Flow cytometry analyzes cells by detecting fluorescent markers on individual cells. Nikolas Pontikos' work automatically analyzes flow cytometry data to identify cell phenotypes, such as naive CD25+ cells, and evaluates associations between cell phenotypes and genetic/clinical factors. His method follows from manually gated data and defines thresholds to automatically gate on markers like CD25. This allows evaluating repeatability of cell phenotype identification over time in large sample sets.

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Analysis Methods in Flow
Cytometry:
Can a Computer Do Better than a Human?
Nikolas Pontikos
PhD Student, Todd Lab
Sackler Lecture Theatre Level 7
Monday 29th October 2012
2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

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What is Flow Cytometry?
Event Forward Side CD4 CD127 CD45RA CD25
Scatter Scatter
1 2110 309 103 254 4 70
2 1565 252 57 278 341 59
... ... ... ... ... ... ...
110,992 964 256 78 199 9 345
1000
800
Side Scatter
400 600200
0 110,992 points
0 1000 2000 3000 4000
Forward Scatter
© 1998-2012 Abcam plc. All rights reserved
1 point = 1 event = 1 cell
2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

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Gating on Forward and Side Scatter
1000
Neutrophils
800
Side Scatter
400 600
Granularity
Lymphocyte Gate
200
Granulocytes
0
0 1000 2000 3000 4000
Forward Scatter
Lymphocytes
Cell Size
CD4+ Lymphocytes CD8+ Lymphocytes
2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

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Manual Gating of Cell Phenotypes
% of CD25+ Naive Cells
% of Memory Cells
2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

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My Work Follows from Manually Gated
Data from this Paper
Naive
2.0
IL2RA associated with T1D Naive CD25- CD25+
Log10 CD45RA Intensity
1.5
1.0
IL2RA gene codes for CD25
Memory
0.5 Memory Memory
CD25- CD25+
0.0
0.0 0.5 1.0 1.5
Log10 CD25 Intensity
2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

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Evaluation of Gating:
Association and Repeatability
of Cell Phenotypes
association of cell phenotypes with:
• IL2RA SNPs (rs12722495, rs2104286 and rs11594656)
• age
• sex
✦ 180 individuals (matched for IL2RA genotype, age and sex).
repeatability of cell phenotypes.
✦ 15 individuals recalled up to 6 months later.
Total of 195 samples.
2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

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Automatic Gating on CD25
CD25 Gate
% of CD25+ Naive Cells over total Naive Cells
4)
Naive
2.0
Naive CD25- CD25+
Log10 CD45RA Intensity
1.5
CD45RA+ (Naive) Gate
1.0
CD45RA- (Memory) Gate
Memory
0.5
Memory Memory
CD25- CD25+
0.0
0.0 0.5 1.0 1.5
Log10 CD25 Intensity
2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

8. Automatic Gating on CD25:
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Defining Threshold
Defining threshold above which cells are CD25 positive: CD25+ Gate Over Time
12
−
manual gating: −
− −
manually gated blank beads + isotype
11
− − −
− −
control + judgment call − −− − −
CD25+ Threshold
− − −− − −− −
− − −−−
− − −− − −
− −
10
− −
− − −
− − − − − −−−
−− −
− − −
−−
− − −
−
−
− − − − −−
−
auto.beads
− − −
− −
−− −
9
automatic gating: − −
−−−
−−−
−
−
− −
−
95th percentile of auto gated blank beads − −
− −
−
− − −
−
−
−− − −
−
manual
8
− −−−
− − − −− − −
−
−− −− − −
− −
− −− − − −
− −− −
− −
− −
7
Mar May Jul Sep
Automatic Gating: Only one CD25 gate for all samples per day.
2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

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Percentage of Naive CD25+ Cell Phenotype:
Association
0.4
● ●
●
0.2 ●
0.0
●
●
−0.2 ●
●
−0.4 auto.beads
manual
●
●
−0.6
rs12722495 rs2104286 rs11594656 Age/10 Male
Auto Gating: SNP and Sex Effect Detected
2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

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Percentage of Naive CD25+ Cell Phenotype:
Repeatability
k
25
gh
CD25+ Naive % Day 2 k
20
ee
g R2
h n
d
15
a p n auto.beads 0.797
o p
b d manual 0.598
a
10
j
bj
c o
m l l
m
5
f
c
f
5 10 15 20 25
CD25+ Naive % Day 1
15 recalled individuals (a, b, c, d, ..., o, p)
Auto Gating: Better Repeatability Than Manual
2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

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Automatic Gating on CD45RA
CD25 Gate
4)
Naive
2.0
Naive CD25- CD25+
Log10 CD45RA Intensity
1.5
CD45RA+ (Naive) Gate
1.0
CD45RA- (Memory) Gate
Memory
0.5
Memory Memory
CD25- CD25+
% of Memory Cells over total Non T Regs
0.0
0.0 0.5 1.0 1.5
Log10 CD25 Intensity
2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

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Cells which Transition from Naive to Memory
Lose Expression of CD45RA
1.0
0.8
Given usual bimodal distribution
0.6
Density
of CD45RA:
0.4
manual gates:
0.2
identify peaks remove
Memory Gate Naive Gate middle bit
0.0
0.0 0.5 1.0 1.5 2.0
Log10 CD45RA Intensity
CD45RA
Memory Naive
2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

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Automatic Gating on CD45RA:
Fitting Mixtures of Two Distributions
1.0
Fit a mixture of two Gaussian (mm)
distributions.
0.8
mm
0.6
Density
0.4 0.2
0.0
0.0 0.5 1.0 1.5 2.0
Log10 CD45RA Intensity
2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

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Automatic Gating on CD45RA:
Fitting Mixtures of Two Distributions
mm posterior
1.0
Fit a mixture of two Gaussian (mm)
95%
distributions.
0.8
mm
0.6
Density
Define the gates by choosing
0.4 0.2
thresholds at which the posterior mm
0.0
probability of group membership
exceeds 95%. 0.0 0.5 1.0 1.5 2.0
Log10 CD45RA Intensity
Memory Gates Naive Gates
2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

15. Automatic Gating on CD45RA:
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Fitting Mixtures of Two Distributions
sp.mm posterior
1.0
95%
Fit a mixture of two semi-parametric
0.8
symmetric distributions (sp.mm)
0.6
Density
0.4 0.2
sp.mm
0.0
0.0 0.5 1.0 1.5 2.0
Log10 CD45RA Intensity
Memory Gates Naive Gates
2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

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Percentage of Memory T Cell Phenotype
1.0
0.8
0.6
Density
0.4
%Memory
manual
manual 66
0.2
sp.mm 66
sp.mm
mm 59 0.0 mm
0.0 0.5 1.0 1.5 2.0
Log10 CD45RA Intensity
Memory Gates Naive Gates
2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

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Percentage of Memory T Cells Phenotype:
Association
0.3
0.2 ●
● ●
●
●
0.1 ● ●
●
● ●
●
● ●
0.0 ●
●
−0.1
sp.mm
mm
−0.2 manual
rs12722495 rs2104286 rs11594656 Age/10 Male
Auto Gating: mm finds no age association
2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

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Percentage of Memory T Cells Phenotype:
Repeatability
j jj
80
e
g
o l g ee
ll p
d p
a d b f np g
a f
a b m n
Memory % Day 2
60
d k of
k b om
m
k n c
c c
40
hh R2
h
sp.mm 0.404
20
mm 0.139
manual 0.768
20 40 60 80
Memory % Day 1
15 recalled individuals (a, b, c, d, ..., o, p)
Auto Gating: Repeatability Compromised by d
2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

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Problem with mm: outliers
Individual d looks completely different on
day two.
j jj
80
e
g
o l g ee
ll p
d p
a d b f np g
a f
a b m n
Memory % Day 2
60
d k of
k b om
m
k n c
c c
40
hh R2
h
sp.mm 0.404
20
mm 0.139
manual 0.768
20 40 60 80
Memory % Day 1
2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

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CD25 Gate
Other Samples From
The Same Day
0.8
Log10 CD45RA Intensity
0.6
2.0
Density
1.5 %Memory
0.4
1.0 manual 52
manual gates
0.2
sp.mm gates 0.5 sp.mm 57
mm gates
0.0
0.0
mm 42
0.0 0.5 1.0 1.5 2.0 0.0 0.5 1.0 1.5 2.0
Log10 CD45RA Intensity Log10 CD25 Intensity
0.8
%Memory
Log10 CD45RA Intensity
manual 34
0.6
2.0
Density
1.5 sp.mm 40
0.4
1.0 mm 62
0.2
0.5
0.0
0.0
0.0 0.5 1.0 1.5 2.0 0.0 0.5 1.0 1.5 2.0
Log10 CD45RA Intensity Log10 CD25 Intensity
Individual d
1.0
0.8
Log10 CD45RA Intensity
%Memory
2.0
manual 57
0.6
Density
1.5
sp.mm 35
0.4
1.0
mm 8
0.2
0.5
0.0
0.0
0.0 0.5 1.0 1.5 2.0 0.0 0.5 1.0 1.5 2.0
Log10 CD45RA Intensity Log10 CD25 Intensity
2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

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A First Approach:
Averaging Over Gate Positions
mm mm mm
Gating Gating Gating
1.0
1.0
1.0
0.8
0.8
0.8
Averaging Gate Positions from
0.6
0.6
0.6
Density
Density
Density
Samples on Same Day:
0.4
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
0.0 0.5 1.0 1.5 2.0 0.0 0.5 1.0 1.5 2.0 0.0 0.5 1.0 1.5 2.0
Log10 CD45RA Intensity Log10 CD45RA Intensity Log10 CD45RA Intensity
1.0
1.0
1.0
learned.mm
0.8
0.8
0.8
0.6
0.6
0.6
Density
Density
Density
0.4
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
0.0 0.5 1.0 1.5 2.0 0.0 0.5 1.0 1.5 2.0 0.0 0.5 1.0 1.5 2.0
Log10 CD45RA Intensity Log10 CD45RA Intensity Log10 CD45RA Intensity
2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

22. Percentage of Memory T Cells Phenotype:
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Association
0.3
●
0.2
● ●
●
0.1 ●
●
●
● ●
0.0
●
learned.mm
−0.1
manual
−0.2
rs12722495 rs2104286 rs11594656 Age/10 Male
Closer Agreement to Manual
2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

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Percentage of Memory T Cells Phenotype:
Repeatability
j jj
80
j jj
80
e
g
o l g ee e g
ee
ll p ll
d
a d b f n np g
a f
p d od f n
a
l g
pp p
a b m a f n g
Memory % Day 2
Memory % Day 2
a obf
60
60
d k of d b
k b om
m k m m
b om
k
k n
k n c c
c c cc
40
40
h R2
hh h
h h
R2 learned.mm 0.666
20
sp.mm 0.404 mm 0.139
20
mm 0.139
manual 0.768
manual 0.768
20 40 60 80
20 40 60 80 Memory % Day 1
Memory % Day 1
Improved Repeatability: better than sp.mm
2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

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A Smarter Approach:
Hierarchical Mixture Model
Parameters fit to one sample influence parameters
fitted to other samples
Parameter Parameter Parameter
Estimation Estimation Estimation
1.0
1.0
1.0
0.8
0.8
0.8
0.6
Density
0.6
0.6
Density
Density
0.4
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0 0.5 1.0 1.5 2.0 0.0
Log10 CD45RA Intensity 0.0 0.5 1.0 1.5 2.0 0.0 0.5 1.0 1.5 2.0
Log10 CD45RA Intensity Log10 CD45RA Intensity
2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

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Can a Computer Do Better than a Human?
In picking a more consistent threshold:
Yes as seen in the case of CD25 thresholding
More complex gating on CD45RA:
Not yet mainly because of outliers
2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

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Future
Dealing with outliers:
- Probabilistic Cell Phenotypes.
- Outlier Detection and Reporting of Anomalies.
Better Model Fitting:
- Different Types of Distributions (skewed
distributions).
- Hierarchical Approach (Bayesian Mixture Models).
Moving Away from Manual Gating:
- Full Auto Gating on Known/Defined Subsets.
- Automatic Gating of Unknown Subsets.
- Development of Automatic Pipeline.
2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

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Acknowledgments
Supervisor: Chris Wallace
Second Supervisor: Anna Petrunkina-Harrison
Calliope Dendrou Stats Group: Immunologists:
Jason Cooper Tony Cutler
Vincent Plagnol Hui Guo Ricardo Ferreira
Xin Yang Marcin Pekalski
Linda Wicker
John Todd
2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data