Data analysis in flow cytometry

Here we cover signal measurement, visualizing multi-color flow cytometry data and gating strategies to quantify your data.

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

Flow cytometry application guide

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Signal measurement 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.

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.

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 one-parameter histogram plotting channel number versus the number of events. The channels are usually viewed on a log scale on the X-axis. 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.

Figure 23. A one-parameter histogram plotting channel number versus the number of events. The channels are usually viewed on a log scale on the X-axis. 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).

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

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.

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). 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.

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

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

Gating strategies to quantify your data

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).

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.

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.

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

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).

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

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)