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Research Techniques Made Simple: Flow Cytometry Richard R. Jahan-Tigh 1,2, Caitriona Ryan 3,4,5, Gerlinde Obermoser 5, Kathryn Schwarzenberger 6 1 Department.

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Presentation on theme: "Research Techniques Made Simple: Flow Cytometry Richard R. Jahan-Tigh 1,2, Caitriona Ryan 3,4,5, Gerlinde Obermoser 5, Kathryn Schwarzenberger 6 1 Department."— Presentation transcript:

1 Research Techniques Made Simple: Flow Cytometry Richard R. Jahan-Tigh 1,2, Caitriona Ryan 3,4,5, Gerlinde Obermoser 5, Kathryn Schwarzenberger 6 1 Department of Dermatology, University of Texas - Houston Medical School, Houston, TX 2 Department of Dermatology, University of Texas MD Anderson Cancer Center, Houston, TX 3 Department of Dermatology, Baylor University Medical Center, Dallas, TX 4 Menter Dermatology Research Institute, Dallas, TX 5 Baylor Institute for Immunology Research, Dallas, TX 6 Department of Dermatology, University of Vermont, Burlington, VT

2 Introduction Measuring properties of cells while in a fluid stream –Flow: cells in motion; Cytometry: measure of cells Analyzes many cells and numerous parameters simultaneously over very short timeframe Can be performed on a variety of tissues - peripheral blood, skin punch biopsies, bone marrow aspirates, tissue culture cell lines Uses fluorescent-labeled antibodies specific to cell-surface markers used to characterize the cell population of interest –Cell surface markers are usually glycoproteins called cluster of differentiation (CD) markers and help differentiate cell subpopulations –Also intracellular proteins, cell membranes, Ca++ flux and many more parameters can be assessed

3 The Flow Cytometry Process Sample is brought into single cell suspension and stained with antibodies of interest. Cells from the sample tube are injected into the sheath stream. Laminar flow of the stream pushes the cells to line up in single file. Cells flow single file past focused laser Scattered light and emitted fluorescence is filtered, translated into electronic signals and graphical display. On a cell sorter, cells are then packed into charged droplets and sorted. 1: Forward scatter detector, 2: side scatter detector, 3: fluorescence detector, 4: filters and mirrors, 5: charged deflection plates

4 Flow Cytometric Cell Sorting = Fluoresence Activated Cell Sorting (FACS) Cells of interest can be separated to very high purity by cell-sorting flow cytometers After cells are interrogated by laser in the flow chamber, the single cell stream is broken very accurately into tiny droplets by a fine nozzle vibrating at ultrasonic frequency Very rapid computation of the signals elicited by the cell in the flow chamber makes it possible to deflect droplets carrying cells of interest using positive, neutral, or negative electric charges Droplets enter an electromagnetic field and are pushed into different sorting containers based on charge

5 Fluorescence As laser interrogates cell, fluorochromes become excited –When fluorochromes leave their excited state, energy is released as photon with a specific wavelength –Photons pass through collection lens and are filtered down specific channels Emission spectra of fluorochromes overlap so single detector may see fluorescence originating from more than one fluorochrome –This spillover must be removed using a mathematical algorithm so that one detector reports signal from only one fluorochrome = compensation

6 Flow Data Plot (1) When laser interrogates a cell, light is scattered in all directions and can be recorded by detectors FSC: light that is scattered in the forward direction (along the same axis the laser is traveling) is detected in the Forward Scatter Channel (FSC) –Intensity of signal depends on cell size and refractive index Combined measurement of FSC and SSC allows differentiation of cell types in a mixed cell population Granulocytes Monocytes Lymphocytes Debris Dead cells RBC Duplicates * ****** SSC: light that is scattered at 90 degrees to the axis of the laser path is detected in the Side Scatter Channel (SSC) –Intensity of this signal is proportional to the intracellular granularity of the cell

7 Flow Data Plot (2) Four ways of displaying identical data: i. Pseudocolor dot plot (allows simultaneous information of rare events (dots) and high-frequency areas with dots of different color); ii. Dot plot where each dot represents one event (note that here only 10,000 events are shown to avoid over-saturation of dots); iii. 5% probability density plot; iv. 5% probability contour plot. iiiiiiiv

8 Gating (1) Isolates a subset of cells on a plot In order to look at parameters specific to only that subset Boolean logic can be used to include or exclude multiple gates Approximate location of lymphocytes selected A single-color histogram of CD3 expression in cells from the selected region (note the cells to the left that stain weakly with CD3) The same population with data for both CD3 and CD4 fluorescence displayed (note better display of the CD3 low expressing cells)

9 Gating (2) Gating strategy to define lymphocyte subsets. (i) Histogram (univariate) plot of CD3 expression. (ii) Pseudocolor plot of CD3 versus CD4. Note that (1) both axes are logarithmic, unlike the linear axes of scatter plots (i); and (2) this display gives a better view on the distribution of CD3 expression than the histogram, in particular for ** CD3 low expressing cells. (iii) Cartoon detailing interpretation of quadrant gates of (ii). CD3+CD4+ cells are displayed in the top right quadrant (46.2% of lymphocytes). Interpretation of quadrants ** Histogram (univariate) plot of CD3 expression Pseudocolor plot of CD3 versus CD4

10 Intracellular Cytokine Staining to Identify Cell Subsets Kagami et al.(JID 130: , 2010) reported an increased frequency of Th17, Th22, and Th1 cells in untreated psoriasis patients. In this figure the dot plots are gated on CD4+ lymphocytes (i.e. only CD4+ cells are being analyzed) that have been stimulated for 6 hours with PMA, followed by staining for IL-17A, IFN- γ, and IL-22. Of note, most cells producing IL-22 do not produce IL-17A or IFN-γ at the same time: In the middle dot plot, a total of 3.2% of cells are positive for IL-22 staining (2.33% cells produce only IL- 22 and not IFN-γ while 0.87% produce both cytokines). Similarly, in the right dot plot a total of 3.27% of cells produce IL-22 (2.99% of cells are single positive for IL-22 and 1.25% are double positive for IL-22 and IL-17A). A similar observation can be made of IL-17A-producing cells.

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