Data analysis in flow cytometry

As the fluorescing cell passes through the laser beam during flow cytometry, it creates a peak or pulse of photon emission, which the PMT detects and converts to a voltage pulse, known as an event (Figure 22). 

The flow cytometer measures the total voltage height and area, with the area correlating directly to the intensity of fluorescence for that event. When no fluorescing cells pass through the optics, no photons are emitted, and therefore no signal is detected.

The pulse area is calculated by adding the height values for each time slice of the pulse as determined by the speed of the analog-to-digital converter (ADC), which is 10 MHz (ie 10 million per second, or 10 per microsecond).

These events are then assigned channels based on pulse intensity (pulse area). This signal can be amplified by turning up the voltage going through the PMT (Figure 23).

When plotted as a histogram, a negative result (no staining) will reveal many events at low fluorescence intensity. In contrast, a positive result gives many events at high fluorescence intensity (Figure 24).

For a positive result, there should be a shift in intensity between the negative control and a positive sample (Figure 25).

There are several ways to visualize and analyze multicolor flow cytometry data (Figure 26). No one way is inherently right or wrong, so it’s essential to determine which approach best represents your data.²

Histograms work best when most cells express a marker of interest, and the staining is bright. The mean fluorescence intensity (MFI) measures brightness and is a relative measure of antigen abundance.

Dot plots can enable you to delineate cell populations using only FS/SS for further analysis, depending on your sample. However, using two fluorescent markers will allow you to separate your population if more than one cell population in your sample has a similar FS/SS profile or shares markers with other cell types.

Converting dot plots to pseudocolor can help highlight where there might be more than one population in regions that are close together. However, converting to contour can help highlight smaller cell populations that did not appear significant in dot plot form.

Drawing gates allow you to quantify your populations. When viewing data as a histogram, gate on a peak to identify the percentage of cells that express a particular marker (Figure 27a). Several tools are available for gating data in dot plots. The quadrant is commonly used as a gate as it easily identifies single or double-positive populations. However, other tools, such as rectangular, elliptical, and polygon gates, are also available (Figure 27b).

A gate represents a subset of your total population. If you drill down on a population and gate within that, you will need to back-calculate your total population. In the example below (Figure 28), 30.1% of the total population are neutrophils, and 14.5% of neutrophils express IL-17a. However, 4.36% (30.1 x 0.145) of the total sample are IL-17a-expressing neutrophils.

Dead cells and doublets can be removed from flow cytometry analysis using various gating strategies (Figure 29).