4 Factors To Improve Flow Cytometry Cell Sorting Speed

Cell sorting owes a lot to Mack Fuwyler  – when he developed the first cell sorter, he started a revolution. Finally, researchers had a tool to isolate specific cells of interest. In the intervening years, more bells and whistles have been added to the base model so that it’s possible to sort multiple populations simultaneously based on multiple markers. It’s also possible to sort individual cells, allowing for a better understanding of the heterogeneity of a phenotypically defined population. It’s hard not to read a paper these days that does not include some level of single-cell genomics work, often aided by sorting.

There are 4 major ways to sort cells. The first way can use magnetic beads coupled to antibodies and pass the cells through a magnetic field. The labeled cells will stick, and the unlabeled cells will remain in the supernatant. The second way is to use some sort of mechanical force like a flapper or air stream that separates the target cells from the bulk population. The third way is the recently introduced microfluidics sorter, which uses microfluidics channels to isolate the target cells. The last method, which is the most common––based on Fuwyler’s work––is the electrostatic cell sorter. This blog will focus on recommendations for electrostatic sorters.

Electrostatic cell sorters are further subdivided into two types based on the location of the laser interrogation point. If it’s the air, it’s a “Jet in Air” sorter, which was the first commercially available sorter. In the second case, the intercept is contained within a flow cell – this is known as a “Stream in Air” sorter. The specifics of these sorters are beyond the scope of this blog, as we are going to focus on some critical considerations that will help you improve your cell sorting experience.

Cell Sorting Factor 1: Choose the correct nozzle size.

A cell sorting experiment can take several hours of preparatory work just to get the sample ready. Due to the isolation that results from staining, the cells may begin dying and clumping. Thus, it’s important to get the cells sorted as quickly as possible. Of course, there is also usually work after the sort, so that needs to be factored into the equation.

First, you should choose the correct nozzle size, which should be 4-to-5 times larger than your cell’s diameter. Nozzle size impacts the sheath pressure. The larger the nozzle, the lower the pressure has to be. Pressure impacts the droplet generation rate, and again, with larger nozzles, the droplet generation rate is lower. So how do you choose? Figure 1 shows some calculations of different cell sizes and recommended nozzle size. The data on cell volume can be found here. To determine the diameter of the cell from this data, you can use this geometric equation: Flow cytometry cell sorting nozzle size calculation

Flow cytometry - average cell diameters and recommended nozzle size for cell sorting

Figure 1: The average diameter of different cells and recommended nozzle size.

As mentioned, the larger the nozzle, the lower the sheath pressure and frequency of droplet generation. Figure 2 from Arnold and Lannigan (2010) shows this relationship.

Relationship between nozzle size, sheath pressure, and droplet generation in cell sorting

Figure 2: Relationship between nozzle size, sheath pressure, and droplet generation.

Cell Sorting Factor 2: Include statistical limitations.

As figure 2 shows, the frequency of droplet generation is given in kiloHertz. This means that a 70 μm nozzle generates between 65,000 and 100,000 droplets per second. So should you sort cells at 100,000 events per second?

No – sorting at that speed will leave you with unhappy and poorly purified cells. Besides the electronic limitations on the system, there are statistical limitations that need to be considered. In this case, we turn to Poisson statistics, which allows us to calculate the probability of a given number of events per unit time. In an ideal world, we would want one cell in a droplet and no competing cells in the leading or lagging droplet. Figure 3 shows the probability that a given drop will contain X number of cells.

Probability that a given drop will contain one or more cells when cell sorting

Figure 3: The probability that a given drop will contain one or more cells.

Based on this data, if we go with a p=0.25 or 1 cell per 4 drops, we have an 80% probability of a drop containing no cells and a 20% chance of a droplet containing a single cell. Therefore, it’s recommended that the event rate be no more than ¼ of the frequency.

Cell Sorting Factor 3: Lower the threshold as much as possible.

After establishing the event rate, you should consider setting the most appropriate threshold. Raising the threshold influences the event rate. With a higher threshold, smaller events are not counted, making the event rate focused on the target cells. In theory, this sounds good. And on an analyzer, it’s not a bad thing… On a sorter, however, it can dramatically and adversely impact the quality of the sorted cells.

Impact of different thresholds on post-sort cell sorting purity

Figure 4: Impact of different thresholds on post-sort purity.

To demonstrate this, BD CS&T beads, which contain both large and small beads, were sorted under two conditions. In the top left panel, the beads were sorted with a 10K FSC threshold. The small events (small gate) are clearly visible. The sort gate (in blue) indicates the events that were sorted. In the top-right panel, the threshold was increased to 50K. This blinded the sorter to the events in the small gate. It doesn’t mean they’re not there – just that they’re not registering as an event.

After sorting, the threshold was reset to 10K FSC, and a post-sort analysis was performed. As you can see, the beads sorted with the 50K threshold are significantly contaminated with the hitherto unseen small beads.

Thus, you need to keep the threshold as low as possible.

Cell Sorting Factor 4: Consider enrichment to speed up the sort rate.

All of this leads to the question of how long will the sort take. To answer this question, it’s necessary to know:

  • The number of cells needed for your downstream application
  • The frequency of the target population
  • The expected recovery from the sorter.

Figure 5 summarizes these relationships.

Relationship between the frequency of a population, expected recovery and time to sort with different droplet frequencies

Figure 5: Relationship between the frequency of a population, expected recovery and time to sort with different droplet frequencies.

Assuming that 100,000 cells are needed for the downstream application, it’s possible to determine the approximate number of cells to stain and how long a given sorter would take (not including setup time, etc.) In the top rows, the frequency of the target population varies. In the middle rows, the sorter recovery varies. And in the bottom rows, the sample processing recovery varies.

If we focus on the frequency of the target population, you can see that for a rare population, sorting at a relatively fast rate of 87,000 a second, it will take over 2 hours to sort 100,000 cells. However, it’s possible to first speed up the sort rate by doing an enrichment of some kind. The most common way is to use magnetic beads to either enrich the target cells or (preferably) deplete the contaminating cells.

Using these data, if we had 200 million cells to sort, with a target frequency of 0.1%, this would take about 2.5 hours. But if we depleted these 200 million cells, removing 90% of the contaminating cells, we would be left with 20 million cells and a target frequency of 1%. We could sort this new sample in about 15 minutes. If the depletion takes an hour, this will save about 75 minutes of time. So when planning for rare events, consider adding a depletion step.

“Time is money,” quipped Benjamin Franklin. This is especially true in science. Getting cells purified for downstream applications can be a long, tedious process. However, it’s critical for understanding biological processes in a phenotypically defined manner. The information in this article can help you better plan your sorting experiments and understand the choices you have to make in order to get the best sort in the shortest time.

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