Planning For Surface Staining Of Cells In Flow Cytometry

One of the most common assays in flow cytometry is the surface labeling of cells with antibodies. Often termed “immunophenotyping”, it allows the researcher to identify, count, and isolate cells of interest in a mix of input cells. Every lab has their own favorite protocol, handed down from some hallowed, chemical-stained notebook, and followed as exactly as making a souffle.

The real questions are, which of those steps are critical, and (with changes in instruments and theory) what other factors should be considered when staining cells? This article will focus on staining immune cells, but the principles apply in general, and specific issues for a specific sample type can be optimized in a similar way.

Cell Preparation

A protocol usually starts with a list of equipment that is needed. After that, the next important component is obtaining and preparing a sample. A good, single-cell suspension is essential for quality flow cytometry.

The source of your primary tissue will guide you down a path for processing. For liquid samples like blood, bone marrow, and spleen, it is pretty easy to make a single-cell suspension.

The trick for liquid suspensions comes in determining whether the red blood cells should be lysed, or otherwise removed. There are two schools of thought on this, the first being to remove the cells using a lysis process, or some centrifugation protocol. This has the advantage of removing the large excess of red blood cells, but also carries the potential risk of losing your target cells. This can be especially true if RBC lysis is performed.

The second school of thought is to use an antibody to identify the RBCs (Ter119 for murine cells, for example) and use that to generate a negative gate in your acquisition software.

The next consideration is which buffer recipe to use for staining. Phosphate-buffered saline is important, but to keep the cells happy, protein needs to be included.

The use of 0.5-1% bovine serum albumin (BSA) (fraction V) is encouraged over bovine serum (fetal or otherwise). The advantage of BSA is you know what is in your buffer, whereas FBS may have compounds that adversely affect your cells, and it must undergo more extensive testing.

In the case of tissue or solid tumor samples, the first place to look is the Worthington Tissue Dissociation guide. This web page is a go-to for those working with solid tissues. It is full of material on different organ types, the best ways to make single-cell suspensions, references, and more.

If the cells re-clump after preparation, one may try adding EDTA or small amounts of DNAse (10U per ml). These help eliminate clumping due to cell surface adhesion molecules and DNA released by dying cells, respectively. This is especially true of tissue/solid tumor samples.

In the last few years, the gentleMACS from Miltenyi Biotec has become popular with researchers seeking to make single-cell suspensions from tissue. BD Biosciences offers a similar product called the Medimachine.

Both machines operate on the same principles, in that the sample is introduced into a small tube that is inserted into a machine. These special tubes are like gentle mini-blenders, and mince the tissue very finely. The addition of appropriate enzymes helps to further reduce the tissue fragments to single cells.

Of course, you must still filter your samples to remove residual chunks from the mixture. There are many different filters on the market at various price points, so it is worth shopping around to find what will work best for you.

The crafty and thrifty researcher might even go to a local fabric store and obtain different samples of fabric to test for the ability to be used as an inexpensive filter. Small Parts, which is now part of Amazon, has sheets of 50 micron mesh for sale that, with appropriate application of scissors or rotary cutter, can be cut into filters for use in the lab. Many other sizes are also available for relatively reasonable prices.

There is no reason to not filter your samples and save everyone from the headaches of clogs.

Minimizing Off-Target Binding

The loss of sensitivity of a flow cytometry measurement can be attributed to several factors. While there is no perfect control to measure this loss of sensitivity, we can design protocols to reduce the impact.

Two such changes include using the proper concentration of antibody for staining, and the proper blocking of the cells to minimize off-target binding. In this context, off-target refers to anything that the antibody binds to that is not directly related to the primary target of the FAb fragment, so both low-affinity binding and Fc receptor mediated binding.

In the presence of excess antibody, antibody will bind low-affinity targets. This causes an increase in background fluorescence, resulting in reduced sensitivity for detection.

One way to mitigate this effect is to titrate your reagents. This process ensures that you identify the best concentration for staining (Figure 1). It is recommended that titrations be carried out in the presence of a viability dye, to reduce problems associated with non-specific uptake and binding by these membrane-compromised dead or dying cells.

Titration should be performed under the specific conditions of the assay. If the cells will be fixed, make sure they are fixed for your titration.

Figure 1: Titration of an antibody. A concatenated file of the data is shown on the left, and the signal intensity (the Staining Index) is calculated and plotted against the antibody concentration — the data is shown on the right.

The second cause of off-target binding is the Fc receptor (FcR). Evolved to perform a specific function when interacting with cells expressing the FcR (such as antigen-presenting cells), Fc binding is a specific process, but one that is detrimental to the assay.

There are several different ways to block cells, and in a recent paper by Andersen and coworkers, they sought to determine the best methods for blocking cells. Specifically, the researchers were attempting to optimize the staining of human monocytes and macrophages, which is mediated by the Fc receptor.

The authors demonstrate that isotype controls are not useful in their analysis — welcome news to many who don’t use these as a control.

In determining the best blocking scenarios, the authors tested several blocking agents, including FcBlock, human and mouse serum, and human and mouse IgG at several concentrations. The conclusion of this work is that, for these cell types, 100 μg/ml of human IgG was the best blocking agent, based on effectiveness and cost.

It goes without saying that the addition of a viability dye in the staining process is important for good analysis. Even in a single color experiment (GFP+ cells, for example), having a viability dye is critical to make sure dead cells are not being counted.

Physical parameters, like forward and side scatter alone, are not sufficient to identify dead cells (Figure 2).

Figure 2: Physical parameters alone cannot determine dead cells. In Figure 2A, dead cells were identified based on their DAPI signal. These cells were plotted on a FSCxSSC plot (Black arrow to plot on bottom left). This gate was applied to the top level and displayed on a FSCxSSC plot, as shown in 2B. A second gate was drawn around small, non-complex cells and the two gates used to generate plots of DAPI vs CD3, as shown on the bottom of 2B. This data clearly shows the cells identified by size parameter are not all dead. Without a viability dye, over 30% of the CD3 cells could have been missed.

Choosing and Prioritizing Controls

What is information about staining cells without a reminder about the proper controls that must be run with each experiment?

These controls include fluorescence controls (unstained, and single-stained compensation controls), fluorescence minus one (FMO) controls, reference controls, unstimulated controls, stimulated controls, instrument controls, and more.

One can argue that controls are most important. After all, the controls are necessary for the researcher to properly interpret their data in the context of the experiment. Without these controls, the data is impossible to interpret. This is even more important in light of the ongoing efforts to improve reproducibility and rigor in our scientific endeavours, rising to answer the challenges that researchers are discussing now (Figure 3).

Figure 3: Concerns over irreproducibility of data and wasted resources from Begley and Ioannidis (2015) Circ Res 116:116-126.

In the initial stages of the development of a panel, every effort should be made to include every control that the researcher can devise. That way, as the data is analyzed and the analysis template developed, the controls can be ranked as CRITICAL, USEFUL, or UNINFORMATIVE for data analysis.

Critical controls for a flow cytometry experiment include things like compensation controls and some FMO controls. Without these controls, the results of the analysis are suspect and should not be trusted. In other words, the data can’t be interpreted if these controls are excluded.

Useful controls would include some FMO controls, reference controls, and autofluorescent controls. These types of controls help with the analysis, and can support the conclusions of the critical controls. For example, in a T-cell panel, a CD3 or CD4 FMO control may be helpful, but may be less critical as there are other controls that ensure the cells are properly identified.

Uninformative controls include the isotype control, and poorly designed reference or stimulation controls. These do not aid in analysis, and can in fact have a negative impact. Take, for example, this figure from Andersen’s paper.

Figure 4: First figure from Andersen’s paper showing the staining of anti-Tie2 antibody and the isotype control.

It is known that the cells of interest express Tie2 at low levels, and this is borne out by the data comparing the background with the anti-Tie2 antibody. However, the isotype control shows almost a full log separation, compared to the positive signal, suggesting that these cells don’t express Tie2, which goes against the known data (Figure 4).

As the analysis workflow is developed, make sure to validate the controls, too.

In conclusion, we have discussed several areas to consider when staining cells. This includes ensuring that sample preparation results in a high quality single-cell suspension, validating the reagents to be used by titration, and not forgetting a viability dye. Finally, this also includes identifying the best controls for the experiment, while eliminating controls that are uninformative or otherwise confound the analysis.

Taken together, a focus on the staining of the cells is a critical first step to ensure high-quality data is returned when the experiment is completed. The more attention to detail at the beginning, the lower the chance of suffering from the GIGO effect.

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