1. The WEB version of these slides can be found on
Detectors, Electronics, Data Analysis
BMS 633/BME 695Y - Week 3 J. Paul Robinson Professor of Immunopharmacology School of Veterinary Medicine, Purdue University
Title page
The title page of the lecture.
The WEB version of these slides can be found on
Hansen Hall, B050
Purdue University
Office:
Fax
WEB
Material is taken from the course text: Howard M. Shapiro, Practical Flow Cytometry, 3nd edition (1994), 4th Ed (2003) Alan R. Liss, New York.
3rd Ed. Shapiro p
4th Ed. Shapiro p

2. 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

3. 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
Detectors
This slide introduces the concept of detectors to students.

4. Characteristics of Light Detection
Red sensitive
PMT
UV line

5. 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))
Silicon photodiodes
The first type of detectors to be covered is silicon photodiodes. This slide covers the properties that are involved in photodiodes.

6. 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 nm (very costly and have lower gain)
PMT
This slide covers the properties behind photomultiplier tubes. This slide may have too much information on it and might be broken down in the future.

7. Signal Detection - PMTs
Secondary emission
Cathode
Anode
Photons in
Amplified
Signal
Out
Signal Detection - PMTs
A visual representation of PMTs that shows how PMTs work.
End
Window
Dynodes
Requires Current on dynodes
Is light sensitive
Sensitive to specific wavelengths
Can be end`(shown) or side window PMTs

8. 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.
© J.Paul Robinson
© J.Paul Robinson
© J.Paul Robinson
Photomultiplier tubes (PMT’s)
An example of PMTs.
PMT
The Bio-Rad Bryte cytometer uses PMTs for forward and wide angle light scatter as well as fluorescence

9. 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
PMTs
This slide covers how PMTs need to be handled with care.

10. 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 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
High Voltage on PMTs
This slide explains the properties of PMTs in regards to voltage.

11. Diode Vs PMT Scatter detectors are frequently diode detectors
Sample stream
Diode Vs PMT
This slide visually compares diodes and PMTs.
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)

12. Spectral Imaging

13. 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 (2ft)
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
Review of Electronics
This slide briefly covers the basic concepts of electronics.

14. 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)
Review of Electronics
Another slide that is used to briefly cover key concepts that are used in electronics.

15. 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
The Coulter Principle
This slide covers the biological parameters of blood in regards to electrical properties.

16. 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.
The Coulter Principle
This slide shows how the electrical properties of blood are manipulated via a Coulter filter for analyzing blood cells.

17. 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
Electrical Opacity
This slide covers how cells impede AC currents.

18. Linear and Log circuits
Linear circuits
Logarithmic circuits
Dynamic range
Fluorescence compensation
Linear and Log circuits
This slide changes the topic of the lecture into the electronics involved behind light collection. In this slide linear and log circuits are introduced and going to be compared.

19. Why use linear amps? Factor reduction 10 100 1000 10000 pulse output
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:
Why use linear amps?
Factor reduction
pulse output
1v mv 10mv 1mv

20. Why use linear amps? Factor reduction 10 100 1000 10000 pulse output
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:
Why use linear amps?
mv 10mv 1mv
Factor reduction
pulse output

21. 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 1V
10V
100mV
How many bits?
Channels

22. Ideal log amp Linear Log amp Log Channels 0 50 100 150 200 250
1 V
10 V
100 mV
Linear
Log amp
1 mV
10 mV
100 mV
1 V
10 V
Ideal log amp
Log
Channels

23. Log amps & dynamic range
Log amps and 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.

24. Log/lin display
Log/lin display

25. 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 l)
Ratio circuits

26. 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
Data Acquisition

27. } System management Operational Steps Sample Preparation
Data Acquisition
Data analysis
Data Reporting }
We will only deal with these in this lecture

28. 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
Data Analysis

29. Data Analysis Main 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
Data Analysis

30. Data Analysis Output goals
Frequency Distributions
Distributions (Gaussian/normal)
Statistical components
Skewness and Kurtosis
Compensation/crosstalk
Reporting
Data Analysis

31. Data Analysis Histograms Bivariate displays Comparing histograms
K-S
Cumulative (Overton) subtraction
constant CV analysis
Bivariate displays
dot plots
linear regression/Least-squares fits
Isometric (2 parameter histogram)
Data Analysis

32. 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
Flow Cytometer Computer Files

33. Data Analysis Software
Instrument Software
Elite Coulter
Bryte HS Bio-Rad
Lysis II Becton Dickinson
Commercial Sources
WinList & Modfit LT Verity Software
ListView & Multicycle Phoenix Software
FloJo Treestar Software
FCS Express Ray Hicks
Flow Explorer Ron Hoebe
Free Flow Software
WinMDI Joe Trotter
MFI Eric Martz
Data Analysis Software

34. WinMDI WinMDI 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:
Excellent Tutorial developed by Dr. Gerald Gregori
WinMDI

35. Precision - C.V. Precision: CV Sensitivity MESF Units
Accuracy and Linearity
Noise
Background
Laser noise
Precision - C.V.
Shapiro’s 7th Law of Flow Cytometry:
No Data Analysis Technique Can Make
Good Data Out of Bad Data!!!

36. Data Acquisition - Listmodev e n t P a r a m 1 P a r a m 2 P a r a m 3 P a r a m 4 F S S S F I T C P E 1 59 1 8 9 2 58 1 1 1 5 9 5 54 6 8 3 3 66 4 6 8 3 5
112 6 8 3 6
115 6 8 3
Data Acquisition - Listmode n
etc

37. Statistical Calculations
Number of events – we always collect this
Mean:
is a measure of central tendency
Standard Deviation:
is a measure of variability
Coefficient of Variation
Statistical Calculations

38. One parameter (frequency) histogram
# of events for
particular
parameter
One parameter frequency histogram
establish regions and calculate coefficient of variation (cv)
cv = st.dev/mean of half peak

39. Coefficient of Variation
%CV Definition =
St.Dev x 100
MEAN
CV=3.0
CV=3.0
MEAN
Coefficient of Variation
Crucial in establishing:
alignment
Fluidic stability
Staining of cells

40. Coefficient of Variation Calculation
Statistical
(Subjective)
Formula
(not boundary
dependent
Objective) • •
Coefficient of Variation • • • • • • • • •
Least-Squares
(Accurate, non-
subjective) • • • • • • • • • • • •

41. Histogram Comparisons
The question here might be:
Is there a difference between these two data sets?
Histogram Overlays
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.

42. Kolmogorov-Smirnov K-S Test
100 50
Cumulative Frequency Distribution
Fluorescnece Intensity
Kolmogorov-Smirnov
Channel Number
A good technique for estimating the differences between histograms

43. Histogram Analysis Normalized Subtraction
Match region
Histogram Analysis
Normalized Subtraction
False Negatives
• Very accurate
• Assumption that control & test histogram are same shape
• Match region finds best amplitude of control to match test histogram

44. Histogram Analysis Integration
“Positive” histogram
Frequency
Histogram Analysis
Integration
False Negatives
False Positives
• Very subjective analysis
• Not easily automated
• Not good for weakly fluorescent signals

45. Histogram Analysis Accumulative Subtraction
Actual
Negatives
Negative Control
Test
Number of Events
Histogram Analysis
Accumulative Subtraction
Actual
Positives
Cumulative Events
• Very accurate
• Assumption that control & test histogram are same shape
• Match region finds best amplitude of control to match test histogram

46. Basic Histogram Operations
Gating 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

47. Gating Example We have here a histogram
By definition it is single parameter
Total cells -5000
Gate M1 determines a region from point A to point B on the X axis (log FITC)
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 A B

48. Gating Example We have here a histogram
By definition it is single parameter
Total cells -5000
Gate M2 determines a region from point A1 to point B1 on the X axis (log FITC) M2
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 A1 B1

49. Multiple Gates Total cells -5000
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). M1 M3 M4 M5 M6 M2

50. 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
Two parameter displays
Note: TIP – Tube identifier Parameter – allows the display of data points for multiple samples
TIG: Time Interval Gating – allow the display of multiple samples over time.

51. Isometric Plot - 3 Parameter view
- simulated surface is created - 2 parameter data plus cell number
- # of particles used as 3rd parameter - 3-D space

52. Density Dot Plot Contour PlotA B
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.

53. More displays Color coded dot plots
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.
In this display, each population has been identified by a different color

54. Kinetic Analysis 2 D plots 50 ng PMA 0 ng PMA Stimulated Unstimulated
Fluorescence
0 ng PMA
Unstimulated
TIME (seconds)
1800
450
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, activated
cells increase the green DCF fluorescence at least 10 times the
initial fluorescence.

55. Some Multi-data display formats
FITC Fluorescence
Mo1
CD4
CD8
CD45
leu11a
CD20
Tube ID
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.
Multiple histograms displayed in a combination format
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
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.

56. The first distribution demonstrates forward gating
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
Forward gate
Back gate
Gating
log PE
2P Scatter
1P Fluorescence
2P Fluorescence

57. Specific Cases - DNA analysis Doublet Discrimination
8 x 125 
m laser beam shape
16 x 64 
m laser beam shape
Peak Fluorescence
Peak Fluorescence
Doublet Discrimination
Clumps
Integral Fluorescence
Integral Fluorescence
Slide 18, 11/11/96 of DNA.ppt

58. From Duque et al, Clin.Immunol.News.
Negative
Positive
HLA-DR T
CD13,33
CD19
TdT
CD10
CD20 Mu
B,T
AMLL
AML
T-ALL
AML-M3
AUL ?
PRE-BI
PRE-BII
PRE-BIII
PRE-BIV
PRE-BV
Decision Tree in Acute Leukemia
Decision Tree in Acute Leukemia
An example of how data analysis can result in a decision process for a data set

59. Multi-color studies generate a lot of data4
color 3
color
2 color
Log Fluorescence
QUADSTATS ++ -- +- -+
Multi-color studies generate a lot of data
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!!

60. 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