5 Gating Strategies To Get Your Flow Cytometry Data Published In Peer-Reviewed Scientific Journals
Written by Tim Bushnell, PhD
“Every block of stone has a statue inside it and it is the task of the sculptor to discover it.” — Michelangelo
When sitting down to perform a new analysis of flow cytometry data, it is much like Michelangelo staring at a piece of marble. There is a story inside the data, and it is the job of the researcher to unravel it.
The critical difference between sculptor and scientist is that while the sculptor is guided by a creative vision, the researcher is guided by very particular laws of nature and a specific method of working through a biological hypothesis to avoid shaping the results to his or her whims.
Science must be objective, or it is simply an exercise in creative sculpting, which does nothing to move science forward.
Thankfully, there are many ways to avoid shaping the results, and instead sifting for the real and actual data that is relevant to the flow cytometry experiment at hand.
Communicating the results of a flow cytometry experiment is where the researcher has the power to make new or subtle findings instantly comprehensible to the audience. This is also where science becomes an art form.
5 Gating Strategies For Publishing Flow Cytometry Data
Gating is a data reduction technique.
While actual cells will not be lost in trying various gating strategies, data points can be eliminated from your population. In other words, you can reuse and refine your gates and plots over and over again without actually losing cells, but you and you alone will determine which events you are displaying. Hopefully, you will objectively choose the right events to display.
To this end, the following hierarchy was created to help you gate your events correctly…
- Flow stability gating — to capture events once the flow stream has stabilized, eliminating effects of clogging, back-pressure, and other instrument issues.
- Pulse geometry gating — to remove doublets from the dataset.
- Forward and side scatter gating — to remove debris and other events of non-interest while preserving cells based on size and or complexity.
- Subsetting gating — to rely on expression of markers and what they identify. Using viability dyes and dump channels further narrow to the cells of interest. This is where Fluorescence Minus One (FMO) controls become critical in defining the populations of interest.
- Backgating — to provide visualization of cells in final gate at higher level.
The details on this hierarchy, including how each fits together sequentially to produce the optimal flow cytometry figure for every experiment, are outlined below…
1. Flow stability gating.
The principle of this step is to ensure a good and even flow stream during the instrument’s run.
Clogging, back-pressure and other instrument-related issues can affect the flow, so eliminating cells that may have been affected by such problems is an important step to cleaning up the data. An example of this is shown in the below plots.
These plots show the sample running evenly over the time of acquisition. The data are plotted against a time parameter versus a scatter parameter. Either forward scatter or side scatter are good choices, as they are both intrinsic measurements of all events passing through the laser intercept.
The red gate on the right-handed plot was used to remove the first seconds of the run where the instrument was in the process of stabilizing the run and not yet ‘flowing’ evenly.
A recent paper published by Fletez-Brant et al., introduced an automated program in R called “flowClean”, which can do this process in an unbiased, automated fashion.
Interestingly, when this program was run on over 29,000 files in the FlowRepository, the authors showed almost 14% had fluorescent anomalies.
Failure to address this problem reduces the sensitivity of all experimental measurements and may result in inaccurate data and results.
2. Pulse geometry gating.
This gate is used to remove doublets from the dataset and is particularly useful with digital data.
When cells pass through the laser intercept and fluoresce, the photons are converted to an electronic pulse in the photomultiplier tube.
The instrument can measure three characteristics: the height of the pulse, the width of the pulse (or time of ‘flight’), and the area of the pulse (see below figure).
In the case of clumps of cells, the transit time increases, thus the area will also increase.
In a plot of the area versus the height measurement, the single cells typically fall along a diagonal, while the clumps of cells will show up with increased area relative to the height.
Using this pulse geometry gate removes these clumps, which is important because flow cytometry analysis is based on single cell analysis, not doublet cell analysis or ‘clump’ analysis.
Another example of pulse geometry gating is shown below. Here, the pulse geometry gate is applied to 487,000 cells and, as shown, over 93.7% of them are single cells. This reduced the initial dataset by 30,000 cells.
3. Forward and side scatter gating.
Forward and side scatter gating is one of the most common gating strategies used in flow cytometry analysis.
The goal is to identify the cells of interest based on the relative size and complexity of the cells, while removing debris and other events that are not of interest.
It is recommended that this gating strategy be as generous as possible, to eliminate ONLY those events that are absolutely not of interest.
As shown in the figure below, the major density of events is captured by this gate. The events with very low FSC and SSC, as well as those with low FSC and high SSC are eliminated. These events represent debris, cell fragments and pyknotic cells. As a result, approximately 45,000 more events have been eliminated from the analysis.
4. Subsetting gating.
This is where the major work of data analysis is done. Subsetting gates rely on the expression levels of markers in the analysis, and what those makers identify.
Using tools like viability dyes and dump channels, more unwanted cells are removed to reveal the data relevant to the experiment and the overall hypothesis behind the experiment.
When designing a panel, adding a viability dye is critical to ensure that dead cells, which can non-specifically take up antibodies, are eliminated from the analysis.
The dump channel is useful for ‘mass’-eliminating specific markers that represent cells that are not of interest to the researcher. For example, when performing a T-cell analysis, one might add markers for B-cells, macrophages, monocytes, and the like into a single channel.
As seen in the above figure, plotting a viability marker against a dump channel eliminated another 189,000 events.
Moving forward, additional analysis to identify the specific cells of interest, in this case CD3+CD4+ cells, continues (as shown below). These additional gates have eliminated over 70% of the events that were initially collected on the flow cytometer.
Of the 487,000 cells that were present in the first plot, there are only 127,785 cells remaining — that is to say, a total of 127,785 CD3+CD4+ cells are present.
Knowing what the relative percentage of your final population is will ensure that sufficient cells are collected for meaningful statistical analyses.
At this point, you should consider your FMO controls, which are used to define the final cellular subsets, in this case CD25+FoxP3+ cells. The FMO control is very useful in addressing issues of how spectral spillover from other fluorochromes in the panel affect the spread of the data in the channel of interest. In the case of the data being analyzed, a gate is drawn on the fully stained sample, and applied to the FMO controls to confirm positioning.
Based on the FMO controls applied to the two right-hand plots below, it is clear that the gate in the left-hand plot is in a good position. Now, the analysis is down to only 2,800 cells.
From here, the researcher would be able to extract additional information, in the form of median fluorescent intensity values, or percentages of cells expressing markers of interest on the identified ‘regulatory T-cells’, as defined by CD3+CD4+CD25+FoxP3+.
This technique allows for the visualization of the cells in the final gate at a higher level. The goal of this gating strategy is to determine if any cells are being missed by the gating strategies that have been previously applied.
As the red dots in the above right-handed plots show, the FSCxSSC gating could be tightened up, reducing the ‘noise’ downstream. Likewise, the viability gate clearly shows why this particular gate is valuable, as there would be a fraction of cells that would be included if this gate was not used.
Once the gating strategy has been developed and validated, it is time to move to extracting the necessary statistics that will be used to answer the biological question. With careful application of the gates discussed above and the proper experimental controls, the researcher should have freed ‘the statue in the stone’. Michelangelo would be proud.
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