The Difference Between Purity, Single Cell, And Recovery Cell Sorting Techniques

Written by Michael Kissner

If you have experience sorting different kinds of cell types on a droplet sorter, you may have noticed that sorting efficiency often seems closely tied to the cell and sample type.

For instance, you may have experienced a better outcome, such as a higher efficiency and more cells recovered, after sorting lymphocytes versus sorting an adherent cell line. There are some fundamental concepts that underpin this phenomenon, and understanding them will help you perform better cell sorting experiments.

What Is Flow Cytometry Cell Sorting Efficiency?

Sorting efficiency, in fundamental terms, is a real-time measurement, generated by the instrument, of how successfully its sorting system is able to resolve cells that we want to sort (target events) from cells we do NOT want to sort (non-target events).

Note that we are talking about the sorting system's ability to resolve events here (the droplets) and NOT the electronics system.

Efficiency is calculated with the following equation:

The results of this equation are highly dependent on two aspects of the sort: 1) the sort mode chosen for the sort and 2) the setup of the sorting system. Sort modes are sets of rules that instruct the instrument on what to do in situations that I often refer to as “ambiguous.”

In order for the instrument's sort output to be acceptable with respect to the researcher's needs, it is not sufficient to simply tell the instrument WHAT to sort (i.e. assign a sort region), but is also critical to tell the instrument HOW to sort the target population. The HOW is determined by the sort modes.

Purity, Single, And Recovery Cell Sorting Modes

If high purity is critical for the downstream application, the sorter must be instructed to exclude any target events from the sort that fall close to any non-target events (usually within one half of a droplet). Otherwise, a non-target event can haphazardly be sorted along with the target events. This kind of sort mode is often called “Purity” or “Purify” mode.

Alternatively, if extremely accurate counting of the output cells is critical for the downstream application-for single cell sorting, for example-“Single” or “Single Cell” mode is often used.

Finally, sometimes it is important to recover every single cell possible from the sort, and there's not much concern for purity. In this case, we tell the instrument to ignore any rules and to sort everything that falls into the sort gate. These modes are often called “Yield” or “Recovery” modes and will always result in efficiencies of 100%.

Target cells that are sorted are termed sorts, and target cells that are not sorted due to violation of the sort mode rules are often called conflicts, coincidences, or aborts.

Although every type of sorter has its own way of implementing sort modes, all sorters must and do include them. In essence, these modes are comprised of combinations of masks that define where a cell can and cannot be in order for a sort to take place.

What Is A Flow Cytometry Cell Sorter “Mask”?

A certain type of mask, often called the “purity mask,” defines how close a non-target cell can come to a target cell in order to mark that target cell for sorting.

Another mask, often called the “yield mask,” defines how many drops should be sorted in order to include a target cell that may be close to a droplet boundary. In this case, the droplet to sort in order to capture this capricious cell is ambiguous and two droplets may be sorted in order to capture it.

Instruments define where cells fall in relation to droplets in relation to the cells' passage through the lasers. The only place on the instrument where it can “see” cells is at the laser-it cannot measure where the cells are when the stream breaks into droplets-so the system effectively predicts where the cells will fall in droplets, to a certain degree of resolution that depends on the instrument's electronics, by relating the timing of cells as they pass through the lasers with respect to the pattern by which droplets form.

It is important to emphasize here that the instrument's determination of cell positions in droplets are predictions, so there is a degree of uncertainty here that requires a buffer zone between events, determined by the masks, to ensure that the sort outcome is as desired. Additionally, cells may speed up or slow down, depending on the sample type and instrument, between the laser interrogation point and the droplet break-off, compounding uncertainty.

Why Cell Sorters Count Droplets, Not Cells

In addition to sort modes, the sort set-up and sort conditions are tightly bound to the efficiency and sort outcome.

The relevant parameters are primarily the droplet frequency, the event rate, and the percent positive (of the total number of events) of the target population. To understand this relationship, it is critical to keep in mind that when we sort we are NOT really sorting cells, but rather, we are sorting droplets.

In other words, the fundamental sorting unit on a droplet deflection sorter is NOT the cell but is the droplets that (ideally) contain the cell. Therefore, in order to sort, the stream of sheath fluid must be partitioned into discrete sorting units or droplets under controlled conditions.

The number of unique partitions depends on the droplet drive frequency. The higher the frequency, the more droplets are generated per second. Most importantly, the rate of droplet formation, once determined at setup, never changes during the sort.

Understanding the difference between cell sorting efficiency, purity, and recovery cell sorting techniques will help you perform better sorting experiments. When performing a sort, make sure you select the proper sort mode, whether it be purity, single cell, or recovery (the latter is sometimes referred to as yield). Remember, cell sorters use masks to predict which droplets, not cells, to sort. By keeping these facts in mind the next time you sort cells, your experiment will be more successful.

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.

12 Flow Cytometry Terms And Definitions Most Scientists Get Wrong

Written by Tim Bushnell, Ph.D.

The most important part of executing a flow cytometry experiment correctly is actually understanding what you are doing. This means you must understand the terms and definitions that are critical to the field of flow cytometry.

As a scientist, you should not just place your faith in a specialized technician. You should not blindly agree with the data you see in front of you without you yourself knowing what 'logicle scaling' means, what 'differential pressure' means, what happens when you change the differential pressure on an instrument, and so on.

You must also be able to communicate your methodologies and results intelligently. This means, for example, knowing the difference between flow cytometry, flow cytometry cell sorting, and FACS analysis: the latter merely being a trademarked term “owned” by a flow cytometry company.

Top 12 Most Commonly Misunderstood Flow Cytometry Terms

To help resolve this confusion, we have worked with our 700+ Mastery Class members to compile a list of the top 12 most commonly unknown or commonly misunderstood flow cytometry terms here…

1. Autofluorescence

Autofluorescence is the term given to describe the natural fluorescence that occurs in cells. The common compounds that give rise to this fluorescence signal include cyclic ring compounds like NAD(P)H, Collagen, and Riboflavin, as well as aromatic amino acids including tyrosine, tryptophan, phenylalanine. These compounds absorb in UV to Blue range (355-488 nm), and emit in the Blue to Green range (350-550 nm).

The consequence of this autofluorescence is the loss of signal resolution in these light ranges and a decrease in signal sensitivity. Autofluorescence typically increases with cell size. Larger cells have more autofluorescence than small cells due to the simple fact that the larger cells often contain more autofluorescent compounds. 

2. Logicle Scaling

Logicle scaling is an implementation of biexponential scaling published by the Herzenberg lab at Stanford. The biexponential scale is a combination of linear and log scaling on a single axis using an arcsine function as its backbone.

The “logicle” implementation of biexponential was implemented in many popular software packages like FACSDiva and FlowJo. Other types of biexponential scaling exist, including Hyperlog.

Biexponential scales are more generally referred to as hybrid scales and include other variations like lin/log or log with negative. More information on logicle scaling can be found here: Parks DR et al. A new “Logicle” display method.

3. Fluorescence Minus One, or FMO Control

The Fluorescence Minus One control, or FMO control is a type of control used to properly interpret flow cytometry data.  It is used to identify and gate cells in the context of data spread due to the multiple fluorochromes in a given panel.

An FMO control contains all the fluorochromes in a panel, except for the one that is being measured. For example, in the 4-color antibody panel, there would be four separate FMO controls, as shown in the table below. The FMO control ensures that any spread of the fluorochromes into the channel of interest is properly identified.

4. Sheath Fluid

Sheath fluid is the solution that runs in a flow cytometer.  Once the sheath fluid is running at laminar flow, the cells are injected into the center of the stream, at a slightly higher pressure. The principles of hydrodynamic focusing cause the cells to align, single file in the direction of flow.

Depending on experimental needs, different formulations of sheath fluid can be used. Many labs purchase pre-mixed phosphate-buffered saline, while other labs use their own Hepes-buffered saline. The latter is particularly useful for high-pressure cell sorting as Hepes controls pH better at high pressure than phosphate buffers do.

Finally, since the sheath and sample core stream do not mix, you can use water as a sheath fluid on analyzers. Adding a small amount (0.1%) of 2-phenoxyethanol will help as this serves as a surfactant, helping keep the system flowing by reducing the surface tension.

5. Sample Injection Port

In flow cytometry, suspended cells are moved through the flow cytometer's tubing all the way to the interrogation point and finally into the waste (or to be sorted and recovered). To do this, the fluidics components of the flow cytometry are required.

The fluidics are comprised of three components. The first component is a running fluid (or sheath fluid – see above), that runs through the system in laminar flow. The movement of this sheath can be achieved by several mechanisms, the most common method using pressure provided by pumps.

The second component of the fluidics is the sample injection port (SIP). This is where the sample is pushed through the tubing to be introduced to the sheath fluid. Based on the principles of hydrodynamic focusing, these cells are strung out, single file, in the direction of the flow, where they will pass the interrogation point.

The third and final main component of the fluidics is the flow cell, which is where the first two components and the suspended cells themselves come together. 

6. Isotype Control

The“ isotype” in isotype control refers to the genetic variation in the heavy and light chains that make up the whole antibody moiety. In mammals, there are 9 possible heavy chain isotypes and two light chain isotypes. Every antibody will have a specific isotype, and this is available on the technical information spec sheet.

For example, you might have an antibody with an isotype of IgG1, kappa. This indicates the heavy chain is of the IgG1 isotype. Where things get interesting is that these isotypes can have different non-specific binding affinity to cells, which has lead to investigators using isotype controls, as a control to identify where cells are positive or negative.

The issue with isotype controls is that they are not proper gating controls. Instead, these controls should only be used for identifying potential blocking problems. For more on this, read the following paper: Herzenberg, LA et al. Interpreting flow cytometry data.

7. Antibody Titration

Titration is the process of identifying the best concentration to use an antibody for a given assay. While the antibody's vendor will provide a specific concentration to use, this may not be appropriate for your assay.

Performing titration is a simple process: fix the cell concentration, the time of incubation, the volume of reaction, and temperature. The graph below displays an antibody that was used to stain 1×106 cells for 20 minutes on ice. To identify the best concentration to use, the modified Staining Index (SI) was calculated and plotted against the concentration. As is shown by this figure, as the concentration increases above 0.5 μg/ml, the SI decreases, due in part to the increase in the background (non-specific staining).

At concentrations below 0.25  μg/ml, the SI decreases because the antibody is no longer at a saturating concentration. Therefore, the best concentration to use is between 0.25-0.5 μg/ml. Titration helps save money and reagents, ensures the optimal concentration of reagent is being used, and avoids background due to high concentration of antibodies.

8. Differential Pressure

Differential pressure-based flow cytometers currently dominate the market. These systems have two pressure regulators. The first is at a constant pressure that sets how fast the fluids run through the system. The second is regulated by you, the scientist.

As the sample pressure goes from low, to medium, to high, the pressure on the sample increases. This results in the volume of the sample increasing (from ~15 ml/min to ~60 ml/min). The difference between the sample pressure and the sheath pressure is the differential pressure. This controls the width of the core stream and the total number of cells passing the laser intercept point. 

9. Jablonski Diagram

The Jablonski diagram illustrates the electronic states of a molecule as well as the transitions between them. These states are arranged vertically by energy, and grouped horizontally by spin multiplicity.

In the below image, nonradiative transitions are indicated by straight arrows and radiative transitions by squiggly arrows. The vibrational states of each electronic state are indicated with parallel horizontal lines. For flow cytometry, it is important to note that the energy of the emission is usually less than that of the absorption. As such, fluorescence normally occurs at lower energies or longer wavelengths.

10. Bandpass, Shortpass, And Longpass Filters

A bandpass filter is a filter that allows light between a set wavelength to pass through it, reflecting only light above and below the set wavelength. For example, a bandpass filter with a wavelength of 550/40nm would allow light between 530nm and 570nm to pass through, but reflect light below 530nm and above 570nm.

A shortpass filter is a filter that allows light over a set wavelength to pass through and reflects light above the set wavelength. For example, a shortpass filter with a wavelength of 450nm would allow light with a wavelength less than 450nm to pass through the filter, but reflect light higher than 450nm.

A longpass filter, on the other hand, is a filter that allows light over a set wavelength to pass through and reflects light below the set wavelength. For example, a longpass filter with a wavelength of 670nm would allow light with a wavelength greater than 670nm to pass through the filter, but reflect light lower than 670nm.

11. Spectral Profile And Spectral Viewer

Every fluorophore has a unique excitation and emission profile which is usually displayed on a spectral viewer, or spectral graph. The combination of the excitation and emission profiles is the fluorophore's spectral profile. Every fluorophore has a peak excitation wavelength (the wavelength at optimal excitation) and a peak emission wavelength (the wavelength of optimal detection). Each fluorophore will also have a much larger range of excitation and emission wavelengths at reduced optimization. This “curve” is what is displayed on a spectral viewer.

The spectral profile of a fluorophore is used to determine the excitation and detection efficiency at any given wavelength. The spectral profile aids in panel design and selecting optimal fluorophores for a given instrument. The spectral profile can also help in determining compensation considerations. There are numerous resources available to view the spectral profiles of various fluorophores, including this resource from eBioscience.

12. FACS Analysis

Flow cytometry is the science of measuring the physical and biochemical processes on cells and cell-like particles. This analysis is performed in an instrument called the flow cytometer.  FACS Analysis is the shorthand expression for this type of cell analysis. The term FACS stands for Fluorescent Activated Cell Sorting, a term first coined by Len Herzenberg in the 1970's, and later trademarked by Becton Dickinson. Since that time, FACS has come to be used as a generic term for all of flow cytometry, even though it is a specific trademarked term.

Understanding the above terms, as well as the proper definition of each term, will help you perform better flow cytometry experiments. It will also help you intelligently communicate your methodologies and results in grant submissions and peer-reviewed paper submissions. By being careful not to misuse words such as FACS Analysis, or misunderstand words such as logicle scaling, you will be seen as competent in the field of flow cytometry and your grants and paper will stand better odds of being awarded and passing the dreaded third reviewer, respectively.

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 Compensate A 4-Color Flow Cytometry Experiment Correctly

Written by Tim Bushnell, Ph.D.

Compensation in flow cytometry is a critical step to ensure accurate interpretation of data. It is also one of the areas that's steeped in mystery, myths and misinformation.

Before jumping into the best practices for compensation of flow cytometry experiments, it's good to show what NOT to do when performing compensation.

Manually adjusting the compensation values based on how the populations look, or so-called 'Cowboy Compensation' (thanks to Joel Sederstrom for the term), is not the correct way to determine proper compensation.

For example, review the following figure, and ask yourself what is the best compensation value? This figure shows FITC on the Y-axis, spilling into the PE channel, on the X-axis…

 

Without knowing the median fluorescent intensity of the positives in the negative channel, or being able to evaluate the spread of the data, it is impossible to determine which of these above plots display the properly compensated values.

4 Steps To Compensating A 4-Color Experiment

The best practices for compensation involve following some very specific rules.These best practices also involve the use of automatic compensation protocols that are available in all major data analysis software packages.

(If you're interested in following along with this blog, you can find the data used in this experiment at this link here.)

Step 1. Choose the correct carrier for compensation.

Compensation is a property of the fluorochrome you're using in your experiments. The role of the carrier is to bring the fluorochrome to the laser intercept point.

The choice of the carrier is up to you, but for antibodies, the use of compensation beads is strongly recommended. Using beads offers several advantages for compensation, including…

• Cells are not wasted when preparing your compensation controls.


• All the antigen is captured in your solutions, not just some of it. This results in the brightest signal possible for your controls.


• Clear positive and negative signals show up on your control plots.


• Autofluorescence is not a factor since all the beads have the same autofluorescence values.

However, beads cannot be used for some dyes, like viability dyes (such as PI, 7AAD, DAPI), fluorescent proteins, and other protein reporters (redox dyes, JC1, Ca++ dyes).

The biggest concern with preparing proper compensation controls is that the fluorescence intensity of the controls must be at least as bright as that of the cells that the compensation will be applied to. Conversely, the amount of antigen the beads are stained with is less critical.

Very often, compensation beads are stained with too much antigen and as a result, the fluorescent signal goes off-scale. When this happens, do NOT turn down the voltage to bring the signal on-scale. Instead, simply re-stain the beads with less antigen. Often times, staining the beads with 1/2 to 1/10 the concentration used on the cells will keep the signal on-scale, while keeping the signal above that of the cells that the compensation is to be applied to.

Step 2:  Collect the data and make sure there is a sufficient number of events.

After staining the carrier, it's time to collect the compensation controls. Since compensation is a statistical calculation, the more data collected, the more accurate the compensation will be.

As shown in this data below, as the number of collected events increases, the compensation values move towards the actual compensation value.

For bead-based compensation, it's recommended to collect at least 10,000 events. For cells, it's recommended to collect at least 30,000 events.

Step 3. Calculate compensation correctly.

As shown in Tung et al., (2004), how compensation is calculated is based on the matrix algebra.

For the above matrix to be calculated correctly, there needs to be a positive and a negative population in each sample. Since the autofluorescence of the positive and negative carrier need to be matched, you should NOT rely on a universal negative.

All major software compensation packages allow for the use of a single control for the negative population, but again, this should be avoided. In the figure below, unstained beads are shown in red, while unstained cells are shown in blue. As the figure shows, if the experiment is being compensated with beads, and a universal negative of unstained cells is being used, compensation will be incorrectly calculated (note the excess of 'Primary Signal').

However, if unstained beads are used in each sample, the resulting compensation values will be correct. As such, make sure ALL of your samples contain a positive and negative fraction in them. You should also make sure that you gate around each positive and negative fraction to define each compensation control for each specific fluorochrome.

Step 4. Apply the compensation values and inspect the results.

Once your compensation values have been calculated, it's time to apply them to your data. At this point in the compensation process, it's important to inspect your results. For example, the below figure displays data that has been properly compensated using beads.

As you can see above, the data is compensated but the display is troublesome. The reason the data is displayed incoherently is because it has yet to be transformed.

Transformation allows the full spread of the data to be visualized, while removing events off the axis. As shown below, when the correct transformation is applied, the data around 'zero' on both the Y-axis and X-axis is re-plotted. Now the data is shown WITHOUT being compressed against these axes.

Automatic compensation is a flow cytometry best practice. When compensating a 4-color experiment make sure you choose the correct carrier for compensation, collect the data and make sure there is a sufficient number of events, calculate compensation correctly, and apply the compensation values and inspect the results. Failure to properly compensate the data will result in erroneous conclusions which may kill an otherwise promising project. For those who must manually compensate due to their instrument, it's best to under-compensate the data and controls and then bring them into a third party software to finalize the compensation using the software's automatic compensation protocols.

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.

What Is Flow Cytometry Light Scatter And How Cell Size And Particle Size Affects It

Written by Mike Kissner

Light scatter is unfortunately one of the more misunderstood concepts in cytometry.

When you first learn flow cytometry, you were likely told the misleading phrase, “Forward scatter signal intensity is proportional to cell size, and side scatter signal intensity is proportional to cell granularity.”

The short story here is that, yes, this can be the case when we’re dealing with typical cells (consider blood granulocytes, which are bigger than lymphocytes, have more intense FSC signals; granulocytes are more “granular” than lymphocytes and thus have more intense SSC signals). However, the truth is that light scatter complexity is belied by this perennial but unsatisfactory introductory explanation.

While particle size (particle radius) certainly does influence light scatter signal, its intensity is a function of a combination of factors, including:

•   wavelength of laser illumination


•   collection angle, and


•   refractive index of the particle and flow medium (sheath) 

It turns out that light scatter intensity has a strong dependence on the relationship between the size of the particle to the wavelength of the laser. More specifically, particles with diameters that are larger than the wavelength of the laser will scatter light with a different pattern than particles that are smaller than the wavelength of the laser.

Keep in mind that we most typically measure scatter using 488 nm excitation (and sometimes 405 nm excitation), so particles or cells with diameters larger than about 0.5 microns will behave differently than particles with diameters significantly smaller than 0.5 microns.

It turns out that there is a physical theory, named after the German physicist Gustav Mie (1869-1957), that predicts and explains the behavior of light scattering of particles larger than the wavelength of illumination. Essentially, Mie’s theory predicts that the intensity of scattered light has a strong angular dependence.

In other words, the intensity of the signal generated from scattered light depends on the angle at which we collect and direct the light towards a detector. Mie’s theory explains why we use a forward scatter (FSC) detector to measure the light scatter signal of typical mammalian cells, which are much bigger than the wavelength of the illumination source (typically the 488 nm laser).

How Small Particles Affect Forward Scatter

Forward scatter detectors collect light at small angles relative to the incident beam and can take advantage of the fact that cells preferentially scatter light in this “forward” direction.

Additionally, because cells scatter so much light in the forward direction, we can save money and use a less sensitive detector to measure this light. As such, forward scattered light is traditionally and often effectively measured with a photodiode, rather than the more sensitive photomultiplier used to measure fluorescence and side scatter.

However, the story is quite different for particles that are smaller than the illumination wavelength (<488 nm or <~0.5 um), like microvesicles and ectosomes.  It turns out that light scatter by particles of this size range is NOT dependent on the angle at which it is measured. This has very significant implications for small particle analysis. Most importantly, because small particles do not preferentially scatter light in the forward direction as do cells, the resolution in this detector may not be sufficient to measure or even identify these particles above background.

How Small Particles Affect Side Scatter

Side scatter, given its orientation in the quieter fluorescence collection path, as well as its traditional detection by the much more sensitive photomultiplier tube, will likely have much better resolution than forward scatter.

Similarly, we often use side scatter as the “trigger” or “threshold” parameter when measuring small things like bacteria, microparticles, or microvesicles for the same reason—better sensitivity allows us to better distinguish and measure small particles above background.

Interestingly, scatter gets dim VERY quickly when particles have diameters below the wavelength of illuminating light, considering that scatter intensity decreases with a dependence on r⁶ of the particle. The bottom line is that small things like extracellular vesicles can be incredibly difficult to detect using scatter signals. This can make publishing flow cytometry data on small particles very difficult.

What Is An Obscuration Bar?

Instrument manufacturers and operators often take advantage of this property of small particle light scatter by installing an adjustable obscuration bar on the forward scatter detector.

The forward scatter obscuration bar is a universal component of this detector that helps to diminish background in the FSC detector by blocking laser light from interacting with the detector. When no particle is present in the laser beam, scattered laser light hits and is blocked by the obscuration bar. On the other hand, when a particle is present in the laser beam, laser light refracted (scattered) by the particle passes over the bar and triggers signal associated with that particle.

An adjustable obscuration bar can be rotated to expose a wider or narrower surface to the laser beam, blocking more or less laser light, respectively, from hitting the detector. To resolve smaller particles from optical noise, it can be helpful to block more laser light from hitting the detector, which can be accomplished by widening the bar.

Moreover, because small particles do not preferentially scatter light in the forward direction, the proportion of signal blocked by the bar of the total signal is less significant than it would be for larger particles, which do preferentially scatter in the forward direction. This strategy is most effective when a photomultiplier tube is used for forward scatter detection than a photodiode, given that the former is much more sensitive than the latter.

This relationship between particle size, wavelength of illumination, and scatter angle can also help explain the side scatter properties of cells. Typical cellular side scatter signal is much more robustly correlated to granularity than forward scatter is to cell size. In fact, the cytoplasmic “granules” that influence side scatter signal are often smaller than 0.5 um and will thus scatter in a non-Mie pattern. Side scatter of mammalian cells and side scatter of small particles are not terribly different after all.

What Is A Refraction Index?

In addition to the wavelength of the laser and the collection angle of the scatter optics, another factor that significantly contributes to the light scattering intensity of a particle is the refractive index of the suspension medium (sheath, which is essentially water) and the particle itself.

For particles that obey Mie’s predictions, light scattering is largely composed of laser light refraction. When a particle or cell is absent at the interrogation point, the laser’s intersection with the particle or cell produces a characteristic refraction, the “ring of diffraction,” that we are most likely acquainted with on sense-in-air cell sorters. This ring of light spreads outwards from the stream in all directions, in the same plane as the laser beam, and is blocked from entering the forward and side scatter detection paths by the obscuration bars in front of each.

However, the presence of a cell in the laser beam changes the composition of the medium through which the light travels. Laser light now passes through the cytoplasm—which contains protein, lipids, and carbohydrates rather than simply the water it passed through in the absence of a cell—which therefore causes the light to bend differently than it does when it passes through the sheath fluid alone. This refraction of light by a cell is a function of the difference in refractive indices (RI) between the media through which light passes, and it causes laser light to bend in such a way that it passes over the obscuration bar and interacts with the detector, generating scatter signal.

Every type of material has an associated refractive index. When light passes from one material to another—say, from the saline of the sheath fluid to the material of the cell and then back through the sheath fluid again—the amount of light that bends due to this transition is proportional to the difference between the refractive indices between the media. The bigger the difference, the more the scatter.

Why Beads Are Not Good For Calibrating Particles

It is precisely this property of light scatter that makes it a terribly unreliable measurement of cell size.

Two particles or cells of exactly the same size may have different refractive indices, due to their composition (e.g. cytoplasmic proteins), and will therefore generate scatter signals with different intensities. This is also precisely why synthetic beads are also terrible size calibrators.

The refractive index of a typical polystyrene bead (1.59) can be significantly different than a cell’s, resulting in very different scatter signals between a bead and a cell of the same diameter. Given the fact that cells are composed of much more water than a polystyrene bead is and that water’s RI is 1.333, polystyrene beads are going to scatter a lot more light than a typical cell would.

According to studies published in Current Protocols In Cytometry, this situation can be even more severe for microvesicles. The study estimates an average and typical RI of a microvesicle to be approximately 1.39. Given the RI of a polystyrene bead at 1.59 and water at 1.333, a microvesicle’s scatter signal may be one to two orders of magnitude lower than that of the polystyrene bead. Here’s the bottom line—beads are not good at calibrating the scale of a scatter parameter in terms of particle size, regardless of whether the particle is a big one or a small one.

However, all is not lost when it comes to identifying microvesicles. Rather than use scatter as a trigger/threshold parameter to identify these kinds of particles and to measure them, the study suggests that fluorescence is a better choice. Small particles can be labeled with a universal dye that causes all membrane-bound particles in a suspension to fluoresce, allowing discrimination of microvesicles from other particles in the solution. There are nuances and caveats to this kind of labeling, but it can provide a much more robust way to identify membrane-bound microparticles than scatter alone.

Light scatter is a fundamental topic to flow cytometry and understanding how cell size and particle size affect light scatter is critical to performing proper flow cytometry experiments. By understanding how small particles affect forward scatter and side scatter, you can collect better data. Knowing what an obscuration bar is and what a refractive index is, as well as how refractive indices are affected by small particles, will help you design experiments that produce publishable data.

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.

What Is A Statistical Analysis T-Test And How To Perform One Using Flow Cytometry Data

Written by Tim Bushnell, Ph.D.

Designing an antibody panel and running samples on a flow cytometer are not the only steps in a flow cytometry experiment.

After you run your experiment, you have to analyze the data. In particular, you need to perform statistical analyses of the data. This is especially true if you’re hoping to publish your data.

Once all the experiments are concluded and the preliminary analysis of the data performed, you must perform statistical analyses on the data to determine if there is significance in the data.

There are several different statistical tests that can be performed depending on the type of data and the comparisons being made. In the case of either making a comparison against a hypothetical mean, or comparison between two populations, the gold standard test is the Student’s T-Test.

What Is A Statistical T-Test?

The T-Test was developed by chemist William Sealy Gosset, who developed the test while working at the Guinness Brewery as a way to monitor the production of their most famous product.

Since he wasn’t allowed to publish his work directly, the paper was published under a pseudonym in the journal, Biometrika.

Before getting into the details of how the T-Test is performed and how the results are interpreted, there are several factors that need to be kept in mind…

The T-Test makes several assumptions about the data:

• The data is from a Gaussian distribution


• The data is continuous


• The sample is a random sample of the population


• The variance of the populations is equal (If not, there are variations on the theme to address this.) 

There are three major variations on the T-Test:

• One-sample T-Test – compares the mean of the experimental sample to a hypothetical mean.


• Unpaired T-Test – compares the mean of the control and experimental samples.


• Paired T-Test – compares the mean of two samples where the observations in one sample can be related to the observation in the second sample. (For example, the effects of treatment on patients where there is a before treatment and after treatment measurement.) 

The three pieces of information needed to perform a T-Test:

• The mean of both samples


• The standard deviation of both samples


• The number of observations

The T-Test compares the differences between the means of two populations to determine if the null hypothesis should be rejected. At a minimum, to perform the T-Test, one needs the means and standard deviations of both populations, and the number of measurements.

The researcher also needs to set the threshold value, also termed the α. We will compare this threshold to the P-value. If the P-value is greater than the α, there is no significance in the data. However, if the P-value is less than the α, there is significance in the data.

What Is A Null Hypothesis (H_O)?

Simply stated, this is a statement about the relationship of the above two populations.  Mathematically, this can be expressed as:

μ_A = μ_B

The null hypothesis makes the assumption that our experimental results are from random variation. If, during the statistical analysis, the data is sufficient to show that random variation is not a sufficient explanation for the data, the alternative hypothesis (H_A) must be accepted. 

A One-Tailed Versus A Two-Tailed T-Test

A T-Test can either be one-tailed or two-tailed. The above example would be an appropriate null hypothesis for a two-tailed T-Test—that is, when the investigators do not know if the treatment will cause an increase or decrease in the measurement. If the investigators expect the treatment will cause an increase OR a decrease, a one-tailed T-Test is more appropriate. 

How To Run A T-Test

In the following example, the researchers sought to determine if the percentage of CD4+ T-cells in patients who had Irumodic Syndrome was increased after treatment with Byphodine.

The percentage of CD4+ T-cells was measured on PBMCs before treatment and one week after treatment. Considering this information, this is how you would proceed to run a T-Test… 

1. Establish the null hypothesis.

“In patients with Irumodic Syndrome, treatment by Byphodine either decreased or caused no change in the percentage of CD4+ T-cells.”

In this case, since the researchers are not concerned if the treatment causes a decrease in the CD4+ cell, a one-tailed T-test will be performed, and can be written as: 

μ_A ≥ μ_B

2. Determine the alternate hypothesis.

“In patients with Irumodic Syndrome, treatment by Byphodine increases the percentage of CD4+ T-cells.” 

3. Establish the threshold.

By convention, the α is typically set to 0.05. This comes from work by R.A. Fisher who stated in his work Statistical Methods for Research Workers (13^th Edition): 

The value for which P=0.05, or 1 in 20, is 1.96 or nearly 2; it is convenient to take this point as a limit in judging whether a deviation ought to be considered significant or not. Deviations exceeding twice the standard deviation are thus formally regarded as significant.

There are cases where the threshold can be changed. Increasing the α makes it easier to show significance at the expense of committing a Type I statistical error (false positive). Decreasing the α makes it hard to show significance, and increases the chance of committing a Type II statistical error (false negative). Care must be taken, however, to ensure that the reason for the change is well-documented and spelled out.

For this example, we will set the α to 0.05. 

4. Collect the flow cytometry data.

Following all best practices, with a well-controlled instrument, all appropriate gating and reference controls used to generate the data table below. 

Table of %CD4+ PBMCs

Pre-treatment	Post-treatment
18.5	26.7
20.1	22.2
25.2	34.5
16.5	23.6
23.3	29.6
22.6	29.1
18.0	40.1
19.3	35.3
17.4	39.5
19.9	31.4

Once this data is entered into our statistical analysis package of choice (we personally use Graphpad Prism), we can generate an appropriate graph: 

In the above case, the data is plotted, with the mean and standard deviation plotted.

When the one-way T-Test is calculated, the P-value is 0.0003, which is lower than the threshold. Therefore, the null hypothesis is rejected, and the alternate hypothesis is accepted. As a result, this data supports the conclusion.

The use of the T-Test makes the assumption that the data follows a normal distribution.  If this is not the case, there are non-parametric tests that will allow for the statistical analysis similar to the T-Test. These include the Wilcoxon test and the Mann-Whitney test. In non-parametric tests, the data is ranked according to the value (from lowest to highest), regardless of where the data comes from.

Non-parametric tests test the null hypothesis that the data is distributed at random, with the alternate hypothesis being that the data is not randomly distributed, but one population has larger values than the other.

The Student’s T-Test is an essential tool in the researcher’s toolkit to confirm that the data generated in the course of the investigation supports the hypothesis driving the research. Proper application of the T-Test (and related non-parametric tests) to determine statistical significance in the data will improve confidence in the conclusions of any published work. Following the steps outlined above will allow the researcher to correctly apply the proper statistical tool for their data.

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.

When Should I Use Flow Cytometry Isotype Controls?

The modern world is changing quickly, and nowhere is that more true than in the world of science. You may have used something last week or last month, only to find out it has become old and obsolete, and all of a sudden you're living in the past.

The Importance Of Isotype Controls In Flow Cytometry

This is true in the field of flow cytometry as well. Take isotype controls, for example. At one point these weren't just the most effective negative controls, they were the only ones! And now, many believe their time is long gone.

But are these claims true? Is it time to put isotype controls out to pasture and move on?

Find out more on isotype controls (◄ read the full article here), and how you should (or shouldn’t!) make use of them in your tests from ExCyte, the leading authority on flow cytometry!

Of course, knowing when to use isotype controls is merely one of the numerous factors you need to know about flow cytometry clinical applications! One more is knowing when to use the right flow cytometry analysis software!!

Flow Cytometry In Clinical Diagnosis

Flow cytometry has made a big impact in the scientific world over the last several years, and its uses have gotten more common.

Running An Effective Flow Cytometry Experiment

So for those serious about boosting their STEM career, or getting it off to a good start, it doesn't get much better than this!

But clinical flow cytometry experiments are not an easy thing to do. Even the most skilled flow cytometrists could wind up missing out on certain crucial details and coming up with bogus or incorrect data.

Follow these Six tips, though, and your knowledge of and proficiency in flow cytometry clinical applications (◄ check out the full post here) will be better for it!

To get more on flow cytometry, like the significance of fluorescence staining and FACS data analysis, these posts will help!