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Cell cycle analysis appears to be deceptively easy in concept, but details are absolutely critical. It is not possible to hide the data if there is poor sample preparation, incorrect dye ratios, too much (or too little) staining time, etc. Forgetting RNAse when using PI will doom your data to failure. Take these basics into account as you move into performing this simple, yet amazingly informative assay.

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 compensation, and being armed with the knowledge, allows the researcher to combat those fairytales that continue to make their rounds in science. It is time to put them to bed and move forward with a full understanding of the process.

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 data.

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.

The flow cytometer is an integral component of any flow cytometry experiment, and special attention should be paid to ensuring that it is working correctly and consistently. As an end-user, the researcher should be able to sit down at a machine and know that it is performing the same way today as it was yesterday and last week. Equally important is that if any changes in instrument performance have occured, the end-user knows how they have been addressed and corrected, rather than letting them fester and potentially affect the results. Quality control measurements can include a variety of targets, such as PMT sensitivity, laser alignment, fluidic stability, background issues, and more.

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.

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.

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 best results possible.

Preparing for rare event analysis requires an understanding of the power and limitation of the instrument to be used. From how fast to run the fluidics, to how the signal is processed to the number of gates that can be used in the sorting experiment, each factor impacts the outcome of the experiment.

For those new to flow cytometry, compensation is confusing at best and terrifying at worst. Likewise, those who have been doing flow cytometry since the analog ages may be holding on to practices that, while suited to the analog instruments, should be left to the annals of history. As such, a lot of time is spent discussing compensation and the best practices for this critical process. There are 3 rules that guide proper compensation, and they’ve been written about extensively since they first appeared in the “Daily Dongle” in 2011. Here, we will review the classic rules and expand upon the tacit assumptions required to fulfill them.

Congratulations, your grant has been funded! Next comes generating data and publishing papers. What was that hypothesis again? It must be in the grant somewhere, right? To avoid even the appearance of HARKing — Hypothesizing After The Results Are Known — it is important to start the statistical analysis process even before the first experiments are performed. This process consists of 5 steps: setting the null hypothesis, establishing a threshold, performing the experiments, performing the statistical tests, and communicating the results. Walk with us as we discuss these steps in an example workflow.