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Blog - A Guide To Establishing Baseline PMT Voltages
In any lab, reproducibility is crucial for long term success. Since many flow cytometry instruments are highly mutable, reproducibility can become a challenge. Unknowingly having a filter set swapped out or an insufficiently powered laser can be detrimental. Therefore, it is vital to ensure your highly tuned instrument is performing at its best, so you have no doubts about the data after you run your sample.

The Lord of The Rings, New Line Cinema.

To assure that instruments are performing optimally over a long period of time, beads with embedded fluorescence are often used to find voltages that both minimize electronic noise and maintain the fluorescence signal in a linear range for each detector. Typically baseline assessments of the specific channels are performed when any major change occurs to the instrument itself. A common misconception is that the voltages automatically assigned by these beads are suitable for your particular experiment. This is not the case, as these beads are used to follow an instrument's health and should not be utilized to establish experiment-specific PMT voltages. If you were to use the baseline voltages for your multiple color assays, you would have optimized your assay for beads rather than your cells.

Comic by Sidney Harris.

There are multiple ways to assess these baseline voltages. Some instruments use specific beads called Cytometer Setup and Tracking (CS&T) beads to determine baseline voltages known as "application settings". These beads are composed of three different fluorescent intensities ranging from dim to bright. After running CS&T baseline, the bead-specific software calculates the standard deviation of electronic noise. For these beads, the PMT voltage that is selected is 10 times greater than the electronic noise for each channel.
If your current setup does not use CS&T beads or you would like to perform this type of baseline assessment manually, you're in luck. There are several other methods for calculating these values using fluorescent beads. In Stephen Perfetto's 2012 Nature Protocol paper, the authors validated a method where you run both a bead that has multiple fluorescent intensities and an unstained non-fluorescent compensation bead in 50 V increments from 350 to 800 V. The unstained compensation beads are meant to represent the background noise. Once these runs have been completed, you then plot the negative population and the positive populations for each voltage increment. The baseline voltage range for each channel is then selected based off the voltage increment that has the greatest separation between the negative and positive populations (based on MFI or SI), while the linearity (the MFI between the different fluorescent intensities) remains consistent. While this protocol is highly practical, it does require a large amount of time and only delivers a baseline range rather than an absolute baseline that the CS&T beads and software provide.

Alternatively, if you are interested in a method that is a bit less cumbersome, you could instead establish the baseline voltages off the inflection point of Rainbow Calibration Particles, peak 2. Similar to the Perfetto method, this baseline assessment would still require you to acquire the beads at varying voltages. But, instead of determining the maximum separation between negative and positive peaks, you would only need to collect the CV of the dim population. For each voltage increment, both the CV and PMT voltage are recorded. These data points would then be plotted for each specific detector. An inflection point at which the CV remains consistent would serve as the baseline voltage for each specific channel. It's worth noting that this method relies entirely on the dim population of beads.

Dexter's Laboratory, Cartoon Network.

While there are tradeoffs to all three of the methods mentioned above, each provides a unique way to standardize your flow cytometer. These baseline settings will help monitor for any changes or issues that might occur due to problems associated with the laser, electronic noise, filters, and PMTs. Any swings in these voltages should be investigated further. Lastly, it's important to remember that these baseline settings are not meant to be experiment-specific. However, following these protocols will set you on the right track to producing reliable, consistent data with your flow cytometer. Stay tuned for a follow-up blog that delves into voltage and PMT settings for experiments.


  1. Quality assurance for polychromatic flow cytometry using a suite of calibration beads 
  2. Flow cytometry controls, instrument setup, and the determination of positivity 
Contributed by Sean Cosgriff.

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Blog - DNA Dyes in Flow Cytometry and Microscopy

"Oh well, Mr. DNA, where did you come from?"
-John Hammond, Jurassic Park. Universal Studios.

As your introductory science classes taught you, DNA is the blueprint for life. And, it tends to be an important focal point in many researchers' work. In this blog, we'll go over how DNA-binding dyes work and how they can be used in flow cytometry and microscopy applications.

Why are we looking for DNA?

DNA can provide lots of insight into the inner workings of a cell. In flow cytometry, we can use DNA-binding dyes to help assess the cell's health. If a dye is not cell-permeant (more on this later), it's excluded by a healthy and intact cell membrane. If that membrane is damaged or the cell is going through apoptosis, the dye can then finds its way to the nucleus and stain DNA. While some people will rely solely on forward and side scatter profiles to assess viability, this is not always reliable and may not include cells in the early stages of apoptosis that have not shrunken down as they die and become debris.

Versatile Helix NP™ Dyes

C57BL/6 mouse thymus cells were fixed using 70% chilled ethanol. The cells were incubated for one hour at -20°C, washed, then stained with Helix NP™ NIR at 5 μM. HeLa cells were fixed, permeabilized, and blocked. Then the cells were intracellularly stained with Alexa Fluor® 488 anti-Cytokeratin (pan reactive)(green) antibody followed by Helix NP™ NIR (red)
DNA dyes can even show you what stage of the cell cycle the cell is currently in. Due to the stoichiometric binding of nucleic acid stains, cells in the G2 or M phase have double the DNA content of a normal cell before dividing and thus, stain with much higher levels of Helix NP™ NIR and have a higher mean fluorescence intensity. DRAQ5™ and CytoPhase™ Violet can be used in a similar manner. In microscopy, the applications for DNA dyes are a little different. DNA dyes are typically used to counterstain cells or tissues. This gives better contextual and localization clues to proteins you've stained with antibodies.

Traits of DNA Dyes

Some of the important traits to understand about your DNA dye include permeability and their mechanism of action. Refer to our handy chart to keep track of these characteristics.


Permeability is a simple enough concept: can the dye get into the cell to bind DNA if the membrane is intact? Permeability will define the type of assay your dye is useful for. If it's cell-permeant, it's less suitable for a viability assay since it will get into every cell regardless of its health status. Instead, it will work better to give you an idea of a total cell count or cell cycle status. If a dye is not cell-permeant, then it can be helpful in assessing viability. For our Helix NP™ dyes, NP indicates they're "Not cell-Permeant" so they're helpful assessors of cell health. Similarly, DRAQ7™ and Propidium Iodide are also excluded by healthy cells with intact membranes. Other dyes like DAPI and 7-AAD are classically considered not to be cell-permeant. However, they are actually semi-permeant, meaning at high concentrations, they'll get through a live cell's membrane and stain, but to a lesser degree than a dead cell would.

Mechanisms of Action

It can also be helpful to understand how these dyes are binding to DNA. Different interactions with DNA have different affinities. The higher the affinity, the tighter the binding and the more easily you can resolve DNA and see more than just a haze around the nucleus. We'll be focusing on dyes that bind the minor groove and intercalators. The minor groove is the narrower of the two grooves formed by the double helix structure of DNA. Minor groove binders generally have a lower affinity than intercalating dyes. Minor groove binders may be a little less flexible as they must follow the groove as it twists around the axis, coming into contact with the edges of base pairs. Binding typically occurs through non-covalent means (i.e., hydrogen bonding of the probe to base pairs).


DNA intercalators actually insert or "intercalate" non-covalently between two sets of adjacent base pairs, causing them to separate and create a pocket for the dye. A part of dye's structure, such as a hydrophobic aromatic ring that bears resemblance to a ring of bases in DNA, can insert itself between sets of base pairs. This can actually cause the DNA to become distorted. Bis-intercalators contain two intercalating moieties connected by a linker that interacts with a groove. As such, they have an even higher affinity than single intercalators and have even been used to image single strands of DNA. Minor groove binders and intercalators can increase in fluorescence due to conformation changes upon binding DNA. It is important to notice that most DNA-binding dyes can be dislodged if the samples are fixed or permeabilized. This is particularly important in scenarios where organic solvents with DNA-denaturing properties are used. For example, methanol is commonly used to help solubilize paraformaldehyde in certain fixation solutions. This can lead to false positives and negatives, making it harder to interpret your data. Alternatively, you can analyze viability with Zombie Dyes, which bind amines on proteins and are more well-suited for fixation and permeabilization conditions.
Hopefully, now you have a better understanding of how DNA-binding dyes work and which one you should choose for your applications. If you want to learn more about DNA dyes, other chemical probes, and their applications, check out our Cell Health and Proliferation Webpage. And if you still have questions, contact our tech support group.

Contributed by Ken Lau, PhD.

So that’s where Spider-Man got that catchphrase.
Comic by the Amoeba Sisters.

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BioLegend Releases Stickers for iOS
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BioLegend Releases Neuro Snap for iOS
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Blog - CRISPR wins the Kavli

30 Rock and Saturday Night Live, NBC Studios

Every once in a while, a scientific advancement pushes the boundaries of human capabilities in ways that seem nothing short of magical.

For their invention of CRISPR-Cas9, Emmanuelle Charpentier, Jennifer A. Doudna, and Virginijus Šikšnys recently received the 2018 Kavli Prize in Nanoscience. Certainly, no (ahem) small feat. But what is CRISPR-Cas9 and how does it work? Today, we will explore a bit of the history behind this revolutionary technology and why is it creating such a large stir in the scientific and lay communities alike.

Nanosurgery for the Genome

First awarded in 2008, the Kavli Prize in Nanoscience recognizes outstanding contributions to study of the absurdly small. CRISPR-Cas9, a new technology designed to manipulate life at the genomic level, is an exciting example of how nanoscience may be applied to solving large problems. CRISPR stands for Clustered Regularly Interspaced Short Palindromic Repeats, which is just a fancy way of saying short nucleotide segments that read the same forward and backwards. Cas9, standing for CRISPR associated protein 9, is an RNA-guided DNA endonuclease. Simply put, the CRISPR-Cas9 system is like a pair of scissors that allow researchers to cut DNA at precise locations to add or delete genetic material.

Co-opting Bacterial Immunity

CRISPR-Cas9 is derived from the mechanism bacteria use to guard against viral infection. Consecutive DNA repeats separated by non-repetitive spacer sequences were first observed in E. coli in 1987 (1). Jansen and colleagues coined the term CRISPR to describe them, and hypothesized a functional relationship with a set of CRISPR-adjacent genes (cas) encoding proteins with helicase and nuclease motifs (2). Researchers later showed that CRISPR spacer sequences were comprised of foreign genetic material, incorporated following bacteriophage infection. Eventually, it was discovered that a complex consisting of Cas proteins and multiple spacer-derived RNA transcripts could provide E. coli immunity against a DNA virus (3). In essence, it was determined that the CRISPR-Cas system amounts to a form of adaptive immunity; bacteria hold onto pieces of viral DNA to "remember" past infections. If the bacterium encounters the virus again, the transcribed RNA is used by Cas as a guide to cleave the foreign DNA.

A new approach to an old idea

Emmanuelle Charpentier, Jennifer A. Doudna, and Virginijus Šikšnys simplified this system around one of these Cas proteins, Cas9 (4,5). They showed that a single RNA guide strand, combined with the Cas9 protein, could be reprogrammed to cleave DNA at a desired site. Non-homologous end joining or homology directed repair in the presence of a DNA repair template produces deletions or insertions. In the lab, researchers can now create short guide sequences of RNA (sgRNA) directed against a target sequence. The sgRNA is then joined with Cas9 in a plasmid and transfected into cells by viral or non-viral means. Aside from knockouts or knock-ins, dead Cas9 (dCas9) lacking nuclease activity can be used to induce gene expression when fused with a transcriptional activator. As a practical laboratory procedure, genomic editing with engineered nucleases has been around for a while. Prior to CRISPR-Cas9, zinc finger nucleases (ZFN) and transcription activator-like effector nucleases (TALEN) were the predominant genomic editing technologies. Both ZFN and TALEN can create site-specific double stranded breaks, but require the difficult and time-consuming process of creating a custom protein for each target sequence. The large advantage of CRISPR-Cas9 is that designing an sgRNA sequence is much more straightforward, and adding more than one sequence makes it easily multiplexable (6).

Limitless possibilities

The excitement generated by CRISPR-Cas9 is understandable, given the multitude of ways mankind stands to benefit from this technology. The applications for treating and eliminating disease are staggering. HIV, a virus that integrates itself into the host genome, is a particularly attractive target for a potential CRISPR-based therapy. Kaminski et al. succeeded in excising portions of integrated HIV-1 DNA in living mice, and again in latently infected CD4+ T lymphocytes from HIV patients (7,8). In the area of cancer medicine, several clinical trials are currently underway investigating PD-1 knockout T cells and CRISPR-Cas9 edited anti-CD19 CAR-T cells (9).

CRISPR-Cas9 has amazing potential not only to treat disease, but to increase crop yields, reduce pesticide use, produce biofuels, or even produce wooly mammoth/elephant hybrids. Mammophants aside, CRISPR-Cas9 represents a valuable tool for research as we even use it at BioLegend to validate knockdown/knockout targets. CRISPR-Cas9 is a promising source of new medicines and an exciting look into what the future may hold.

Image courtesy of Dzu-Doodles.

  1. Ishino Y, et al. 1987. Journal of Bacteriology. 169(12): 5429-33.
  2. Jansen R, et al. 2002. Molecular Microbiology. 43(6): 1565-75.
  3. Brouns S, et al. 2008. Science. 321(5891): 960-964.
  4. Jinek M, et al. 2012. Science. 337(6096): 816-21.
  5. Karvelis T, et al. 2013. Biochem Soc Trans. 41(6): 1401-6.
  6. Wang H, et al. 2013. Cell. 153(4): 910-8.
  7. Kaminski R, et al. 2016. Gene Ther. 23(8-9): 690-5.
  8. Kaminski R, et al. 2016. Scientific Reports. 6:22555.
  9. Zhan T, et al. 2018. Seminars Cancer Biology. S1044-579X(17)30274-2.
Contributed by Christopher Dougher, PhD.

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Blog - Curve Fitting for Immunoassays: ELISA and Multiplex Bead Based Assays (LEGENDplex™)
Traditional sandwich ELISAs and bead-based multiplex immunoassays, such as LEGENDplex™, are frequently used to detect and quantify specific analytes within a biological sample. These samples include serum, plasma, cell culture supernatants, and other biological matrices. In order to determine the concentration of an analyte within a sample, one must run a standard, or calibration, curve. The production of a standard curve requires the use of known concentrations of the analyte being assayed. Performing a quantitative immunoassay asks one to plot an x-y plot that shows the relationship between this standard (analyte of interest) with the readout of the assay, e.g. optical density (OD) for ELISA and mean fluorescence intensity (MFI) for LEGENDplex™. The concentration of the analyte in the sample can then be calculated using the OD or MFI.

Before samples can be analyzed, it is important to choose the best curve fit model to achieve the most accurate and reliable results. Thankfully, if you choose the appropriate software, the analysis will be done for you and you do not need to use all of the formulas discussed later in the blog. However, they are important for understanding what curve to choose for your analysis.

Linear Regression and Sum of Squared Residuals

The most straightforward way to analyze your immunoassay data is to use a linear regression curve fit. This generally means plotting the concentration vs. the assay readout (OD for ELISA or MFI for LEGENDplex™) and using that equation we all learned in basic algebra: y = mx + b

You remember that, right?!? The concentration is generally represented as x, the assay readout as y, with m referring to the slope and b referring to the y-intercept where x = 0.
Your aim is to find values for the slope (m) and y-intercept (b) that minimize the absolute distance from the data point to the curve, also known as the residual.
The ideal assumption is that the best-fit linear curve will be a line that passes as close as possible to all data points from the standard curve. The question that arises from this is, "How is this assessed?" This is where the concept of a 'residual' is introduced. Since the best fit line will be the one that passes closest to all data points, it should seem natural that we could simply sum the residuals of all data points and the line with the lowest sum would be the best.

However, there is an underlying problem here that needs to be addressed. Take an over-simplified example where we are looking at residuals from just 2 data points, A & B. Now, imagine we fit 2 linear curves to the data. The first gives residuals of A = 1 and B = 9, and the second gives A = 5 and B = 5. If we sum the residuals, both curves give the same answer of 10. This is problematic since mathematically they are "equivalent", but clearly the second curve fits the data better as it passes closer to both data points. More simply put, 5 for each is a better fit than 1 and 9. 

The solution to this issue is to square the residual values first, and then add them together. By transforming the data like this, curves with poorer fits and larger residuals will be scored higher and become less desirable. To revisit the example from above, 52 + 52 = 50, and 12 + 92 = 82. Rather than being mathematically equivalent, now the better fit curve has the lower sum of squared residuals.

This is referred to as sum of squared residuals, and the smaller this number is, the better the curve fit your data.

Got it? Easy, right?

The Simpsons™, Fox Television.
However, since immunoassays are used for biological measurements, they almost never follow a linear response. In fact, the instruments that we use to measure these responses (OD, MFI, etc.) also have upper limits. Have you ever seen a biological system that increases or decreases forever as the curve moves towards infinity? No. Due to the complex nature of the biological systems being assayed, a more complex form of modeling must be used.

Non-linear Curve Models: 4-Parameter Logistic (4PL)

Immunoassay standard curves typically produce an S-shaped sigmoidal curve, which requires a different kind of mathematical modeling called logistic regression, that allows for curve fitting beyond the linear range of the curve. This new range is referred to as the logistic range, and is most simply described by a 4PL curve. This type of modeling still uses the underlying concept of summing the square of the residuals, but instead of minimizing residuals for a straight line, we're now doing so with an S-shaped curve that is defined by the following parameters.
This type of analysis uses an equation that has a maximum and minimum incorporated into it, and 4 parameters, hence the name. If your data produces a symmetrical, S-shaped curve, a 4PL fit should be sufficient to analyze your data.

Non-linear Curve Models: 5-Parameter Logistic (5PL)

At times when running an ELISA, or more complex multiplexing assays such as LEGENDplex™, you may not get a pretty, symmetrical curve. What do you do then? There is an additional parameter that can be added to the 4PL equation, thus allowing one to do a 5PL curve fit. This fifth parameter takes into account an asymmetry factor, g, and provides a better fit when the curve does not have symmetry.

Futurama™, Fox Television.

How well does your model fit your data?

Using the appropriate curve fitting model is important for generating reliable, high quality data. The "goodness of the curve fit" refers to how well a curve fits the data that has been generated. Linear regression uses the R2 value as a good representation of the "goodness of fit". A curve is considered to have a very good fit when the R2 value is over 0.99.

We have discussed how linear regression analysis may not be the best for complex biological assays, and the need for more complex modeling is generally necessary. When using a 4PL or 5PL analysis, evaluating the "goodness of fit" is a little more complicated. Two methods of doing so are to measure the recovery of the standards and to perform a spike recovery. 

Recovery of Standards

The recovery of standards allows one to measure the accuracy of the observed concentration that was calculated for the expected concentrations of each standard. Basically, you calculate the concentration of each standard and compare it to the actual concentration using the following equation:

The closer the recovery is to 100%, the better the curve fit model being used. The general rule of thumb says for accurate quantification, the recovery should fall between 80-120%. Using logistic regression (4PL or 5PL), rather than linear regression, will allow for more accurate quantitation across a wider range.

Spike & Recovery

Spike and recovery is used to test the accuracy of your assay. Spike and recovery experiments are generally used to assess if your sample matrix (plasma, serum, etc.) is causing interference with the ability of the capture and detection antibodies to bind to the target protein being assayed. By adding, or "spiking", a known concentration of the recombinant standard into your sample and comparing this to the same concentration of recombinant spiked into the standard diluent, or blank, you can assess whether anything in your sample matrix is causing interference. You should always include your sample with no spiked recombinant so you are able to measure any endogenous protein that may already be there. All three of these samples are measured and concentrations are determined relative to the standard curve. As with the standard recovery, your spike recovery should also fall between 80-120%. Learn more about spike recovery and it's importance in a previous blog: The Matrix Effect: What is it? And more to avoid it! 


The most common curve fitting models used for ELISAs and multiplexing immunoassays are linear regression and logistic regression. As discussed, the results for biological assays may not fall within the linear portion of the curve, so the need for logistic regression analysis such as 4PL or 5PL is almost always recommended. If your data produces a symmetrical curve, a 4PL curve fit will be sufficient. On the other hand, if the curve produced is asymmetrical, it is best to introduce that fifth parameter and use a 5PL curve fit.
Contributed by Kellie Johnson.

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Podcast Episode - Kissing Disease Autoimmunity, Nipah Virus, and Premature Births
In our newest podcast, we talk about autoimmune diseases linked to the Kissing Disease, concerns over the emerging Nipah Virus, and how fetal immunity may be causing babies to be born early.


Epstein-Barr Virus and Autoimmunity (2:20-12:00)
Nipah Virus Outbreaks (12:01-20:35)
Bacteria Coats itself with IgA for Protection (23:23-32:23)
Out of Whack Fetal Immune Systems Prompt Preterm Labor (35:45-43:33)

Keywords: autoimmunity, kissing disease, mononucleosis, Nipah Virus, herpes, Epstein-barr virus, cancer, bats, reservoir, Bacteroides fragilis, IgA, gut, mucosa, bacteria, pregnancy, premature birth, fetal immunity, preterm

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Blog - Rule the Magnet! Tips on improving your magnetic cell separation results

Obtain good purity and yield from your magnetic separation experiments.
Become Magneto (of the bench). Marvel Enterprises.

Our MojoSort™ magnetic cell separation reagents, as well as others in the market, offer convenient, efficient, and cost-effective ways to obtain purified cells of interest. But, as with any other procedure, you may run into some difficulty with your separation experiment. Perhaps your cells are not as pure as you'd want/expect, and you don't know how to get rid of the cells you don't want? Or, maybe the resulting number of cells you get out in the end (i.e. yield) is not very high, as if you're somehow losing a lot of cells in the process? In this blog, we'll discuss some of the common issues that can yield suboptimal results from a magnetic separation experiment, how to overcome them, and how you can bring some mojo back to your experiment!

Types of MojoSort™ Products

First, let's clarify some terminology that will be referred to in this entry, as well as in the nomenclature of some our MojoSort™ products. You can also refer to our previous blog to review some of the terminology being used below:

Positive vs. Negative Selection:

After positive selection, the final cells of interest will be bound to the magnet. Our "Selection Kits" include cell type-specific antibodies, along with nanobead-conjugated secondary reagents (e.g. Streptavidin-nanobeads) to purify the cells of interest as advertised. You can find an example of one of our selection kits with the MojoSort™ Human CD14 Selection Kit. In a negative selection (or isolation) experiment, the cells of interest are not bound by the magnet and are ultimately eluted out in the negative fraction. All other unwanted cells are depleted out in the magnetic-bound fraction via the use of antibody-nanobead cocktails. One major advantage of using negative selection is the cells of interest in the end are not bound to magnetic particles. This may be preferable if there are any concerns about the interference of magnetic particles in a sensitive downstream bioassay or application. The difficulty in designing this on your own is that you must be able to efficiently deplete all other cells in a given sample in order to get good purity, which might be difficult to do comprehensively without trial and error. Our isolation kits are optimized to deplete out all other cells in a given sample except for the cells advertised. For instance, our MojoSort™ Human NK Cell Isolation Kit contains a cocktail of antibodies to deplete out all other cells from healthy PBMC samples except NK cells, as analyzed by CD56+CD3- surface marker phenotype shown on the product webpage.

Direct vs. two-step isolation:

Direct separation refers to the use of magnetic particles that are directly conjugated to the antibodies. Two-step utilizes antigen-specific primary antibodies, followed by Streptavidin-magnetic particles (in the case where antibodies are biotinylated), or other magnetically-bound secondary reagents, such as our anti-PE and anti-APC nanobeads. MojoSort™ products that are labeled as "nanobeads" contain antibodies that are directly conjugated to magnetic particles, and our "selection kits" or "isolation kits" contains reagents for a two-step selection method.
Suppose you perform a magnetic isolation experiment for B cells on murine splenocytes, stain and analyze using PE anti-mouse CD19, and you obtain the following result (under "reality"):
So, how can you break down your result and make improvements to your experiment?

Identify the perpetrator. 

With low purity, the source of "contamination" is likely originating from one cell population. Try and identify this population. When assessing purity via flow, analyze the plots using multiple markers and parameters instead of just looking at a marker associated with the cells of interest in a histogram. Knowledge of the starting cell populations/contents is useful here, as is having a good set of controls to identify them. If you are using a "home-made" magnetic depletion cocktail instead of a pre-optimized kit from manufacturers, honing into particular cell populations allow you to pinpoint where you need to optimize your experiment, such as adding markers specifically for those cells to better deplete, and titrating associated reagents.

A couple of additional tips here:
  • Back-gate: when analyzing the sample via flow cytometry for the marker pertaining to your cells of interest, gate the contaminating cells for analysis. For instance, if you're isolating human CD3+ T cells and you're finding a lot of CD3- cells from your PBMC preparation, gate the CD3- cells to see if you can find any parameters, such as FSC/SSC scatter profiles, to possibly identify the contaminating cells. By looking at the scatter profile, you may be able to distinguish the contaminating population is actually from monocytes or dead cells. 
  • Anticipate failure: This sounds pessimistic, but if you're trying a separation protocol for the first time, it can help you save some steps, samples, and time getting a working protocol for you in the long run. Based on your knowledge of the starting material, you can pre-plan a multicolor panel of general phenotyping markers to co-stain, not just to label the final cells of interest but also the anticipated contaminants. Ask yourself - If my purified sample was to be contaminated by another cell type, who would it likely be, based on the sample? Do you need tips on how to identify some of these cells, and what the expected cell frequencies are in a starting material? You can take a look at our essential phenotyping markers page to get a quick idea for a panel, and a previous blog entry on expected cell populations. Of course, you should also carry an unsorted, starting material cell population as well for comparison after separation.

Co-staining a sample and analyzing on a two-dimensional bivariate plot instead of a histogram can reveal a lot of information. In the B Cell isolation example above, co-staining with APC anti-mouse CD3 suggests that the remaining CD19- population (red box) is primarily T cells (~32%). Perhaps this is where you can optimize your experiment, to try and remove more T cells from your sample:

The cells are too concentrated in the separation experiment

A common issue leading to poor magnetic cell separation is a staining reaction with a sample that is too highly concentrated with cells. I know that sometimes it's cumbersome to count out the cells you have in your sample, and just taking 100 µL straight out of your original sample suspension allows you to cut so much time and hassle! However, this step is crucial - if your sample is too concentrated, it can affect antibody (and any other secondary reagent) binding its target antigen sufficiently and/or increase non-specific binding, causing more cell aggregation, amongst other unwanted consequences. Ultimately, this can affect both purity and yield. If using a column, applying too many cells per run can also clog it. Make sure you are following manufacturers instructions on how many cells can be subjected to each cell separation assay. Also verify the column manufacturer's recommendation so samples are diluted to a sufficient volume before they're added to the columns, and pre-equilibrate columns with proper buffers as necessary.

For most MojoSort™ reagents, a standard test consists of 1 x 10^7 cells in a 100 µL reaction volume. You can always scale up as needed, but try to always keep the same cell/volume ratio. When starting with less than 10^7 cells, I'd recommend staying with the 100 µL reaction volume. Manual cell counting can yield variability, so make sure you have a relatively precise number. If you're concerned about precision, try counting a couple of replicates.
Don't get too physical

Take your time, and be gentle. If using our MojoSort™ magnetic separator (or equivalent), if you rotate the tube while inside the magnet during incubation, it can cause the magnetically-bound cells to come loose from the magnet. So, during this incubation step, leave the tube alone. Don't aggressively shake the tube to get your unbound samples out- use a smooth, decanting motion to take out your negative fraction. If using a column, let gravity do the work and allow time for samples to naturally drip through. Don't plunge the bound samples out until all of the negative fraction has dripped through.

Pour out cells in one smooth motion. Just like Dr. Evil. Naturally, he has great MojoSort™ technique.
New Line Cinema.
Low yield? Labeling too much... or too little?

The likely cause for your yield being lower than expected is that your cells are ending up in the wrong fraction. In a positive selection experiment, the cells are likely being washed away. For negative isolation, you're depleting out too many of your cells of interest. You can confirm this by taking both positive/negative fractions for flow analysis, but in either case, the first thing you can try is to optimize reagent use:
  • Positive selection - to make sure you're not losing your cells of interest in the washed out fractions, try increasing the amount of antibody/secondary reagents for selection. Too many cells in the reaction (discussed above) can also cause this.
  • Isolation - titrate down antibody/secondary reagents. The likelihood is that the excess reagents are depleting out too many cells non-specifically.

For MojoSort™ reagents, contact our tech support team ( to get sample yields obtained from our in-house testing. Also note that increasing yield can potentially concomitantly compromise purity. The key here is to find a "sweet-spot" that has a good balance of both.

Follow recommended protocol! 

This one's relatively straightforward - if you are using one of our pre-designed kits, we’ve developed optimized protocols, which are available on their respective product webpages or you can view our Protocol tables on our MojorSort™ page. It's best to start with what's been validated to work in BioLegend labs first, before proceeding to optimize and modify steps and reagents as desired in your future experiments. To see how our MojoSort™ reagents and magnets are used, check out our awesome protocol video below:

The points mentioned here are by no means comprehensive as there are numerous other factors and considerations not covered here that can affect your magnetic separation outcome. However, if you encounter low purity and/or yield, hopefully some of the tips and suggestions above may help as your first steps to success. You can check out the MojoSort™ webpage for an overview on our reagents, FAQs, and protocols by clicking on the various available tabs. For any additional suggestions on how to design and improve your magnetic separation experiments, contact us at!
Contributed by Kenta Yamamoto, PhD.

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Blog - A Guide to Tandem Dyes and Degradation
In this blog, we'll describe the common characteristics of tandem dyes, discuss their advantages in multicolor flow cytometry, and address a common myth about tandem degradation.

What is a tandem dye?

A tandem dye is composed of two covalently attached fluorescent molecules. One of these molecules is either a protein-based (i.e., PE, APC) or synthetic dye (i.e., BV421™) characterized by a large extinction coefficient (capacity to absorb energy) that serves as a donor, while the second molecule is a small synthetic dye that serves as an acceptor (i.e., Cy5, Cy7).

Why do we use tandem dyes?

As you may already know, the number of fluorophores one is able to use at the same time is limited by the number of lasers and detectors on the cytometer (Table 1). The most advanced cytometer instruments on the current market have 5 lasers including ultraviolet (355 nm), violet (405 nm), blue (488 nm), green (532 nm) or yellow-green (561 nm), and red (633 nm) and the potential to integrate 8 parameters off of each laser. However, there are not currently enough spectrally distinct fluorophores yet to fill out this configuration. Without tandem fluorophores, each laser would only be suitable to discretely excite a small number of fluorophores, thus severely limiting the total number of parameters able to be detected.

Table 1. Example of multicolor flow panel diversification based on available lasers, detectors, and instrument.

Further expansion of the number of detectors off of each laser has allowed us to increase flow panels up to 21 colors /23 parameters (Table 1). It is possible to achieve because the tandem dyes use the same excitation characteristics as the donor dye but possess emission properties of the acceptor. Therefore, though donor dyes and their tandems are excited by the same laser, they can be used in the same panel by utilizing different detectors to readout their emission. As an example, Figure 1 demonstrates the excitation and emission spectrum of BV421™ and its tandems.

The further expansion of multicolor panels can be achieved by using a spectral cytometer, such as Cytek's Aurora™ Spectral Cytometer. This instrument has 48 fluorescent channels with three lasers. Theoretically, it can detect 42 fluorophores as long as those fluorophores have distinct emission profiles. A spectral detection system does not utilize traditional methods of compensation calculation and allows a simultaneous use of fluorophores with significant overlapping emission spectrum (i.e. BV750™ and BV785™) while having minimal compensation concerns.

Figure 1. Excitation and emission spectra of BV421™ and its tandems, BV570™, BV605™, BV650™, BV711™, BV750™, BV785™.

How do tandem dyes work?

The donor fluorophore absorbs light energy of the specific wavelength. Upon excitation, energy is transferred from the donor to the acceptor through a phenomenon called Förster resonance energy transfer (FRET), also known as fluorescence resonance energy transfer. The acceptor emits the transferred energy as fluorescent light. When FRET efficiency is high, a strong signal will be observed in the acceptor channel and a weaker signal in the donor channel. Please note, the FRET efficiency is never 100%, which means that some spillover or bleeding in the donor channel is expected.

I see a fluorescence signal in the donor channel. Is my tandem degrading?

NO. This is a common myth. The donor and acceptor dyes are covalently conjugated and don't typically fall apart. As mentioned earlier some spillover or bleeding into the donor channel is expected since FRET efficiency is not 100%. To accurately compensate spillover, it is important to use the same antibody used in the multicolor sample as the single color compensation control and to treat them identically as the multicolor sample especially in light and fixative exposure. However, if you observe a strong signal in the donor channel and a weak signal in the acceptor channel, this is an indication of low FRET efficiency that is caused by one of the following factors:
  • Photobleaching. Always protect tandem dyes from light or other sources of oxidative stress as they are highly susceptible to photobleaching resulting from oxidation.

  • Exposure to freezing temperature. Do not freeze tandem dye antibody conjugates as it might result in denaturation of the protein-based donor fluorophore.

  • Fixation and Permeabilization. Don't leave your cells in fixative for long periods of time as it will result in increase of the autofluorescence and cause greater harm to the tandems. It is strongly advised to wash the cells after fixation and replace the buffer with the FluoroFix™ Buffer. You can also perform surface staining after fixation. However, be sure to check our Fixation page for compatibility of our antibody clones with fixation.
As an example of how different fixatives may affect the fluorescence signal of tandem dyes, Figure 2 demonstrates the fluorescence signal from cells stained with a PerCP/Cy5.5-conjugated antibody and exposed to 1%PFA or True-Phos™ Perm buffer. Exposure to the True-Phos™ Perm buffer, which is methanol-based, leads to the denaturation of PerCP since this is a protein based dye. It results in the loss of signal for PerCP but not for its acceptor, Cy5.5 whose fluorescence can be detected in the Alexa Fluor® 700 channel.

Figure 2. A) Fluorescence intensity of PerCP/Cy5.5 anti-CD14 antibody staining upon exposure to 1% PFA or True-Phos™ Perm buffer solution. B) Dot plots demonstrating appearance of signal in Alexa Fluor® 700 channel upon exposure of PerCP/Cy5.5 to True-Phos™ Perm buffer.

Hopefully, now you have a better understanding of tandem dyes and are familiar with guidelines on their use and handling when planning and doing your experiments. As always, if you have any questions while planning your experiments, feel free to reach out to our technical services team at You can learn more about these fluorophores with our tandem dyes webpage.
Contributed by Ekaterina Zvezdova, PhD.
View a full listing of Trademarks and Patents.

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Podcast Episode - 8-Bit Science: Level One
In our new podcast, we look at the science behind popular video game franchises like Resident Evil, Final Fantasy VII, and The Last of Us!


Final Fantasy VII (Podcast from 2:35-14:20)
Resident Evil (Podcast from 14:20-26:50)
The Last of Us (Podcast from 31:50 to 44:00; Spoilers from 41:20 to 42:18)
Viruses in the Resident Evil Series
The Last of Us Part II
Stages of Infection in the Last of Us
Cordyceps: Attack of the Killer Fungi-Planet Earth with David Attenborough

Intro music “The Pirate and the Dancer” by artist Rolemusic at the Free Music Archive.

Keywords: video games, science, podcast, resident evil, final fantasy VII, Jenova, CRISPR, gene splicing, Sephiroth, virus, T-virus, mitochondria, retrovirus, reverse transcriptase, Ebola, Atari, playstation, The Last of Us, Cordyceps, fungus

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