Tools to Improve Your Panel Design – Determining Antigen Density
When a researcher chooses to use flow cytometry to answer a scientific question, they first have to build a polychromatic panel that will take advantage of the power of the technology and experimental design.
When we set up to use flow cytometry to answer a scientific question, we have to design a polychromatic panel that will allow us to identify the cells of interest – the target of the research. To identify these cells, we need to build a panel that takes advantage of the relative brightness of the fluorochromes, the expression level of the different proteins on the cell, and the performance of the instrument, among other things.
In its most basic form, the goal is to maximize the sensitivity of the measurement on the target cells. We achieve this by pairing brighter fluorochromes with lower expressed targets, while minimizing the loss of resolution in the target channels.
One of the first steps in the process is to rank the expression level of the targets on the cells from the major sub-setting markers (CD3, CD4, CD19, etc.) to the targets of our investigation. To determine the answer to these questions, we need to research the literature. Fortunately, there are several resources available to help with this work.
The first is the website Benchsci.com. This website uses AI to curate published papers, identifying targets, clones, cells, assays, and more. When you search for a given target, you are able to see the data where it was used, which allows you to make a more informed decision as to the utility of the target. Benchsci.com also introduced a tool that takes advantage of their AI to help select reagents for your research.
The second resource is a publication by Kalina and colleagues (2019). In this paper, the authors measured the expression levels of CD antigens from 1 to 100 on 47 immune cell subsets. They used PE-labeled antibodies to calculate expression levels. All the data are accessible at www.hcdm.com. Example data, providing an overview of the expression levels are shown in Figure 1.
The third resource is a paper by Amir and colleagues (2019). They used mass cytometry to do the same thing that Kalina and colleagues did using fluorescent tags. In this paper, the authors screened 350 antibodies and used the automated analysis platform Astrolabe. The data, which will allow the researcher to explore their target antigens, are available at this site.
Previously, determining the expression level of different antigens was a combination of guesswork and detective work. You had to read multiple journals to get an idea of what others had published, this only got more complex and time consuming when antigens started to have multiple clones. Enter automation and Artificial Intelligence in the guise of Benchsci.com. This tool is an excellent resource to help researchers identify the proper clone to use for their work.
Additionally, two groups used different approaches to provide the research community with the tools to determine the antigen density on different cellular subsets. Both have made their data publicly available so that the research community can take full advantage of these resources.
Thus, the first step of panel design has gone from frustrating and annoying to a simple matter of turning your web browser to three sites to get all the information you need. In the next blog on panel design, we’ll discuss the similarities and differences between panel design for traditional fluorescent flow cytometry and spectral cytometry, with a dash of mass cytometry thrown in for good measure.