page 1 © 1988-2006 j.paul robinson, purdue university bms 602 lecture 9.ppt bms 631 - lecture 10x...

© 1988-2006 J.Paul Robinson, Purdue University BMS 602 LECTURE 9.PPT

BMS 631 - LECTURE 10xFlow Cytometry: Theory

Bindley Bioscience CenterPurdue UniversityOffice: 494 0757Fax 494 0517email; [email protected]

WEB http://www.cyto.purdue.edu

Multiparameter Data Analysis3rd Ed. Shapiro p 207-214

J. Paul RobinsonProfessor of ImmunopharmacologyProfessor of Biomedical EngineeringPurdue University


Data Analysis

• Gating• Data displays

– histogram– dot plot– isometric display– contour plot– chromatic (color) plots– 3 D projection


Gating

•Real-time gating vs. software gating•Establishing regions•Gating strategies•Quadrant analysis•Complex or Boolean gates•Back gating


Real-Time vs. Software Real-Time vs. Software GatingGating

Real-time or live gating:-restrict the data that will be accepted by a computer (some characteristic must be metbefore data is stored)

Software or analysis gating:-excludes certain stored data from a particular analysis procedure


Establishing RegionsEstablishing Regions•Establishing regions:

-objective or subjective?-training/skill/practice

•Possible shapes: -rectangles-ellipses-free-hand-quadrants

•Statistics


Region 1 establishedRegion 1 established Gated on Region 1Gated on Region 1

Using GatesUsing Gates

log

PE


Quadrant AnalysisQuadrant Analysis

log

PE

(+ +)( - +)

(+ -)(- -)


Drawing Regions: Sample Preparation

Sample Quality

B.subtilisB.subtilis spores spores B.subtilis B.subtilis veg. + spores veg. + spores

Debris

Spores Spores

Debris

Vegetative

Data removedFrom analysis


Complex or Boolean GatingWith two overlapping regions, several options are available:


Boolean GatingNot Region 1:Not Region 1:


Boolean GatingNot Region 2:Not Region 2:


Boolean GatingRegion 1 or Region 2:Region 1 or Region 2:


Boolean Gating

Region 1 and Region 2:Region 1 and Region 2:


Not (Region1 and Region 2):Not (Region1 and Region 2):

Boolean Gating


0 200 400 600 800 1000

0 2

00 4

00 6

00 8

0010

00

Side Scatter Projection

Forw

ard

Sca

tter P

roje

ctio

n

Light Scatter Gating

Forward Scatter Projection

90 Degree Scatter

Neutrophils

Lymphocytes

Monocytes

For

war

d S

catte

r

Human white blood cells


Back-Gating

Region 4 establishedRegion 4 established Back-gating using Region 4Back-gating using Region 4

log

PE

Back gateBack gate


3 Parameter Data DisplayIsometric Display


Methods that can change results:

1. Doublet discrimination

2. Time as a quality control parameter

Example: DNA content-need to eliminate debris & clumps-need to gate out doublets-maintain constant flow rate


DNA Histogram

G0-G1

S

G2-M

Fluorescence Intensity

# of

Eve

nts

TimeC

ount

s

A B C

Gating out bad data


Multi-color studies generate a lot of data

1 2 3 4 5 6 7 8 9 10

3 color4 color 5 color

Log Fluorescence

QUADSTATS

Log

Flu

ores

cenc

e

++

-- +-

-+

Log Fluorescence

QUADSTATS

Log

Flu

ores

cenc

e

++

-- +-

-+

Log Fluorescence

QUADSTATS

Log

Flu

ores

cenc

e

++

-- +-

-+

Log Fluorescence

QUADSTATS

Log

Flu

ores

cenc

e

++

-- +-

-+

Log Fluorescence

QUADSTATS

Log

Flu

ores

cenc

e

++

-- +-

-+

Log Fluorescence

QUADSTATS

Log

Flu

ores

cenc

e

++

-- +-

-+

Log Fluorescence

QUADSTATS

Log

Flu

ores

cenc

e

++

-- +-

-+

Log Fluorescence

QUADSTATS

Log

Flu

ores

cenc

e

++

-- +-

-+

Log Fluorescence

QUADSTATS

Log

Flu

ores

cenc

e

++

-- +-

-+

Log Fluorescence

QUADSTATS

Log

Flu

ores

cenc

e

++

-- +-

-+

Log Fluorescence

QUADSTATS

Log

Flu

ores

cenc

e

++

-- +-

-+

Log Fluorescence

QUADSTATS

Log

Flu

ores

cenc

e

++

-- +-

-+

Log Fluorescence

QUADSTATS

Log

Flu

ores

cenc

e

++

-- +-

-+

Log Fluorescence

QUADSTATS

Log

Flu

ores

cenc

e

++

-- +-

-+

Log Fluorescence

QUADSTATS

Log

Flu

ores

cenc

e

++

-- +-

-+

Log Fluorescence

QUADSTATS

Log

Flu

ores

cenc

e

++

-- +-

-+

Log Fluorescence

QUADSTATS

Log

Flu

ores

cenc

e

++

-- +-

-+

Log Fluorescence

QUADSTATS

Log

Flu

ores

cenc

e

++

-- +-

-+

Log Fluorescence

QUADSTATS

Log

Flu

ores

cenc

e

++

-- +-

-+

Log Fluorescence

QUADSTATS

Log

Flu

ores

cenc

e

++

-- +-

-+

Log Fluorescence

QUADSTATS

Log

Flu

ores

cenc

e

++

-- +-

-+

Log Fluorescence

QUADSTATS

Log

Flu

ores

cenc

e

++

-- +-

-+

Log Fluorescence

QUADSTATS

Log

Flu

ores

cenc

e

++

-- +-

-+

Log Fluorescence

QUADSTATS

Log

Flu

ores

cenc

e

++

-- +-

-+

Log Fluorescence

QUADSTATS

Log

Flu

ores

cenc

e

++

-- +-

-+

Log Fluorescence

QUADSTATS

Log

Flu

ores

cenc

e

++

-- +-

-+

Log Fluorescence

QUADSTATS

Log

Flu

ores

cenc

e

++

-- +-

-+

Log Fluorescence

QUADSTATS

Log

Flu

ores

cenc

e

++

-- +-

-+

Log Fluorescence

QUADSTATS

Log

Flu

ores

cenc

e

++

-- +-

-+

Log Fluorescence

QUADSTATS

Log

Flu

ores

cenc

e

++

-- +-

-+


Contour plots Dot plots


• This figure shows two examples of simultaneous 2 color immunophenotyping. In figure 3 (a) the directly labeled MABs used were CD4-PE / CD8-FITC. In this example approximately 50% of the cells were positive for CD4 and 23% positive for CD8. These percentages were calculated based upon the settings of the negative control for 2% positivity. Right figure shows a similar situation for CD2-PE / CD19-FITC.

Typical phenotypic analysis histograms


Kinetic Analysis

50 ng PMAStimulated

Fluo

resc

ence

Fluo

resc

ence

0 ng PMAUnstimulated

TIME (seconds)0 1800450 900 1350

TIME (seconds)0 1800450 900 1350

Figure: This figure shows an example of stimulation of neutrophils by PMA (50 nm/ml). On the left the unstimulated cells show no increase in DCF fluorescence . On the right, activatedcells increase the green DCF fluorescence at least 10 times the initial fluorescence.


Color Coded Dot Plots

R1

R2

R3

R4

10 20 30 40 50 60 70 80 90 100 120

FSC-Height -->

1020

3040

5060

7080

9010

012

0

SS

C-H

eigh

t -->

10 1 10 2 10 3 10 4

FL4-Height -->

101

102

103

104

FL2-

Hei

ght -

->

Key to understanding this figure, is the notion that differentpopulations can be identified by colors and the relationship of these populations to one another can be monitored.


Contour Plot with Projection

90 Light Scatter 60 50 40 30 20 100

Fo

rwa

rd S

catt

er

60

50

40

30

20

10

0


3 Color Combinations

Negatives

Positives

4+4=8

FITC

PE

APC 4+4+4=12


3 Color Combinations

FITC

PE

APC 4+4+4=12

PE-FITC Pattern

FITCFITC

TR-FITC Pattern

FITCFITC

TR-PE Pattern

PhycoerytherinPhycoerytherin

.1.11

1010

010

010

0010

00

.1.111

1010

100

100

1000

1000

.1.111

1010

100

100

1000

1000

.1.1 11 1010 100100 10001000 .1.1 11 1010 100100 10001000 .1.1 11 1010 100100 10001000

CD4CD4

CD5CD5 CD38CD38

K/LK/L

CD45CD45CD3CD3

NegativeNegative NegativeNegative

CD10CD10

HLA-DR

HLA-DR

CD20CD20CD8CD8

CD7CD7

CD2CD2

CD8CD8

CD8 (dim)CD8 (dim)CD2CD2

NegativeNegative CD4CD4 CD5CD5

KK

Tex

as R

edT

exas

Red

Tex

as R

edT

exas

Red

Ph

yco

eryt

her

inP

hyc

oer

yth

erin

CD3CD3

CD3CD3

IgMIgM

IgGIgG

CD3CD3

CD8CD8CD3CD3

CD20CD20

IgGIgG

LL

CD20CD20

CD20CD20

CD45CD45


Lasers used for multicolor studies


Innovative Data Analysis


Some advanced ways of showing data relationships

20

60

100

Enrico Lugli et al, Università di Modena e Reggio EmiliaOral Presentation Immunology section 15.30-17.30 today

Classification ?


Spectral analysis allows classification


Conclusions• The more parameters you have, the more complex

the analysis will be• But…when you have more parameters (variables)

you have more opportunities for population discrimination

• Display of data in histogram and dotplot formats assists the analysis process

• Displays in 3D are nice but not particularly useful for analysis.

• Multiple parameter displays such as PCA or LDA are more useful for high content data sets

•

page 1 © 1988-2006 j.paul robinson, purdue university bms 602 lecture 9.ppt bms 631 - lecture 10x...

Documents

purdue university bms

boolean gating slide

gating realtime gating

boolean gating region

analysis slide

live gating

gate slide

software gating realtime