  B  can  be  expressed   without  Perforin.     What  had  the  suspect  done  to  the   Granzyme  B+  Perforin-­‐  cells?  
  • 91. Count 4: Felony Failure to Wash During  a  vigorous  interroga2on,  suspect  broke   down  and  admided  that  he  had  forgoden  to   add  GrzB  an2body  during  staining.     Tried  to  cover  up  crime  by  adding  an2body  at   the  cytometer,  but  forgot  to  wash  it  out.     Moral:  Inves2gate  paderns  that  aren’t   consistent  with  biology.  Technical  mistakes   can  lead  to  non-­‐specific  binding.  
  • 92. Count 5: Misrepresentation without Titration At  trial,  defense  adorney  submided  evidence  claiming   that  “Pra2p’s  an2body  conjugates  never  work  reliably,”   hoping  to  undermine  the  case  against  his  client.          
  • 93. Count 5: Misrepresentation without Titration Witnesses  report,  however,  that  defendant  “is  always   rushing,”  and  “seems  to  finish  staining  way  before   anyone  else  in  the  lab.”     Surveillance  footage  revealed  that  suspect  stained  at   4C  for  5  minutes.     FCU  2tra2ons  showed  that  reagent  was  fine.     Titra2on  under  same  condiEons  as  experiment  shows   that  staining  will  work  even  at  4C,  but  you  need  much   more  an2body.           0 5 10 15 20 Dilution 0 102 103 104 105 B515-A:CD8FITC 0 102 103 104 105 B515-A:CD8FITC 2' 37C 2' 4C 0 5 5' 4C 5' 37C 15 20 tion 15' rt 0 5 10 15 20 Dilution5' rt 0 5 10 15 20 Dilution 2hrs rt 0 5 10 15 20 Dilution 6hrs rt 0 5 5  minutes   4C   15  minutes   RT   Dilu6on  
  • 94. Summary Resolving Problems in Flow Cytometry Data •  is easier when instrument, reagents, and staining procedures are optimized. •  requires examination of ALL parameters (hidden problems can exist with certain marker combinations that aren’t present in gating tree). •  is based on pattern recognition, with some patterns indicating a very easy to diagnose problem. www.isac-net.org
  • 95. Welcome to FCU, Now Here Are Your Cases “Report of missing CCR7+ central memory cells.” CD45RA  FITC   CCR7  A700   Mul2color  Panel   Percentage of CD45RA- CCR7+ cells is too low in multicolor panel. CD45RA  FITC   CCR7  PE   2-­‐color  Test   What happened?
  • 96. Welcome to FCU, Now Here Are Your Cases “Report of missing CCR7+ central memory cells.” CD45RA  FITC   CCR7  A700   Mul2color  Panel   Percentage of CD45RA- CCR7+ cells is too low in multicolor panel. CD45RA  FITC   CCR7  PE   2-­‐color  Test   What happened? 1)  Titration of A700 reagent? 2)  Spreading error from another marker in panel? 3)  Poor reagent choice for CCR7?
  • 97. Welcome to FCU, Now Here Are Your Cases “Domestic dispute, disagreement about whether new antibody works.” 10 0 10 1 10 2 10 3 10 0 10 1 10 2 10 3 Compensated New  AnEbody   CD4  Cy5PE   Who’s right? *  Compensated  data  
  • 98. Welcome to FCU, Now Here Are Your Cases “Domestic dispute, disagreement about whether new antibody works.” 10 0 10 1 10 2 10 3 10 0 10 1 10 2 10 3 Compensated New  AnEbody   CD4  Cy5PE   Who’s right? *  Compensated  data   Events piling up on axes Spreading of negative distribution “Holes” in distribution: appear to separate uniform populations
  • 99. Welcome to FCU, Now Here Are Your Cases “Domestic dispute, disagreement about whether new antibody works.” 10 0 10 1 10 2 10 3 10 0 10 1 10 2 10 3 Compensated New  AnEbody   CD4  Cy5PE   Who’s right? *  Compensated  data   Events piling up on axes Spreading of negative distribution “Holes” in distribution: appear to separate uniform populations
  • 100. Welcome to FCU, Now Here Are Your Cases “Fraud: Vendor sold me a kit for making cell lines from single sorted cells. Most of the cells didn’t grown into lines. Vendor claims user error.” What could have gone wrong? Full Panel
  • 101. Welcome to FCU, Now Here Are Your Cases “Fraud: Vendor sold me a kit for making cell lines from single sorted cells. Most of the cells didn’t grown into lines. Vendor claims user error.” What could have gone wrong? 1)  Not really CMV+, just fluorochrome aggregates. 2)  No viability marker, all/most cells are dead. Full Panel
  • 102. Welcome to FCU, Now Here Are Your Cases “Fraud: Vendor sold me a kit for making cell lines from single sorted cells. Most of the cells didn’t grown into lines. Vendor claims user error.” What could have gone wrong? 1)  Not really CMV+, just fluorochrome aggregates. 2)  No viability marker, all/most cells are dead. Full Panel Viability   Gate  
  • 103. Welcome to FCU, Now Here Are Your Cases “Serial criminal on the loose. Find the crimes…” Experimental   Stain   CD4 A488 CD3 PE CD8 Cy7PE DAPI Lymphs   (Stained  today)   Beads   No stain 1.  Set  voltages   Beads   CD3 FITC Lymphs   CD3 PE DAPI Beads   CD4 Cy7PE Beads   DAPI Single     Colors   Universal     Nega2ve   Beads   No stain (Stained  last  week  and  fixed)  2.  Compensa2on   Lymphs   No stain 3.  Ga2ng  
  • 104. Welcome to FCU, Now Here Are Your Cases “Serial criminal on the loose. Find the crimes…” Experimental   Stain   CD4 A488 CD3 PE CD8 Cy7PE DAPI Lymphs   (Stained  today)   Beads   No stain 1.  Set  voltages   Beads   CD3 FITC Lymphs   CD3 PE DAPI Beads   CD4 Cy7PE Beads   DAPI Single     Colors   Universal     Nega2ve   Beads   No stain (Stained  last  week  and  fixed)  2.  Compensa2on   Lymphs   No stain 3.  Ga2ng  
  • 105. Review   Recognize Problematic Staining Patterns Troubleshoot Experiments and Analysis Characteristic patterns, tools to identify trouble, strategies to troubleshoot Guidelines to Improve Cytometry Experiments. Stop and Setup, Cautiously Experiment, Drive New Tools for QC
  • 106.   Thank  you!     Help  us  compile  more  troubleshoo2ng  examples!           If  you  have  good  troubleshoo2ng  examples,  please  email:   pchadop@mail.nih.gov       Perhaps  we  can  have  a  public,  anonymous  repository  for   such  data  through  FlowRepository  or  CytoU.