4 Gating Controls Your Flow Cytometry Experiment Needs To Improve Reproducibility

Written by Tim Bushnell, PhD

To reproduce reliably in flow cytometry, one must control the gate.

The identification of the target cells of an experiment is the critical first step to performing the secondary analysis that will be used to judge the biological hypothesis and is done by peeling away the layers of cells that do not meet the criteria.

This involves the data reduction method of 'gating' with the researcher as gatekeeper, controlling what may pass and what shall not pass, based on the controls designed for the specific experiment.

It is disappointing to realize that in the paper, Maecker et al., the authors evaluated different models for conducting clinical trials and found that individual labs experienced a ~20% CV in the data analysis whereas a central lab showed only a ~4% variance in data analysis.

One of the best ways to improve gating is to ensure the most appropriate controls are identified and collected in the experiment.

How these controls are used to identify the population of interest is also critical to improving this process. There are 4 common gating controls that can be used for improving gating consistency and reproducibility:

1. Fluorescence Minus One (FMO control).

The term Fluorescence Minus One (FMO) was first introduced in this Cytometry paper in 2001. The FMO control is designed to identify the effects of spectral overlap of fluorochromes into the channel of interest.

This overlap can reduce the sensitivity of measurement in the channel of interest and make identifying the true positive population difficult. The FMO control is performed by staining the cells of interest with all fluorochromes except one. When the data is displayed, the spread of the data in the channel of interest becomes apparent, as shown in the figure below.

Here, human PBMCs were stained with FITC, PE, CY5.5 PE and APC. The left panel shows the unstained sample and the right panel, the fully stained sample. The middle panel shows the PE FMO control.

If the unstained control was used to set positivity, as shown by the red line, it would appear all the cells would be PE positive. However, when the same cells are viewed in the context of the FMO control, it becomes clear that there is spread of the signal, and based on the blue FMO bound line, it is clear these cells are not PE positive.

The FMO control is a valuable control and should be run with all combinations during panel development. Through this development cycle, the researcher will be able to identify the critical FMO controls that are necessary for proper gate placement.

The FMO control is especially essential when attempting to measure rare events, identify emergent markers, or where there is a continuum of expression.

2. Internal Negative Controls (INCs).

Internal Negative Controls (INCs) are those cells in the staining sample that do not express the marker of interest. Unlike the FMO control, where one reagent is left out, the INC is exposed to all the markers, but biologically does not express the marker of interest.

In this case, the INC can help identify and address proper gating when there is non-specific binding of the antibody. This control takes advantage of the fact that we know a bit of the biology of the system and do not expect that the INC cells will bind with the target marker. This, of course, needs to be confirmed in the literature and through experimentation, but leads to a powerful control for proper gate placement. 

In this figure, the data on the left comes from the identified INC cells. They are plotted against CD4+, which is our population of interest.

To help set the gate, a quadrant marker can be used to help track the boundary of the INC. As can be seen, the target cells are clearly positive for the marker of interest, and the INC helps ensure we have identified the correct gate.

3. Unstimulated control.

A third control, useful for stimulation experiments, is the unstimulated control that Maecker and Trotter discuss in their paper from 2006.

The unstimulated control again relies on the biology of the system to assist in setting the proper gate. The unstimulated control also takes into account the background binding of the target antibody, since the unstimulated cells should not be expressing the target. 

As shown in this figure, there is some background binding of the Activation Maker target on the un-stimulated cells. The FMO (left panel) is used to correct for issues of spectral spreading into the Activation Maker channel, but alone does not allow the proper gate placement. It is only when the FMO is combined with the unstimulated control that the best gate placement identified.

4. Isotype control.

The final control to consider is the isotype control. The concept is that one stains cells with an irrelevant antibody that has the same isotype as the target antibody and labeled with the same fluorochrome. This is supposed to allow for identification of the background binding caused by the specific antibody isotype.

The use of this control remains controversial.  

Several papers, such as this one from Keeney et al., call into question the use of isotype controls for setting gates. Maecker and Trotter caution on reliance of the isotype control, and show an excellent figure (Figure 2) where PE-labeled isotype controls show wide variability of staining on small lymphocytes.

When using an isotype control, one makes several assumptions:

1. That the affinity of the variable region on the isotype has similar characteristics for secondary targets as the target antibody.


2. There are no primary targets for the isotype Ab to bind to (and do you know what the primary target is for the isotype?).


3. The fluorochrome to protein (F/P) ratio is the same (and how do you titrate an isotype control?)

We cannot easily know the answer to #1 or #2 and must trust the vendor that the Ab target will not bind to the cells of interest.

Other than with large fluorochromes (PE, APC, etc.), where the F/P is usually 1:1 (due to the size of these fluorochromes), antibodies can have very dramatic optimal F/P ratios for FITC and the Alexa dyes (for example), that have to be optimized out during labelling.

This information therefore has to be collected by the vendor during QC and provided to the customer, something not always readily available on websites. 

The isotype control becomes another variable to be tested, validated, and optimized for marginal gain as a gating control. As Maecker and Trotter state,

“…It is thus a hit-or-miss prospect to find an isotype control that truly matches the background staining of a particular test antibody. And, remembering that we are using the isotype control to help us define the true level of background staining, this becomes a circular proposition…”

Where isotype controls can assist researchers is in assessing the success of the blocking of the cells.  In this case, if the cells are poorly blocked, the isotype control can reveal that, but should not be used to set gates.

In the continuing efforts to ensure consistent and reproducible data, the proper use of controls to establish the boundaries of gates is critical. With the exception of the isotype control, each of the controls discussed above serve a specific role in that process, and should be part of every experiment. This will help reduce the variability in the data in a given experiment, and when the use is communicated (or demonstrated) in publications, it will assist researchers seeking to reproduce the data in achieving similar results, while helping to reduce data analysis variability between institutions.

To learn more about getting your flow cytometry data published and to get access to all of our advanced materials including 20 training videos, presentations, workbooks, and private group membership, get on the Flow Cytometry Mastery Class wait list.

How To Improve Reproducibility Through The Automated Analysis Of Flow Cytometry Data

Written by Ryan Brinkman, Ph.D.

Editor's Note:  Reproducibility continues to be a critical area that all researchers need to be aware of. From the NIH's focus on reproducibility in grant applications, to a renewed focus by reviewers on the way data has been analyzed and presented, it is imperative that researchers keep up on best practices to ensure they pass these hurdles. 

One area that flow cytometry researchers should be focusing on is the emerging changes in the area of automated data analysis. Over the last five years there have been dramatic changes and improvements in these programs and workflows. As Dr. Brinkman discusses below, the automated analysis of flow cytometry data is coming into its own. 

Flow cytometry (FCM) datasets that are currently being generated will be two orders of magnitude larger than any that exist today, and new instruments, both flow and mass cytometry, have increased the number of parameters measured for each single cell by 50% (to 30).

Even in 14 dimensional datasets there are 16,384 possible cell populations of interest pre-sample (1). The information contained within large and complex single cell datasets can only be realized with approaches to effectively curate, integrate, analyze, interpret, and share these datasets.

What Is Reproducibility And Automated Analysis?

While there are many steps in the analysis pipeline that can benefit from automated approaches for which approaches have been developed (Figure 1), a major bottleneck in the analysis of flow cytometry data is in the identification of cell populations.

Manual analytical techniques lack the capacity and rigour to bring out the full potential of signals latent in the data (1, 2) and its subjectivity has been identified to be the primary source of variation between analytic results (3, 4).

Figure 1: Typical flow cytometry automated analysis workflows.

Analysis usually starts with several pre-processing steps (blue boxes) followed by identification of cell populations of interest (orange boxes) and visualization.

To address this problem, the computational cytometry community has developed a collection of widely used approaches for the high throughput analysis of FCM and Mass Cytometry (CyTOF) (5). Methods have been extensively evaluated against manual analysis through the Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) project (6,7,8) and have been found to meet and in many cases exceed the performance of manual analysis.

Only by taking advantage of cutting-edge computational abilities will we be able to realize the full potential of data sets now being generated and be able to keep up with the quick rate of progress and advancement in our fields.

Further Reading (References hyperlinked above)…

1. Aghaeepour N, Chattopadhyay PK, Ganesan A, O'Neill K, Zare H, Jalali A, … Brinkman, RR. Early immunologic correlates of HIV protection can be identified from computational analysis of complex multivariate T-cell flow cytometry assays. Bioinformatics 2012, 28(7):1009-16.


2. O'Neill K, Aghaeepour N, Špidlen J, Brinkman RR. Flow cytometry bioinformatics. PLoS Comput Biol 2013. 9(12):e1003365.


3. Maecker H, Rinfret A, D'Souza P, Darden J, Roig E, Landry C, … Sekaly R. Standardization of cytokine flow cytometry assays. BMC Immunol 2005. 6:13.


4. Qiu P, Simonds E, Bendall S, Gibbs KJ, Bruggner R, Linderman M, … Plevritis S. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat Biotechnol 2011. 29:886-91.


5. Kvistborg P, Gouttefangeas C, Aghaeepour N, Cazaly A, Chattopadhyay PK, Chan C, … Maurer D. Thinking Outside the Gate: Single-Cell Assessments in Multiple Dimensions. Immunity 2015. 42(4):591-92.


6. Aghaeepour N, Chattopadhyay P, Chikina M, Dhaene T, Van Gassen S, Kursa M, …, Brinkman RR. A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes. Cytometry 2016, 89(1):16-21.


7. Aghaeepour N, Finak G, TheFlowCAPConsortium, TheDREAMConsortium, Hoos H, Mosmann T, … Scheuermann RH. Critical assessment of automated flow cytometry data analysis techniques. Nature Methods 2013. 10(3):228-238.


8. Finak G, Langweiler M, Jaimes M, Malek M, Taghiyar J, Korin Y, … McCoy J. Standardizing Flow Cytometry Immunophenotyping Analysis from the Human ImmunoPhenotyping Consortium. Scientific Reports 2016. 6:20686.

As mentioned above, FCM datasets will soon be two orders of magnitude larger than those that exist today. As such, researchers must keep up on best practices for data reproducibility, especially in the area of automated data analysis. This will ensure that the field of flow cytometry and scientific research overall maintains its integrity while continuing to advance rapidly.

To learn more about how to improve reproducibility through automated analysis, and to get access to all of our advanced materials including 20 training videos, presentations, workbooks, and private group membership, get on the Flow Cytometry Mastery Class wait list.