Important Controls For Your Flow Cytometry Lab
No researcher wants to discover that the results of a long, careful experiment are confounded by an uncontrolled variable. To assist in data interpretation, you must build careful controls into your experimental workflow. These controls minimize the effects of confounding variables in the experiment while helping to identify the changes related to the independent variable. When designing a flow cytometry experiment, what controls should you consider? Below are a few experimental controls that can dramatically enhance reproducibility in your flow cytometry experiments.
Addressing fluorescence compensation is not possible without proper controls, so it is critical to prepare controls of high quality. Compensation is a consequence of spectral overlap, which occurs because the emission spectra of fluorochromes is broader than the standard filters used to measure the specific (or major) emission. This leads to the detection of the fluorochrome in secondary channels.
The more colors measured within a single experiment, the more crowded the spectrum becomes, and this affects the sensitivity of your measurements. Compensation is a mathematical process that deals with this problem by removing a percentage of the total signal from each detector.
This percentage corresponds to the amount of spillover signal that is contributed to any given detector by all the other fluorophores being used in the experiment. The compensation calculation relies on the fundamental concept that the amount of spillover of a fluorophore (e.g. fluorophore A) into a detector (e.g. fluorophore B) is defined by the ratio of A’s signal in B’s detector to A’s signal in its own detector. This relationship between two detectors, called spillover coefficient, defines the spillover regardless of the amount of dye, so it can be used correctly for the spectral overlap in a typical experimental setting in which the amounts of dye can vary widely between and within samples.
Fluorescence Minus One
Experimental samples are meaningless if you can’t easily establish the positive from negative population While this may be easy for some major phenotyping markers, it becomes more complex if you are analyzing rare events or determining the positivity of emergent targets, such as activation markers.
How can you convince reviewers that you didn’t make an error and placed your gate in the proper place? How are you going to account for the data spread that occurs with compensation? In any multicolor flow cytometry experiment, the answer to your gating troubles is to use fluorescence minus one controls. FMO controls are samples that contain all the antibodies you are testing in your experimental samples – minus one of them.
When analyzing the excluded parameter in an FMO control, you give yourself a strong negative control to work with. It’s a strong negative control because the left out marker in the FMO control allows you to take into account how the other stains in your panel affect the left out parameter. FMO controls are required for accurately discriminating positive versus negative signals, high versus low (or variable) antigen expression levels, and more. Even simple 2- or 3-color experiments reveal the need for FMO controls when drawing gates.
Reagent controls – titration and isoclonal.
Reagent control methods include titration and isoclonal controls. In your experiments, it is important to validate the amount of antibody used for staining. If too much antibody is used, there will be an increase in non-specific binding, which reduces sensitivity. Yet too little antibody, and the cells will not be saturated – this too results 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.
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, as shown below.
Identifying the target cells of an experiment is key to evaluating the biological hypothesis. Populations of cells that do not meet established criteria must be removed. Using the data-reduction method known as “gating,” the researcher acts as a kind of gatekeeper, controlling what can pass based on the controls designed for a specific experiment. There are 3 major controls that qualify as gating methods, described here in summary: First, there is the fluorescence minus one control (FMO). As we already discussed, this control is designed to identify the effects of spectral overlap of fluorochromes into the channel of interest.
Second, there are internal negative controls (INC): 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, it does not express the marker of interest. The INC can help identify and address proper gating when there is non-specific binding of the antibody.
The use of the isotype control remains controversial and is not recommended as a gating control. Based on the assumptions that must be made for this control, it should be relied on to determine positivity.
The third option in gating is the unstimulated control, which is useful for stimulation experiments. This gating method relies on the biology of the system to assist in setting the proper gate. It takes into account the background binding of the target antibody since the unstimulated cells should not be expressing the target.
In conclusion, to improve reproducibility, you must examine and understand the many controls available to a researcher. Minimize the effects of confounding variables in your experiment by using appropriate control methods to identify changes caused by the independent variable and exercise any confounding variables that may attempt to haunt your experiment.
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