Written by Michael Kissner
If you have experience sorting different kinds of cell types on a droplet sorter, you may have noticed that sorting efficiency often seems closely tied to the cell and sample type.
For instance, you may have experienced a better outcome, such as a higher efficiency and more cells recovered, after sorting lymphocytes versus sorting an adherent cell line. There are some fundamental concepts that underpin this phenomenon, and understanding them will help you perform better cell sorting experiments.
What Is Flow Cytometry Cell Sorting Efficiency?
Sorting efficiency, in fundamental terms, is a real-time measurement, generated by the instrument, of how successfully its sorting system is able to resolve cells that we want to sort (target events) from cells we do NOT want to sort (non-target events).
Note that we are talking about the sorting system’s ability to resolve events here (the droplets) and NOT the electronics system.
Efficiency is calculated with the following equation:
The results of this equation are highly dependent on two aspects of the sort: 1) the sort mode chosen for the sort and 2) the setup of the sorting system. Sort modes are sets of rules that instruct the instrument on what to do in situations that I often refer to as “ambiguous.”
In order for the instrument’s sort output to be acceptable with respect to the researcher’s needs, it is not sufficient to simply tell the instrument WHAT to sort (i.e. assign a sort region), but is also critical to tell the instrument HOW to sort the target population. The HOW is determined by the sort modes.
Purity, Single, And Recovery Cell Sorting Modes
If high purity is critical for the downstream application, the sorter must be instructed to exclude any target events from the sort that fall close to any non-target events (usually within one half of a droplet). Otherwise, a non-target event can haphazardly be sorted along with the target events. This kind of sort mode is often called “Purity” or “Purify” mode.
Alternatively, if extremely accurate counting of the output cells is critical for the downstream application—for single cell sorting, for example—“Single” or “Single Cell” mode is often used.
Finally, sometimes it is important to recover every single cell possible from the sort, and there’s not much concern for purity. In this case, we tell the instrument to ignore any rules and to sort everything that falls into the sort gate. These modes are often called “Yield” or “Recovery” modes and will always result in efficiencies of 100%.
Target cells that are sorted are termed sorts, and target cells that are not sorted due to violation of the sort mode rules are often called conflicts, coincidences, or aborts.
Although every type of sorter has its own way of implementing sort modes, all sorters must and do include them. In essence, these modes are comprised of combinations of masks that define where a cell can and cannot be in order for a sort to take place.
What Is A Flow Cytometry Cell Sorter “Mask”?
A certain type of mask, often called the “purity mask,” defines how close a non-target cell can come to a target cell in order to mark that target cell for sorting.
Another mask, often called the “yield mask,” defines how many drops should be sorted in order to include a target cell that may be close to a droplet boundary. In this case, the droplet to sort in order to capture this capricious cell is ambiguous and two droplets may be sorted in order to capture it.
Instruments define where cells fall in relation to droplets in relation to the cells’ passage through the lasers. The only place on the instrument where it can “see” cells is at the laser—it cannot measure where the cells are when the stream breaks into droplets—so the system effectively predicts where the cells will fall in droplets, to a certain degree of resolution that depends on the instrument’s electronics, by relating the timing of cells as they pass through the lasers with respect to the pattern by which droplets form.
It is important to emphasize here that the instrument’s determination of cell positions in droplets are predictions, so there is a degree of uncertainty here that requires a buffer zone between events, determined by the masks, to ensure that the sort outcome is as desired. Additionally, cells may speed up or slow down, depending on the sample type and instrument, between the laser interrogation point and the droplet break-off, compounding uncertainty.
Why Cell Sorters Count Droplets, Not Cells
In addition to sort modes, the sort set-up and sort conditions are tightly bound to the efficiency and sort outcome.
The relevant parameters are primarily the droplet frequency, the event rate, and the percent positive (of the total number of events) of the target population. To understand this relationship, it is critical to keep in mind that when we sort we are NOT really sorting cells, but rather, we are sorting droplets.
In other words, the fundamental sorting unit on a droplet deflection sorter is NOT the cell but is the droplets that (ideally) contain the cell. Therefore, in order to sort, the stream of sheath fluid must be partitioned into discrete sorting units or droplets under controlled conditions.
The number of unique partitions depends on the droplet drive frequency. The higher the frequency, the more droplets are generated per second. Most importantly, the rate of droplet formation, once determined at setup, never changes during the sort.
Understanding the difference between cell sorting efficiency, purity, and recovery cell sorting techniques will help you perform better sorting experiments. When performing a sort, make sure you select the proper sort mode, whether it be purity, single cell, or recovery (the latter is sometimes referred to as yield). Remember, cell sorters use masks to predict which droplets, not cells, to sort. By keeping these facts in mind the next time you sort cells, your experiment will be more successful.
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