Experimental Controls For Reproducible Flow Cytometry Measurements

With the increased focus on reproducibility of scientific data, it is important to look at how data is interpreted. To assist in data interpretation, the scientific method requires that controls are built into the experimental workflow. These controls are essential to minimize the effects of variables in the experiment so that changes caused by the independent variable can be properly elucidated. In fact, one of Begley’s 6 rules, as described by Bruce Booth, asks if the positive and negative controls were both shown.

What types of controls should be considered when designing a flow cytometry experiment?

Focus controls to minimize confounding variability. Sample processing, for example, can be controlled using a reference control. Where to properly set gates can be addressed using the FMO control. Controls for treatment can include Unstimulated and Stimulated controls. Reagent controls ensure that the reagents are working, and are at the correct concentration. Compensation controls are critical — these have been discussed in detail elsewhere. Of course, there are some controls that do not actually control for what they are used for, such as the isotype control.

1. Reference controls.

The purpose of a reference control is to determine if the process — from sample preparation through staining — has been performed consistently. It also allows for a reference range to be established that reflects the inherent variability in the preparation process.

Identifying a reference control is an important step in the panel design/validation process. This control should be readily accessible: for example, a large number of frozen PMBCs from a single source, or a defined mouse strain.

This sample must also reflect the expected staining pattern in sufficient detail to allow for verification that the antibodies properly labeled the targets.

When staining an experimental sample, the reference control is also stained.

If it behaves differently than you would expect on common plots, then there is likely a problem with the experiment and you need to troubleshoot.

It’s a great indicator of the health of your experiment. An example of this data is shown in Figure 1.

Figure 1: Tracking the results of staining a reference control.

This figure shows the results of 8 independent experiments, with the mean and SD shown. In the case of outliers, 2 examples shown by red arrows, it is critical to identify the root cause of the reason for the variation.

An added benefit of the reference control is that it can be used as a training tool for new users.

Since the expected range is known, having them stain the reference control helps them gain confidence in their technique.

Before the reference control is used up, it is critical to perform an overlap experiment. Run the new control 3-5 times in parallel with the old control to determine the differences between the old and new control ranges. Don’t forget to document!!!

2. Fluorescence minus one control.

A very important control for data interpretation is the fluorescence minus one, or the FMO, control. This is a gating control that is used to identify positive from negative. It is designed to reveal the spread of the data as it addresses the contribution of error measurements to the channel of interest from all the other fluorochromes in the panel.

As the name implies, cells are stained with all fluorochromes in the panel, except the one of interest. An example of this is shown below, in Figure 2.

Figure 2: FMO control in a 5-color panel to identify the proper placement of gates.

The red-dashed line represents the unstained boundary for the data. The middle panel represents the FMO control.

The staining above the red line implies that those cells are positive for the PE marker. However, since that tube doesn’t have PE, those cells cannot be positive.

The true boundary is shown by the blue line. The arrow on the far right panel shows the spread of the data — this is caused by the other fluorochromes in the panel spilling over into the channel of interest.

FMO controls are critical for setting gates, especially for rare events, emerging antigens, or any case where sensitivity is important to the measurement.

During the panel development phase, it’s good practice to run all possible FMO controls. From there, identify those controls that are essential for identifying the target cells, and run those with every panel.

3. Unstimulated control.

When performing a stimulation experiment, it is valuable to run both a stimulated and unstimulated control.

The stimulated control should be cells treated with a very powerful stimulant. This ensures that the cells can be stimulated, that the reagents are working, and it provides an upper limit for expected results.

The unstimulated control is also critical. In this case, the cells are not stimulated so that background signal can be identified. Shown here, is data from the 2006 Maecker and Trotter paper. This figure shows SEB stimulated cells, looking at CD4 expression on the y-axis and IL-2 production on the x-axis.

Figure 3: Controls for stimulation experiments. From Maecker and Trotter (2006) Figure 3.

The fully stained sample is shown at the top, and the FMO control is in the bottom middle and reveals the spectral contribution of the other fluorochromes in the panel to the PE channel. On the right is the isotype control, but more on this topic later.

The unstimulated sample, on the left, should have no IL-2 PE positivity.

Starting with the FMO control, and adjusting for the background staining of the IL-2 antibody using the unstimulated sample, allows for correct gate placement.

4. Isotype control.

The isotype control has been used in flow cytometry for many years. The theory behind this control is that non-specific binding of a given antibody isotype can be determined using an antibody of the same isotype as the antibody of interest, but to an irrelevant target.

For example, if your antibody of interest is a mouse IgG1, κ, clone MOPC-21 is an appropriate isotype control. The problem is that MOPC-21 has been around since the 1970s and the target is still unknown. This illustrates the assumptions that are made when using isotype controls

1. The isotype control has the same affinity and characteristics for secondary targets as the original target antibody does.
2. There are no primary targets for the isotype control to bind.
3. The fluorochrome-to-protein ratio is the same on the target antibody as it is on the isotype control.

Historically, isotypes have been used to set gates and determine positivity. However, since the answers to these 3 questions are unclear, it is not a true control, but rather another experimental variable.

Thus, the isotype control is not an effective or worthwhile control, and you are better off focusing on other controls.

In the 2006 Maecker and Trotter paper, the authors showed the following figure (Figure 4, left panel).

Figure 4: Isotype Control data.

The cells, “small lymphocytes”, were identified by scatter characteristics and the staining of 3 different isotype controls is shown. The red line is added for emphasis.

More recently, a paper by Andersen and colleagues attempted to identify the best methods for blocking their cells of interest. The first figure shows the results of staining for a known target (Tie-2) on the surface of the cells of interest. The corresponding isotype control staining is also shown and, based on that staining, the interpretation of the data would be that the cells do not express Tie-2, which is known to be false.

Expanding on this finding, the authors attempted to identify the best blocking strategy for their target cells. The results of this data are shown in Figure 5.

Figure 5: Results of testing different blocking reagents.

The authors compared the Median Fluorescence Intensity (MFI) of the unstained cells in the channel of interest in the absence of an isotype control to the MFI of the cells stained with the appropriate isotype control.

Different blocking strategies were performed and the cells stained with the isotype control. The results suggested that Human IgG was best for blocking, due to low cost and stability.

5. Reagent Controls

A. Titration

One important experimental control is to validate the amount of antibody being used for staining. If too much antibody is used, there will be an increase in non-specific binding, reducing sensitivity. Too little antibody, and the cells are not saturated — again, resulting in reduced sensitivity.

The best way to determine the optimal antibody concentration is to perform a titration experiment. In a titration experiment, you vary the amount of antibody used in staining, while holding other variables — incubation time, temperature, and cell concentration — constant. After acquiring the data, calculate the staining index for each concentration. An example of a titration experiment is shown below in Figure 6.

Figure 6: Example of antibody titration.

The plot on the right of concentration vs staining index shows that at low or high antibody concentrations, the SI decreases. The boxed region between is the optimal staining range. Splitting the difference between the 2 shoulders provides a best recommendation for the antibody concentration to use.

B. Isoclonal control

The isoclonal control was originally published to demonstrate that the cells of interest were not binding the fluorochrome on the antibodies, as has been shown for CD64. The isoclonal control is a great way to show that you have specific binding.

To perform the isoclonal control, mix unlabeled antibody of the same clone to compete with the binding of the original antibody. As shown in Figure 7, increasing the ratio of unlabeled antibody results in a decrease in staining.

Figure 7: Isoclonal control demonstrating specific binding.

In conclusion, getting into the mindset to improve the reproducibility of flow cytometry experiments requires a hard look at the appropriate controls to use in each experiment. These controls are essential tools for proper data interpretation, and should be referred to in any communication about the data and shown in supplemental figures at a minimum. Further, consider showing all the data by uploading it to the FlowRepository.

In the end, it is in everyone’s interest to provide the best data, with all the necessary information, to reproduce and expand the findings. As Isaac Newton said, “If I have seen further than others, it is by standing on the shoulders of giants.” That is how science makes progress.

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