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© 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
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
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