Flow cytometry interpretation and data analysis are crucial to getting the correct answers from your experiments.¹

Signal measurement in flow cytometry

Flow cytometry measures multiple parameters such as fluorescence intensity, forward scatter, and side scatter to analyze different cell populations. These parameters allow for the identification and characterization of specific cell populations and different cell types within a heterogeneous mixture.

As the fluorescing cell passes through the fluid stream and the laser beam during flow cytometry, emitted light is detected and analyzed. The photomultiplier tube (PMT) detects this emitted light and converts it to a voltage pulse, known as a distinct event, with each distinct event corresponding to a single cell or particle passing through the laser (Figure 22).

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

Figure 22. The PMT measures the pulse area of the voltage created each time a fluorescing cell releases photons as it passes through the detectors.

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).

Figure 23. A single parameter histogram typically represents the distribution of signal intensity for one measured characteristic, plotting channel number versus the number of events. The channels are usually viewed on a log scale on the X-axis, while the y axis shows the number of events. Each event is given a channel number depending on its measured intensity; the more intense the fluorescence, the higher the channel number the event is assigned.

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). Histograms, dot plots, scatter plots, density plots, and other graphical representations are commonly used to visualize flow cytometry data, allowing for the analysis of multiple parameters and the distinction of different cell populations.

Figure 24. Fluorescence intensity measurements for a negative (left) and positive (right) result.

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

Figure 25. Anti-CCR2 antibody (ab21667) staining of human PBMC gated on monocytes. Data is from an anonymous review.

Visualizing multicolor flow cytometry data

There are several ways to visualize and analyze multicolor flow cytometry data (Figure 26). Flow cytometry data is typically represented using graphical representations such as histograms, scatter plots, and density plots to visualize multiple parameters and facilitate the identification of different cell populations. No one way is inherently right or wrong, so it’s essential to determine which approach best represents your data².

Histograms display a single parameter, with the x axis showing signal intensity and the y axis representing the number of events. They 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, scatter plots, and density plots are used to analyze and distinguish different cell populations and specific cell populations based on multiple parameters. 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. Interest based gating strategies can be applied to focus on specific cell populations of interest.

Converting dot plots to pseudocolor or using other graphical representations such as contour plots and density plots can help in analyzing expression patterns across different cell types. Converting to contour can help highlight smaller cell populations that did not appear significant in dot plot form.

Analyzing and comparing these graphical representations allows for the identification of different cell populations and the analysis of marker expression. Cells based analysis strategies utilize these plots to distinguish and analyze specific cell populations.

Figure 26. Examples of different ways of visualizing flow cytometry data.

Gating strategies to quantify your data

Gating strategies can be interest based, focusing on specific cell populations or characteristics of interest within a heterogeneous sample.

Drawing gates allows 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). Each distinct event in the data represents a single cell that can be analyzed to identify specific cell populations. Gating methods enable the analysis of different cell populations and different cell types based on marker expression. After using gates, subsequent analysis and statistical analysis are performed to validate and interpret the results.

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).

Figure 27. Examples of gating strategies. a) Gating on a peak when viewing data as a histogram. b) Gating when viewing data as a dot plot.

Calculating percentages from gated cell populations

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.

The calculated percentages can be used to analyze and compare different cell populations and specific cell populations that have been identified through gating. Subsequent analysis and statistical analysis are performed on the quantified data to ensure accurate interpretation, allowing the identified populations to be thoroughly analyzed for meaningful biological insights.

Figure 28. Example of calculating percentages of the total population after drilling down into a gated population.

Eliminating dead cells and doublets

Dead cells and doublets can be removed from flow cytometry analysis using various gating strategies (Figure 29). Side scatter and forward scatter are used to distinguish dead cells and doublets from viable cells based on their light scatter properties. Plotting forward and side scatter allows for the identification of different cell populations and specific cell populations during the gating process. Each distinct event is analyzed to ensure only viable, single cells are included in the final analysis. Cells based methods are commonly used to remove unwanted events and improve data quality.

Figure 29. Examples of gating strategies to eliminate dead cells and doublets from flow cytometry analysis.

References

1. Herzenberg, L.A.,, Tung, J.,, Moore, W.A.,, et al. Interpreting flow cytometry data: a guide for the perplexed Nature Immunology 7 (7),681-685 (2006)