3 – Data analysis

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.

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.

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.

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.

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…

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…

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.

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.

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.