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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.

Since the first laser was mounted to create the first flow cytometer, there has been a push for more - more lasers, more detectors, more colors. As a result, today’s researchers require a large number of lasers and detectors to ensure current panels can be run and new, expanded panels can be developed. This can be problematic because, in general, making one decision to improve a cell analyzer can limit the analyzer in other ways. It may seem like an impossible task, but the team of Bio-Rad and Propel Laboratories, collaborated to bring the ZE5™ Cell Analyzer to the market and, with thoughtful design, the Analyzer answers these challenges, resulting in a high-end, easy to use, automated flow cytometer.

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.

There are several areas that researchers can focus on to improve the reproducibility of their flow cytometry experiments. From instrument quality control, through validation of reagents, to reporting out the findings, a little effort will go a long way to ensure that flow cytometry data is robust, reproducible, and accurately reported to the greater scientific community. Initiatives by ISAC have further offered additional levels of standards to support these initiatives, which were developed even before the Reproducibility Crisis came to a head in both scientific and popular literature.

FCS Express is the ideal data analysis software program to use when analyzing your flow cytometry experiments because it is the most user-friendly program available that is both aligned with current data analysis best practices and maintains rigorous quality control standards.

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.

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.

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.

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.

Getting a clear signal with reduced noise is an essential component to good data. Adding a threshold when acquiring flow cytometry data is one way to do that. It reduces the number of events by setting a bar that a signal pulse must clear before it is counted as an event. Depending on the importance of the data, the downstream applications for the data (or sorted cells) will dictate how critical the threshold is. In combination with proper sample preparation, appropriate thresholding will reduce debris and ensure best outcome.

The best way to take out the fear and agony of setting voltages is to use some optimization methods. The peak 2 method is a useful and robust method of identifying optimal PMT voltage ranges. Refining that to the voltage walk with the actual cells and fluorochromes of interest will further improve sensitivity, which is especially critical for rare cell populations or emergent antigens. This article describes how to set up, monitor, and maintain optimal voltage settings for your flow cytometry experiment.

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.