Avoid Data Loss By Following These Steps To Set Your Flow Cytometry Gates Correctly

How are you defining your gates?

When you do primary analysis or single tube analysis, it’s important that you make sure your gates are set correctly.

You need to know that you can find the populations that you’re interested in so you can extract the appropriate data.

This is what allows you to do your secondary or statistical analysis confidently.

What Gates Should You Be Setting Up In Your Flow Experiments

At the beginning of the experimental design process, it is a good idea to sketch out a hypothetical gating strategy. This sketch will help evaluate the panel and ensure that those markers that need good resolution are visualized with brighter fluorochromes.

In any gating strategy, gates to address machine and processing issues should be used to exclude those events that could confound the analysis. These gates include a flow stability gate, a doublet discrimination gate, a ‘schmutz’ gate and finally a gate to eliminate dead cells and if a dump channel is in the panel, this is where it can be included.

Figure 1: Gating Strategy showing the primary gates (top) and secondary gates (bottom), along with FMO controls (smaller plots bottom right).

Once these gates have been used to clean up the data, gating using the antibodies comes next. The first couple of gates are typically the major lineage markers (e.g. CD3, CD20, etc), followed by the subsetting markers, which are used to identify the population that the experiment has been built around.

The flow stability gate is going to help you identify where there were issues with the flow rate. But if you don’t have good flow at the beginning, this data may impact data interpretation. Sometimes, at the very end of your run you’ll get events that are falling off because the tube is dry, so you want to eliminate those events as well. This is especially important if you have discontinuity or issues during the run because you had microclogging. With advances in analytical packages written in R and other languages, this process has been automated. One of these programs is called FlowAI. In the figure below, the raw data is shown on top. After FlowAI is run, two gates ‘FlowAIBadEvents’ and FlowAIGoodEvents’ are generated and downstream analysis can continue.

Figure 2: Automatic removal of events that are identified as ‘anomalies’

From this flow stability gate, pulse geometry can be used to remove doublets. As a reminder, when a photon is detected, an electronic pulse is generated. This has three characteristics – a pulse height, a pulse width (or time of flight) and the pulse area – the integral of height and width. If two cells stick together, the pulse width will increase, so the pulse area will increase without a proportional increase in height.

Figure 3: The electronic pulse of a single (top) and a doublet (bottom) showing how the different parameters would change.

The third gate here is called this the forward scatter gate. You can use this gate to get rid of small pinocytotic cells that are side scatter complex and forward scatter small. This gate is also used to get rid of the debris in the lower left-hand corner, as well as events that are off the scale. You should use the forward scatter gate as a cleanup gate and let the antibodies do the heavy lifting.

The last gate is the viability and dump channel gate.

Using , these four gates, 9flow stability gate, a doublet discrimination gate, a ‘schmutz’ gate and a gate to eliminate dead cells or a dump channel) will get you to the point where you can then start doing additional analysis and use antibodies to identify populations.

Remember gating is a data reduction tool so a good rule of thumb is to be generous with each gate in the initial analysis. Tighten these gates up based on the consequences and results of the impact on your target populations and based on the appropriate controls.

Start with major subset identifiers, and then get more and more specific until you meet the population of interest. Then you can take that population on for your secondary analysis.

Once you have chosen the types of gates you are using you need to determine how you will define those gates. Here are 3 things to remember when defining your gates…

1. Don’t use an isotype control.

Historically, researchers used a tool called the isotype control to define positive from negative. The theory for using isotype controls was that an antibody with the same isotype as my antibody of interest will reveal the non-specific binding of my target antibody.

A useful control should be one that changes a single variable. In the case of an isotype control, there are some large assumptions that are made which invalidate the utility of this reagent as a useful control.

1. That the isotype control has the same affinity for off-targets as the experimental antibody.
2. There are no primary targets for the isotype control
3. That the fluorochrome to protein ratio is the same for the isotype control and target antibody.

These are some pretty big assumptions. Take for example the mouse IgG2a, 𝜅 isotype control clone MOPC-173. This was first described in the 1970’s, but no target was identified. Vendors selling this reagent generally state that it has been tested against major lineage subsets under various conditions. Does this testing resolve assumption A and B?

Looking at the F/P ratio, for some reagents (PE-labeled, for example) steric hindrance prevents more than one label per antigen. However, for smaller molecules, different antibodies can have different optimal F/P ratios. This figure, from ThermoFisher’s website, shows this issue clearly. This graph plots the F/P ratio of two different molecules (FITC and AlexaFluor 488) and the impact on fluorescene. Notice that the FITC tops out at ratio of 6 for this molecule, whereas Alexa488 keeps getting brighter. Thus, the brightness of your reagent is tied to the F/P ratio. If the F/P on the target antigen is lower than for the isotype control, the observed signal for the isotype could be higer for this reason, not due to more binding.

Figure 4: Impact of F/P ratio on fluorescence signal.

Reviewing the literature in Maecker and Trotter’s paper from 2006 they show the binding of three different Isotype controls (PE-labeled) compared to unstained cells that are gated on ‘small lymphocytes’. Notice the differences in background staining.

Figure 5: Isotype control staining from Figure 2 of Maecker and Trotter (2006). The red line is set at 10², based on the peak of the unstained control (added for comparison).

The authors conclude that “…it is a hit-or-miss prospect to find an isotype control that truly matches the background staining of a particular test antibody…”

In a paper by Andersen and colleagues (2016), this point was further driven home. These researchers were looking to optimize the blocking of the cells they study. In figure one from this paper, the authors show the staining anti-Tie2, which is known to be expressed at low levels on the target cells. An isotype control was included in the analysis and based on that isotype control staining, the cells would be considered Tie-2 negative. The authors state that the cells were stained with antibodies that were “…same isotype, fluorescence conjugation, and manufacturer, at the same staining concentration (2 μg/mL)...”

Figure 6: Erroneous isotype control staining

The authors conclude their paper stating “…Due to the unpredictable nature of isotype controls, we recommend not to use such controls for determining background signal, or for gating in flow cytometry. Instead, nonspecific staining should be alleviated by use of a blocking reagent….”

The only valid use of an isotype control is to demonstrate that the samples were blocked sufficiently. It should not be used for setting positivity.

2. Use a fluorescence minus one control.

The fluorescence minus one control (FMO) is a control where cells are labeled with all the staining reagents except one. This empty channel can be used to determine the impact how the sensitivity of the channel is impacted by the other flourochromes in the experiment. An example of a PE FMO control is shown below.

Figure 7: PE FMO Control

The red line represents where the negative/positive line would be if the unstained cells were used to determine this. However, in the middle panel, which has not been stained with PE, there are cells above this line. These cells cannot be positive for PE, since there is none in the stain buffer. Thus, the correct boundary is the blue line. The black arrow shows the spread of the data caused by spillover from the dyes in other channels into the PE channel.

The FMO control is essential when the accurate determination of positive is critical. This can include rare events, emergent antigens, and dim markers. During the optimization phase of panel design, it is recommended to run all FMO controls to determine which controls are critical for identifying the target cells of interest. When the panel moves into validation, only those FMO controls that will be needed are used.

3. Use an unstimulated sample.

When performing a stimulation experiment, researchers can take advantage of the biology of the system to set positivity from negativity using an unstimulated control. This is demonstrated in the figure below, from the 2006 Maecker and Trotter paper, figure 3.

Figure 8: Using an unstimulated control to set positivity.

The blue line represents where the isotype control positive/negative boundary would be set and the red line represents where the FMO control boundary would be set. Notice how the FMO would overestimate the responding cells and the isotype control would underestimate this value.

In this case, the unstimulated cell takes into account the spread of the data that the FMO reveals, and the non-specific binding that the isotype control ‘in theory’ would reveal. Having a biological control is an excellent addition to an experiment, and should be explored during the optimization phase of panel design.

One last recommendation to help set the gates. When gating on an unstimulated or FMO gate, consider using a cutoff percentage. This is where the researcher sets a gate such that there is no more than some percentage of events in the control gate. A good cutoff percentage is 0.1%, which is derived from the critical values of a normal distribution. Events in this region are greater than 3 times the standard deviation of the distribution away from the mean.

Gating is a critical process in data analysis. Identifying the correct populations to extract information for secondary analysis is essential for robust analysis and conclusions. Use the FMO and biological controls while avoiding the temptation to use an isotype control for setting positivity. Since gating is a data reduction technique, it is important that these gates are set correctly, and importantly explained in a publication where the data is used. Consider the MiFlowCyt standard as a way to communicate this information. If you do those things you can set your gates with confidence and have high quality downstream results.

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