4 Steps To Validate Flow Cytometry Antibodies And Improve Reproducibility
Written by Tim Bushnell, PhD
Reproducibility is the name of the game in science.
For scientific results to be valuable, they must be reproducible. The current crisis in scientific reproducibility has been well-highlighted and has spurred the NIH to initiate a reproducibility and rigor initiative for grant applications.
There is significant concern about the quality and consistency of antibodies in scientific research.
Bradbury & Plückthun published a letter in Nature, signed by over 100 researchers, that called for standardizing antibodies by moving away from reliance on traditional monoclonal and polyclonal production towards generation of recombinant antibodies.
They estimate that $350 million a year is wasted on bad antibodies in the US alone. They go on to estimate that to produce recombinant binding reagents to 20,000 human genes would cost about $1 billion — three years worth of wasted antibodies.
Until such robust reagents are available, it is the responsibility of the researcher to be vigilant in their use of antibodies.
This is especially critical in the realm of flow cytometry. Until we have validated recombinant antibodies, there are a few steps that can be integrated into the research workflow to ensure that the traditionally produced reagents are working as intended.
1. Always titrate.
The FAb fragment of an antibody binds with high affinity to the epitope against which the antibody was raised. However, at high concentrations, the intended targets become saturated and the antibody will bind to low affinity targets.
This increases the background fluorescence measured by the flow cytometer.
To avoid this off-target binding, it is paramount to titrate antibodies before using them in experimental work.
Titration should be carried out under the same conditions that the experiments will be performed, with the only variable being the concentration of antibody.
Starting with the vendor-recommended concentration, I recommend an 8-point serial-dilution curve for titration of reagents that have not been used previously. It is good to include at least one point near the recommended concentration.
Interpreting titration data can be performed in several ways.
Fig. 1: Histogram view of titration data showing positive and negative populations.
The first approach is to display histograms and estimate the best separation between the positives and negatives by eye (see Figure 1 for an example). This can be useful for well-separated populations, but it is not always clear for dim or rare populations.
To best examine the data, it is recommended that one extract the median fluorescent intensities and use a standardized metric for comparison.
Fig. 2: Concatenated data (left) and plot of three different standardized metrics (right) for sample titration data.
As can be seen in Figure 2, each of these three methods results in the same conclusion for the ideal concentration.
The Staining Index and Separation Index take into account the spread of the data of the negative population, which can be seen in the concatenated file above. This spread represents non-specific binding of the reagent to the negative cells at high concentration.
No matter which way you choose to analyze the titration, knowing the best concentration at which to use your antibody is essential for generating high-quality data.
2. Validate specificity.
The specificity of each reagent used should be validated. While it is assumed that the vendor is doing this, knowing how to validate the reagent in your system is important.
In the excellent article on controls by Hulspas and co-workers, even the same clone from different vendors can perform differently. As shown below in Figure 3, two PE labeled antibodies against human CD34 (clone 581) from different vendors exhibited different characteristics.
Fig. 3: Figure 5 from Hulspas et al. (2009) Cytometry B 76:355-64. True staining is shown by the red circle on the bottom bivariant plot. In both the bivariants and the histograms, there is significant non-target binding from one vendor’s product.
Inspecting cells by microscopy after labeling, or developing positive and negative target lines using techniques like CRISPR and siRNA, can further assist in providing the researcher with confidence in the specificity of antibody binding.
In addition to running the samples required to evaluate binding properties, it is also critical to have the correct controls for interpreting the results.
3. Be wary of isotypes.
Isotype controls continue to be used as a means to “identify” the background binding of a given antibody isotype.
Though this topic has been covered in detail, it bears repeating that in the effort to improve reproducibility, relying on the isotype control to determine background binding is not a robust method.
Andersen and co-workers recently published an excellent paper regarding reducing Fc receptor mediated binding on monocytes and macrophages while also showing the issues related to reliance on isotype controls (see Figure 4).
Figure 4: In the absence of blocking, the isotype control (IgG1) binds at a much higher level than the specific binding for Tie2, a protein known to be expressed at low levels on monocytes. From Andersen et al. (2016) Cytometry A 89:1001-1009.
Maybe it is, as Keeney and co-authors put it, “time to let go” of the isotype control.
4. Integrate critical controls.
Using every control possible — especially during panel development — is essential to establishing that protocols and reagents are working well.
Fluorescent labeling can impact antibody binding and render a good antibody bad. Likewise, some cells can bind the fluorochrome directly.
The use of the isoclonic control can help ensure that binding is specific, and not due to interactions with the fluorochrome.
To use this control, incubate cells with fluorescent conjugate and increasing amounts of unconjugated antibody. Specific binding is established when a decrease in fluorescence is observed (see Figure 5 for an example of an isoclonic control experiment).
Figure 5: As increasing amounts of unlabeled antibody are added to the staining mixture, the intensity of the CD19 population decreases, indicating that antibody binding is mediated through the antibody, and not due to the fluorochrome.
If fluorescence is not reduced in the presence of unconjugated antibody, a different fluorochrome should be considered.
Another useful control is the internal negative control.
Internal negative control cells are usually identified during the panel development and validation phase, and represent a population of cells that are known to not express the target protein.
Thus, a single stained sample can reveal non-specific binding issues with the target antibody, based on the biology of the cell populations within it.
A lot has been written about using the Fluorescence Minus One (FMO) control to identify true negatives based on the spectral spillover into the channel of interest (see Figure 6 for an example).
The FMO control ensures that contribution to the spread of the fluorescence signal in the target channel by the other fluorochromes is accounted for during analysis.
Figure 6: Using the unstained control to determine positivity, as shown by the red line, would cause all cells to appear PE positive. Using the blue FMO line to bound the data, it is clear the cells in question are not PE positive.
Until we have access to well-validated recombinant antibodies produced under tightly regulated conditions, researchers need to exercise good judgment regarding these critical biological reagents. These 4 steps outlined above will help ensure that your results are consistent and reproducible. This will both reassure your reviewers that your data is of high quality, and allow for researchers at other institutions to successfully replicate your results. In addition, identifying antibody duds early on will save you time and money in the long run. Don’t shirk the work of ensuring your antibodies are working correctly and targeting the right proteins.
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