Skip to content

In the flow cytometry community, SPADE (Spanning-tree Progression Analysis of Density-normalized Events) is a favored algorithm for dealing with highly multidimensional or otherwise complex datasets. Like tSNE, SPADE extracts information across events in your data unsupervised and presents the result in a unique visual format. Given the growing popularity of this kind of algorithm for dealing with complex datasets, we decided to test the SPADE algorithm in 5 software packages, including Cytobank, FCS Express, FlowJo, R, and the original, free software made available by the author of SPADE. Which was the fastest?

Read More

FlowJo is a powerful tool for performing and analyzing flow cytometry experiments, if you know how to use it to the fullest. This includes understanding embedding and using keywords, the FlowJo compensation wizard, spillover spreading matrix, FlowJo and R, and creating tables in FlowJo. Extending your use of FJ using these hacks will help organize your data, improve analysis and make your exported data easier to understand and explain to others. Take a few moments and explore all you can do with FJ beyond just gating populations.

Read More

It is necessary to sort through hundreds of thousands or millions of cells to find the few events of interest. With such low event numbers, we move away from the comfortable domain of the Gaussian distribution and move into the realm of Poisson statistics. There are 3 points to consider to build confidence in the data that the events being counted are truly events of interest and not random events that just happen to fall into the gates of interest.

Read More

With the added emphasis on reproducibility, it is critical to look at every step where experiments can be improved. No single step makes an experiment more reproducible, rather it is a process, making changes at each stage that leads to reproducibility. Antibodies comprise a critical component that needs to be reviewed. As Bradbury et al. in a commentary in Nature pointed out, the global spending on antibodies is about $1.6 billion a year, and it is estimated about half of that money is spent on “bad” antibodies. This does not include the additional costs of wasted time and effort by the researcher using these bad antibodies. Using tools to identify the best reagent to use, considering a switch to recombinant antibodies, and properly validating reagents for use in an assay, are 3 steps that will improve the reproducibility of your experiments.

Read More

Dyes exist for the detection of everything from large nucleic acids to reactive oxygen species, and from lipid aggregates to small ions. Concentrations of physiologically important ions such as sodium, potassium, and calcium can be important indicators of health and disease. Calcium ions play an especially critical role in cellular signaling. As a signaling messenger, calcium is involved in everything from muscle contractions, to cell motility, to enzyme activity. Calcium experiments can be very informative, and with the advent of cheaper UV lasers, more and more researchers can use ratiometric measurements to evaluate the signaling processes in phenotypically defined populations.

Read More

You are probably familiar with the term, “doublet discrimination” or “doublet exclusion”, and have likely included this flow cytometry measurement into at least some (if not all) of your gating strategies. Even though you may utilize this important gating strategy, you may not have had the chance to delve deeper to explore exactly what doublets are and why it’s critical to exclude them. This article aims to give you insight on the what, why, and how of doublet discrimination.

Read More

For those working in the signaling field, having the ability to take a sample and phenotypically identify it, while knowing what is happening inside the cell to the target molecules of choice opens up a host of new opportunities. These assays are amenable to high throughput setup, meaning that biologically relevant outcomes in pre-clinical drug discovery can be measured directly. All told, with a little forethought, some careful planning and validation, and our helpful tips, phosphoflow assays are within your reach.

Read More

Flow cytometry is a numbers game. There are percentages of a population, fluorescence intensity measurements, sample averages, data normalization, and more. Many of these common calculations are useful, but surrounded by misconceptions. This primer will help you decide which calculation to use, when to use it, and how to interpret the results.

Read More

Measuring the receptor occupancy of a given target showcases the power of flow cytometry. With the right reagents, best practices, and attention to detail, this assay can become a mainstay in your research toolkit. It extends quantitative flow cytometry to the next level, to determine a complete biological picture of how efficiently a given target is being bound. This also serves as the basis for even more fine-analysis when combined with assessment of downstream targets that the engagement of the receptor by the target antibody may affect. Phosphorylation, cell cycle arrest, and protein expression are all within reach, resulting in an even more complete picture of the process, that will ultimately give the medical community a fuller understanding of how these potential therapeutics work and when to use them. This is truly personalized medicine at its fullest potential.

Read More

The ultimate goal of any experiment is to analyze data and determine whether it supports or disproves a given hypothesis. To do that, scientists turn to statistics. If we wish to compare either a single group to a theoretical hypothesis, or two different groups, and these groups are normally distributed, the test of choice is the Student’s t-Test. To perform the t-Test, it is critical to start from the beginning of the experiment to establish several parameters, including the type of test, the null hypothesis, the assumptions about the data, the number of samples to be analyzed (Power of the experiment), and the threshold. The experiments are performed, and only then, after the primary analysis is completed, is statistical testing performed.

Read More

Mass cytometry panels routinely include 30 or more markers, but traditional analysis methods like bivariate gating can’t adequately parse the resulting high-dimensional data. Spanning-tree progression analysis of density-normalized events (SPADE) is one of the most commonly used computational tools for visualizing and interpreting data sets from mass cytometry and multidimensional fluorescence flow cytometry experiments. There are two key parameters in SPADE that you can adjust in order get the best results possible: downsampling, and target number of nodes or k. Knowing how to properly set these values will enable you to enhance the quality of your analysis.

Read More

We hope this explanation sheds some light on scaling. Knowing how to properly display your data is a critical part of scientific communication. Remember to use linear scaling for most scatter parameters, or when you need to visualize small changes, and log scaling for most fluorescence parameters, or when you need to visualize a wide range of values. As always in flow cytometry, there are certainly exceptions, but armed with this knowledge, you should be able to make educated judgements about which scale types to use in various assays and to better interpret your data.

Read More

Until we have access to well-validated recombinant antibodies produced under tightly regulated conditions, researchers need to exercise good judgment regarding these critical biological reagents. These 4 steps will help ensure that your results are consistent and reproducible. This will both reassure your reviewers that your data is of high quality, and allow for researchers at other institutions to successfully replicate your results. In addition, identifying antibody duds early on will save you time and money in the long run. Don’t shirk the work of ensuring your antibodies are working correctly and targeting the right proteins.

Read More

Considerations that must be made when choosing fluorochromes include the brightness of the dyes in question, the instrument configuration, and the staining protocol. Each of these factors will impact the quality of the data because of issues related to spectral spillover, staining, loss of signal because of tandem dye degradation, the ability to get an antibody/fluorochrome into a cell, and more. It takes time and effort to develop and optimize a panel. If one fluorochrome doesn’t work, consider why it may have failed and look for alternatives.

Read More

It is critical to prepare for your statistical analysis at the beginning of the experimental design process. This will ensure the correct data is extracted, the proper test applied, and that sufficient replicates are obtained so that if an effect is to be found, it will be found. Here are five considerations to implement into your experimental design to ensure the best statistical methods so that your data stands up to review.

Read More

Fluorescence compensation is not possible without proper controls, so it is critical to spend the time and effort to generate high-quality controls in the preparation of an experiment. For a compensation control to be considered “good” or “proper”, each compensation control must be as bright as or brighter than the experimental stain, autofluorescence should be the same for the positive and negative populations used for the compensation calculation in each channel, and the fluorophore used must be the exact fluorophore (i.e. same molecular structure) that is used in the experimental sample.

Read More

While controls are critical for minimizing the effects of variables in your flow cytometry experiments, choosing the right controls are essential. When your research is published, reviewers need to see that your variables have been analyzed properly. Evaluating strengths and weaknesses will give you information and back up arguments for the case for or against isotype controls. Here’s a review of what isotype controls are and if you need to use them.

Read More

Flow cytometry (FCM) datasets that are currently being generated will be two orders of magnitude larger than any that exist today. Reproducibility continues to be a critical area that all researchers need to be aware of and researchers need to keep up on best practices to stay relevant. One area that flow cytometry researchers should be focusing on is the emerging changes in the area of automated data analysis. This brief article explains why.

Read More

When sitting down to perform a new analysis of flow cytometry data, the researcher is guided by very particular laws of nature and a very specific method of working through a biological hypothesis to avoid shaping the results to his or her whims. Following these 5 data analysis and gating strategies through the hierarchy described in this article, researchers are provided with several strategies for identifying and displaying the most relevant data from their flow cytometry experiments.

Read More

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

Read More

Cellular proliferation is a critical component in biological systems. While normal cell proliferation keeps the body functioning, abnormal proliferation (such as in cancer) can be a target for therapy. There are several critical components in developing, validating and optimizing an assay to make these measures using flow cytometry. Knowing the steps to optimize these assays and properly interpret the results will help ensure the best data and best opportunities are pursued.

Read More

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. Manually adjusting the compensation values based on how the populations look, or so-called ‘Cowboy Compensation’, is not the correct way to determine proper compensation. The best practices for compensation involve following some very specific rules. Here are 4 steps to correctly compensating 4+ color flow cytometry experiments.

Read More

Methods sections in scientific papers are often unable to capture all the critical data necessary to accurately reproduce the results in another lab. Here, information is provided on two specific ways in which flow cytometry researchers are effectively communicating flow cytometry data and metadata to the greater flow cytometry community to improve reproducibility and consistency. These two ways include first, the use of the MIFlowCyt standard and second, sharing data using the Flow Repository.

Read More

Reproducibility is a critical component of the scientific process. One cannot publish data if the experiments cannot be replicated.

Unfortunately, as Begley and Ellis pointed out in a commentary in Nature that when Amgen attempted to reproduce 53 “landmark” papers in the area of cancer research, only 6 papers were “confirmed.” What does that mean to you, the flow cytometry researcher? To avoid publishing errors that reviews despise, it’s important to follow and promote the best practices in the field, thus ensuring that your data is reproducible to investigators attempting to validate your research. In particular, you must follow these 5 experimental tips.

Read More

There are several methods for analyzing live, dead, and apoptotic cells by flow cytometry.

As cells die, the membrane becomes permeable. This allows for antibodies to penetrate the cells, which can now mimic live cells. For this and other reasons, it’s important to remove dead cells from further analysis during your flow cytometry experiments. For example, let’s say you merely need to generate an accurate cell count. If you fail to remove your dead cells first, you might think you’re seeding 10,000 cells, but in reality only 7,000 of your cells are actually viable. Since the dead cells in your sample will not divide, your culture will take extra time to reach the needed level of confluence. Don’t make the mistake of forgetting to add a live-dead cell marker to your next flow cytometry experiment. Here are the top 3 markers available to you.

Read More

Flow Cytometry is a remarkably powerful tool for the study of T cells. It has been successfully used for many decades to accurately visualize and enumerate a variety of T cell subsets. With a large sensitivity range for fluorescent probes, >95% sampling efficiency, and the ability to sort populations of interest for further study, fluorescent-based cytometry remains a tool of choice for T cell analysis. The key is to define your T cell populations of interest with correct gating strategies and to back up your T cell subset findings with functional analysis of these subsets. A cell’s actions should guide its definition, not the other way around.

Read More

Microvesicles originate from cells and have the same analysis requirements as cells. For these and other reasons, flow cytometry is a popular choice for microvesicle analysis. However, there are pitfalls with small particle flow cytometry that have led to many conflicting publications. The only way to avoid these mistakes is to first identify them and then take measures to prevent them. The following are 4 common mistakes researchers make when preparing microvesicle flow cytometry experiments, as well as how to prevent these mistakes.

Read More

All the experiments and experience in the world do not count if you are unable to communicate your results to the scientific community. As part of that communication process, your paper will undergo the dreaded ‘Peer-Review’ process. If you wish your paper to survive this process, you must collect, analyze, and present your flow cytometry data properly—before you submit your paper. A review of the following questions, as well as how to answer them, will help ensure your paper is not rejected. Here are 5 specific questions reviewers will ask when reviewing your flow cytometry data.

Read More

With the increased development of fluorescently conjugated monoclonal antibodies came more applications with potential clinical impact.

In bone marrow transplantation, studies using hematopoietic cytokines made it feasible to gather stem cells from peripheral blood. It was also shown that reconstitution of bone marrow was accelerated when using cell from peripheral blood rather than bone marrow. Many more clinical flow cytoemtry applications have been developed. All of which should follow these 6 keys of running clinical flow cytometry experiments.

Read More

Written by Tim Bushnell, PhD Flow cytometry data analysis is getting more complex. Gone is the rule of 2-3 color experiments. Even beginners are starting with 5+ color assays, and the adoption of mass cytometry has the potential to increase our headaches even more. Current data analysis methods are good for single tubes or small cohort…

Read More

Manual compensation is the process of adjusting the compensation based on how the data visually looks.

If you have manually compensated data in your lab notebook–strike it out now. Manual compensation results in overcompensated data, yielding incorrect conclusions. If you have issues, explore what those problems are and work to resolve them rather than making up fiction by manual compensation. Here are three keys to automatically compensating your data.

Read More

Written by Tim Bushnell, PhD BD Biosciences brand of digital flow cytometers, including the FACSCanto, the LSR-II, FACSAria and Fortessa, utilize a software acquisition program known as FACSDiva. Diva is aptly named as it can be a difficult program to master. However, Diva has come along way over the past 10 years and many improvements…

Read More

Written by Tim Bushnell, PhD Cell death is a fact of biological life.  How, when, where and most importantly, why cells die, can have huge biological consequences on the path an organism may take. Apoptosis, or programed cell death, can result in a selective advantage for an organism. Fingers, for example, are the result of apoptosis…

Read More

Written by Tim Bushnell, PhD I often have researchers come into the core wanting to look at the activation and downstream signaling events that occur in different immune cells. These events occur in response to signals such as cytokines, chemokines, various receptor ligands, and the engagement of the T cell or B cell receptors. The…

Read More

Written by Tim Bushnell, PhD Cell proliferation can readily be measured by flow cytometry.  Depending on the research question, there are several different techniques that can be used. Cell counting experiments This relatively straightforward experiment where the investigator uses one of several counting techniques to see if there is an increase in the populations. This…

Read More

A variation on biexponential scaling similar to logicle scaling. The biexonential scale is a combination of linear and log scaling on a single axis using an arcsine function as its backbone. Biexponential scales are more generally referred to as hybrid scales and include other variations like lin/log or log with negative. More information on Hyperlog…

Read More

Written by Tim Bushnell, PhD Cell cycle analysis by flow cytometry uses a DNA binding dye, such as propidium iodide (PI), 7- aminoactinomycin D (7-AAD) or 4’,6-diamidino-2phenylindole (DAPI), to determine the cell cycle state of a cell population. The Gap1 (G1) phase of an eukaryotic cell is defined as having 2C DNA. The synthesis (or…

Read More

Written by Tim Bushnell, PhD DNA cell cycle analysis is a very powerful technique in flow cytometry. It is deceptively easy, but there are several critical things to remember to ensure successful analysis. Collect enough events. Cell cycle analysis involves fitting of the data using one of several mathematical models that describe the behavior of…

Read More

Written by Tim Bushnell, PhD With the ability to capture expression data at the single cell level through many thousands of cells in a short time, flow cytometry data is very numbers rich. The importance of those numbers and how to use them in hypothesis testing is critical to ensure the robustness of the analysis.…

Read More

Written by Tim Bushnell, PhD After completing the perfect staining and cytometry run, the hard work begins – data analysis.  To properly identify the cells of interest, it is critical to pull together knowledge of the biology with the controls run in the experiment to properly place the regions of interest that will be dictate…

Read More

Written by Tim Bushnell, PhD Depending on the experimental design, many researchers will be doing complex assays that will require statistical analysis to determine if the hypothesis being tested is statistically significant or not. Unfortunately, many researchers go about this analysis the wrong way, resulting in spurious conclusions. The following points are guides to help…

Read More

Written by Tim Bushnell, PhD After completing the perfect staining and cytometry run, the hard work begins – data analysis. To properly identify the cells of interest, it is critical to pull together knowledge of the biology with the controls run in the experiment to properly place the regions of interest that will be dictate…

Read More

Written by Tim Bushnell, PhD Understanding statistics and fow cytometry statistical analysis is critical to understanding flow cytometry data. One of the powers of flow cytometry is the fact that we generate large amounts of data that are amenable to statistical analysis of our populations of interest.  Using the standard set of statistical analysis tools…

Read More

Written by Tim Bushnell, PhD Flow cytometry is the science of measure 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 short-hand expression for this type of cell analysis The term FACS stands for Fluorescent Activated Cell Sorting, a…

Read More