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How to Optimize Flow Cytometry Hardware For Rare Event Analysis

Written by Tim Bushnell, PhD

“Not everything that can be counted counts and not everything that counts can be counted.” — William Bruce Cameron (but often misattributed to Albert Einstein)

What does this quote mean in terms of flow cytometry? Flow cytometry can yield multi-parametric data on millions of cells, which makes it an excellent tool for the detection of rare biological events — cells with a frequency of less than 1 in 1,000.

With the development and commercialization of tools such as the Symphony, the ZE5, and others which can measure 20 or more fluorescent parameters at the same time, researchers now have the ability to characterize miniscule population subsets that continue to inspire more and more complex questions.

When planning experiments to detect — and potentially sort — rare events using flow cytometry, we need to optimize our hardware to ensure that optimal signals are being generated and that rare events of interest are not lost in the system noise. This noise is also exacerbated by poor practices when running the flow cytometer.

There are 3 areas of hardware limitations that we need to consider when performing rare event flow cytometry.

1. Speed of the fluidics

The first step in running cells on the flow cytometer is setting up the fluidics to ensure the best flow possible while minimizing coincident events and data spread.

Hydrodynamic focusing is the process which focuses our cells inside the core stream, pushes them along, and spreads them out along the velocity axis, so that the cells line up single file and go through the focal point of the laser beam.

But, if the differential pressure is increased, what happens?

An increase in differential pressure between the sheath fluid and the sample fluid being introduced to the flow cytometer causes the core stream to widen. And, as it widens, more cells can pass through the laser per unit time.

There are 2 reasons why this is a concern, especially for rare event analysis:

  1. 2 cells can pass through the laser at the same time, resulting in what is measured as a doublet, and therefore both must be excluded.

By having to exclude more cells, the chances of detecting a rare event decrease.

FIGURE 1: Impact of increasing differential pressure on flow cytometry data.

  1. As the core stream widens, the cells at the edge are more poorly illuminated, and therefore emit less intensely.

When we increase differential pressure, we increase the flow rate and core stream width, allowing the cells to move and meander within the core stream. Some of these cells will not be exposed to the full laser power.

Therefore, the CV of the data spreads and we lose resolution between 2 populations, as seen in the graph below, on the right.

FIGURE 2: Effects of differential pressure on flow cytometry data. Peak CVs spread at higher flow rates.

Thus, there is a trade off between speed of acquisition and the quality of your resolution.

Best practice for rare event analysis is to run the system at low differential pressure so that the event rate is no more than 10,000 events per second (depending on your instrument).

It is often even better to run at a lower rate, such as 5,000 events per second. While this means that acquisition time will take twice as long, the quality of data will be improved. Is the trade-off worth it? For rare event analysis, it is almost a requirement.

Newer technology, like acoustic focusing from Thermo Fisher, is helping to diminish this effect. Acoustic focusing uses a standing acoustical wave that forces the cells into the center of the core stream, allowing you to run much, much faster than a traditional flow cytometer, without the data spreading found in conventional systems relying on hydrodynamic focusing alone.

2. Coincident events and aborts

What is a coincident event and how does this impact the data?

It all starts with the measurement of the electronic pulse. The schematic of pulse generation is shown in Figure 3.

As a cell passes the laser intercept, photons are received by the PMT, which converts the photons into photocurrent. When the cell is fully inside the laser, the maximal number of photons is being generated and pulse reaches the peak (the height measurement) before falling back to 0.

Figure 3: Pulse generation as a cell passes through the laser.

A problem arises if a second cell passes into the laser intercept before the first pulse finishes being processed, and both events will be aborted, resulting in lost data.

Thus, the size of the pulse matters.

The size of the pulse is ultimately going to be the size of the cell plus the beam height.

A hypothetical 5-micron cell and a 20-micron laser beam yields a 25-micron pulse. The stream of a typical analyzer travels at 5 meters per second and 30 meters per second for a sorter. Thus, it takes roughly 0.83 microseconds on a cell sorter for the typical pulse to be processed.

On some instruments, there is an additional period added to this processing time, called the window extension, on BD instruments. This extension increases the time that the system is looking for a pulse and is depicted in Figure 4.

Figure 4: The impact of window extension on pulse processing.

Imagine a cell has just passed through the laser intercept and the pulse is being processed. The next event cannot enter this extended window space until this first cell leaves the window, otherwise it’s considered a coincident event and excluded. This window can be increased or decreased, based on the size of the cell.

The consequence of altering window extension is shown in Figure 5. Window extension was increased and the number of electronic aborts were measured using 2 different sort masks after approximately 600,000 events.

At higher window extensions, there can be as much as a 13% loss of events.

Figure 5: The effect of window extension on abort rate based on 2 different sort masks.

3. Electronic limitations set by manufacturers

The final piece of the hardware are those limitations that have been set by the vendors. These limitations can include the maximal number of events allowed in a file, the number of events per second that can be acquired, the flow rate, the number of gates in a gating hierarchy, and more.

It is critical to understand these limitations while planning the details of the experiment. For analytical flow, you may need to acquire multiple files of the same tube to ensure collection of sufficient event numbers. With sorting, gating hierarchy limitations require careful thought on how to identify the target cells.

Preparing for rare event analysis requires an understanding of the power and limitation of the instrument to be used. From how fast to run the fluidics, to how the signal is processed, to the number of gates that can be used in the sorting experiment, each factor impacts the outcome of the experiment. With these hardware limitations understood, the next step is to understand how to address the sample preparation and identification of the target cells.

To learn more about How to Optimize Flow Cytometry Hardware For Rare Event Analysis, and to get access to all of our advanced materials including 20 training videos, presentations, workbooks, and private group membership, get on the Flow Cytometry Mastery Class wait list.

Tim Bushnell, PhD

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