What Is A Flow Cytometry Laser And How Flow Cytomtery Optics Function

Written by Tim Bushnell, PhD

The optical system of a flow cytometer consists of an elegant coordination of many components that function concordantly and synchronously to generate the signals that we need to measure in order to shed light on the biology at hand.

Understanding the optical system of a flow cytometer may seem unnecessary for performing a typical experiment, but the more you know about your instrument, the better you will be at understanding nuanced aspects of your data, as well as troubleshooting any potential issues that may arise during an experiment.

A cytometer's optical system can be broken down into two major parts: A) lasers, and B) lenses, mirrors, and filters.

Lasers and some lenses comprise the excitation optics that generate optical signal, while other lenses, mirrors, and filters form the emission optics which collect optical signal. A brief description of the roles of each of these components is listed below, followed by more detailed descriptions.

Part I of this article will focus on lasers. Look for Part II for the remaining discussion on lenses, mirrors and filters.

• Lasers illuminate the stream with coherent, focused light of specific wavelength (energy) and power. This illumination facilitates the generation of fluorescence signals from cells labeled with fluorophores and light scatter signal from redirected laser light.

• Lenses focus laser light and collect light scatter and fluorescence optical signal, and direct this signal to the optical detection path.

• Mirrors are responsible for directing light through the detection path and partitioning it so that fluorescence and scattered light are directed to the appropriate detectors.

• Filters, placed in front of detectors, function to restrict the light that is introduced to the PMT detectors so that each detector can be dedicated to measure fluorescence from a specific set of fluorophores.

Before diving deeper into the optical components, it is worth discussing some fundamental concepts about electromagnetic radiation, or light.  While we typically think of light as something that is visible, the electromagnetic spectrum spans a very large range, of which visible light is only a small portion.

Figure 1 below from NASA illustrates the limited range occupied by visible light in the entire spectrum.

Electromagnetic radiation is characterized by its wavelength. Wavelength is inversely proportional to energy, so the longer the wavelength of light, the lower the energy.

Electromagnetic radiation with very short wavelengths, like gamma (~10 picometers) or X-rays (0.01 – 10 nanometers), has very high energy - high enough to break covalent bonds and wreak havoc on biological systems. Those with longer wavelengths, like microwaves (1 mm – 1 m) and radio waves (whose wavelengths can be in the kilometer range), are lower in energy.

Flow cytometry is primarily concerned with the visible spectrum, which occupies a portion of the spectrum in about the middle, with wavelengths of about 380 – 700 nanometers or so.

The spectral range that is utilized in flow cytometry is actually a bit wider than the true visible spectrum, typically between ~350 nm to ~800 nm. The wavelength of visible light determines its “color”. Ultraviolet light, the highest energy light used in flow cytometry of wavelengths below about 400 nm, is not visible. The “cytometric spectrum” can be very roughly conceptualized as follows:

• ultraviolet light occupies the mid-to-high 300 nm range


• violet light occupies the low 400 nm range of the spectrum


• blue light occupies the mid-to-high 400 nm range


• green light occupies the low 500 nm range


• yellow light occupies the mid 500 range


• orange light occupies the high 500 nm range


• red light occupies the range above about 600 nm


• light above ~700 nm is not visible.

When it comes to your flow cytometer's lasers, there are 4 factors that you should understand. These 4 factors are…

1. Coherence.

In order to measure fluorescence from labeled cells, a light source is necessary to produce this fluorescence. Light can be generated in several ways, but the most effective way for the conditions and configurations of flow cytometry is by utilizing the laser.

Lasers, whose name is actually an acronym (Light Amplification by Stimulated Emission of Radiation), are especially suited for flow cytometry for two primary reasons…

First, laser light is coherent. Second, laser output is of a very narrow energy range - the wavelength of light can be specified with high precision.

Coherence is the best thing about lasers as far as flow cytometry is concerned. In technical language, this means that all of the light that is emitted by the laser, according to Shapiro, is “in phase with and propagating in the same direction.”

In practical terms, this means that all of the photonic power of a laser can be directed and focused onto a very small spot. Unlike microscopy, particles flowing through a cytometer spend a very short amount of time in the illumination spot. Given a stream velocity of 20 meters per second, a beam spot of 20 micrometers, and a 15 micrometer cell, the cell is illuminated for only 0.015 microseconds. That's not a whole lot of time.

To maximize the likelihood that sufficient fluorescence events are produced by a labeled cell, and in order to best measure that fluorescence, it is necessary to bombard that cell with as many photons as possible.

The coherence property of lasers ensures that the photon density at the illumination point is high enough to allow us to precisely measure the fluorescence necessary to glean useful biological information.

In contrast, other kinds of light sources, such as arc lamps or LEDs, which are commonly used in fluorescence microscopy, are not coherent. Their light output travels in all directions from the origin, and specialized optics are required to gather this light and direct it onto a measurement point.

Another benefit of lasers is that they can be designed to produce light of a very narrow spectral, or wavelength, range.

Unlike a typical fluorescent or arc lamp, which output white light, or a wide spectral band of photons, laser output can be tuned, depending on the construction and materials of the laser, to produce a particular color, or wavelength, of light. Laser lines commonly used in flow cytometry are: 355 nm, 375 nm, 405 nm, 488 nm, 530 nm, 561 nm, and 640 nm.

2. Spontaneous and stimulated emissions.

The way that lasers work is interesting. The laser consists of material, called the lasing medium, typically through which electrical energy is pumped. This causes electrons in the medium to be excited, or transition to higher energy states. When the electrons fall back to lower energy states, a photon is generated.

The emission of these initial photons results from spontaneous emission - they are not in-phase or polarized and are not necessarily of the same energy (wavelength), as reported by Shapiro. However, the incredible thing, which Einstein showed, is that when electrons of a molecule or atom are excited to a higher energy state, the presence of a photon nearby with a particular energy will increase the probability that the excited molecule will EMIT a photon with the same energy.

This is called, in contrast to spontaneous emission, stimulated emission, and is the logic behind the word “stimulated” in the acronym “laser”.

In other words, photons have a “mob mentality.” If one if doing something (i.e. propagating in a certain direction with a certain wavelength), other photons like to follow-suit and do the same.

By equipping cytometers with multiple lasers, each outputting a specific wavelength, a spatially-separated system can be constructed in which fluorophores are illuminated and excited at distinct points on the stream.

In this kind of system, each laser is focused on its own spot, or interrogation point, on the stream, so only fluorophores with excitation spectra in the range of the laser's wavelength will be excited at each interrogation point.

By constructing a system like this, it is possible to simultaneously measure and differentiate fluorophores which have very similar emission spectra but different excitation like, for example, PE-Cy7 and APC-Cy7.

Both of these fluorophores emit photons at the same wavelength (Cy7's emission spectrum, whose maximum is in the high 700 nm range). However, PE-Cy7's excitation spectrum is largely restricted to the 488 nm or 561 nm laser lines, while APC-Cy7 excitation spectrum is largely restricted to the 640 nm laser.

Spatially-separated systems can differentiate between these two fluorophores because each beam spot, or interrogation point, is associated with its own dedicated collection path. In other words, fluorescence signal from 561 nm excitation is routed exclusively into the 561 nm collection path, while fluorescence signal from the 640 nm collection path is routed exclusively into its own, separate path.

The simultaneous detection of both of these fluorophores would not be possible if PE-Cy7 and APC-Cy7 were excited at the same point in time and space.

This is only possible when the illumination source of a single interrogation point consists of a very narrow range of wavelengths.

3. Colinear systems.

In colinear systems, on the other hand, multiple lasers are focused on the same spot. This can be a cost-effective and convenient way to accommodate two excitation lines when two separate beam spots (interrogation points) are not practical or possible.

However, these systems carry the caveat of the inability to simultaneously measure two fluorophores with very similar emission spectra, as described above.

For example, it is very challenging to measure and distinguish Brilliant Violet 786 from APC-Cy7 simultaneously using a 405-640 nm collinear system. Both Brilliant Violet 786 and APC-Cy7 fluorescence over largely the same range of wavelengths but excite at very different wavelengths (~405 nm for Brilliant Violet 786 and ~640 for APC-Cy7).

Since both of these dyes will be excited at the interrogation point in a collinear system and then be collected into the same optical path, both will be measured by the same PMT. In contrast, Brilliant Violet 786 and APC-Cy7 can be measured simultaneously on a spatially-separated system.

4. Lasing mediums.

There are a few different kinds of lasers, with respect to the lasing medium, and while a detailed discussion of this aspect is beyond the scope of this article, most lasers in current cytometers are solid-state lasers.

In these kinds of lasers, the lasing medium is a solid, as opposed to a gas or plasma. Lasers these days are much smaller, have lower power requirements, and do not require the amount of warm up time that they used to in the past.

The evolution of laser design is one of the reasons that cytometers and sorters have gotten so much smaller in the last decade. The era of the “benchtop” cytometer has in large part been facilitated by the development of smaller lasers without sacrificing output power.

In a flow cytometer, lasers must be shaped by the excitation optics before they reach the interrogation point and interact with cells. This shape is typically elliptical which results in a Gaussian energy profile. In other words, the photons are most “dense” in the middle of the beam and taper off towards the edges.

Figure 2 below illustrates this property of the elliptical laser shape.

Because energy is densest in the center of beam, cells must flow through this portion of the spot in order to generate most fluorescence. When flow rates are high, the core stream through which cells flow widens, which results in more variation in cells' positions in the beam.

Cells located towards the edge of the beam will be exposed to fewer photons and generate less fluorescence, while cells that flow through the center of beam will be exposed to more photons and generate more fluorescence.

Figure 3 below illustrates how flow rate can affect cells' positions in the laser beam.

Finally, another helpful property of lasers is that their output, or power (in milliwatts), can also be specified. Laser power, which is essentially a measurement of how many photons are output per unit time, is usually adjustable on a cytometer but sometimes not.

Typical powers range from 20 mW to 100 mW, although some cytometers are equipped with very high-powered and considerably dangerous lasers of up to several hundred mW. Although not always apparent in practice, the more power the better (when it comes to lasers).

The more photons that a fluorophore sees, the higher the chance that that fluorophore will fluoresce and the higher the chance that a useful biological measurement can be made.

Regarding power and safety, with the exception of UV lasers (~355 nm), lasers on cytometers in the typical range of powers are not terribly dangerous, as long as care is taken to not look directly into the beam. That being said, leave laser alignment to your service engineers unless you've had formal laser safety training. Any laser can be dangerous in the right context.

For further reading, check out the following: Shapiro, H.M. Practical Flow Cytometry. New York: John Wiley & Sons, 2005.

Part I of a look into your cytometer's optics focused on the dynamics of the lasers and their application in flow cytometry. Understanding the coherence property of lasers and how they impact fluorescence, along with principals of emissions, use of colinear systems and lansing mediums helps you understand the intricacy of the equipment you're using while providing you with the opportunity to troubleshoot during your 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.

4 Gating Controls Your Flow Cytometry Experiment Needs To Improve Reproducibility

Written by Tim Bushnell, PhD

To reproduce reliably in flow cytometry, one must control the gate.

The identification of the target cells of an experiment is the critical first step to performing the secondary analysis that will be used to judge the biological hypothesis and is done by peeling away the layers of cells that do not meet the criteria.

This involves the data reduction method of 'gating' with the researcher as gatekeeper, controlling what may pass and what shall not pass, based on the controls designed for the specific experiment.

It is disappointing to realize that in the paper, Maecker et al., the authors evaluated different models for conducting clinical trials and found that individual labs experienced a ~20% CV in the data analysis whereas a central lab showed only a ~4% variance in data analysis.

One of the best ways to improve gating is to ensure the most appropriate controls are identified and collected in the experiment.

How these controls are used to identify the population of interest is also critical to improving this process. There are 4 common gating controls that can be used for improving gating consistency and reproducibility:

1. Fluorescence Minus One (FMO control).

The term Fluorescence Minus One (FMO) was first introduced in this Cytometry paper in 2001. The FMO control is designed to identify the effects of spectral overlap of fluorochromes into the channel of interest.

This overlap can reduce the sensitivity of measurement in the channel of interest and make identifying the true positive population difficult. The FMO control is performed by staining the cells of interest with all fluorochromes except one. When the data is displayed, the spread of the data in the channel of interest becomes apparent, as shown in the figure below.

Here, human PBMCs were stained with FITC, PE, CY5.5 PE and APC. The left panel shows the unstained sample and the right panel, the fully stained sample. The middle panel shows the PE FMO control.

If the unstained control was used to set positivity, as shown by the red line, it would appear all the cells would be PE positive. However, when the same cells are viewed in the context of the FMO control, it becomes clear that there is spread of the signal, and based on the blue FMO bound line, it is clear these cells are not PE positive.

The FMO control is a valuable control and should be run with all combinations during panel development. Through this development cycle, the researcher will be able to identify the critical FMO controls that are necessary for proper gate placement.

The FMO control is especially essential when attempting to measure rare events, identify emergent markers, or where there is a continuum of expression.

2. Internal Negative Controls (INCs).

Internal Negative Controls (INCs) are those cells in the staining sample that do not express the marker of interest. Unlike the FMO control, where one reagent is left out, the INC is exposed to all the markers, but biologically does not express the marker of interest.

In this case, the INC can help identify and address proper gating when there is non-specific binding of the antibody. This control takes advantage of the fact that we know a bit of the biology of the system and do not expect that the INC cells will bind with the target marker. This, of course, needs to be confirmed in the literature and through experimentation, but leads to a powerful control for proper gate placement. 

In this figure, the data on the left comes from the identified INC cells. They are plotted against CD4+, which is our population of interest.

To help set the gate, a quadrant marker can be used to help track the boundary of the INC. As can be seen, the target cells are clearly positive for the marker of interest, and the INC helps ensure we have identified the correct gate.

3. Unstimulated control.

A third control, useful for stimulation experiments, is the unstimulated control that Maecker and Trotter discuss in their paper from 2006.

The unstimulated control again relies on the biology of the system to assist in setting the proper gate. The unstimulated control also takes into account the background binding of the target antibody, since the unstimulated cells should not be expressing the target. 

As shown in this figure, there is some background binding of the Activation Maker target on the un-stimulated cells. The FMO (left panel) is used to correct for issues of spectral spreading into the Activation Maker channel, but alone does not allow the proper gate placement. It is only when the FMO is combined with the unstimulated control that the best gate placement identified.

4. Isotype control.

The final control to consider is the isotype control. The concept is that one stains cells with an irrelevant antibody that has the same isotype as the target antibody and labeled with the same fluorochrome. This is supposed to allow for identification of the background binding caused by the specific antibody isotype.

The use of this control remains controversial.  

Several papers, such as this one from Keeney et al., call into question the use of isotype controls for setting gates. Maecker and Trotter caution on reliance of the isotype control, and show an excellent figure (Figure 2) where PE-labeled isotype controls show wide variability of staining on small lymphocytes.

When using an isotype control, one makes several assumptions:

1. That the affinity of the variable region on the isotype has similar characteristics for secondary targets as the target antibody.


2. There are no primary targets for the isotype Ab to bind to (and do you know what the primary target is for the isotype?).


3. The fluorochrome to protein (F/P) ratio is the same (and how do you titrate an isotype control?)

We cannot easily know the answer to #1 or #2 and must trust the vendor that the Ab target will not bind to the cells of interest.

Other than with large fluorochromes (PE, APC, etc.), where the F/P is usually 1:1 (due to the size of these fluorochromes), antibodies can have very dramatic optimal F/P ratios for FITC and the Alexa dyes (for example), that have to be optimized out during labelling.

This information therefore has to be collected by the vendor during QC and provided to the customer, something not always readily available on websites. 

The isotype control becomes another variable to be tested, validated, and optimized for marginal gain as a gating control. As Maecker and Trotter state,

“…It is thus a hit-or-miss prospect to find an isotype control that truly matches the background staining of a particular test antibody. And, remembering that we are using the isotype control to help us define the true level of background staining, this becomes a circular proposition…”

Where isotype controls can assist researchers is in assessing the success of the blocking of the cells.  In this case, if the cells are poorly blocked, the isotype control can reveal that, but should not be used to set gates.

In the continuing efforts to ensure consistent and reproducible data, the proper use of controls to establish the boundaries of gates is critical. With the exception of the isotype control, each of the controls discussed above serve a specific role in that process, and should be part of every experiment. This will help reduce the variability in the data in a given experiment, and when the use is communicated (or demonstrated) in publications, it will assist researchers seeking to reproduce the data in achieving similar results, while helping to reduce data analysis variability between institutions.

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.

How To Improve Reproducibility Through The Automated Analysis Of Flow Cytometry Data

Written by Ryan Brinkman, Ph.D.

Editor's Note:  Reproducibility continues to be a critical area that all researchers need to be aware of. From the NIH's focus on reproducibility in grant applications, to a renewed focus by reviewers on the way data has been analyzed and presented, it is imperative that researchers keep up on best practices to ensure they pass these hurdles. 

One area that flow cytometry researchers should be focusing on is the emerging changes in the area of automated data analysis. Over the last five years there have been dramatic changes and improvements in these programs and workflows. As Dr. Brinkman discusses below, the automated analysis of flow cytometry data is coming into its own. 

Flow cytometry (FCM) datasets that are currently being generated will be two orders of magnitude larger than any that exist today, and new instruments, both flow and mass cytometry, have increased the number of parameters measured for each single cell by 50% (to 30).

Even in 14 dimensional datasets there are 16,384 possible cell populations of interest pre-sample (1). The information contained within large and complex single cell datasets can only be realized with approaches to effectively curate, integrate, analyze, interpret, and share these datasets.

What Is Reproducibility And Automated Analysis?

While there are many steps in the analysis pipeline that can benefit from automated approaches for which approaches have been developed (Figure 1), a major bottleneck in the analysis of flow cytometry data is in the identification of cell populations.

Manual analytical techniques lack the capacity and rigour to bring out the full potential of signals latent in the data (1, 2) and its subjectivity has been identified to be the primary source of variation between analytic results (3, 4).

Figure 1: Typical flow cytometry automated analysis workflows.

Analysis usually starts with several pre-processing steps (blue boxes) followed by identification of cell populations of interest (orange boxes) and visualization.

To address this problem, the computational cytometry community has developed a collection of widely used approaches for the high throughput analysis of FCM and Mass Cytometry (CyTOF) (5). Methods have been extensively evaluated against manual analysis through the Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) project (6,7,8) and have been found to meet and in many cases exceed the performance of manual analysis.

Only by taking advantage of cutting-edge computational abilities will we be able to realize the full potential of data sets now being generated and be able to keep up with the quick rate of progress and advancement in our fields.

Further Reading (References hyperlinked above)…

1. Aghaeepour N, Chattopadhyay PK, Ganesan A, O'Neill K, Zare H, Jalali A, … Brinkman, RR. Early immunologic correlates of HIV protection can be identified from computational analysis of complex multivariate T-cell flow cytometry assays. Bioinformatics 2012, 28(7):1009-16.


2. O'Neill K, Aghaeepour N, Špidlen J, Brinkman RR. Flow cytometry bioinformatics. PLoS Comput Biol 2013. 9(12):e1003365.


3. Maecker H, Rinfret A, D'Souza P, Darden J, Roig E, Landry C, … Sekaly R. Standardization of cytokine flow cytometry assays. BMC Immunol 2005. 6:13.


4. Qiu P, Simonds E, Bendall S, Gibbs KJ, Bruggner R, Linderman M, … Plevritis S. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat Biotechnol 2011. 29:886-91.


5. Kvistborg P, Gouttefangeas C, Aghaeepour N, Cazaly A, Chattopadhyay PK, Chan C, … Maurer D. Thinking Outside the Gate: Single-Cell Assessments in Multiple Dimensions. Immunity 2015. 42(4):591-92.


6. Aghaeepour N, Chattopadhyay P, Chikina M, Dhaene T, Van Gassen S, Kursa M, …, Brinkman RR. A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes. Cytometry 2016, 89(1):16-21.


7. Aghaeepour N, Finak G, TheFlowCAPConsortium, TheDREAMConsortium, Hoos H, Mosmann T, … Scheuermann RH. Critical assessment of automated flow cytometry data analysis techniques. Nature Methods 2013. 10(3):228-238.


8. Finak G, Langweiler M, Jaimes M, Malek M, Taghiyar J, Korin Y, … McCoy J. Standardizing Flow Cytometry Immunophenotyping Analysis from the Human ImmunoPhenotyping Consortium. Scientific Reports 2016. 6:20686.

As mentioned above, FCM datasets will soon be two orders of magnitude larger than those that exist today. As such, researchers must keep up on best practices for data reproducibility, especially in the area of automated data analysis. This will ensure that the field of flow cytometry and scientific research overall maintains its integrity while continuing to advance rapidly.

To learn more about how to improve reproducibility through automated 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.

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.