4 – Experimental Design

The topic of compensation is a critical one for the cytometrist to understand. It requires adherence to some specific rules, an understanding of how the instrument works, and how fluorescence occurs. Poor or incorrect compensation can easily lead to incorrect conclusions, and decreases the reliability and robustness of the data generated. It is critical to question the wisdom of the “Protocol’s Book” and understand that the “truths” in this book are not always correct anymore. The new user doesn’t necessarily know any differently, and for this reason there are suboptimal practices that permeate flow cytometry experiments to this day. Understanding…

Understanding the 3 rules of compensation, and applying them to your everyday workflows, is an essential step in good, consistent, and reproducible flow cytometry data. Making sure the controls are bright, and treated the same way, is essential. Don’t bring unfixed controls when your samples are fixed, as the controls will not reflect the spectra from the fixed samples. Make sure not to rely on the “Universal Negative”, use a single sample to set background, and collect enough events to make sure an accurate measurement is made, as this will further improve the quality of your control and therefore the…

3 different theories on compensation are discussed. The first, non-pensaton, is not recommended, and only possible under a narrowly defined instrument. The second, manual compensation, is also not recommended for anything more than 2 fluorochromes. It is error prone and subject to the researcher’s judgement, unless statistics are invoked and then it becomes a tedious and difficult exercise in algebra. For polychromatic flow cytometry, best practices in flow cytometry is to use the automated compensation methodologies. This will ensure consistent and accurate compensation, if some rules are followed.

Why is the speed of the algorithm so important? Why worry when you can just set up the analysis and go for lunch? If you’re like me, when I’m analyzing data, I like to stay in that mindset. Distractions, like a long break, can impact the train of thought about the analysis. Additionally, with long run-times, it is depressing to return to the data and see the calculation stopped prematurely because of an incorrect parameter or some other error.

With the increased focus on reproducibility of scientific data, it is important to look at how data is interpreted. To assist in data interpretation, the scientific method requires that controls are built into the experimental workflow. These controls are essential to minimize the effects of variables in the experiment so that changes caused by the independent variable can be properly elucidated. Getting into the mindset to improve the reproducibility of flow cytometry experiments requires a hard look at the appropriate controls to use in each experiment.

Stem cells, circulating tumor cells, and minimal residual disease in cancer patients were all discovered through the power of rare event flow cytometry. When preparing for rare event analysis, sample preparation and data analysis must be taken into account at the beginning. How will we stain our cells? How will we analyze our cells? What controls will we use to help us identify our rare events? What statistical methods do we use to analyze our results? Here are 5 procedural limitations that impact the quality of rare event flow cytometry data and how to optimize your assay to get the…

Here, we cover 5 lessons from the trenches of flow cytometry looking at important aspects of how best practices have changed over time, which practices need to be adopted, and which are outdated. Put those old, coffee-stained protocols away and take advantage of the best practices for digital instruments to write new and improved ones (coffee stains optional). Your data will thank you.

One of the most common assays in flow cytometry is the surface labeling of cells with antibodies. Often termed “immunophenotyping”, it allows the researcher to identify, count, and isolate cells of interest in a mix of input cells. Every lab has their own favorite protocol to move from sample to cytometer, handed down from some hallowed, chemical-stained notebook, and followed as exactly as making a souffle. The real questions are, which of those steps are critical, and what other factors should be considered when staining cells? This article will focus on staining immune cells, but the principles apply in general,…

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.

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.

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.