Biomedical technologies for blood
cell measurements
Introduction to the terminology, types of
measurements, capabilities of flow cytometry, uses &
applications
• Comparison between flow cytometry and fluorescence
microscopy
• Scatter
• Fluorescence
• Sensitivity, precision of measurements, statistics,
populations
•Speed, combinatorial measurements (multiparameter)
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 1
•
•
•
•
•
What can Flow Cytometry
Do?
Enumerate particles in suspension
Determine “biologicals” from “non-biologicals”
Separate “live” from “dead” particles
Evaluate 105 to 5x106 particles/min
Measure particle-scatter as well as innate
fluorescence or 2o fluorescence
• Sort single particles for subsequent analysis
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 2
Flow Cytometry Publications/year
3345
2899
Papers
2,713
2,445
2700
2,332
2400
2100
1,855
1800
1,494
1500
1,232
1,078
1200
940
811
900
611
480
600
223
113
300
300
0
13
28
79
00
1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
YEARS
Data taken from Medline search using the keywords: “flow Cytometry”
Papanicolaou
1941 - originally studies the reproductive system of
primates during the estrous cycle and observed changes in
cells exfoliated from the female genital tract during the
the cycle
- mixed a series of stains to identify changes he observed
- Developed
for using quantitative cytology and
morphology for the exfoliative cytologic diagnosis of cervical
carcinoma in humans
- developed sets of critical stains and interpretations
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 4
Gucker - 1947
• Developed a flow cytometer for detection of bacteria
in aerosols
• Published paper in 1947 (work was done during WWII
and was classified).
• Goal was rapid identification of airborne bacteria and
spores used in biological warfare
• Instrument: Sheath of filtered air flowing through a
dark-field flow illuminated chamber. Light source was
a Ford headlamp, PMT detector (very early use of PMT)
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 5
P.J. Crossland-Taylor
Sheath Flow Principle
“Provided there is no turbulence,
the wide column of particles will
then be accelerated to form a
narrow column surrounded by
fluid of the same refractive index
which in turn is enclosed in a
tube which will not interfere with
observation of its axial content.”
A Device for Counting Small Particles
Suspended in a Fluid through a Tube
P.J. Crosland-Taylor
Bland-Sutton Institute of Pathology
Middlesex Hospital, London, W.1. June 17, 1952
Nature 171: 37-38, 1953
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 6
Wallace Coulter
Wallace Coulter - Coulter orifice - 1956 (as early as 1948) - measured changes in electrical
conductance as cells suspended in saline passed through a
small orifice
• 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
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 7
The Coulter Principle
•2 chambers filled with a conductive
saline fluid are separated by a small
orifice (100mm 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.
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 8
Wallace Coulter - Coulter orifice - 19481956
Cell
counter
vacuum
orifice
1998 photo
© J.Paul Robinson
Instrument Components
Fluidics: Specimen, sorting, rate of data collection
Optics: Light source(s), detectors, spectral separation
Electronics: Control, pulse collection, pulse analysis,
triggering, time delay, data display, gating, sort control,
light and detector control
Data Analysis: Data display & analysis,
multivariate/simultaneous solutions, identification of sort
populations, quantitation
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 10
What are the principles?
• Hydrodynamically focused stream of
particles
• Light scattered by a laser or arc lamp
• Specific fluorescence detection
• Electrostatic particle separation for
sorting
• Multivariate data analysis capability
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 11
Richard Sweet
Richard Sweet developed the electrostatic ink-jet printer
which was the principle used by Mack Fulwyler to create a
cell-sorter.
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 12
Mack Fulwyler
Mack Fulwyler - sorter 1965 - electronic cell volume 1965 - at
Los Alamos National Labs - this instrument separated cells based
on electronic cell volume (same principle as the Coulter counter)
and used electrostatic deflection to sort. The cells sorted were
RBC because they observed a bimodal distribution of cell volume
when counting cells - the sorting principle was based on that
developed for the inkjet printer by Richard Sweet at Stanford in
1965.
Electronic Cell Volume
After determining that the bimodal distribution was artifactual, this group
were able to sort neutrophils and lymphocytes from blood.
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 13
The mysterous red cell problem
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 14
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 15
Kamentsky
He also built a fluidic cell-sorter to evaluate the cells identified in
his RCS An RCS was sent to Stanford for use by Leonard
Herzenberg . The unit was also the model for the Technicon D
instrument guilt by Technicon.
1970 Model
“Cytograph” currently
at Purdue University
1998 photo
© J.Paul Robinson
Kamensky’s first benchtop instrument the Cytograph. This measured
scatter using a He-Ne laser. This particular instrument was a model prior to
the fluorescence detection model.
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 16
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 17
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 18
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 19
Hydrodynamics and Fluid
Systems
• Cells are always in suspension
• The usual fluid for cells is saline
• The sheath fluid can be saline or
water
• The sheath must be saline for sorting
• Samples are driven either by syringes
or by pressure systems
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 20
Fluidics
• Need to have cells in suspension flow in single file
through an illuminated volume
• In most instruments, accomplished by injecting
sample into a sheath fluid as it passes through a
small (50-300 µm) orifice
• When conditions are right, sample fluid flows in a
central core that does not mix with the sheath
fluid
• This is termed Laminar flow (Sheath Flow Principle)
• The introduction of a large volume into a small
volume in such a way that it becomes “focused”
along an axis is called Hydrodynamic Focusing
[RFM]
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 21
Fluidics - Laminar Flow
• Whether flow will be laminar can be
determined from the Reynolds number
Re

d v

where
d  tube diameter
  density of fluid
v  mean velocity of fluid
  viscosity of fluid
• When Re < 2300, flow is always laminar
• When Re > 2300, flow can be turbulent
[RFM]
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 22
Fluidics
Notice how the ink is
focused into a tight stream
as it is drawn into the tube
under laminar flow
conditions.
Notice also how the
position of the inner ink
stream is influenced by
the position of the ink
source.
[RFM]
V. Kachel, H. Fellner-Feldegg & E. Menke - MLM Chapt. 3
Fluidics Systems
Positive Pressure Systems
• Based upon differential pressure
between sample and sheath fluid.
• Require balanced positive pressure
via either air or nitrogen
• Flow rate varies between 6-10 ms-1
+++
+++
+++
Positive Displacement Syringe Systems
1-2 ms-1 flow rate
Syringe
Fixed volume (50 ml or 100 ml)
Absolute number calculations possible
Usually fully enclosed flow cells
Flowcell
100 ml
•
•
•
•
3-way valve
Sample
Waste
Sample loop
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 24
Syringe systems
• Bryte HS
Cytometer
Syringe
3 way valve
1998 photo
© J.Paul Robinson
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 25
Fluidics - Particle Orientation and Deformation
“a: Native human
erythrocytes near the
margin of the core stream
of a short tube (orifice).
The cells are uniformly
oriented and elongated by
the hydrodynamic forces
of the inlet flow.
b: In the turbulent flow
near the tube wall, the
cells are deformed and
disoriented in a very
individual way. v>3 m/s.”
[RFM]
V. Kachel, et al. - MLM Chapt. 3
Closed flow cells
Laser
direction
1998 photo
© J.Paul Robinson
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 27
Fluidics - Flow Chambers
Flow through
cuvette (sense
in quartz)
[RFM]
H.B. Steen - MLM Chapt. 2
Flow chamber blockage
A human hair blocks the flow
cell channel. Complete
disruption of the flow results.
1998 photo
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
© J.Paul Robinson
Page 29
The Elements of Flow
Sorting
•
•
•
•
•
•
Sample Preparation
Hardware Setup
Droplet formation
Timing
Coincidence - Purity and Efficiency
Sterile Sorting Concepts
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 30
Fluorescence Activated
Cell Sorting
488 nm laser
FALS Sensor
Fluorescence detector
-
+
Charged Plates
Single cells sorted
into test tubes
Purdue University Cytometry
Droplet formation
As liquid is ejected into
air, it will form droplets.
By vibrating the nozzle at
a defined frequency, the
size of these droplets and
the position along the
stream where they form
can be controlled with
great precision.
Last Attached
Droplet
Satelite
droplet
(Murphy)
T. Lindmo, D.C. Peters & R.G Sweet - MLM Chapt. 8
Droplet break off
Video2.mpg
Video of the droplet formation in a sort
stream from a Cytomation instrument.
Source: Purdue CDROM vol 4, 1998
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 33
Laser power
E=h and E=hc/
• One photon from a 488 nm argon laser has an energy of
E=
6.63x10-34 joule-seconds x 3x108
488 x 10-3
= 4.08x10-19 J
• To get 1 joule out of a 488 nm laser you need 2.45 x 1018
photons
• 1 watt (W) = 1 joule/second a 10 mW laser at 488 nm is
putting out 2.45x1016 photons/sec
• UV Laser at 325 nm is putting out 1.63x1018 photons/sec
• He-Ne laser at 653 nm is putting out 3.18x1018 photons/sec
Shapiro p 77
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 34
Light Scatter
• Materials scatter light at wavelengths at which they
do not absorb
• If we consider the visible spectrum to be 350-850
nm then small particles (< 1/10 ) scatter rather
than absorb light
• For small particles (molecular up to sub micron) the
Rayleigh scatter intensity at 0o and 180o are about
the same
• For larger particles (i.e. size from 1/4 to tens of
wavelengths) larger amounts of scatter occur in the
forward not the side scatter direction - this is
called Mie Scatter (after Gustav Mie) - this is how
we come up with forward scatter be related to size
Shapiro p 79
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 35
Optics for forward scatter
iris
Laser
beam
scatter
detector
blocker
Stream in air or a
round capillary
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 36
Brewster’s Angle
• Brewster’s angle is the angle at which the reflected
light is linearly polarized normal to the plane incidence
• At the end of the plasma tube, light can leave through
a particular angle (Brewster’s angle) and essentially be
highly polarized
• Maximum polarization occurs when the angle between
reflected and transmitted light is 90o
thus Ør + Øt = 90o
since sin (90-x) = cos x
Snell’s provides (sin Øi / cos Øi ) = n2/n1
Ør = tan -1 (n2/n1)
Ør is Brewster’s angle
Shapiro p 82
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 37
Brewster’s Angle
1998 photo
© J.Paul Robinson
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 38
Fluorescence
• Excitation Spectrum
– Intensity of emission as a function of exciting
wavelength
• Chromophores are components of molecules which
absorb light
• They are generally aromatic rings
• The wavelength of absorption is related to the
size of the chromophores
• Smaller chromophores, higher energy (shorter
wavelength)
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 39
Fluorescence
• Stokes Shift
Fluorescnece Intensity
– is the energy difference between the
lowest energy peak of absorbance and
the highest energy of emission
Fluorescein
molecule
Stokes Shift is 25 nm
495 nm
Wavelength
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
520 nm
Page 40
Properties of Fluorescent
Molecules









Large extinction coefficient at the region of excitation
High quantum yield
Optimal excitation wavelength
Photostability
Excited-state lifetime
Minimal perturbation by probe
Dye molecules must be close to but below saturation
levels for optimum emission
Fluorescence emission is longer than the exciting
wavelength
The energy of the light increases with reduction of
wavelength
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 41
Fluorescence
Resonance Energy
Transfer
Molecule 1
Molecule 2
Fluorescence
Fluorescence
ACCEPTOR
DONOR
Absorbance
Absorbance
Wavelength
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 42
Absorption
• Basic quantum mechanics requires that molecules
absorb energy as quanta (photons) based upon a
criteria specific for each molecular structure
• Absorption of a photon raises the molecule from
ground state to an excited state
• Total energy is the sum of all components
(electronic, vibrational, rotational, translations,
spin orientation energies) (vibrational energies are
quite small)
• The structure of the molecule dictates the likelyhood of absorption of energy to raise the energy
state to an excited one
Shapiro p 84
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 43
Mercury Arc Lamps
Lens
Arc
Lens
1998 photo
© J.Paul Robinson
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
1998 photo
© J.Paul Robinson
Page 44
Arc Lamp Excitation
Spectra Xe Lamp
Irradiance at 0.5 m (mW m-2 nm-1)



Hg Lamp



J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt


Shapiro p 99
Page 45
Laser Power & Noise
Light Amplification by Stimulated Emission of Radiation
• Laser light is coherent and monochromatic (same
frequency and wavelength)
• This means the emitted radiation is in phase with and
propagating in the same direction as the stimulating
radiation
• ION lasers use electromagnetic energy to produce and
confine the ionized gas plasma which serves as the
lasing medium.
• Lasers can be continuous wave (CW) or pulsed (where
flashlamps provide the pulse)
• Laser efficiency is variable - argon ion lasers are about
0.01% efficient (1 W needs 10KW power)
Shapiro p 106
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 46
Lasers
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 47
Goals of Light Collection
•
•
•
•
•
•
Maximum signal, minimum noise
Maximum area of collection
Inexpensive system if possible
Easy alignment
Reduced heat generation
Reduced power requirement
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 48
Optical Collection systems
He-Cd Laser
Argon Laser
He-Ne Laser
1998 photo
© J.Paul Robinson
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 49
Interference in Thin Films
• Small amounts of incident light are reflected at
the interface between two material of different
RI
• Thickness of the material will alter the
constructive or destructive interference patterns
- increasing or decreasing certain wavelengths
• Optical filters can thus be created that
“interfere” with the normal transmission of light
Shapiro p 82
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 50
Interference and Diffraction:
Gratings
• Diffraction essentially describes a departure
from theoretical geometric optics
• Thus a sharp objet casts an alternating
shadow of light and dark “patterns” because
of interference
• Diffraction is the component that limits
resolution
Shapiro p 83
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 51
Interference filters
• They are composed of transparent
glass or quartz substrate on which
multiple thin layers of dielectric
material, sometimes separated by
spacer layers .
• Permit great selectivity.
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 52
Optical Filters
• Interference filters:
Dichroic, Dielectric, reflective filters…….reflect
the unwanted wavelengths
• Absorptive filters:
Colour glass filters…..absorb the unwanted
wavelengths
Dichroic Filter/Mirror at 45 deg
Light Source
Transmitted Light
Reflected light
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 53
Standard Band Pass Filters
630 nm BandPass Filter
White Light Source
Transmitted Light
620 -640 nm Light
Standard Long and Short Pass Filters
520 nm Long Pass Filter
Light Source
Transmitted Light
>520 nm
Light
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
575 nm Short Pass Filter
Light Source
Transmitted Light
<575 nm
Light
Page 54
Transmission determination
• Constructive and destructive
interference occurs between
reflections from various layers
• Transmission determined by :
– thickness of the dielectric layers
– number of these layers
– angle of incidence light on the filters
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 55
Optical Design
PMT 5
PMT 4
Sample
PMT 3
Flow cell
Dichroic
Filters
Scatter
Sensor
PMT 2
PMT 1
Laser
Bandpass
Filters
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)
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 57
Signal Detection - PMTs
Secondary emission
Cathode
Anode
Amplified
Signal
Out
Photons
in
End
Window
Dynodes
• Requires Current on dynodes
• Is light sensitive
• Sensitive to specific wavelengths
• Can be end`(shown) or side window PMTs
Diode Vs PMT
• Scatter detectors are frequently diode detectors
Sample stream
1998 photo
1998 photo
© J.Paul Robinson
© J.Paul Robinson
Back of Elite forward scatter detector
showing the preamp
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Front view of Elite forward scatter detector
showing the beam-dump and video camera
signal collector (laser beam is superimposed)
Page 59
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
Page 60
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Linear and Log circuits
•
•
•
•
Linear circuits
Logarithmic circuits
Dynamic range
Fluorescence compensation
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 61
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 010V before performing a digital conversion.
• Assuming this, with 4 decades and a maximum signal of 10 V
we have:
Factor reduction 10
pulse output
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
1
100
100mv
1000
10mv
10000
1mv
Page 62
How many bits?
• Assume we convert linear analog signals using an 8
bit ADC - we have 256 channels of range (2n) (28256) corresponding to the range 0-10 V
• Channels difference is 10/256=40mV per channel
1V
10V
100mV
0
50
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
100
150
Channels
200
250
Page 63
Ideal log amp
1V
10 V
100 mV
Linear
0
Log amp
1 mV
50
10 mV
100
150
100 mV
200
1V
250
10 V
Log
0
50
100
150
Channels
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
200
250
Page 64
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.
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 65
Data Acquisition
• Each measurement from each
detector is referred to as a
“variable” or in flow parlance a
“parameter”
• Data are acquired as a “list” of the
values for each “parameter” (variable)
for each “event” (cell)
[RFM]
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 66
Data Acquisition - Listmode
Event Param1 Param2 Param3 Param4
FS
SS
FITC
PE
1
50
100
80
90
2
55
110
150
95
3
110
60
80
30
[RFM]
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 67
Data Presentation Formats
• Histogram
• Dot plot
• Contour plot
• 3D plots
• Dot plot with projection
• Overviews (multiple histograms)
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 68
CD45
CD8
CD4
CD8
leu11a
CD20
Mo1
FITC Fluorescence
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 69
Data Analysis
•
•
•
•
•
Frequency Distributions
Gaussian distribution
Normal distributions
Statistics
Skewness and Kurtosis
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 70
Coefficient of Variation
%CV Definition =
St.Dev x 100
MEAN
CV=3.0
CV=3.0
MEAN
Crucial in establishing:
• Alignment
• Fluidic stability
• Staining of cells
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 71
Precision - C.V.
•
•
•
•
•
•
•
•
Precision: CV
Sensitivity
MESF Units (Mean Equivalent Soluble Fluorescein)
Accuracy and Linearity
Noise
Background
Laser noise
Shapiro’s 7th Law of Flow Cytometry:
“No Data Analysis Technique Can Make
Good Data Out of Bad Data!!!”
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 72
One parameter frequency
histogram
# of events for
particular
parameter
establish regions and calculate coefficient of variation (cv)
cv = stdev/mean of half peak
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 73
Histogram Analysis
Normalized Subtraction
Match region
False Negatives
• Very accurate
• Assumption that control & test histogram are same shape
• Match region finds best amplitude of control to match test
histogram
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 74
Kolmogorov-Smirnov
K-S Test
Fluorescnece Intensity
100
50
0
50
100
Channel Number
50
100
A good technique for estimating the differences between histograms
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 75
Histogram Analysis
Integration
Frequency
“Positive” histogram
False Negatives
False Positives
• Very subjective analysis
• Not easily automated
• Not good for weakly fluorescent signals
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 76
Histogram Analysis
Accumulative Subtraction
Cumulative Events
Number of Events
Actual
Negatives
Negative Control
Test
Actual
Positives
• Very accurate
• Assumption that control & test histogram are same shape
• Match region finds best amplitude of control to match test histogram
Page 77
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Histogram Overlays
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Page 78
Density Dot Plot
Contour Plot
Color of dots can give indication
of frequency of events
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
Identify subpopulations
with proper contour lines
Page 79
Forward gate
log PE
Back gate
1P Fluorescence
2P Fluorescence
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
2P Scatter
Page 80
Isometric Plot
- simulated surface is created
- # of particles used as 3rd parameter
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
3 Parameter
- 3 parameter data
- 3-D space
Page 81
Multi-color studies
generate a lot of data
++
--
+-
++
--
+-
++
--
+-
+-
++
--
+-
++
--
+-
QUADSTATS
Log Fluorescence
QUADSTATS
-+
-+
++
--
+-
++
--
+-
QUADSTATS
Log Fluorescence
QUADSTATS
-+
-+
++
--
+-
-+
++
--
+-
QUADSTATS
Log Fluorescence
QUADSTATS
-+
10
Log Fluorescence
--
QUADSTATS
Log Fluorescence
QUADSTATS
-+
++
Log Fluorescence
+-
-+
Log Fluorescence
--
QUADSTATS
Log Fluorescence
QUADSTATS
-+
++
Log Fluorescence
+-
-+
Log Fluorescence
--
QUADSTATS
Log Fluorescence
QUADSTATS
-+
++
9
QUADSTATS
-+
++
--
+-
-+
++
--
+-
Log Fluorescence
QUADSTATS
Log Fluorescence
+-
+-
Log Fluorescence
-+
8
Log Fluorescence
--
--
QUADSTATS
7
Log Fluorescence
++
++
6
Log Fluorescence
-+
-+
5
Log Fluorescence
+-
Log Fluorescence
--
+-
QUADSTATS
-+
++
--
+-
Log Fluorescence
Log Fluorescence
QUADSTATS
QUADSTATS
QUADSTATS
QUADSTATS
QUADSTATS
QUADSTATS
QUADSTATS
QUADSTATS
QUADSTATS
QUADSTATS
-+
++
--
+-
Log Fluorescence
-+
++
--
+-
Log Fluorescence
-+
++
--
+-
Log Fluorescence
-+
++
--
+-
Log Fluorescence
-+
++
--
+-
Log Fluorescence
-+
++
--
+-
Log Fluorescence
-+
++
--
+-
Log Fluorescence
-+
++
--
+-
Log Fluorescence
-+
++
--
+-
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
++
--
QUADSTATS
Log Fluorescence
QUADSTATS
-+
++
4
Log Fluorescence
+-
Log Fluorescence
-+
Log Fluorescence
--
QUADSTATS
Log Fluorescence
++
Log Fluorescence
Log Fluorescence
QUADSTATS
3
Log Fluorescence
2
Log Fluorescence
1
Log Fluorescence
3 color
-+
5 color
4 color
-+
++
--
+-
Log Fluorescence
From Duque et al, Clin.Immunol.News.
APRE-BV
PRE-BIV
Mu
Negative
Positive
PRE-BIII
PRE-BII
CD20
AUL
PRE-BI
CD10
TdT
AMLL
AML
AML-M3
?
CD19
B,T
CD13,33
T-ALL
CD13,33
T
HLA-DR
Decision Tree in Acute Leukemia
J.Paul Robinson, Purdue University EE 520 lecture 2000.ppt
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