# BMS 633BME 695 Y Week 3 Detectors Electronics

- Slides: 60

BMS 633/BME 695 Y - Week 3 Detectors, Electronics, Data Analysis J. Paul Robinson Professor of Immunopharmacology School of Veterinary Medicine, Purdue University The WEB version of these slides can be found on http: //tinyurl. com/385 ss Hansen Hall, B 050 Purdue University Office: 494 0757 Fax 494 0517 email: [email protected] cyto. purdue. edu WEB http: //www. cyto. purdue. edu Material is taken from the course text: Howard M. Shapiro, Practical Flow Cytometry, 3 nd edition (1994), 4 th Ed (2003) Alan R. Liss, New York. © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT 3 rd Ed. Shapiro p 127 -133 4 th Ed. Shapiro p 160 -256

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 633 A –BME 695 Y 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 633 A –BME 695 Y LECTURE 3. PPT

Characteristics of Light Detection Red sensitive PMT UV line © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y 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 633 A –BME 695 Y 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 (Ga. As) cathodes operate from 300 850 nm (very costly and have lower gain) © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT

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 © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y 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 forward scatter and a PMT for side scatter. © J. Paul Robinson The Bio Rad Bryte cytometer uses PMTs forward and wide angle light scatter as well as fluorescence © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT PMT © J. Paul Robinson

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 633 A –BME 695 Y 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 633 A –BME 695 Y LECTURE 3. PPT

Diode Vs PMT • Scatter detectors are frequently diode detectors Sample stream Back of Elite forward scatter detector showing the preamp © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT Front view of Elite forward scatter detector showing the beam dump and video camera signal collector (laser beam is superimposed)

Spectral Imaging © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y 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 (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 © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y 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=(R 2+(Xl Xc)2)½ (X = inductive reactance, X = capacitive reactance) l © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT c

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 633 A –BME 695 Y LECTURE 3. PPT

The Coulter Principle • 2 chambers filled with a conductive saline fluid are separated by a small orifice (100 m 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 633 A –BME 695 Y 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 633 A –BME 695 Y LECTURE 3. PPT

Linear and Log circuits • • Linear circuits Logarithmic circuits Dynamic range Fluorescence compensation © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y 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 10 V before performing a digital conversion. • Assuming this, with 4 decades and a maximum signal of 10 V we have: Factor reduction 10 pulse output 1 v © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT 10000 100 mv 1 mv

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 10 V before performing a digital conversion. • Assuming this, with 4 decades and a maximum signal of 10 V we have: Factor reduction 10 pulse output 1 © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT 100 mv 1000 10 mv 10000 1 mv

How many bits? • Assume we convert linear analog signals using an 8 bit ADC we have 256 channels of range (2 n) (28 256) corresponding to the range 0 10 V • Channels difference is 10/256=40 m. V per channel 1 V 100 m. V 0 50 100 150 Channels © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT 200 250

Ideal log amp 1 V 100 m. V 10 V Linear 0 Log amp 1 m. V 50 10 m. V 150 100 m. V 200 1 V 250 10 V Log 0 50 100 © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT 150 Channels 200 250

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 linear scale are expanded in the log scale. © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT

Log/lin display © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y 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. • p. H can also be measured using ratio circuits • Calcium ratio (using Indo 1 we can ratio the long and short l) © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y 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 633 A –BME 695 Y LECTURE 3. PPT

System management Operational Steps 1. Sample Preparation 2. Data Acquisition 3. Data analysis 4. Data Reporting © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT } We will only deal with these in this lecture

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 633 A –BME 695 Y LECTURE 3. PPT

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

Data Analysis Output goals • • • Frequency Distributions (Gaussian/normal) Statistical components Skewness and Kurtosis Compensation/crosstalk Reporting © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y 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 633 A –BME 695 Y 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) f ormat 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 633 A –BME 695 Y LECTURE 3. PPT

Data Analysis Software Instrument Software Elite 4. 0 Bryte HS 2. 0 Lysis II Coulter Bio Rad Becton Dickinson Commercial Sources Win. List & Modfit LT List. View & Multicycle Flo. Jo FCS Express Flow Explorer Verity Software Phoenix Software Treestar Software Ray Hicks Ron Hoebe Free Flow Software Win. MDI MFI Joe Trotter Eric Martz © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT

Win. MDI 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 Gregori http: //www. cyto. purdue. edu/flowcyt/labinfo. htm © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT

Precision C. V. • • Precision: CV Sensitivity MESF Units Accuracy and Linearity Noise Background Laser noise Shapiro’s 7 th Law of Flow Cytometry: No Data Analysis Technique Can Make Good Data Out of Bad Data!!! © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT

Data Acquisition Listmode Event Param 1 Param 2 Param 3 Param 4 FS SS FITC PE 1 2 3 4 5 6 59 58 54 66 112 115 100 110 60 60 n etc © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT 80 150 80 80 90 95 30 30

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

One parameter (frequency) histogram # of events for particular parameter establish regions and calculate coefficient of variation (cv) cv = st. dev/mean of half peak © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT

Coefficient of Variation %CV Definition = St. Dev x 100 MEAN CV=3. 0 MEAN Crucial in establishing: • alignment • Fluidic stability • Staining of cells © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT

Coefficient of Variation Calculation Statistical (Subjective) • • • • • • • © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT Formula (not boundary dependent Objective) Least Squares (Accurate, non subjective)

Histogram Comparisons The question here might be: Is there a difference between these two data sets? 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. © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT

Kolmogorov Smirnov Fluorescnece Intensity Cumulative Frequency Distribution K S Test 0 50 100 Channel Number 50 100 A good technique for estimating the differences between histograms © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT

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

Histogram Analysis Integration Frequency “Positive” histogram False Negatives False Positives • Very subjective analysis • Not easily automated • Not good for weakly fluorescent signals © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT

Histogram Analysis Accumulative Subtraction Negative Control Cumulative Events Number of Events Actual Negatives Test Actual Positives • Very accurate • Assumption that control & test histogram are same shape • Match region finds best amplitude of control to match test histogram © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT

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

Gating Example Total cells 5000 We have here a histogram By definition it is single parameter Gate M 1 determines a region from point A to point B on the X axis (log FITC) A B © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT Within the boundaries of A B, the gate M 1 gives is the total number of cells within the range A B – the number of cells is 4900

Gating Example Total cells 5000 We have here a histogram By definition it is single parameter Gate M 2 determines a region from point A 1 to point B 1 on the X axis (log FITC) M 2 A 1 B 1 © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT Within the boundaries of A 1 B 1, the gate M 2 gives is the total number of cells within the range A 1 B 1 which is 4, 700

Multiple Gates Total cells 5000 M 1 M 3 M 4 M 5 M 6 M 2 © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT 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 M 6, excluding gate M 3 – (M 6 M 3) would give you the same result as adding gates M 1 and M 4 (M 1+M 4).

Multiple parameter displays Following display are important in flow • Dot plot • Density dot plot • Contour plot • Isometric plot • 3 D projection • Complex displays – TIP and TIG 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. © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT

Isometric Plot 3 Parameter view simulated surface is created # of particles used as 3 rd parameter © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT 2 parameter data plus cell number 3 D space

Density Dot Plot A 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 90 ls is Ninety Degree light scatter or orthogonal light scatter. ) © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT Contour Plot B 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.

More displays Color coded dot plots In this display, each population has been identified by a different color © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT 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.

Kinetic Analysis 2 D plots 50 ng PMA Stimulated 0 450 900 1350 1800 TIME (seconds) Flu o res cen ce ce 0 ng PMA Unstimulated 0 450 900 1350 1800 TIME (seconds) 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, activated cells increase the green DCF fluorescence at least 10 times the initial fluorescence. © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT

Some Multi data display formats + ++ +++ Mo 1 CD 20 be ID leu 11 a Tu CD 4 CD 8 FITC Fluorescence 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 © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT 1 2 3 4 5 6 7 8 9 CD 45 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 2 n =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.

Forward gate Back gate log PE 1 P Fluorescence 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 2 P Fluorescence © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT 2 P Scatter

Specific Cases - DNA analysis Doublet Discrimination 8 x 125 m laser beam shape Peak Fluorescence 16 x 64 m laser beam shape Clumps Integral Fluorescence Slide 18, 11/11/96 of DNA. ppt © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT Integral Fluorescence

From Duque et al, Clin. Immunol. News. APRE-BV PRE-BIV Mu Negative Positive PRE-BIII PRE-BII CD 20 AUL PRE-BI CD 10 Td. T AMLL AML-M 3 ? CD 19 B, T CD 13, 33 T-ALL CD 13, 33 T HLA-DR Decision Tree in Acute Leukemia An example of how data analysis can result in a decision process for a data set © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT

Multi color studies generate a lot of data +- ++ -- +- QUADSTATS -+ ++ -- +- Log Fluorescence QUADSTATS -+ ++ -- +- 10 QUADSTATS -+ ++ -- +- QUADSTATS Log Fluorescence ++ 9 Log Fluorescence +- -+ Log Fluorescence -- QUADSTATS Log Fluorescence QUADSTATS -+ ++ Log Fluorescence +- Log Fluorescence -+ 8 Log Fluorescence QUADSTATS -+ ++ -- +- Log Fluorescence QUADSTATS Log Fluorescence -- -- QUADSTATS 7 Log Fluorescence ++ ++ 6 Log Fluorescence -+ -+ 5 Log Fluorescence +- Log Fluorescence -- +- QUADSTATS -+ ++ -- +- Log Fluorescence QUADSTATS QUADSTATS QUADSTATS -+ ++ -- +- 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 QUADSTATS 3 Log Fluorescence 2 Log Fluorescence 1 Log Fluorescence 2 color -+ 4 color 3 color -+ ++ -- +- Log Fluorescence 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 633 A –BME 695 Y 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 © 1988 -2004 J. Paul Robinson, Purdue University BMS 633 A –BME 695 Y LECTURE 3. PPT