Scientists use flow cytometry to differentiate between different types of cells or microscopic organisms. It is a tool used in many applications such as medical diagnostics or forensic pathology. While this experimental technique is fairly easy to accomplish, the analysis of the complex data produced by the flow cytometer is more difficult due to the multiple experimental factors and/or cytometer parameters. As such, it is routine for cytometric data to be visualized and analyzed using sophisticated professional programs such as CELLQuest or FlowJo. Familiarity with flow cytometry techniques, machinery and software is necessary in order to understand the results produced by these experiments.
Understanding Flow Cytometry Data
Clarify the aim of the experiment by asking, "What was the question or hypothesis being investigated?" This will be required to adjust the raw results to the appropriate format and settings for further analysis using statistical cytometry software. Make whatever changes are necessary in order to have the data displayed with the relevant settings (e.g. positive cells, negative gates, fluorescence intensity, cell populations etc.).
Find gates. Cells can be grouped or simply observed clustered together on a density plot or contour diagram. The groups often separate depending on their identity. If one group stains very intensely for a particular marker or antibody, it is concluded that the members of that group all have the identity of the specific cell- type, which expresses that marker. It is common to find cells that are positive for more than one of these markers, and these cells are usually an intermediate and denoted as "double-positive."
Look at scattergraphs. The way the groups of cells spread out in a scatter plot is an indication of the size of the cells. Cells with very large or high scatters are typically large cells; however, they may be large simply because they contain a high proportion of cytoplasm, or they may be high because they have a very large nucleus. Depending on the biology being investigated, this will of course, vary widely between experiments.
Look at numbers. Adjust the plots to display different parameters on one axis (usually the X-axis) while keeping the counts on the Y-axis. This indicates the proportion of the sample population that are positive for that particular parameter, as a peak will normally be observed in a positively-stained sample, which will be absent from the negative control sample.
Look at multiple-parameter histograms. By adjusting the X-axis and the Y-axis to each represent a different parameter that was investigated during the experiment, it is possible to get a deeper understanding of the properties of the sample. For example, by setting the X-axis to red fluorescence and the Y-axis to green fluorescence, quadrant-style gates can be computed for the sample to show four regions of a quadrant in which cells are present and stained for either red or green fluorescence, both colors, or none at all. This allows a heterogeneous sample to be divided into its component parts and any overlapping entities to be visualized as well as quantified.