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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 will be placed on the WWW at the following address: http://www.cyto.purdue.edu/educate J.Paul Robinson, Ph.D.

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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 will be placed on the WWW at the following address: http://www.cyto.purdue.edu/educate. J.Paul Robinson, Ph.D. Data Analysis. - PowerPoint PPT Presentation

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  • Workshop TutorialPolish Cytometry Society 1998Analysis of flow cytometric data - data collection, principles ofgating and histogram analysis

    This presentation will be placed on the WWW at the following address:http://www.cyto.purdue.edu/educateJ.Paul Robinson, Ph.D.

  • Data AnalysisData acquisition vs. data analysisData analysis softwareData displayEstablishing regions and gatingAnalysis methods that can change results

  • Data AcquisitionEach measurement from each detector is referred to as a parameter or variableData are acquired as a list of the values for each parameter (variable) for each event (cell)[RFM]

  • Data Analysis SoftwareInstrument SoftwareElite 4.0CoulterBryte HS 2.0 Bio-RadLysis IIBecton-Dickinson

    Commercial SourcesWinList & Modfit LT Verity SoftwareListView & Multicycle Phoenix Software

    Free Flow SoftwareWinMDIWeb

  • Pros & Cons of Free Flow SoftwareAdvantages:works with listmode files from many types of aquisition softwaresource code is availablemany people use these packagesFREE!!!!!

    Disadvantages:little or no technical supportlittle or no documentation often no tutorialsBUGS!!

  • Flow Cytometer Computer FilesListmode files-correlated data file where each event is listed sequentially, parameter by parameter-large file size

    Histogram filesuncorrelated data used for display only

    Flow cytometry standard (FCS 2.0) format used to save datause other software programs to analyze data

  • Types of Listmode data Ungated Listmode Gated Listmode collection

  • Data Acquisition - Listmode[RFM]

    Event

    Param1

    FS

    Param2

    SS

    Param3

    FITC

    Param4

    PE

    1

    50

    100

    80

    90

    2

    55

    110

    150

    95

    3

    110

    60

    80

    30

  • Listmode FileFILEVERSION;1.15BTRIEVE;7058418 IBA13-2 collected 6/27/95;28/6/1995;;2;1PARAM;LS1;400;LS2;600;FL1;460;FL2;400;FL3;300;WID;TIMGAIN;1;1;2;2;0THRES;0;10;0;5;0;5;1;11;0;5FLUIDIC;3;4;1;7.20FCM;357;2;2;1;1;0;0;1;1;1;1;0;1;1;0;1SUBTRACT;15;0AUTOCYCLE;1;0;0;0;0;0;0;-1;1;20000;7;0;1;0;1;0PCBUFFER;374000;1SERIAL;2ADBOARD;1;0;0;30;0PEAKAREA;0;0;0;0CALIBRATION;300;300;300;300;0;0;0;0MAINWINDOW;1PRINTHEADER; Flow cytometry Report;Win-Bryte software - Bryte-HS flow cytometerPRINTFOOTER; Purdue University Cytometry LaboratoriesPRINTSTATISTICS;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;5ROI;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;0ROI;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;0ROI;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;0ROIDATACYTO;7;20022;4845.7;98.7;0.0ROIDATAHIST;1;2.4;16712;52;53;4044.6;2.4;53;83.5;0.0ROIDATAHIST;2;2.2;1875;128;128;531.3;1.7;128;7.8;0.0HISTOGRAM;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;255HISTOGRAM;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;255CYTOGRAM;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;255HISTOGRAM;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;255CYTOGRAM;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;255LISTDATA226;0;426;426;243;81;0412;225;8;426;426;239;0380;262;2;427;427;245;0578;348;0;412;412;239;0529;377;0;431;431;240;0420;203;0;438;438;243;0444;221;0;427;0;240;0383;215;0;421;421;238;0735;716;0;1027;1027;280;0499;228;0;431;431;239;0531;328;0;433;433;241;0520;218;0;423;423;243;0471;252;0;425;425;244;0652;298;0;441;441;240;0561;291;0;421;421;240;0608;307;0;415;415;241;0541;231;0;424;424;245;0

  • Listmode data Analysis226;0;426;426;243;81;0PARAM;LS1;400;LS2;600;FL1;460;FL2;400;FL3;300;WID;TIMLS1LS2FL1FL2FL3WIDTIM226 0426426243 81 0

  • One parameter frequency histogram

    establish regions and calculate coefficient of variation (cv)cv = stdev/mean of half peak# of events forparticular parameter

  • Establishing RegionsEstablishing regions:-objective or subjective?-training/skill/practice

    Possible shapes: -rectangles-ellipses-free-hand-quadrantsStatisticsR1

  • GatingReal-time gating vs. software gatingEstablishing regionsGating strategiesQuadrant analysisComplex or Boolean gatesBack gating

  • Real-Time vs. Software GatingReal-time or live gating:-restrict the data that will be accepted by a computer (some characteristic must be metbefore data is stored) (This is not encouraged)

    Software or analysis gating:-excludes certain stored data from a particular analysis procedure

  • Region 1 establishedGated on Region 1Using Gateslog PER1

  • 90 Degree Scatter0 200 400 600 8001000815203040501002001000LymphocytesMonocytesNeutrophilsSide Scatter ProjectionLight Scatter GatingScale

  • log PE Back gateForward gate

  • Quadrant AnalysisThe Square Cell PrincipleIs It necessary?Why is it used?

  • Quadrant Analysislog PE(+ +)( - +)(+ -)(- -)Log FITC (525 nm)Log PE (575 +/- 20 nm)1234

  • Complex or Boolean GatingWith two overlapping regions, several options are available:

    R1R2

  • Boolean GatingNot Region 2:

  • Boolean GatingRegion 1 or Region 2:

  • Boolean GatingRegion 1 and Region 2:

  • Not (Region1 and Region 2):Boolean Gating

  • Back GatingRegion 4 establishedBackgating using Region 4log PE Back gate

  • Drawing Regions: Sample PreparationSample Quality

    B.subtilis sporesB.subtilis veg. + sporesDebrisSporesSporesDebrisVegetative loglogloglog

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

  • 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

  • Peak FluorescenceIntegral FluorescenceDoublet Discrimination- selection at the time of collection- 1:1 ratio

  • Time as a quality Control Parameterblockage 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.

  • ConclusionThis workshop has discussed the following ideas:1. Nature of data2. Analysis software and techniques3. Gating and region definition4. Forward and Back gating5. Boolean operators on flow data6. Quality control systems

    Presentation given to the Polish Society for Cytometry meeting, Gdansk, 20 October, 1998.Material on this site is copyrighted by J.Paul Robinson, however, all the material may be freely used for non-commercial educational use. Examples include lectures, tutorials and lecture notes. If slides and material are used, appropriate acknowledgement should be as follows:

    Taken/modified from material prepared by J.Paul Robinson, Purdue University, West Lafayette, Indiana.

    For contact purposes:J.Paul Robinson, Ph.D.Professor of ImmunopharmacologyPurdue University, West Lafayette, In. Ph (765) 494-0757Fax (765) 494-0517http://www.cyto.purdue.eduemail [email protected]

    Date Published to the WEB 25 October, 1998

    SummaryThis workshop will concentrate on understanding the nature of data analysis for flow cytometry. We will discuss the structure of the files, both listmode and histograms, techniques for gating populations (forward and back gating), the use of time and histogram analysis. Various examples will be provided and routine problems in data analysis will be identified. More detailed notes will be provided for the workshop session.

    .Data CollectionMost flow cytometers collect between 3 and 10 "parameters" or "variables". Each variable can be used to discriminate some component of the cell populations. The standard for data collection has been known as the "Flow Cytometry Standard" or FCS data file structure. Data can be collected in either histogram mode or listmode. It is now routine to collect flow cytometry data in listmode because there are few constraints in the size of data collection files. Originally, flow cytometers did not have good network capability, small hard disks (5-20 Mb, and floppy drive that could save only about 1 Mb of data). With high capacity hard disks and Ethernet access now routine, listmode is the most useful collection modality. Data can be collected in a gated or ungated mode. If the data are ungated, all events registered by the instruments electronic circuits will be saved in listmode files. If gated collection is chosen, it is possible to collect a listmode file of only those events that satisfy certain criteria established by the operator.

    .Analysis SoftwareThere are many ways of analyzing flow cytometry data. The first and probably most important packages are those provided on the instruments used in the laboratory to collect the initial data. Often the manufacturer provides a limited analysis package, however, frequently a great deal of the needed analysis can be obtained directly from the instrument package. If more complex gating and comparison are required, it is mostly necessary to move the data to an off-line computer with an alternative data analysis package. Many freeware packages are available today that are excellent. Several examples are WinMDI written by Joe Trotter.

    .Data structureAs noted above the file structure of the collected data is normally saved in a format known as FSC (Flow Cytometry Standard). This was originally proposed by Murphy et al (ref) to encourage all manufacturers and software engineers to comply with the standard to allow relatively easy data exchange between users of different instruments. Most manufacturers comply with a reasonable extent and a number of conversion utilities exist in software packages to allow cross-analysis of data from different instruments. The current standard is FCS-2, which adds a number of additional components to the file structure.

    Each file collected has a number of keywords, PMT (Photomultiplier Tube) values and gating regions associated with it. Some listmode file structures also contain the instrument setup used to collect the original data. While each instrument collects files of different internal structure, in essence the information collected retains a relatively common format allowing 3rd party software manufacturers to create value added analysis packages.

    .List Mode DataEssentially however, a listmode file is a series of numbers which sequentially list data for each variable collected on each cell. This is shown in the associated table provided.

    .Histogram DataHistograms are by definition frequency distributions for whatever variable of interest. Frequently we use the term histogram to incorrectly describe various types of data representations.

    .Gating: Principles and TermsThere are a number of terms associated with flow cytometry analysis. One important concept is the idea of creating an electronic gate. A gate can also be referred to as a bitmap or region. Usually these terms are synonymous in usage, although they can mean slightly different things to different manufacturers.

    .Forward GatingForward gating occurs when we select criteria to be applied to a data set such as an initial light scatter bitmap.

    This cartoon indicates how data can be presented. This is a contour plot with a PROJECTION histogram of each of the variables shown on the contour plot. For example if contour plots are to be presented, contour levels must be provided (see box at left). Without the contour levels, there is no way to know how the data were actually derived - for example, the lowest level might be 1 cell or even 100 cells - so the data must be provided.

    The second issue of importance is the labeling of the axes. It is NOT acceptable to label axes as FL! or FL2. This shows an unscientific understanding of the nature of data presentation and should NEVER be used. It is best to identify the name of the parameter/variable represented and if applicable, the wavelength of the signal. If the signal is LOG data, this should be indicated also..Back GatingWhen we backgate - our goal is to identify certain endpoint criteria and apply them to the data set. As example in a whole blood assay, we can use scatter gating to identify which cells might be monocytes. However, there may be some overlap with other cell types making accurate analysis impossible. By adding a fluorescent marker that will identify those monocytes we can place a bitmap on the positive regions of the fluorescence histogram and backgate that to identify the scatter of only those cells that are positive with the fluorescence marker. This is now a backgated population. Backgating can be sequential based on several criteria.

    .Histogram AnalysisThere are many ways to analyze a histogram. Some very simple methods are often satisfactory to obtain the required information. For example, there might be an interest in identifying positive from negative expressing cells. If there is a significant difference in the fluorescence distributions, a simple positive gate will give you the answer you need. If however, there is overlap between the populations a more sophisticated approach might be needed. This is the area where significant caution must be taken as analysis now becomes more complex. Comparison between histograms requires considerable care. Statistical methods must be used to determine significant difference between populations. The coefficient of variation (CV) is an important measure of the variation that exists between cells within a population. Other statistical components will be introduced.

    .Back GatingWhen we backgate - our goal is to identify certain endpoint criteria and apply them to the data set. As example in a whole blood assay, we can use scatter gating to identify which cells might be monocytes. However, there may be some overlap with other cell types making accurate analysis impossible. By adding a fluorescent marker that will identify those monocytes we can place a bitmap on the positive regions of the fluorescence histogram and backgate that to identify the scatter of only those cells that are positive with the fluorescence marker. This is now a backgated population. Backgating can be sequential based on several criteria.

    Time was first proposed as a quality control parameter by Watson. IN the above example the DNA histogram (Top left) appears to have an abnormal G0-G1 population. By collecting time as a parameter, an observation can be made as to the rate of cell collection. As can be seen at the right of this histogram, the collection rate appears to have been significantly reduced at region B. This may have been caused by a blockage in the flow cell or some other non-biological problem. If this caused turbulence in the flow cell, a significant impact might be seen on the CV of the fluorescence histogram. By removing the data collected during the time gate B (by using a Boolean operator [a NOT B], only data collected at the appropriate rate is included in the data analysis as seen on the lower left histogram. A normal time histogram is shown on the lower right.This Workshop was entitled:Analysis of flow cytometric data - data collection, principles of gating and histogram analysis and was presented at the 4th Polish Cytometry Society Congress in Gdansk, Poland, 18 October, 1998.

    J. Paul RobinsonProfessor of ImmunopharmacologyPurdue UniversityWest Lafayette, USA

    Material copyright by J.Paul Robinson. See first slide notes for use information. Date Published to the WEB 25 October, 1998