What Is Fluorescent Activated Cell Sorting And 4 Other Questions About FACS Data Analysis

Written By Tim Bushnell, PhD

Prior to the mid-1960's, the ability to study a defined cell type was severely limited.

Researchers had to use centrifugation methods, such as differential centrifugation, rate zonal centrifugation, or isopycnic centrifugation, to define cell types.

All of these methods would allow separation of cells based on the property of the particles within different separation medias, but didn't allow for very fine resolution of the cell populations. 

That all changed starting in the mid-1960's, when Mack Fulwyler published the first true cell sorter, which combined the power of cell characterization by the Coulter principle with the electrostatic separation of droplets developed by Richard Sweet (and used in inkjet printers).

For the first time, researchers could rapidly isolate individual cells based on more precise physical characteristics.    

4 Common Questions About FACS Analysis

Early cell sorting technology eventually found its way into the Herzenberg lab at Stanford University, where a talented research group added lasers and developed what is now known as the “Fluorescence Activated Cell Sorter”, or 'FACS' machine.

This first instrument had a single laser and two detectors, capable of measuring one fluorescence and 'forward scatter'.

With advances in areas of electronics, lasers, optics, and fluorochromes, instruments are now available that can measure as many as 15+ simultaneous fluorochromes and sort at rates of 20,000 events per second.

Cell sorting technology has come a long way, but many scientists still struggle to answer basic questions about FACS analysis. Here are the 4 most common FACS-related questions…

1. What is FACS and how does it work?

The term FACS is held as trademark by BD Bioscience, but the word has become accepted as a reference for any cell sorter, regardless of vendor.

FACS combines the traditional power of flow cytometry and couples it with the ability to isolate the cells of interest.

The most common FACS systems on the market use electrostatic separation, although there are some systems that use a physical or microfluidics design for isolation of the cells.

Just about every cell sorter is also a standard flow cytometer. As such, cells are stained following standard methods and introduced into the sorting machine by gentle pressure.

From there, the cells undergo hydrodynamic focusing and flow, single file, towards the laser intercept point(s), as the below figure shows. 

Next, the flow stream is vibrated at some frequency, breaking it into many thousands of droplets. Some of these droplets contain the cells of interest. It is to these droplets that an electric charge is applied.

As the droplet flies free, it enters an electrostatic field and based on the applied electric charge, is deflected to a collection tube. Those droplets that do not get a charge are discarded as waste.

There are some technical differences between the various electrostatic sorters on the market. These differences are predominantly based on where the cells are interrogated.

2. What are the range of cell types that can be sorted by FACS?

The cell type that can be sorted is limited to the size of the cell, the quality of the instrument, and the ingenuity of the investigator.

Cell sorters have a nozzle, and the size of the nozzle dictates how large (or small) a cell can be sorted. Most often, cells should be 4-5 times smaller than the nozzle being used.

Most sorters on the market today can sort from very small cells (bacteria) to very large cells. There is even a special sorter that can sort very large clumps of cells and even small organisms.

3. How fast can a FACS instrument process cells?

When it comes to the processing speed of a cell sorter, there are two points to consider.

The first point to consider is the inverse relationship between the size of the nozzle and the frequency of droplet generation that will produce a stable stream.

The below table shows the frequency of sorting for several different nozzle sizes. You can see that there is a range of frequencies, which are related to the pressure of the system. The pressure of the system has to be balanced with the nozzle size to produce a stable stream.  

The second point to consider regarding the speed of the cell sorter is related to how many events per second the system should run. This relates the need for purity of the sorted product and the poison distribution of events within the fragmented stream.

If there are too many events based on the frequency, this leads to the decreased purity and loss of recovered cells. 

As the above figure shows, there is a greater chance of having two cells next to each other, or multiple cells in one drop, when the event rate approaches the frequency of droplet generation. A good rule of thumb is an event rate at ¼ the frequency, as the below table shows.

Now it becomes possible to calculate how long a sort might take. For example, sorting at 60 kHz, at a rate of 15,000 events/second, if one needs 100,000 cells for a downstream application, and the cells are at a frequency of 1%, will take at least ((100,000 cells)/(frequency))/15,000 about 667 seconds or 11 minutes for this sort.  Assuming a 50% recovery would double the number of input cells needed, thus increasing the time to 22 minutes or so.

4. What topics should someone new to cell sorting consider?

There are several important tips that can help a researcher who is new to cell sorting and help ensure the best possible outcome for the experiment…

1. Talk to the operator(s) of the cell sorter. They are friendly and will be able to provide a wealth of information on planning and executing the experiment. Enter into their good graces by making them part of the process to ensure they care about your cells as much as you do.


2. Review the protocol. Go over the staining protocol and make sure everything is ready before beginning the process. Do the back of the envelop calculation to make sure you know how many cells will be needed. Always assume a 50% loss from the cell sorter (due to electronic aborts, coincident events, cells dying post-sort, etc.).


3. Coat the tubes. Coating your experimental tubes goes a long way to ensure that the charged droplets don't stick to the plastic of the catch tube. Neutralizing that charge by coating with some protein can improve recover post sort.


4. Filter the cells. Nothing ruins a sort like a clog. Remember Howard Shaprio's First Law of Flow Cytometry – “A 51

4 Biggest Mistakes Scientists Make During Multicolor Flow Cytometry Cell Sorting Experiments

Written By Mike Kissner

Multicolor cell sorting is a complicated process and certain scientific errors can be common.

Unsuccessful multicolor sorts can result in erroneous data and inconclusive results. Successful multicolor sorts, on the other hand, can give excellent results and lead to dynamic conclusions.

Successful multicolor cell sorting requires special attention to planning.

Using specific setup strategies for your experiment can create a streamlined system for an otherwise complicated process. For example, these critical steps and strategies for multicolor sorting experiments can save you time and maximize your results.

When setting up a multicolor experiment, the most common mistakes are failing to set PMT voltages properly, failing to use a viability dye, failing to address doublet discrimination properly, and failing to set the right sort regions and gates. Eliminating these 4 mistakes is important for any kind of flow cytometry experiment, but particularly for flow cytometry cell sorting experiments.

The following 4 mistakes should be avoided prior to the setup phase, which should be executed immediately before the sort. This setup phase should be included as part of the planning, optimization, and trial process of the experiment to give you the best cell sorting results possible.

Here are 4 common multicolor cell sorting mistakes you should avoid…

1. Failing to set the PMT voltages properly.

When setting up a multicolor experiment, the most saliently critical step is to set PMT voltages and to do so properly.

The overarching theme to this portion of experimental setup, as with most anything in flow cytometry, is to maximize signal-to-background resolution.

As such, setting voltages using an unstained sample to place the negative peak in the first log quadrant (or any other desired position in the plot) may not, and often doesn't, accomplish the goal of maximizing sensitivity in each channel.

Keep in mind that PMTs do not perform maximally (i.e. convert photons to electrons as efficiently as possible) at every single voltage setting. Moreover, in order to ensure that a detector is operating at peak performance, a sample that contains both negative and positive populations must be used.

An unstained sample provides perspective with respect to the negative population only, so it cannot be used to determine how well a stained population will be resolved from an unstained population.

In general, the danger arises when the voltage is set too low, which may result in suboptimal photoelectron generation and signal detection.

When measuring signal in channels in which cells tend to autofluoresce, like the green region of the spectrum, setting voltages based on the position of the unstained may result in a PMT voltage that is too low. Conversely, setting voltages based on the position of the unstained in red channels, in which cells autofluoresce very little, may result in the voltage being set too high, which in turn may result in the positive population to be off-scale once the full-stain is acquired.

If using BD instruments controlled by FACSDiva (e.g. FACSAria, LSR II, LSR Fortessa) the CS&T system can help to determine minimum baseline voltages, or the minimum voltage at which that detector should be operated. There are some excellent references that provide extensive and thorough methods to accomplish the same goal.

In general, there some useful rules of thumb that can help guide you along the most optimal path for setting your PMT voltages properly.

First, voltages must be set so that no stained population is off-scale. This is critical both from a visualization perspective (no one likes to look at data where staining is smashed up against the high end of the scale), and from a measurement one. The very high portion of the scale may not be in the linear range of the detector and may not facilitate proper signal measurement. Again, this goal can only be accomplished by running a sample with a clear positive population.

Be wary of using compensation beads to set voltages.

Staining can be very bright, which may result in a tendency to reduce voltage to possibly suboptimal settings. After checking to make sure no staining is off-scale, adjust the voltages, usually by increasing them, so that the separation between positive and negative populations is clear and maximized as best as possible.

One common practice in flow cytometry is the tendency to adjust PMT settings with the specific aim of minimizing percent overlap in the compensation matrix. Remember, the primary goal in setting voltages is to ensure that the resolution between positive and negative is maximized.

The percent overlap is not a particularly good indicator of whether separation is maximized.

As long the voltages are set so that no populations are off-scale, the detectors are operating in linear range, and that positive and negative are well separated, do not worry about the compensation percentages, assuming that compensation was set up properly. Instead, let the data speak for itself.

Always ensure that the PMT voltages are the same for each control. Compensation will not be calculated correctly if voltages in all channels are not consistent between controls.

2. Failing to use a viability dye.

Antibodies have a tendency to stick to dead cells, which will result in false positives that may drastically compromise purity.

This can be devastating for a sort, especially when the cells will be used for downstream molecular applications that rely on high-integrity sort purities. Moreover, while false-positive dead cells including in the sort fraction may not grow in cell-based assays, their presence may affect cellular processes of other cells present.

There are many nice choices for viability dyes and there are two kinds whose mechanisms are different: DNA-binding dyes and amine-reactive dyes.

DNA-binding dyes, like propidium iodide (PI), DAPI, and 7-AAD, are typically positively charged molecules with strong DNA-affinity that cannot pass through intact cell membranes. Thus, they only stain the DNA of dead or dying cells with compromised membranes. These dyes are good choices because staining is very rapid, so the dye can be added very soon before the sort and does not require a separate staining step.

Because the stain is present in the buffer in excess, DNA-binding dyes provide a “real-time” indicator of cell death; cells that die during the sort will allow dye into the nucleus and will begin to fluoresce.

While typically not a concern for sorting, these dyes cannot be used with fixed cells because the dye-DNA binding is non-covalent and equilibrium-driven.

If cells are fixed after staining, dye that dissociates from DNA in cells that were dead before the fixation may stain DNA of cells that were live before the fixation, given that fixation disrupts cell membrane integrity. Alternatively, amine-reactive dyes, often called “fixable” dyes, bind covalently to free amines on and in the cell. Staining with these kinds of dyes must be performed during an independent staining step.

Dye will enter and stain cells that are dead and have compromised membranes, so staining intensity of dead cells will be much higher than that of live cells, which permit binding of the dye to only those amines on the cell surface. After staining, cells can be fixed if desired, due to the fact that the dye-amine bond is covalent and not equilibrium-driven, so staining integrity will be preserved after fixation.

In general, DNA-binding dyes are preferable to amine reactive dyes for sorting, given their ease of use and “real-time” properties, so stick with the many choices available for these when designing a panel.

Reagent manufacturers have devised DNA-binding viability in many flavors, so finding one that fits into your panel should not be a difficult task. The SYTOX^Ⓡ dyes, manufactured by ThermoFisher, can be a good choice.

One nice thing is to combine a viability dye with a dump channel, or a channel used to gate out cells that are positive for a marker or multiple markers, to remove “lineage-negative” cells from analysis, for example. Since both the dump and viability dye channels are used to gate out cells that are stained, both can be combined into one channel, which can free up another channel on the cytometer for a another marker.

3. Failing to discriminate between doublets and single cells.

Doublets occur when two cells pass through the interrogation point so close together that the instrument treats them as one event.

When this occurs, the pulses from a doublet event measured in the FSC detector look like those illustrated in the figure below.

The height of a doublet pulse will generally be equivalent to the height of a single-particle pulse.

However, because a doublet pulse is essentially the merger of two single-particle pulses, the area and width of such a pulse will be larger than that of a single-particle pulse. We can take advantage of the disparity between the pulse parameters of single particle and doublet signals to distinguish the two from each other.

Typically, area is plotted against height, height is plotted against width, or area is plotted against width. All cells must have a measurable signal in the parameter chosen, so forward scatter or side scatter are usually utilized.

Also, two doublet discrimination gates, one utilizing FSC and the other utilizing SSC, can be included for more robust doublet identification. While doublet discrimination is important for any kind of flow cytometry experiment, it is especially critical for cell sorting.

Failure to discriminate doublets from single cells can severely compromise the purity of a multicolor cell sort.

A doublet event may incorporate one cell that fulfills the sort logic AND another cell that does not fulfill the sort logic. Because the sorter has identified both of these cells into one event, the entire event - both the target cell and the non-target cell - will be sorted, resulting in both a target and non-target cell in the collection fraction.

The presence of doublets does not necessarily indicate poor performance of a sorter.

Doublet events are a normal and expected aspect of a flow cytometry experiment and whose frequencies are dictated by how the cells are dispersed into a stream. Denser suspensions and sticker cell types can certainly influence dispersion, so do not be dismayed if doublets are observed. The most important thing is to find and eliminate them.

4. Failure to set the right sort regions and gates.

Setting the right sort regions and gates is especially critical for sorting, given that all set-up must be perfect before the sort begins in order to achieve results of the highest caliber. Gates should be set based on the boundaries of positivity determined by FMO controls to ensure that only true positive cells are sorted.

Keep in mind that populations in flow cytometry are distributions with inherent variances or widths.

The width of a population is primarily a function of both the number of fluorophores bound to (immunofluorescence) or expressed by (fluorescent proteins) the cell as well as the measurement variation. The fluorescence of a single theoretical cell passed through a cytometer 1,000 times will be measured differently each time and will give rise to its own “population”.

The degree to which this is the case depends on many factors, including laser power, collection efficiency of the instrument, and wavelength of detection. The lower the population falls on a log scale, the more this error will be revealed in the same way that error is revealed by compensation resulting in spillover spreading.

Lower decades on a log scale contain fewer bins, or fluorescence intensity values, than decades higher on the log scale, so a distribution with the same variance will look broader in the second decade of a log scale than in the third decade.

In the above example, the GFP+ population falls very close to the GFP- population and the two populations overlap.

As such, it is critical in this case to position the region that classifies events as GFP+ far enough away from the negative population to ensure that no GFP- cells fall into the GFP+ region as a result of measurement imprecision. Moreover, the distribution of the negative population reflects no fluorescence signal whatsoever, and there is no meaning to where a cell falls in that distribution.

For the most part, assuming the autofluorescence of all cells in the negative population is the same, a cell on the left side of the negative distribution is no different than a cell on the right side of a distribution. As such, do not expect a “pure” population if the sort region encompasses a specific portion of the non-fluorescent population.

When run back through the instrument for a purity check, the entire negative distribution will be repopulated, given that there is absolutely no difference between unstained cells with regards to where they appear on the scale.

As a tip, it is often better to distinguish dim GFP signal from background on a two-dimensional dot-plot than on a histogram, as illustrated below.

By plotting GFP, or any other signal for that matter, on a plot against another parameter that is not being utilized in the experiment, low-expressing cells can be distinguished from the autofluorescence of non-expressing cells due to how the cells are distributed in both channels, as illustrated above.

The figure below, from Arnold and Lannigan, clearly emphasizes the importance of setting sort gates conservatively when signal is dim. Failure to do so can severely impact purity by permitting non-expressing cells into the sort region.

The above figure is from a paper published in Current Protocols in Cytometry by Arnold et al. Here, Panels A and C shows the effect when the sort gate, R1, is placed too close to the negative population (R2). Because this gate encroaches on the negative distribution, it does not distinguish non-expressing cells from expressing cells. Purity is poor using this gate.

When the sort gate is positioned more conservatively, purity is much higher.

Keep this in mind when setting gates for dimly expressing cells. It can make the difference between a successful sort and a suboptimal one.

Multicolor flow cytometry sorting experiments, while sometimes challenging, are not unsurmountable. When setting up a multicolor experiment, the most saliently critical step is to set PMT voltages properly. In addition, using a viability dye and addressing doublet discrimination and setting the right sort regions and gates is important for any kind of flow cytometry experiment, but particularly for cell sorting. Utilizing the tips described here as well as the abundant other resources available to help optimize multicolor staining, should help clarify some of the more difficult aspects of setting up and executing this kind of cytometry experiment.

To learn more about getting your flow cytometry data published 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.

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+.

5. Backgating.

Backgating is a technique that should be applied at the very end of your gating analysis.

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.

To learn more about how to get your flow cytometry data published 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.

How To Create A Flow Cytometry Quality Assurance Protocol For Your Lab

By EditaMotyčáková, Ph.D.

Editor's note: This is based on Edita's experiences implementing the proposals reported in Perfetto et al., (2012) Nature Protocols 7:2067.  She submitted this to the ExCyte Mastery Class and developed the Excel sheet (attached) to assist others in tracking their QC data.

Developing and implementing a new Quality Control (QC) protocol can sometimes be a daunting task.

With the continued emphasis on reproducibility in science, QC programs are an essential step that cytometrists are encouraged to both develop and implement.

This QC program comprises several assays that are focused on three important characteristics of the flow cytometer:

(1) Optimal instrument setting (e.g. instrument optimization)

(2) Cytometer sensitivity (e.g. instrument calibration)

(3) Monitoring of day-to-day variability in measurement (e.g. quality assurance)

QC Program Step #1 - Instrument Optimization

Instrument optimization assays include protocols useful for determination of laser power, photoelectron efficiency, testing of filter characteristics, evaluation of signal synchronization, and laser delay determination.

These are central characteristics of the flow cytometer and understanding these values at installation help ensure that when changes are made, the system is performing as well as when it was first brought into service.

In addition to making these measurements at installation, instruments should also be optimized whenever the optical pathways are changed, including: changing a filter installation, installation of a new laser, and/or realignment because of a new flow cell. Basically, whenever an optical pathway is changed. This gives the user a baseline to know how the instrument is performing and a reference for when there are issues.

Instrument optimizations do require some specialized equipment, namely a laser power meter and super-reflecting mirror to perform any of these tests.

QC Program Step #2 - Cytometer Calibration

There are two separate protocols necessary for cytometer calibration:

• Determining the sensitivity of PMTs


• Validation of PMT sensitivity

To complete the first protocol, three bead sets are needed. For this work, you should use:

• Single peak beads – BC Flow-Set Pro Fluorospheres


• Multipeak beads – DakoFluorospheres


• Unstained beads – DakoFluorospheres

To generate the top graphs in the below figure, the voltage was plotted against the Signal-to-Background (S-T-B). This is calculated by dividing the median fluorescent intensity (MFI) of a well-separated peak by the background MFI. In this case, I chose the 4th peak, as it was nicely displayed and recognized throughout most of voltage range and on most detectors.

These data also allow for the determination of PMT linearity, which is measured as difference between MFIs of two adjacent peaks from multiple-peak beads divided by MFI of lower peak from selected peak pair. Like the PMT calibration, this value is plotted over the voltage range.

To interpret the top graphs in the above figure, you need to determine the voltage with the highest S-T-B.  As shown above, the FL1 detector (BP525/30) is most sensitive at 450 V, whereas the FL3 detector (BP620/30) is most sensitive at 600 V. Equipped with this information, when you run a new experiment, you should use the voltage with the highest S-T-B ratio for primary detector as your default voltage.

The PMT linearity showed that the FL1 detector gives linear response throughout the tested voltage range, while in the case of FL3 the lower voltage (below 450V) setting should be avoided because the response to the fluorescence intensity is not linear.  

Remember that compensation cannot be correctly calculated if the signal is not in the linear range of the PMT.

Validation of the PMT voltages is performed to confirm the results of the optimization step above. The protocol for this requires particles (CompBeads) that give a single peak when stained with antibodies. For this experiment, three antibodies were chosen that had been previously characterized (titrated) to ensure the optimal signal.

Here, polystyrene microparticles were used, which bind any mouse kappa light chain-bearing immunoglobulin, as well as three fluorochrome labeled antibodies (mouse CD16-PC5, CD45-PC7 and CD36-FITC), which are suitable for three different detectors. The titration curve showed that the CompBeads can be stained in ratio 1:20, 1:20 and 1:5 for CD16-PC5, CD45-PC7 and CD36-FITC, respectively.

As a second step, CompBeads were mixed with a negative control (without any binding capacity) and labeled individually with an appropriate marker. Finally, the primary detector (FL4 for PC5, FL5 for PC7 and FL1 for FITC) was set to gain highest fluorescence response (see below).

The highest fluorescence for primary detectors was determined as 670 V at FL4 (for CD16-PC5), 500 V at FL5 (for CD45-FL5) and 500 V at FL1 (for CD36-FITC).

The next step was to set the secondary channels to a minimal MFI. For this, a wide range of detector voltages (400-700 V) were tested. The voltage with the lowest MFI found in PMT linear region was chosen (see below).

As you can see in the above graphs, decreasing MFI values were found on the primary channels (yellow bars), despite being set on a single voltage during the course of all measurements.

Notice how drastically the setting of secondary channels can influence results on primary channels.

When calibrating your cytometer, the final step is to measure rainbow single-peak beads using the same primary/secondary channel settings that were used for your individual fluorochrome.

The repeated measurements (n=20) serves for determination of the target value range for CV, which is the highest value found within +- 1 SD or +- 10 % of mean value.

In the above figure you can see that the FL5 detector has the lowest sensitivity in comparison to the other detectors, particularly when compared to the FL1 detector. Even if FL5 detector is set to achieve the highest MFI, its response is much lower than the response achieved by the FL1 detector.

QC Program Step #3 – Implement QC Checkpoints

After spending the time and effort to perform the above measurements to optimize your instrument, it's important to implement and monitor how the system is changing over time.

Implementing the proper Quality Assurance (QA) checkpoints is the only way ensure that your flow cytometer is functioning properly over time. It's also the only way to determine what the issue is and how to fix it if there are deviations in these checkpoints.

To implement these QA checkpoints, data from three different bead sets must be recorded and analyzed. The beads include:

•      Single peak bead


•      Multiple peak bead


•      Unstained bead

The parameters to be monitored or “checked” include:

•      PMT voltage


•      CV of the single peak bead


•      MFI of a defined peak (peak 4)


•      MFI of the unstained beads

Altogether, this allows for three calculations that can be used to assess the overall quality of the instrument over time. These calculations are:

•      Accuracy (voltage setting as a function of time)


•      Precision (CV as a function of time)


•      Sensitivity (S-T-B ratio as a function of time)

Once the above data are collected, they must be plotted for analysis. The most common plot is the Levey-Jennings plot, which shows the daily data, a running average and lines representing tolerance ranges.

These plots are commonly defined relative on the stringency needs of the investigator to either +/- 2 standard deviations from the mean, or +/- 5% of the mean value. Typical plots are shown below.

If your instrument does not perform this analysis automatically, or if you're interested in having a second QA protocol that is independent from your vendor's protocol, you can download this QA protocol spreadsheet and use it to monitor your data over time.

The above QA protocol spreadsheet has been developed for a FC500 instrument, but the logic can be applied to any number of detectors for any system.

The spreadsheet is organized as follows:

• Tab 1:  Quality control data – this is the place to put the data that will be the basis of the calculations. Make sure to include the bead type and LOT number.  This is especially critical when there is a change to the bead lot. When coming to the end of a lot of beads, it is good to order the new lot and perform an overlap experiment where the old and new lots of beads are run in parallel and any changes to target values can be identified and noted.


• Tab 2: Accuracy – here the data is used to calculate the change in voltage over time.


• Tab 3: Precision – here the coefficient of variation (CV) is calculated using a single bead over time.


• Tab 4: Sensitivity – here the changes in S-T-B are calculated over time.

By adding your data to the first tab, all the graphs and tables will be updated, allowing for a rapid check and confirmation that your instrument is performing within acceptable ranges. A Levey-Jennings plot for detector is shown with both the +/-2 SD and +/-5% of the mean for each value. This helps you visualize the changes over time, and identify trends before they become problems.

Implementing a system of quality assurance protocols of this nature lends confidence to the data collected, especially for those researchers performing longitudinal studies. Optimal instrument setting, cytometer sensitivity, and monitoring of day-to-day variability in measurement leads to improved assurance for those using this instrument to collect their critical data. QC programs will continue to be prudent measures for cytometrists to take as they align with the current emphasis on quality and reproducibility.

To learn more about how to create a flow cytometry quality assurance protocol for your lab 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.

How To Analyze FACS Data And Prepare Flow Cytometry Figures For Scientific Papers

Written by Tim Bushnell, PhD

“It would be possible to describe everything scientifically, but it would make no sense; it would be without meaning, as if you described a Beethoven symphony as a variation of wave pressure.”

― Albert Einstein

 

The goal of any scientific process, as you know, requires the communication of the data that supports or refutes the hypothesis under testing.

Before it is deemed worthy of publication, it must survive the process of peer review ― where the data is laid bare before a group of experts in the field who judge the material impartially (usually) and in secret ― then pass judgment on the suitability of the information for publication.

The presentation of your data must be clear.

As such, choosing the right flow figures to communicate your data is essential.

Good handwriting (formerly known as proper penmanship) and drawings might have been enough to convince peers in the distant past, but not today.

Today, the expectation is that you'll choose the right flow figures from all that are available, selecting the ones that reflect your data accurately and without confusion.

There is so much data and so little time that it is essential to present information in the clearest, most concise way.

As Einstein once said: “Everything must be made as simple as possible. But not simpler.”

Presenting the data in the best possible format, highlighting your results while avoiding glitz that can make the integrity of your data suspicious, is key.

At first glance, flow cytometry data is very visual.

Analysis techniques rely on presentations using univariate (a.k.a. histograms), bivariate (a.k.a. dot plots) and even higher order plots (3D plots, SPADE trees, etc.).

The huge caveat with falling in love with any of these types of plots is in knowing the plots used for flow analysis are more often than not a means to an end.

Their purpose is to extract numeric values (such as percent positive or median fluorescent intensity) from the data ― the real value of the data to be presented.

Here are the benefits and drawbacks of popular flow figures to consider when presenting your data:

1. Histograms.

Histograms tend to be the most abused of figures for presenting flow cytometry data.

These plots show the intensity of expression versus the number of events.

Typically, figures are shown with data from different conditions shown on one graph, often with an offset as below…

Histograms are useful for cell cycle and proliferation analysis, but are less useful for presenting data for several reasons:

• No relationship between different markers (can't identify double positive cells)


• Subtle populations lost in larger distribution (no rare events)


• Shape is dependent on binning (different for different instruments and analysis tools)


• Peak height is a function of the number of events and spread of the data

2. Scatter Graphs.

The real data that is important are the numbers extracted from these graphs. As such, scatter plots should be seen as a way to summarize the real data.

The power of the scatter graph shows several things:

• The number of the experiments that were performed in generating the data


• The average of the data


• The spread of the data


• The significance of the data

3. Bivariant plots.

Bivariant plots have some utility in presenting the manner in which the populations of interest were identified.

Bivariant plots show the relationship between two different markers, allowing for more complex phenotypes to be identified and important populations of interest to be isolated via gating.

The original bivariant plot was the 'dot plot', a figure that showed the relationship between two variables, but lacked detail in terms of the intensity of the number of events in a given region.

4. Density Plots.

The dot plot led to the development of the 'density' plot ― a way to show not just expression levels, but the relative number (i.e. density) of events in a given region.

Three such density plots are shown below (generated in FlowJo v9)…

Each of these plots show the same thing, just in slightly different ways, so pick the one you are most comfortable with and use it.

5. Contour Plots.

The other way to show the density of your data is to use a contour plot.  Like the above density plots, these show the relative intensity of the data using contour lines. In this case, each line contains x% (as defined by the plot).

In the plot below, the lines are at 5% of the population, so the outermost line contains 95% of the cells, the second line 90% and so on.  

The closer the lines are together, the steeper the 'island' of cells. Unfortunately, contour plots are not good at showing the outliers. The best strategy here is to couple a contour plot with a dot plot, allowing your rare events to be displayed (shown below in the plot on the right).

One concern reviewers may have over the contour plot that can prevent your data from being published is that these plots do not convey a sense of the number of events on the plot. This is a common criticism of all bivariate plots.

As shown in this figure, only a few points make a very compelling plot (or seemingly compelling plot)…

The solution to this problem is to indicate the number of events on a given plot. This will give reviewers and all readers an indication of the magnitude of the data involved in the analysis.

6. Gating Strategy (All Plots).

The gating strategy used is of great interest to the reader of a paper or grant. It is also a common criticism of flow cytometry data in general. Why? Because…

Gating is a subjective art form.  

At least, gating can be a subjective art form. In a Nature Immunology paper, Maecker and other researchers performed a series of studies concluding that…

In other words…

Since the conclusions from the study will be based on the populations of interest as defined by the gating strategy, getting this consistent, and communicating how the gating strategy was established, is a critical piece of data to share.

An excellent example of this can be seen in any of the published OMIPs, such as OMIP-3 by Wei et al. (see below).

The above presentation of the gating strategy is valuable for dispelling that myth that gating is a subjective art form.  

As new automated analytical techniques become more widespread, they will also help in addressing this issue while adding a level of confidence that the data extracted for downstream statistical analysis has come from a robust, vetted process.

When preparing figures for publication, the scientific question and hypothesis that forms the basis of the paper must be central and all the figures must be in support of that. The flow cytometry data that forms the basis of the conclusions should be presented clearly and concisely. While it provides pretty pictures and colorful layouts, the meat of the data are the numbers ― percentages of populations, fluorescent intensity levels and the like ― these are what will convince the reader that the hypothesis tested is valid and well thought-out.

To learn more about getting your flow cytometry data published 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.

4 Core Techniques For Improving Fluorescence Activated Cell Sorting Results

Written by Mike Kissner.

Cell sorters have become more sophisticated to rival the multicolor capabilities of analytical cytometers, offering up to seven lasers and a score or more of detectors.

Likewise, cell sorting experiments have also become more complicated in terms of the number of markers and fluorophores utilized to identify target populations, and more complicated in terms of understanding an increasing canon of flow cytometry and cell sorting definitions.

These days, sorts of up to 12 or more colors are not uncommon.

While multicolor sorts are very feasible and can yield excellent results, success is always a product of very careful planning and optimization.

Attempting a 12-color sort of precious samples without trial runs will almost always result in failure and require a return to the drawing board to perform the optimization steps that were initially foregone.

Additionally, all setup steps, especially compensation, must be performed on-the-spot rather than during offline data analysis, as is commonly done in analytical experiments.

Satisfactory results require that all controls and settings must be perfected during the setup phase immediately before the sort.

The following 4 core technique will help you through the planning, optimization, and trial process to give you successful multicolor sorting experiments.

1. Choose your fluorophores more intelligently.

One of first steps to designing a multicolor experiment is to determine which fluorophores to use to detect target markers.

When going about this task, keep in mind that compensation in and of itself is not something to be feared.

Compensation is unavoidable when venturing into the world of multicolor experiments, and the methodology for choosing fluorophores is to do so in a way that maximizes signal detection above background.

First, obtain the optical configuration – the lasers, their power, and the filter sets –  of the instrument that you will be using to sort.

Once you have determined which spectral bands can be detected by your instrument, utilize an online spectral analyzer tool to find optimal fluorophores for that instrument.

There are several options, and they are all useful. Here are some to try…

BD Spectrum Viewer

ThermoSpectraviewer

BioLegendSpectraanalyzer

Use these tools in combination with a reagent search on the manufacturers' websites to find conjugates that are optimal for the instrument.

This can be facilitated using panel design tools like Chromocyte, Fluorish, and Fluorofinder.

When choosing reagents, the general rule is to match fluorophore brightness with antigen density.

Keep in mind that all fluorophores are not created equal.

Some are much brighter than others: specifically, they emit more photons and have higher probabilities of detection than others.

For example, Brilliant Violet 421, PE, and APC/Alexa 647 are brighter than PE-Cy5 and FITC, which in turn are brighter than Pacific Blue.

With the advent of polymer dyes like the Brilliant Violets, there are more choices for bright fluorophores than there were in the past.

Reserve bright fluorophores, like APC or Brilliant Violet 421, for low-expression markers like CD25 or CD56.

Dimmer markers, like FITC, can adequately be used to detect bright antigens like CD45 or CD3.

Additionally, choose fluorophores to minimize the amount of overlap between them as much as possible, while not neglecting the importance of the relationship of brightness and antigen density.

On multilaser instruments with multiple beam spots and detection paths for each laser, it can often be very helpful to choose fluorophores so that detection is spread across the lasers and detection paths rather than saturating a single laser with several fluorophores.

On these kinds of instruments, fluorophores are excited and detected at different points in time as the cells pass through the interrogation points, which minimizes overlap and permits detection of fluorophores with the similar emission peaks but different excitation maxima (e.g. PE-Cy7 and APC-Cy7).

Online spectral analyzer tools permit visualization of fluorescence spectra based on laser line and will also overlay typical filter sets to get a sense of how much overlap there may be between detectors.

Keep in mind that overlap depends on laser power, filter sets, and reagent quality (especially in the case of tandem dyes) so the actual overlap on your instrument may be quite different.

The topic of choosing dyes in relation to compensation is a very tricky and difficult one.

In essence, compensation itself is not a problem.

Instrument acquisition software using the proper controls does an excellent job of calculating and correcting for spectral spillover between detectors: the overlap alone is not a problem that cannot be handled.

The big problem, which can result in severe loss of signal-to-noise resolution between stained and unstained populations, is something called spillover spreading.

Spillover spreading is a visual consequence of the process of compensation.

Compensation does not cause it but rather, reveals it.

The detection of every fluorophore is associated with a degree of error that is inherent to the measurement.

In other words, if the same cell stained with a particular dye was passed through the instrument 1,000 times, the fluorescence from the dye would be detected slightly differently during each measurement, which gives rise to a distribution or population.

The amount of variation in this distribution depends on many factors, including the power of the laser, the spectral region being measured (red fluorophores are lower energy, which gives rise to more variation in how many photons are detected at the photocathode during the measurement), the filter set used to detect the dye being measured, and the dye itself.

As with all measurements, dyes detected in channels other than their primary channels (spillover) are subject to the same variation.

During the process of compensation, the population measured (the FITC+ population in the example below) in the spillover channel (the 585/40 channel in the example below; typically used to measure a dye like PE) is shifted down on the log scale so that its median matches that of the negative population.

This process causes the FITC population to appear wider or more “spread out” than it appeared to be before compensation.

This phenomenon is essentially a visualization artifact of logarithmic scaling.

The portion of the scale in which the data resided before compensation is more compact (contains more data “bins” to which measurement values can be assigned) than the portion of the scale that results after compensation, so the population appears to be wider compensated compared to uncompensated.

The critical point about this phenomenon is that the process of compensation has absolutely no impact on the actual variation of the population being compensation; all it does is reveal this error.

Spillover spreading can have significant impact on the resolution between positive and negative populations and this specifically is the reason why avoiding spillover, when possible and reasonable, is important.

This topic can become very complicated very quickly, but the main takeaway is to be mindful of channels that receive spillover from other channels.

The degree to which spillover spreading occurs depends on many things – laser power, filter set, and the fluorophore itself – but, generally, the more spillover from other fluorophores that occurs in a particular channel and the more compensation required for that detector, the more spreading there will be.

Interestingly, a channel used for PE detection can receive significant spillover from other channels and can be problematic in this regard.

So even though PE is a terrific fluorophore in terms of brightness, it may be wise to be wary of using this channel.

While this is not necessarily reason to avoid using PE, it is something to be aware of and can help design smart and well-crafted panels.

For example, utilizing markers that are not co-expressed with the marker that PE is used to detect will help mitigate the extent to which spillover spreading is a problem.

There are some excellent and thorough resources out there that can assist in both better understanding and dealing with spillover spreading. Two of these are listed below:

Nguyen R et al. Cytometry Part A.

Perfetto S et al. Nature Reviews Immunology.

2. Titrate your antibodies correctly.

This step of setting up a panel should be performed regardless of whether the experiment is a sorting experiment or a solely analytical one.

In flow cytometry, it is always important to keep in mind that almost everything we do is geared towards maximizing signal-to-noise resolution to provide the best possible chance to distinguish stained and unstained cells.

The better that we can distinguish positives from negatives, the more robust our identification of target cells and the better the sorting results will be.

Titration is a critical first step in ensuring that signal-to-noise and staining index are as high as they can be.

Although understandably daunting and arduous, with panels that include large numbers of fluorophores, the initial time investment will be well worth it.

When a suboptimal amount of antibody is used, the ultimate result is the same regardless of whether the concentration was too high or too low – compromised population identification.

When the amount of antibody is too high, non-specific binding of the antibody to cells that do not express the target antigen will occur.

When this happens, the negative population may both expand and increase in fluorescence intensity as compared to a non-stained sample.

Both of these effects influence how well the positive population can be discerned from the negative population and are components of the staining index, which is a an excellent measurement of separation.

Staining Index (SI) = MedianPositive-MedianNegative2 x Standard DeviationNegative

When too little antibody is used, the fluorescence intensity of the positive will decrease, yielding compromised separation and determination of positivity.

Be aware that the manufacturer's recommended antibody volume “per test” is often much higher than what is needed.

The correct volume may be orders of magnitude lower than the recommended volume.

There are many nuanced methods for performing titrations, but they all rely on serial dilutions of a starting amount of antibody, often the manufacturer's recommendation or slightly more.

However you titrate, make sure to choose the amount that generated staining with the highest staining index.

Keep in mind that only the Staining Index takes any widening of the negative into account compared to the S/N (Median_Positive – Median_Negative), so SI can, but not necessarily, be a better choice for calculating the right titer.

3. Test and optimize the instrument being used.

After the markers, fluorophores, and titrations have been worked out, be sure to run a few pilot experiments before the actual sort.

There is always a chance that some unforeseen issue may crop up even after everything has been optimized.

Even more critically, make sure to test the stain on the instrument that you will be using to sort.

It is very tempting, given the typical price difference in core facilities between time on an analytical cytometer compared with time on a cell sorter, to use an analyzer to test the stain, however, there is significant danger in this approach.

The robustness of a stain and the cardinal ability to distinguish stained populations from unstained populations can be highly dependent on the fundamentals of a cytometer like laser power, filter set, laser geometry and even the detectors themselves.

Sometimes, two cytometers are configured to detect the same fluorophore with different laser paths and excitation sources.

For example, PE can be detected either under 488 nm excitation conditions or 561 nm excitation conditions, and the stain may look significantly different under each of these conditions.

Additionally, the stain may look very different under the typical, more sensitive cuvette-driven interrogation points in analytical cytometers than under the jet-in-air interrogation points in many cell sorters.

4. Use more and better controls.

This can not be emphasized enough, especially when sorting, as all of the setup must be performed immediately before the sort.

There are two primary kinds of controls to that are essential: compensation controls and controls that determine positivity in a channel.

Proper compensation can only be calculated properly by using high-quality controls.

In order for a control to be acceptable, a clear positive and negative population must be discernible, and there must be sufficient cells in each population to collect a file with at least a few thousand events in both the positive and negative gates.

Correctly calculating compensation is not dependent on signal intensity.

The stain will be compensated correctly as long as the control is brighter or as bright as the staining intensity of the actual stain.

Both stained cells and beads can generate high-quality compensation controls.

If you anticipate that one of your stains will be dim or contain a low frequency of positive (or negative cells), beads are a better option.

As far as beads are concerned, be sure to use antibody capture beads, which are available from a variety of sources (BD™ Comp Beads, eBioscienceOneCompeBeads, Thermo Fisher Flow Cytometry Compensation Beads, among others), and not hard-dyed beads.

Antibody capture beads contain both a population of beads that bind antibodies so they can be stained with your experimental fluorophore as well a population of negative beads that will not bind antibodies.

Be sure to choose the product appropriate for the antibodies you are using in your experiment – some are species and/or isotype-specific and may not bind every antibody in your panel.

What about the autofluorescence component of compensation?

What happens if the autofluorescence of cells being used for compensation is different than the autofluorescence of the cells being measured in the experimental conditions?

It turns out that autofluorescence does not affect the compensation as long as the autofluorescence of the positive and the negative in each control is the same.

If this is the case, autofluorescence will not factor into the math of compensation, so it has no impact on compensation.

This can be tricky when compensating for a marker like CD14.

CD14+ cells, primarily monocytes, display a different autofluorescence profile than most of the CD14- cells (e.g. lymphocytes), so autofluorescence may impact the compensation calculation. In this case, antibody capture beads are the better choice.

It is perfectly acceptable if some compensation controls are generated with beads and others with cells.

Be sure that each control contains a negative population so that autofluorescence of the positive population and the negative population is the same.

“Universal negatives” are not appropriate in this case, as autofluorescence will be different among the compensation control samples.

What about compensating for fluorescent proteins?

Because it is critical that the fluorophore used in a compensation control is the same fluorophore being used in the full stained sample, a sample of FITC-stained cells is not a good compensation control for a GFP signal.

Clontech offers beads that are spectrally matched to GFP or mCherry.

For other fluorescent proteins, it is optimal to utilize a cell line that expresses each singly.

This can be arduous, especially if a transfection must be performed, but it really is the best way to properly compensate.

Controls that help determine what is positive and what is not positive are trickier.

We don't have an extensive arsenal of controls at our disposal to do this, but the ones we do have can be very powerful.

Unstained cells, while often used, are not the best choice.

They may be passable for smaller panels, but they don't tell us anything about background staining or spillover spreading, so they can often result in false positives, which can be devastating for sorting.

Isotype controls, while perhaps helpful to determine whether there is a general non-specific binding problem with the sample, can display different non-specific binding patterns than the antibody being used in the full stain, so they may not be terribly relevant either.

When staining is bright with a particular marker in a particular channel, we really don't need (or have) controls to determine what is positive and negative.

When the separation is good, it is clear which populations are stained and which are unstained.

Keep in mind that staining above background itself does not necessarily mean that a population is positive in that it expresses the target marker.

Non-specific binding of antibodies to dead cells, for example, may result in a signal that appears positive for a marker but in reality is not.

What is more critical are controls that help us to determine what is stained and what is not stained when fluorescence is dim and there isn't a clear bimodal distribution in the channels being measured.

These controls are called fluorescence minus one, or FMO controls, and are generated by staining the experimental sample with every antibody conjugate in the panel except for one.

For example, the FITC FMO control will be stained with every other marker except FITC.

FMO controls help us to determine the effect that our old friend spillover spreading has on the stain, which is an effect that no other type of control can reveal.

Below is a famous figure from a 2004 paper from the NIH that very clearly shows why FMO controls are necessary.

 

Perfetto S et al. Nature Reviews Immunology.

This figure shows, with real data, that bounds of positive staining are different depending on whether an unstained sample is used or an FMO control is used.

The unstained sample overestimates the amount of positive staining because it fails to take into account the background expansion as a result of spillover spreading.

In other words, the brighter fluorescence of the FMO control, compared to the unstained sample, in the PE detector in the figure above is due to the combined effect of FITC, Cy5PE, and Cy7PE.

The latter two are not represented on these plots but, regardless, have an impact on the fluorescence measured in the PE detector.

While it is often not necessary to prepare FMO controls for every single channel, it is wise to do so during optimization steps and the first time that an experiment is executed on the sorter to get a sense of the extent that spillover spreading is a problem.

Channels that often do not require FMO controls are those in which there is significant separation between stained and unstained populations and those which received little spillover from other channels.

These properties of a stain can be predicted to a certain degree, but even though it can certainly be arduous, be sure to cover all bases by preparing FMO controls for all channels the first few times the experiment is run in-full.

Choosing fluorophores to maximize signal detection and utilizing compensation controls to minimize spillover spreading are important first steps in planning your execution for this kind of cytometry experiment. Titrating your antibodies and optimizing your equipment while maintaining proper controls that you've set up and performed in advance are critical first steps to performing accurate multicolor sorting experiments.

To learn more about getting your flow cytometry data published 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.