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Workshop Tutorial Polish Cytometry Society 1998 Analysis of flow cytometric data - data collection, principles of gating and histogram analysis This presentation.

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Presentation on theme: "Workshop Tutorial Polish Cytometry Society 1998 Analysis of flow cytometric data - data collection, principles of gating and histogram analysis This presentation."— Presentation transcript:

1 Workshop Tutorial Polish Cytometry Society 1998 Analysis of flow cytometric data - data collection, principles of gating and histogram analysis This presentation will be placed on the WWW at the following address: http://www.cyto.purdue.edu/educate J.Paul Robinson, Ph.D.

2 Data Analysis Data acquisition vs. data analysis Data analysis software Data display Establishing regions and gating Analysis methods that can change results

3 Data Acquisition Each measurement from each detector is referred to as a “parameter” or “variable” Data are acquired as a “list” of the values for each “parameter” (variable) for each “event” (cell) [RFM]

4 Data Analysis Software Instrument Software Elite 4.0Coulter Bryte HS 2.0 Bio-Rad Lysis IIBecton-Dickinson Commercial Sources WinList & Modfit LT Verity Software ListView & Multicycle Phoenix Software Free Flow Software WinMDIWeb

5 Pros & Cons of Free Flow Software Advantages: works with listmode files from many types of aquisition software source code is available many people use these packages FREE!!!!! Disadvantages: little or no technical support little or no documentation often no tutorials BUGS!! Don’t forget to mention that the CDROM has all the free software on it!!!

6 Flow Cytometer 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) ­format used to save data ­use other software programs to analyze data

7 Types of Listmode data Ungated Listmode Gated Listmode collection

8 Data Acquisition - Listmode [RFM]

9 Listmode File FILEVERSION;1.15 BTRIEVE;7058418 IBA13-2 collected 6/27/95;28/6/1995;;2;1 PARAM;LS1;400;LS2;600;FL1;460;FL2;400;FL3;300;WID;TIM GAIN;1;1;2;2;0 THRES;0;10;0;5;0;5;1;11;0;5 FLUIDIC;3;4;1;7.20 FCM;357;2;2;1;1;0;0;1;1;1;1;0;1;1;0;1 SUBTRACT;15;0 AUTOCYCLE;1;0;0;0;0;0;0;-1;1;20000;7;0;1;0;1;0 PCBUFFER;374000;1 SERIAL;2 ADBOARD;1;0;0;30;0 PEAKAREA;0;0;0;0 CALIBRATION;300;300;300;300;0;0;0;0 MAINWINDOW;1 PRINTHEADER; Flow cytometry Report;Win-Bryte software - Bryte-HS flow cytometer PRINTFOOTER; Purdue University Cytometry Laboratories PRINTSTATISTICS;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;0;0;1;20;15;1;8;12;5;0;0;5 ROI;1;1;3;3;4;45;65;61;65;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;bird;1;1;1;0;1;1;1;0;1;1;0;;-1;-1;3;0;255;0;0 ROI;2;1;3;3;0;121;41;141;41;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;human;1;1;1;0;1;1;1;0;1;1;0;;-1;-1;3;0;255;0;0 ROI;7;3;5;1;0;0;18;24;63;52;48;16;2;0;2;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;;1;1;1;0;1;1;1;0;1;1;0;;-1;-1;3;5;255;0;0 ROIDATACYTO;7;20022;4845.7;98.7;0.0 ROIDATAHIST;1;2.4;16712;52;53;4044.6;2.4;53;83.5;0.0 ROIDATAHIST;2;2.2;1875;128;128;531.3;1.7;128;7.8;0.0 HISTOGRAM;FALS;24;49;290;320;256;0;Count;1;1;0;0;0;255;0;255;0;0;0;0;0;255;1;1;C;0;1;1;1;S;0;-1;-1;-1;-1;-1;-1;-1;-1;-1;-1;-1;-1;-1;1;1;1;255;0;255 HISTOGRAM;SIDE-SCATTER;290;49;556;320;256;1;Count;1;1;0;0;0;255;0;255;0;0;0;0;0;255;1;1;C;0;1;1;1;S;0;-1;-1;-1;-1;-1;-1;-1;-1;-1;-1;-1;-1;- 1;1;1;1;255;0;255 CYTOGRAM;LS1-LS2;24;320;290;591;64;1;0;1;1;0;0;0;255;0;255;0;0;0;0;0;0;1;1;O;0;1;0;0;S;0;6;-1;-1;-1;-1;-1;-1;-1;-1;-1;6;-1;- 1;1;0;1;0;A;B;C;D;1;1;255;0;0;32;32;0;255;0;255 HISTOGRAM;FL2;290;320;556;591;256;3;Count;1;1;0;0;0;255;0;255;0;0;0;255;0;0;1;1;C;10;1;1;1;S;0;0;1;-1;-1;-1;-1;-1;-1;-1;-1;0;7;-1;1;1;1;255;0;255 CYTOGRAM;FL2-TIM;556;320;822;591;64;6;3;1;1;0;0;0;255;0;255;0;0;0;0;0;0;1;1;I;0;1;0;0;S;0;-1;-1;-1;-1;-1;-1;-1;-1;-1;-1;-1;-1;- 1;1;0;1;0;A;B;C;D;1;1;255;0;0;32;32;0;255;0;255 LISTDATA 226;0;426;426;243;81;0 412;225;8;426;426;239;0 380;262;2;427;427;245;0 578;348;0;412;412;239;0 529;377;0;431;431;240;0 420;203;0;438;438;243;0 444;221;0;427;0;240;0 383;215;0;421;421;238;0 735;716;0;1027;1027;280;0 499;228;0;431;431;239;0 531;328;0;433;433;241;0 520;218;0;423;423;243;0 471;252;0;425;425;244;0 652;298;0;441;441;240;0 561;291;0;421;421;240;0 608;307;0;415;415;241;0 541;231;0;424;424;245;0

10 Listmode data Analysis 226;0;426;426;243;81;0 PARAM;LS1;400;LS2;600;FL1;460;FL2;400;FL3;300;WID;TIM LS1LS2FL1FL2FL3WIDTIM 226 0426426243 81 0

11 One parameter frequency histogram establish regions and calculate coefficient of variation (cv) cv = stdev/mean of half peak # of events for particularparameter

12 Establishing Regions Establishing regions: -objective or subjective? -training/skill/practice Possible shapes: -rectangles -ellipses -free-hand -quadrants Statistics R1

13 Gating Real-time gating vs. software gatingReal-time gating vs. software gating Establishing regionsEstablishing regions Gating strategiesGating strategies Quadrant analysisQuadrant analysis Complex or Boolean gatesComplex or Boolean gates Back gatingBack gating

14 Real-Time vs. Software Gating Real-time or live gating: -restrict the data that will be accepted by a computer (some characteristic must be met before data is stored) (This is not encouraged) Software or analysis gating: -excludes certain stored data from a particular analysis procedure

15 Region 1 established Gated on Region 1 Using Gates log PE R1

16 90 Degree Scatter 0 200 400 600 8001000 8 15 20 30 40 50 100 200 1000 Lymphocytes Monocytes Neutrophils Side Scatter Projection Light Scatter Gating Scale

17 log PE Back gate 1P Fluorescence 2P Fluorescence 2P Scatter 90 deg Scatter FALS Scatter Forward gate

18 Quadrant Analysis The Square Cell Principle Is It necessary? Why is it used?

19 Quadrant Analysis log PE (+ +)( - +) (+ -) (- -) Log FITC (525 nm) Log PE (575 +/- 20 nm) 1 2 3 4

20 Complex or Boolean Gating With two overlapping regions, several options are available: R1R2

21 Boolean Gating Not Region 2:

22 Boolean Gating Region 1 or Region 2:

23 Boolean Gating Region 1 and Region 2:

24 Not (Region1 and Region 2): Boolean Gating

25 Back Gating Region 4 established Backgating using Region 4 log PE Back gate

26 Drawing Regions: Sample Preparation Sample Quality B.subtilis sporesB.subtilis veg. + spores Debris Spores Debris Vegetative log

27 FITC + PE+ FITC+ APC+ PE + APC+ Multi Parameter Data Display

28 Methods that can change results: 1. Doublet discrimination 2. Time as a quality control parameter Example: DNA content -need to eliminate debris & clumps -need to gate out doublets -maintain constant flow rate

29 Peak Fluorescence Integral Fluorescence Doublet Discrimination - selection at the time of collection - 1:1 ratio

30 A B Time as a quality Control Parameter “blockage” data can be removed by gating the time histogram. Abnormal histogram caused by turbulence (not biological variation) Normalized histogram after subtraction of list mdoe data collected during the blockage shown at B on right.

31 Conclusion This workshop has discussed the following ideas: 1. Nature of data 2. Analysis software and techniques 3. Gating and region definition 4. Forward and Back gating 5. Boolean operators on flow data 6. Quality control systems

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