+ All Categories
Home > Documents > © 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT BMS 633/BME 695Y...

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT BMS 633/BME 695Y...

Date post: 20-Dec-2015
Category:
View: 217 times
Download: 1 times
Share this document with a friend
60
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT BMS 633/BME 695Y - Week 3 J. Paul Robinson Professor of Immunopharmacology School of Veterinary Medicine, Purdue University Hansen Hall, B050 Purdue University Office: 494 0757 Fax 494 0517 email: [email protected] WEB http://www.cyto.purdue.edu Detectors, Electronics, Data Analysis 3 rd Ed. Shapiro p127-133 4 th Ed. Shapiro p160-256 Material is taken from the course text: Howard M. Shapiro, Practical Flow Cytometry, 3nd edition (1994), 4 th Ed (2003) Alan R. Liss, New York. The WEB version of these slides can be found on http://tinyurl.com/385ss
Transcript

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

BMS 633/BME 695Y - Week 3

J. Paul RobinsonProfessor of Immunopharmacology

School of Veterinary Medicine, Purdue University

Hansen Hall, B050Purdue UniversityOffice: 494 0757Fax 494 0517email: [email protected] http://www.cyto.purdue.edu

Detectors, Electronics, Data Analysis

3rd Ed. Shapiro p127-133

4th Ed. Shapiro p160-256

Material is taken from the course text: Howard M. Shapiro, Practical Flow Cytometry, 3nd edition (1994), 4th Ed (2003) Alan R. Liss, New York.

The WEB version of these slides can be found on

http://tinyurl.com/385ss

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Learning goals

• Students will lean about the nature of detection systems of flow cytometry– Their use, characteristics, benefits and

problems– The types of detection systems used– The way data points are collected and used– The principles of data analysis and reporting

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Detectors

• Light must be converted from photons into volts to be measured

• We must select the correct detector system according to how many photons we have available

• In general, we use photodiodes for scatter, and absorption and PMTs for fluorescence

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Characteristics of Light Detection

Red sensitivePMT

UV line

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Silicon photodiodes• A silicon photodiode produces current when photons impinge

upon it (example : solar cells)

• Does not require an external power source to operate

• Peak sensitivity is about 900 nm

• At 900 nm the responsivity is about 0.5 amperes/watt, at 500 nm it is 0.28 A/W

• Are usually operated in the photovoltaic mode (no external voltage) (alternative is photoconductive mode with a bias voltage)

• Have no gain so must have external amps• quantum efficiency ()% = 100 x ((electrons out)/(photons in))

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

PMT• Produce current at their anodes when photons impinge upon their light-

sensitive cathodes

• Require external powersource

• Their gain is as high as 107 electrons out per photon in

• Noise can be generated from thermionic emission of electrons - this is called “dark current”

• If very low levels of signal are available, PMTs are often cooled to reduce heat effects

• Spectral response of PMTs is determined by the composition of the photocathode

• Bi-alkali PMTs have peak sensitivity at 400 nm

• Multialkali PMTs extend to 750 nm

• Gallium Arsenide (GaAs) cathodes operate from 300-850 nm (very costly and have lower gain)

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Signal Detection - PMTs

Cathode Anode

Dynodes

Photons in

AmplifiedSignal Out

EndWindow

• Requires Current on dynodes• Is light sensitive• Sensitive to specific wavelengths• Can be end`(shown) or side window PMTs

Secondary emission

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Photomultiplier tubes (PMT’s)The PMTs in an Elite. 3 PMTs are shown, the other 2 have been removed to show their positions. A diode detector is used for forward scatter and a PMT for side scatter.

The Bio-Rad Bryte cytometer uses PMTs for forward and wide angle light scatter as well as fluorescence

© J.Paul Robinson

© J.Paul Robinson

© J.Paul Robinson

PMT

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

PMTs• High voltage regulation is critical because the relationship

between the high voltage and the PMT gain is non-linear (almost logarithmic)

• PMTs must be shielded from stray light and magnetic fields

• Room light will destroy a PMT if connected to a power supply

• There are side-window and end-window PMTs

• While photodiodes are efficient, they produce too small a signal to be useful for fluorescence

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

High Voltage on PMTs• The voltage on the PMT is applied to the dynodes

• This increases the “sensitivity” of the PMT

• A low signal will require higher voltages on the PMT to measure the signal

• When the voltage is applied, the PMT is very sensitive and if exposed to light will be destroyed

• Background noise on PMTs is termed “dark noise”

• PMTs generally have a voltage range from 1-2000 volts

• Changing the gain on a PMT should be linear over the gain range

• Changing the voltage on the PMT is NOT a linear function of response

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Diode Vs PMT• Scatter detectors are frequently diode detectors

Back of Elite forward scatter detector showing the preamp

Front view of Elite forward scatter detector showing the beam-dump and video camera signal collector (laser beam is superimposed)

Sample stream

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Spectral Imaging

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Review of Electronics• Based on Ohm’s Law, the flow of a current of 1 Amp through a

material of resistance of R ohms () produces a drop in electrical potential or a voltage difference of V volts across the resistance such that V=IR

• DC - direct current - the polarity of a current source remains the same when the current is DC

• AC - Alternative current - this is generated by using a magnetic field (generator) to convert mechanical into electrical energy - the polarity changes with motion

V(t) = Vmax sin (2ft)• A wire loop or coil exhibits inductance and responds to alternative

current in a frequency dependent fashion.• AC produces a changing magnetic field - generates a voltage opposite

in polarity to the applied voltage• In an inductance of 1 Henry (H) on a voltage of 1 volt is induced by a

current changing at the rate of 1 Amp/second - this property is called reactance

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Review of Electronics• Reactance like resistance provides an impediment to the flow of current, but

unlike resistance is dependent on the frequency of the current

• If a DC current is applied to a capacitor a transient current flows but stops when the potential difference between the conductors equals the potential of the source

• The capacitance measured in Farads (F) is equal to the amount of charge on either electrode in Coulombs divided by the potential difference between the electrodes in volts - 1 Farad = 1 coulomb/volt

• DC current will not flow “through” a capacitor - AC current will and the higher the frequency the better the conduction

• In a circuit that contains both inductance and capacitance, one cancels the other out

• The combined effect of resistance, inductive reactance and capacitive reactance is referred to as impedance (Z) of the circuit

• Impedance is not the sum of resistance and reactance• z=(R2+(Xl-Xc)2)½ (Xl = inductive reactance, Xc = capacitive reactance)

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

The Coulter Principle• Cells are relatively poor conductors• Blood is a suspension of cells in plasma which is a

relatively good conductor• Previously it was known that the cellular fraction of

blood could be estimated from the conductance of blood

• As the ratio of cells to plasma increases the conductance of blood decreases

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

The Coulter Principle•2 chambers filled with a conductive saline fluid are separated by a small orifice (100m or less)

•Thus, most of the resistance or impedance is now in the orifice.

•By connecting a constant DC current between 2 electrodes (one in each chamber), the impedance remains constant. If a cell passes through the orifice, it displaces an equivalent volume of saline and so increases the impedance.

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Electrical Opacity

• This is similar to impedance, except that you use an AC current across the electrodes of a coulter cell

• When the frequency used is in the radio frequency range (RF) the parameter measured is known as electrical opacity

• This reflects the AC impedance of cells and is dependent on cellular structure and less on size

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Linear and Log circuits

• Linear circuits

• Logarithmic circuits

• Dynamic range

• Fluorescence compensation

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Why use linear amps?

• The problem with compensation is that it needs to be performed on linear data, not logarithmic data. Thus, either the entire electronics must be built in linear electronics, which requires at least 16 bit A-D converters, or a supplementary system must be inserted between the preamp and the display.

• We need the dynamic range for immunologic type markers, but we can’t calculate the compensation easily using log amps - certainly not without complex math.

• Flow cytometers amplify signals to values ranging between 0-10V before performing a digital conversion.

• Assuming this, with 4 decades and a maximum signal of 10 V we have:

10 100 1000 10000

1v 100mv 10mv 1mv

Factor reduction

pulse output

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Why use linear amps?• The problem with compensation is that it needs to be performed on

linear data, not logarithmic data. Thus, either the entire electronics must be built in linear electronics, which requires at least 16 bit A-D converters, or a supplementary system must be inserted between the preamp and the display.

• We need the dynamic range for immunologic type markers, but we can’t calculate the compensation easily using log amps - certainly not without complex math.

• Flow cytometers amplify signals to values ranging between 0-10V before performing a digital conversion.

• Assuming this, with 4 decades and a maximum signal of 10 V we have:

10 100 1000 10000

1 100mv 10mv 1mv

Factor reduction

pulse output

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

How many bits?

• Assume we convert linear analog signals using an 8 bit ADC - we have 256 channels of range (2n) (28-256) corresponding to the range 0-10 V

• Channels difference is 10/256=40mV per channel

0 50 100 150 200 250

10V1V

100mV

Channels

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Ideal log amp

0 50 100 150 200 250

10 V1 V

100 mV

0 50 100 150 200 250

10 V1 mV

Channels

Linear

Log

1 V100 mV10 mVLog amp

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Log amps & dynamic range

Compare the data plotted on a linear scale (above) and a 4 decade log scale (below). The date are identical, except for the scale of the x axis. Note the data compacted at the lower end of the the linear scale are expanded in the log scale.

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Log/lin display

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Ratio circuits• Ratio circuits are analog circuits which produce an output

proportional to the ratio of the 2 input signals.• They are usually made from modules called analog multipliers. • Examples are calculation of surface density or antigenic receptor

sites by dividing the number of bound molecules by the cell surface area.

• e.g. Could use 2/3 power of volume to obtain surface area - but few cytometers make this parameter so can use the square of the cell diameter of scatter instead to approximate.

• pH can also be measured using ratio circuits• Calcium ratio (using Indo-1 we can ratio the long and short

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Data Acquisition• operations which are required to make measurements of a specified physical characteristic(s) of cells in sample

• Each measurement from each detector is referred to as a variable or “parameter”

• Data are acquired as a “list” of the values for each variable (“parameter” ) for each event (“cell”)

• Purpose is to store data • And to convert data to numerical form

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

System management

Operational Steps

1. Sample Preparation

2. Data Acquisition

3. Data analysis

4. Data Reporting

We will only deal with these in this lecture}

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Data Analysis

Issues to define

•Data acquisition vs. data analysis

•Data analysis software

•Data display

•Establishing Regions of Interest (ROI) and gating

•Analysis methods that can change results

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Data AnalysisMain tasks

• Cell counting

• Population discrimination

• A-D conversion of data

• Dynamic range must be appropriate

• DSP for pulses if appropriate

• Data rates and data acquisition

• Preprocessing for data acquisition

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Data AnalysisOutput goals

• Frequency Distributions

• Distributions (Gaussian/normal)

• Statistical components

• Skewness and Kurtosis

• Compensation/crosstalk

• Reporting

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Data Analysis

• Histograms– Comparing histograms

• K-S• Cumulative (Overton) subtraction• constant CV analysis

• Bivariate displays– dot plots– linear regression/Least-squares fits– Isometric (2 parameter histogram)

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Flow Cytometry Computer Files

•Listmode files -correlated data file where each event is listed sequentially,

parameter by parameter-large file size

•Histogram files uncorrelated data used for display only

•Flow cytometry standard (FCS 2.0, FCS 3.0) format used to save data use other software programs to analyze data

Note: No cytometry manufacturer abides strictly by the FCS standard

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Data Analysis SoftwareInstrument Software

Elite 4.0 CoulterBryte HS 2.0 Bio-RadLysis II Becton Dickinson

Commercial SourcesWinList & Modfit LT Verity SoftwareListView & Multicycle Phoenix SoftwareFloJo Treestar SoftwareFCS Express Ray HicksFlow Explorer Ron Hoebe

Free Flow SoftwareWinMDI Joe TrotterMFI Eric Martz

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

WinMDIWinMDI or Windows Multiple Document Interface

-requires Windows 3.1, Windows 95, Windows NT or OS/2

Developed by Joe Trotter at the Scripps Institute

Available FREE from Internet:http://facs.scripps.edu/software.html

Excellent Tutorial developed by Dr. Gerald Gregorihttp://www.cyto.purdue.edu/flowcyt/labinfo/labinfo.htm

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Precision - C.V.• Precision: CV• Sensitivity• MESF Units• Accuracy and Linearity• Noise• Background• Laser noise

Shapiro’s 7th Law of Flow Cytometry:Shapiro’s 7th Law of Flow Cytometry:No Data Analysis Technique Can Make No Data Analysis Technique Can Make

Good Data Out of Bad Data!!!Good Data Out of Bad Data!!!

Shapiro’s 7th Law of Flow Cytometry:Shapiro’s 7th Law of Flow Cytometry:No Data Analysis Technique Can Make No Data Analysis Technique Can Make

Good Data Out of Bad Data!!!Good Data Out of Bad Data!!!

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Data Acquisition - Listmode

Event Param1FS

Param2SS

Param3FITC

Param4PE

1 59 100 80 902 58 110 150 953 54 60 80 30

4 60 80 305 60 80 306 60 80 30

66112115

etcn

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Statistical CalculationsNumber of events – we always collect this

Mean:• is a measure of central tendency

Standard Deviation: • is a measure of variability

Coefficient of Variation

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

One parameter (frequency) histogram

establish regions and calculate coefficient of variation (cv)cv = st.dev/mean of half peak

# of events forparticular parameter

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Coefficient of Variation

Crucial in establishing:• alignment• Fluidic stability• Staining of cells

MEAN

CV=3.0

CV=3.0

%CV Definition = St.Dev x 100MEAN

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Coefficient of VariationCalculation

• •

•••••••••••••

•••••• ••

Statistical(Subjective)

Formula(not boundarydependentObjective)

Least-Squares(Accurate, non-subjective)

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Histogram ComparisonsHistogram Comparisons

We compare histograms to determine if there is a difference between them. If there is, we can make a statement of difference based on statistics. Since we are usually measuring biological phenomena, our conclusion will be related to the biological difference perhaps.

The question here might be:Is there a difference between these two data sets?

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Kolmogorov-SmirnovK-S Test

Flu

ores

cnec

e In

tens

ity

Channel Number

Cum

ulat

ive

Fre

quen

cy D

istr

ibut

ion

50

100

0 50 100 50 100

A good technique for estimating the differences between histograms

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Histogram AnalysisNormalized Subtraction

• Very accurate• Assumption that control & test histogram are same shape• Match region finds best amplitude of control to match test histogram

False Negatives

Match region

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Histogram AnalysisIntegration

• Very subjective analysis• Not easily automated• Not good for weakly fluorescent signals

False PositivesFalse Negatives

Fre

quen

cy“Positive” histogram

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Histogram AnalysisAccumulative Subtraction

• Very accurate• Assumption that control & test histogram are same shape• Match region finds best amplitude of control to match test histogram

Negative ControlActualNegatives

TestN

umbe

r of

Eve

nts

Cum

ulat

ive

Eve

nts

ActualPositives

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Basic Histogram OperationsGating or Region of Interest (ROI) selection• 1. A gate is a region of interest• Gates can be applied to any histogram• Gates or ROI can also be applied to mult-

parameter plots• Gates are applied to select out cells with a desired

characteristic.• Gates can be additive – this means the results are

compounded in the data analysis

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Gating ExampleWe have here a histogramBy definition it is single parameter

Gate M1 determines a region from point A to point B on the X axis (log FITC)

A B

Within the boundaries of A-B, the gate M1 gives is the total number of cells within the range A-B – the number of cells is 4900

Total cells -5000

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Gating ExampleWe have here a histogramBy definition it is single parameter

Gate M2 determines a region from point A1 to point B1 on the X axis (log FITC)

A1 B1

Within the boundaries of A1-B1, the gate M2 gives is the total number of cells within the range A1-B1 which is 4,700

M2

Total cells -5000

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Multiple Gates

Any number of gates can be applied to a histogram. Gates can be inclusive, exclusive or “either or”.

For example, you could select all cells that satisfy gate M6, excluding gate M3 – (M6-M3) would give you the same result as adding gates M1 and M4 (M1+M4).

M3

M2

Total cells -5000M1

M4

M5M6

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Multiple parameter displays

Following display are important in flow

•Dot plot

• Density dot plot

• Contour plot

• Isometric plot

•3D projection

•Complex displays – TIP and TIG displays

Note: TIP – Tube identifier Parameter – allows the display of data points for multiple samplesTIG: Time Interval Gating – allow the display of multiple samples over time.

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Isometric Plot - 3 Parameter view

- simulated surface is created - 2 parameter data plus cell number- # of particles used as 3rd parameter - 3-D space

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Density Dot Plot Contour Plot

A: Color of dots gives an indication of the identify of subpopulations. e.g. in the above plot the green dots are high density and the mauve are low density areas (FS is Forward Scatter and 90ls is Ninety Degree light scatter or orthogonal light scatter.)

B: The color of lines in each contour provides an indication of the number of events in that level of the plot. e.g. in the above plot the green are high density and the mauve are low density with proper contour lines. The data sets of A and B are identical.

A B

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

More displaysColor coded dot plots

In this display, each population has been identified by a different color

Here, the multiple colors are in the lymphocyte gate. All of the se cells are identified on the left plot. When applied to the scatter plot, there is a region with multiple colors.

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

2 D plotsKinetic 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 9.3.4 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.

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Some Multi-data display formats

FITC Fluorescence

Mo1

CD4 CD8

CD8

CD45

leu11a

CD20

Tube

ID

1 2 3 4 5 6 7 8 9

--- --+ -++ -+- +-- +-+ ++- +++

Multiple histograms displayed in a combination format

This is the “Phenogram” format which displays all of the possible binary combinations of a set of fluorochromes – in this case there are 3 colors (n) so there are 2n =8 combinations.

Robinson, J. Paul, Durack, Gary & Kelley, Stephen: "An innovation in flow cytometry data collection & analysis producing a correlated multiple sample analysis in a single file". Cytometry 12:82-90,1991.

J. Paul Robinson, K.Ragheb, G. Lawler,S.Kelley, & G. Durack: Rapid Multivariate Analysis and Display of cross-reacting antibodies on Human Leukocytes. Cytometry 13:75-82,1992

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

log

PE Back gate

Forward gate

1P Fluorescence 2P Fluorescence 2P Scatter

The first distribution demonstrates forward gating. Cell fluorescence is gated based on their scatter characteristics. Below fluorescence is used to “backgate” the fluorescence signal onto the scatter dotplot

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Slide 18, 11/11/96 of DNA.ppt

Specific Cases - DNA analysisDoublet Discrimination

Integral FluorescenceIntegral Fluorescence

Peak

Flu

ores

cenc

e

Peak

Flu

ores

cenc

e

8 x 125 m laser beam shape

16 x 64 m laser beam shape

Clumps

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Decision Tree in Acute LeukemiaAn example of how data analysis can result in a decision process for a

data set

ANegativePositive

HLA-DR

TCD13,33

CD19

TdT

CD10

CD20

Mu

B,T

AMLL AML

T-ALL

AML-M3

AUL

?

PRE-BI

PRE-BII

PRE-BIII

PRE-BIVPRE-BV

CD13,33

From Duque et al, Clin.Immunol.News.

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Multi-color studies generate a lot of data

1 2 3 4 5 6 7 8 9 10

2 color3color

4color

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

QUADSTATSLo

g F

luor

esce

nce

++

-- +-

-+

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

QUADSTATSLo

g F

luor

esce

nce

++

-- +-

-+

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

QUADSTATSLo

g F

luor

esce

nce

++

-- +-

-+

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

++

-- +-

-+

This example shows how complex the analysis can become for a large set of data with many variables. Represented are the number of dual plots that would have to be displayed to represent the possible number of combinations. It should be noted of course that you cannot display 3 or more dimensions in 2 dimensional space!!

© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT

Summary of Material

• There are 2 primary types of detectors used in flow cytometers

• These have different sensitivities and applications

• We collect data in log space mostly because we need a large dynamic range (this is difficult to do in linear space because of limits and costs of hardware)

• Data acquisition and analysis

• Types of data formats and presentation formats

• Data analysis techniques such as gating, forward and back gating


Recommended