Procedural Limitations That Impact The Quality Of Rare Event Flow Cytometry

Having dealt with the hardware issues related to rare event analysis in the first part of this series, it is time to turn to our second focus: how samples are prepared.

Stem cells, circulating tumor cells, and minimal residual disease in cancer patients were all discovered through the power of rare event flow cytometry. When preparing for rare event analysis, sample preparation and data analysis must be taken into account at the beginning.

How will we stain our cells? How will we analyze our cells? What controls will we use to help us identify our rare events? What statistical methods do we use to analyze our results? Here are 6 procedural limitations that impact the quality of rare event flow cytometry data and how to optimize your assay to get the best results possible.

1. Staining your cells.

In every lab, there is the “Notebook”, the collection of time-tested protocols handed down from the PI and guarded by the senior technician. In the confines of the “Notebook” is the “Protocol” to stain cells for flow cytometry. What worked 30 years ago is good enough for today, right?

There are many things that have changed since the time the “Protocol” was written, including the best practices for staining cells, choosing fluorochromes, setting voltages, and more. It is time to review the “Protocol” and see if it has stood up to the test of time.

Flow cytometry requires a single-cell suspension. Cell debris and clumps compromise data at best, and can clog the instrument, thus ruining the whole experiment, at worst. Even with liquid samples (i.e. blood, marine water, bacterial cultures), care must be taken in how the cells are treated.

For example, check that the protocol states the RCF (Relative Centrifugal Force) to spin the cells down. Do not rely on RPM (Revolutions Per Minute) as a measure. For mammalian cells, a good rule of thumb is to pellet the cells around 180 x g. Below, is a handy chart that shows the relation between rotor radius (in mm) and the speed (in RPM) to achieve a given RCF.

You can calculate this yourself with the following equation:RCF=(RPM/1000)2*r*1.118

For those new to working with cells, it’s important to point this out. A quick “zip” spin in the microfuge is going to result in nothing more than debris.

Remember to filter the cells as well. Typically, this is done before staining, but is sometimes required afterwards as well if the cells are clumpy.

For adherent cells, try to keep them cold and add EDTA, which will chelate Ca⁺⁺ that is needed for cell-cell adhesion.

During sample preparation, it is critical to remember that the microscope is your best friend. Nothing helps to confirm clumpy cell preps, before they cause issues on the cytometer, better than the microscope and the Mark I eyeball.

For solid tissues (like tumors, lungs, etc.), each tissue will have to be optimized independently. One of the best references out there is the Worthington Tissue Dissociation Guide.

Having prepared a good cell suspension, make sure to block it properly. Andersen’s paper is a great guideline for determining the best blocking strategy for your cells. Don’t forget to titrate your antibodies!

Consider using a master mix as well, which contains all the antibodies that will be used. This makes it easier to stain multiple samples without trying to measure 0.25 𝞵l of each reagent.

2. Using a stability flow gate.

After staining, we turn our attention to issues that can arise when the sample is placed on the flow cytometer. These factors can impact your ability to find the cells of interest.

For example, while we expect uniform flow, this may not always be the case. Micro-clogging at the input end of the system, or clogs in the waste path, can impact the speed of the cytometer. In both cases, the consequences are the same: the data will be incomplete, as the cells will arrive at the downstream laser intercepts either too quickly or to slowly, and the pulses from these cells will not be matched up properly.

Additionally, it takes a few seconds to stabilize the flow at the beginning, and if the tube is run dry at the end, there will be a large increase in event number due to air bubbles into the stream.

To address these issues during data analysis, a flow stability gate should be considered as the first gate in the data analysis workflow.

Figure 1: Flow stability gating to remove uneven flow.

Using this plot of time versus forward scatter, it is obvious where there were issues. In this case, the major issues with this file are at the beginning and end. If there are flow stability issues in the middle of the file, or even if there are a few, one can create a series of gates and use boolean logic to remove these regions.

You can set flow stability gates manually, but there are also some new tools, including programs called FlowClean and FlowAI, which will automatically do this for you as part of the analysis process.

In the paper discussing FlowClean, they actually used it on files from the flow repository, and they found something in the order of 13% of the data files that were in the repository to have these types of abnormalities.

3. Removing artifacts — doublet discrimination.

Even with the best prepared samples, artifacts caused by cell aggregates can occur. To remove these artifacts, you need to apply doublet discrimination. This technique takes advantage of the fact that we understand how the geometries of the pulses are related.

In the image below, the top plot shows a voltage pulse for a single pulse.

It has a height and a width — how long it took to pass through the laser intercept.

And, the integral of the height over the width is the area, shaded blue.

Figure 2: Removal of doublets by pulse geometry gating.

Now, if we have two cells that enter the flow cell together, or enter into the laser intercept together, or are in such close proximity that they look like they’re together, you may see something that looks like the lower plot.

So now, the width is increased to 3 microseconds and the area is also increased.

That can be manifested by looking at the larger plot on the right, which depicts forward scatter height versus forward scatter area.

Cells along the diagonal are the cells we want. Cells outside this diagonal line represent doublets that we want to exclude.

Recently, Hazen et al discussed how the area scaling factor in the DIVA based instruments impacts the data quality and sort results. As the authors point out, since CS&T sets the ASF based on a small particle, this value may not be best for larger cell types. This article provides good guidance for developing protocols to train investigators to adjust the ASF on their instruments. This is especially important for sorting, but will benefit analysis of larger cells as well.

4. Removing cellular debris.

Many people are trained to use forward and side scatter to identify their cells of interest. Forward scatter and side scatter properties can change due to fixation, permeabilization, sample conditions, activation or an inactivation, or a disease state. Thus, it is better to let antibodies help identify the cells of interest, as they are binding specific targets.

There is still a place for the forward and side scatter gate, which is to clean up the data some more. This is affectionately called the schmutz gate.

This gate should be very generous, removing those events that are off-scale, those cells in the lower left of the plot, and the cell-debris events that don’t add to the data.

It is also a good time to remove the small, pyknotic events.

5. Eliminating dead cells.

With compromised membranes, dead cells can masquerade as live cells with bizarre phenotypes because they will uptake any antibody in the staining buffer. There are 2 ways we can improve our analysis: by eliminating dead cells using a viability dye, and by using a dump channel.

A dump channel is a mixture of antibodies to targets that are not of interest for downstream analysis. For example, trying to identify murine HSC, a mixture of B220, Ter119, CD3, CD4, GR1, and CD11b is often added in the same color — these are all markers of maturity and the HSC is defined as being undifferentiated.

Viability dyes like cell-imperant dyes including PI, 7-AAD, ToPro3, and DAPI are DNA-binding dyes that cannot enter cells with intact membranes. Cells with compromised membranes, however, will allow these dyes in where they will complex with DNA and fluoresce brightly.

These viability dyes are useful for both cell sorting and live cell analysis.

Amine reactive dyes, which bind to the amine group of proteins, are a second class of viability dyes. These are excellent dyes for assays involving the measurement of intracellular targets. There are so many different colors of these dyes, it makes it very easy to add them to any panel.

Shown in Figure 3, we combine a viability dye and a dump channel to remove uninteresting or dead cells, and take only live cells of interest forward in the analysis.

Figure 3: Combining the dump channel and viability dye in one plot to identify the cells of interest.

One trick with dump channel reagents is to get them all labeled with biotin, and this allows the use of a streptavidin-labeled reagent.

The 5 gates above are useful in cleaning up the data of issues that occur due to instrument-related issue, as well as eliminating unwanted cells.

6. Placing your gates.

Gate placement is very important because gating is an all-or-nothing process. When you put a gate on the plot, the cells that are inside the gate continue forward, and the cells outside of the gate are thrown away, even if they’re only one pixel away.

It is critical to place gates correctly, using the controls that have been run with the experiment, to help with this process. One of these controls is fluorescence minus one or the FMO control.

Figure 4 shows how to use an FMO. Start by gating on the fully stained sample before applying the gate to the FMO controls. If the gate is poorly placed, and there is a large number of events in the gate on the FMO plot, adjust the gate until the number is reduced to an acceptable level. You can set this percentage to whatever you are comfortable with. A good place to start is 0.1%, which is based on the fact that 99.7% of the data in a normal distribution is within 3 standard deviations from the mean. Whatever the percentage, make sure to explicitly state it.

Figure 4: The FMO control used for setting and confirming gates.

FMO controls are those cells stained with all antibodies except for one. These controls are essential for setting up good gates, especially for rare event analysis and emergent markers.

During the process of panel development, start with all the FMO controls, and during the validation, identify the FMOs that are critical for identifying the populations of interest.

The other thing that’s very useful for placing your gates is having a known positive control and a known negative control to help define your populations of interest.

By the same token, a reference control — a sample with consistently and reproducibly known behaviour in your assay, that is frozen down, and thawed and stained every time you run the experiment — is also useful for setting your gates properly.

Rare event analysis requires patience. It involves optimizing the sample processing and straining. It involves understanding how the flow cytometer works and monitoring the run to identify if things go wrong. It involves properly designing the analysis workflow and using all the information that is in the flow cytometry data file. Couple that with ensuring the experiment has been properly designed for statistical analysis, and it will be possible to identify these rare events with rigor.

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