Cell sorting is a combination of a numbers game (Recovery), quality of output (Purity) and speed. For any experiment, the end goal is going to be measured by these three characteristics, and as soon as one of these measures is more heavily favored, the other two must be compromised in some manner. When designing a sorting experiment, start with the question of what will the cells be used for after sorting, and how many cells will you need for those experiments? That will set the minimum recovery that is needed. The second question is how pure do you need the…
Cell sorting is a combination of a numbers game (Recovery), quality of output (Purity) and speed. For any experiment, the end goal is going to be measured by these three characteristics, and as soon as one of these measures is more heavily favored, the other two must be compromised in some manner. When designing a sorting experiment, start with the question of what will the cells be used for after sorting, and how many cells will you need for those experiments? That will set the minimum recovery that is needed. The second question is how pure do you need the…
As a researcher, you want to achieve the best cell sorting possible. So, how can you achieve that? There are clear strategies you can use to achieve great cell sorting results, including finding your ideal sample concentration, using magnetic sorting to enrich your population, suspending cells in the right buffer to avoid cell clumps, changing your instrument settings when sorting small cells, and optimizing your sample preparation and instrument when sorting large cells.
Cell cycle seems like such a straightforward assay. At its heart, it is a one-color assay and should be a simple protocol to follow. However, as discussed before, fixation and dye concentrations are critical. Once those are optimized, it becomes important to run the cells low and slow in order to get the best quality histograms for analysis — the topic of another blog. Adding the critical CEN and TEN controls will help standardize the assay, and ensure consistency and reproducibility between runs while helping identify non-standard (aneuploid, polyploid) populations from normal ploidy. Trying to isolate and focus on specific…
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…
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.
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…
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.