Analysis Methods in Flow Cytometry

1 of 27

2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

Analysis Methods in Flow Cytometry:

Can a Computer Do Better than a Human?

Nikolas Pontikos

Sackler Lecture Theatre Level 7Monday 29th October 2012

PhD Student, Todd Lab

2 of 27

2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

What is Flow Cytometry?

© 1998-2012 Abcam plc. All rights reserved

Event ForwardScatter

SideScatter

CD4 CD127 CD45RA CD25

1 2110 309 103 254 4 70

2 1565 252 57 278 341 59

... ... ... ... ... ... ...

110,992 964 256 78 199 9 345

0 1000 2000 3000 4000

020

040

060

080

010

00

Forward Scatter

Side

Sca

tter

110,992 points

1 point = 1 event = 1 cell

3 of 27

2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

Gating on Forward and Side Scatter

0 1000 2000 3000 4000

020

040

060

080

010

00

Forward Scatter

Side

Sca

tter

Lymphocytes

Granulocytes

Neutrophils

CD4+ Lymphocytes CD8+ Lymphocytes

Granularity

Cell Size

Lymphocyte Gate

4 of 27

2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

Manual Gating of Cell Phenotypes

% of CD25+ Naive Cells

% of Memory Cells

5 of 27

2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

My Work Follows from Manually Gated Data from this Paper

IL2RA gene codes for CD25

Memory CD25-

0.0 0.5 1.0 1.5

0.0

0.5

1.0

1.5

2.0

Log10 CD25 Intensity

Log 1

0 CD

45R

A In

tens

ity Naive CD25-Naive CD25+

MemoryCD25+

Memory

IL2RA associated with T1D

6 of 27

2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

Evaluation of Gating:Association and Repeatability

of Cell Phenotypes

repeatability of cell phenotypes.

✦ 180 individuals (matched for IL2RA genotype, age and sex).

✦ 15 individuals recalled up to 6 months later.Total of 195 samples.

association of cell phenotypes with:

• IL2RA SNPs (rs12722495, rs2104286 and rs11594656)

• age

• sex

7 of 27

2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

Memory CD25-

0.0 0.5 1.0 1.5

0.0

0.5

1.0

1.5

2.0

Log10 CD25 Intensity

Log 1

0 CD

45R

A In

tens

ity

4)Naive CD25-

CD25 Gate

CD45RA+ (Naive) Gate

CD45RA- (Memory) Gate

Naive CD25+

MemoryCD25+

Memory

% of CD25+ Naive Cells over total Naive Cells

Automatic Gating on CD25

8 of 27

2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

Automatic Gating on CD25:Defining Threshold

Defining threshold above which cells are CD25 positive:

automatic gating:

95th percentile of auto gated blank beads

Automatic Gating: Only one CD25 gate for all samples per day.

auto.beads

manual

manual gating:

manually gated blank beads + isotype

control + judgment call

−

−

−

−

−

−

−

−−

−

−

−

−

−

−

− −−−−

−

−

−

−

−−

−

−

−

−

−−−

−−

−−

−−

−−−

−

−

−−−

−−−−−−−−−

−

−−

−

−− −−−

−−−−−−

−

−−−−−−−−−−−−−−−−−−−−−−−−−−

−−−

−

−−−−−

−

−−−

−−−

−

−

−−

−−−

−−

−−

−−−−−−−

−−−

−−−−−−−−−−

−−

−

−−−−−−

−−−−−−

−−

−

−−

−

−−−−

−−

−

−

−

−−

−

−−

−

−

−

−

−

−−−

−

− −

−

− −−−

Mar May Jul Sep

78

910

1112

CD25+ Gate Over Time

CD

25+

Thre

shol

d

9 of 27

2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

Percentage of Naive CD25+ Cell Phenotype:

Association

Auto Gating: SNP and Sex Effect Detected

auto.beadsmanual

−0.6

−0.4

−0.2

0.0

0.2

0.4

rs12722495 rs2104286 rs11594656 Age/10 Male

●

●

●

●

●

●

●

●

●

●

10 of 27

2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

Percentage of Naive CD25+ Cell Phenotype:

Repeatability

a

b

c

d

e

f

gh

j

k

lm

n

o

p

5 10 15 20 25

510

1520

25

CD25+ Naive % Day 1

CD

25+

Nai

ve %

Day

2

a

b

c

d

e

f

gh

j

k

lm

n

o

p

R2

auto.beads 0.797

manual 0.598

15 recalled individuals (a, b, c, d, ..., o, p)

Auto Gating: Better Repeatability Than Manual

11 of 27

2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

Memory CD25-

0.0 0.5 1.0 1.5

0.0

0.5

1.0

1.5

2.0

Log10 CD25 Intensity

Log 1

0 CD

45R

A In

tens

ity

4)Naive CD25-

CD25 Gate

CD45RA+ (Naive) Gate

CD45RA- (Memory) Gate

Naive CD25+

MemoryCD25+

Memory

Automatic Gating on CD45RA

% of Memory Cells over total Non T Regs

12 of 27

2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

Cells which Transition from Naive to Memory

Lose Expression of CD45RA

Memory Naive

CD45RA

Memory Gate Naive Gate

manual gates:

identify peaks remove

middle bit

0.0 0.5 1.0 1.5 2.0

0.0

0.2

0.4

0.6

0.8

1.0

Log10 CD45RA Intensity

Den

sity

Given usual bimodal distribution

of CD45RA:

13 of 27

2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

Automatic Gating on CD45RA:

Fitting Mixtures of Two Distributions

Fit a mixture of two Gaussian (mm)

distributions.

0.0 0.5 1.0 1.5 2.0

0.0

0.2

0.4

0.6

0.8

1.0

Log10 CD45RA Intensity

Den

sity

mm

14 of 27

2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

Automatic Gating on CD45RA:

Fitting Mixtures of Two Distributions

Fit a mixture of two Gaussian (mm)

distributions.

mm

mm posterior

95%

Memory Gates Naive Gates

0.0 0.5 1.0 1.5 2.0

0.0

0.2

0.4

0.6

0.8

1.0

Log10 CD45RA Intensity

Den

sity

mm

Define the gates by choosing

thresholds at which the posterior

probability of group membership

exceeds 95%.

15 of 27

2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

Automatic Gating on CD45RA:

Fitting Mixtures of Two Distributions

sp.mm

sp.mm posterior

95%

Memory Gates Naive Gates

0.0 0.5 1.0 1.5 2.0

0.0

0.2

0.4

0.6

0.8

1.0

Log10 CD45RA Intensity

Den

sity

Fit a mixture of two semi-parametric

symmetric distributions (sp.mm)

16 of 27

2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

mm

sp.mm

manual

Naive GatesMemory Gates

%Memory

manual 66

sp.mm 66

mm 59

Percentage of Memory T Cell Phenotype

0.0 0.5 1.0 1.5 2.0

0.0

0.2

0.4

0.6

0.8

1.0

Log10 CD45RA Intensity

Den

sity

17 of 27

2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

Percentage of Memory T Cells Phenotype:

Association

Auto Gating: mm finds no age association

sp.mm

mm

manual−0.2

−0.1

0.0

0.1

0.2

0.3

rs12722495 rs2104286 rs11594656 Age/10 Male

●

●

●

● ●

●

●

●

●

●

●

●

●

●

●

18 of 27

2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

Percentage of Memory T Cells Phenotype:

Repeatability

15 recalled individuals (a, b, c, d, ..., o, p)

Auto Gating: Repeatability Compromised by d

a b

c

d

e

f

g

h

j

k

l

m n

o p

20 40 60 80

2040

6080

Memory % Day 1

Mem

ory

% D

ay 2

ab

c

d

e

f

g

h

j

k

l

m

no

pa

b

c

d

e

fg

h

j

k

l

mn

o

p

R2

sp.mm 0.404

mm 0.139

manual 0.768

19 of 27

2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

Problem with mm: outliers

Individual d looks completely different on day two.

a b

c

d

e

f

g

h

j

k

l

m n

o p

20 40 60 80

2040

6080

Memory % Day 1

Mem

ory

% D

ay 2

ab

c

d

e

f

g

h

j

k

l

m

no

pa

b

c

d

e

fg

h

j

k

l

mn

o

p

R2

sp.mm 0.404

mm 0.139

manual 0.768

20 of 27

2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

Other Samples From The Same Day

0.0 0.5 1.0 1.5 2.0

0.0

0.2

0.4

0.6

0.8

Log10 CD45RA Intensity

Den

sity

Log10 CD25 Intensity

Log 1

0 CD

45R

A In

tens

ity

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

0.0

0.2

0.4

0.6

0.8

1.0

Log10 CD45RA Intensity

Den

sity

Log10 CD25 Intensity

Log 1

0 CD

45R

A In

tens

ity

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

0.0

0.2

0.4

0.6

0.8

Log10 CD45RA Intensity

Den

sity

Log10 CD25 Intensity

Log 1

0 CD

45R

A In

tens

ity

0.0 0.5 1.0 1.5 2.0

0.0

0.5

1.0

1.5

2.0

Individual d

manual gatessp.mm gatesmm gates

CD25 Gate

%Memory

manual 52

sp.mm 57

mm 42

%Memory

manual 34

sp.mm 40

mm 62

%Memory

manual 57

sp.mm 35

mm 8

21 of 27

2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

A First Approach:Averaging Over Gate Positions

Averaging Gate Positions from Samples on Same Day:

mmGating

0.0 0.5 1.0 1.5 2.0

0.0

0.2

0.4

0.6

0.8

1.0

Log10 CD45RA Intensity

Den

sity

0.0 0.5 1.0 1.5 2.0

0.0

0.2

0.4

0.6

0.8

1.0

Log10 CD45RA Intensity

Den

sity

0.0 0.5 1.0 1.5 2.0

0.0

0.2

0.4

0.6

0.8

1.0

Log10 CD45RA Intensity

Den

sity

0.0 0.5 1.0 1.5 2.0

0.0

0.2

0.4

0.6

0.8

1.0

Log10 CD45RA Intensity

Den

sity

0.0 0.5 1.0 1.5 2.0

0.0

0.2

0.4

0.6

0.8

1.0

Log10 CD45RA Intensity

Den

sity

0.0 0.5 1.0 1.5 2.0

0.0

0.2

0.4

0.6

0.8

1.0

Log10 CD45RA Intensity

Den

sity

learned.mm

mmGating

mmGating

22 of 27

2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

Closer Agreement to Manual

learned.mm

manual

Percentage of Memory T Cells Phenotype:

Association

−0.2

−0.1

0.0

0.1

0.2

0.3

rs12722495 rs2104286 rs11594656 Age/10 Male

●

●

●

● ●

●

●

●

●

●

23 of 27

2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

Improved Repeatability: better than sp.mm

a b

c

de

fg

h

j

k

l

m

no p

20 40 60 80

2040

6080

Memory % Day 1M

emor

y %

Day

2

ab

c

d

e

f

g

h

j

k

l

m

no

pa

b

c

d

e

fg

h

j

k

l

mn

o

p

R2

learned.mm 0.666

mm 0.139

manual 0.768

a b

c

d

e

f

g

h

j

k

l

m n

o p

20 40 60 80

2040

6080

Memory % Day 1

Mem

ory

% D

ay 2

ab

c

d

e

f

g

h

j

k

l

m

no

pa

b

c

d

e

fg

h

j

k

l

mn

o

p

R2

sp.mm 0.404

mm 0.139

manual 0.768

Percentage of Memory T Cells Phenotype:

Repeatability

24 of 27

2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

A Smarter Approach:Hierarchical Mixture Model

Parameters fit to one sample influence parameters fitted to other samples

ParameterEstimation

ParameterEstimation

ParameterEstimation

0.0 0.5 1.0 1.5 2.0

0.0

0.2

0.4

0.6

0.8

1.0

Log10 CD45RA Intensity

Den

sity

0.0 0.5 1.0 1.5 2.0

0.0

0.2

0.4

0.6

0.8

1.0

Log10 CD45RA Intensity

Den

sity

0.0 0.5 1.0 1.5 2.00.

00.

20.

40.

60.

81.

0

Log10 CD45RA Intensity

Den

sity

25 of 27

2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

Can a Computer Do Better than a Human?

In picking a more consistent threshold:

More complex gating on CD45RA:

Yes as seen in the case of CD25 thresholding

Not yet mainly because of outliers

26 of 27

2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

Future

Moving Away from Manual Gating:-Full Auto Gating on Known/Defined Subsets.

-Automatic Gating of Unknown Subsets.

-Development of Automatic Pipeline.

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).

27 of 27

2012-10-29, Nikolas Pontikos, Automatic Analysis of Flow Cytometry Data

Acknowledgments

Calliope Dendrou

Vincent Plagnol

Linda Wicker

John Todd

Stats Group:

Jason Cooper

Hui Guo

Xin Yang

Immunologists:

Tony Cutler

Ricardo Ferreira

Marcin Pekalski

Supervisor: Chris Wallace

Second Supervisor: Anna Petrunkina-Harrison