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Read this article to learn about the principles of flowcytometry data analysis.
Data Analysis:
Gates and Regions:
An important principle of flow cytometry data analysis is to selectively visualize the cells of interest while eliminating results from unwanted particles, e.g., dead cells and debris.
This procedure is called gating. Cells have traditionally been gated according to physical characteristics. For instance, subcellular debris and clumps can be distinguished from single cells by size, estimated by forward scatter.
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Also, dead cells have lower forward scatter and higher side scatter than living cells. Lysed whole blood cell analysis is the most common application of gating, and Fig. 15.10 depicts typical graphs for SSC versus FSC when using large cell numbers. The different physical properties of granulocytes, monocytes and lymphocytes allow them to be distinguished from each other and from cellular contaminants.
On the density plot, each dot or point represents an individual cell that has passed through the instrument. Yellow/green hotspots indicate large numbers of events resulting from discrete populations of cells. The colours give the graph a three-dimensional feel. After a little experience, discerning the various subtypes of blood cells is relatively straightforward.
Contour diagrams are an alternative way to demonstrate the same data. Joined lines represent similar numbers of cells. The graph takes on the appearance of a geographical survey map, which, in principle, closely resembles the density plot. It is a matter of preference but sometimes discrete populations of cells are easier to visualize on contour diagrams, e.g., compare monocytes in Fig.15.10.
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Newer gating strategies utilize fluorescence parameters along with scatter parameters. Once again, blood can be used to demonstrate this principle.
Above on the left is a FSC/SSC plot for human lysed whole blood using smaller numbers of cells than in Figure 15.10. The lymphocytes, monocytes and granulocytes have been gated as region 1 (Rl), region 2 (R2) and region 3 (R3), respectively. ‘Region’ simply refers to an area drawn on a plot displaying flow cytometry data.
On the right the same cells are now plotted as SSC on the y-axis versus CD45 fluorescence on the x-axis. CD45 is a marker expressed on all white blood cells at varying intensities but is absent on red blood cells. In relative terms, lymphocytes have a low SSC and high CD45 count (R4), granulocytes have a high SSC and low CD45 count (R6), while monocytes are somewhere in between the other two (R5).
The major difference between the lymphocytes gated in R1 and those gated in R4 is the absence of red blood cells in the latter, making it a much purer preparation. This highlights the usefulness of gating strategies that combine a scatter parameter with a fluorescence parameter.
Single-Parameter Histograms:
These are graphs that display a single measurement parameter (relative fluorescence or light scatter intensity) on the x-axis and the number of events (cell count) on the у-axis. The histogram in Fig.15.12 looks very basic but is useful for evaluating the total number of cells in a sample that possess the physical properties selected for or which express the marker of interest. Cells with the desired characteristics are known as the positive data set.
Ideally, flow cytometry will produce a single distinct peak that can be interpreted as the positive data set. However, in many situations, flow analysis is performed on a mixed population of cells resulting in several peaks on the histogram. In order to identify the positive data set, flow cytometry should be repeated in the presence of an appropriate negative iso-type control (see Fig. 15.13).
Analytical software packages that accompany flow cytometry instruments make measuring the % of positive-staining cells in histograms easy. For example, the F4/80 histogram is shown again below with statistics for R2 and R3 (known on this type of graph as ‘bar regions’).
In Fig. 15.14, 99.83% of the negative control (blue outline) is in R2. 28.14% of cells (red shade) ‘stain negative’ for F4/80 (R2) compared to 71.86% in the positive data set (R3). Additional statistics about the peaks (median and standard deviation) is also provided automatically here but this will vary with the software. A similar type of analysis will be generated for two parameter histograms.
Two-Parameter Histograms:
These are graphs that display two measurement parameters, one on the x-axis and one on the y-axis, and the cell count as a density (dot) plot or contour map. The parameters could be SSC, FSC or fluorescence. Another example is the dual-colour fluorescence histogram presented below. Lymphocytes were stained with anti-CD3 in the FITC channel (x-axis) and anti-HLA-DR in the PE channel (y-axis). CD3 and HLA-DR are markers for T cells and B cells, respectively.
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In Fig. 15.15, R2 encompasses the PE-labelled B cells note their positive shift along the PE axis. R5 contains the FITC-labelled T cells (positively shifted along the FITC axis). The top right quadrant contains a few activated T cells (about 4% in this sample) that possess some 11 LA DR expression also.
As these stain with both antibody markers they are grouped in their own region (R3). R4 contains cells negative for both FITC and PE no shift). Currently, flow cytometry can be performed on samples labelled with up to 17 fluorescence markers simultaneously. Therefore, a single experiment can yield a large set of data for analysis using various two- parameter histograms.
