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5 Flow Cytometry Errors Reviewers Despise

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ExCyte is a professional flow cytometry consulting company. Our mission is to provide expert instruction and to help leaders in the field connect and grow. We specialize in providing education opportunities to anyone utilizing flow cytometry or imaging in their research.

ExCyte’s courses provide a complex understanding of flow cytometry, instantly improving the attendees’ knowledge in experimental design, instrumentation, compensation, data analysis, and figure preparation. Trained experts in the field of cytometry provide unbiased lecture style training in basic and advanced topics in cytometry at a reasonable cost to the researcher.

Rejected
We all know that flow cytometry makes individual measurements on large populations of cells, it allows for statistical analysis of the data, lending strength to a researcher’s conclusions.

Likewise, the isolation of very complex populations by flow cytometry cell sorting can help lead to a richer understanding of the intricate biology at the genomic, proteomic and functional level.

As a reviewer of papers and grants, I am always especially interested in the details of HOW the experiments were performed because that is the critical foundation for what the data is able to tell us–and what it can NOT tell us.

Mistakes That Mark You As Untrained

It may sound harsh but there are some errors that reviewers hate seeing. The good news is that by learning what reviewers dislike (or even despise), you can learn how to run better and more accurate experiments. Here are 5 errors reviewers really don’t like seeing:

1. Failure to plan out the entire experiment from the end backwards.

Consider the statistics you will need to show in your grant or paper at the very beginning. Before the first reagent is ordered or cells processed, it is critical to start at the end. This means understanding the biological hypothesis and what statistical approaches will be used to test the significance of that hypothesis.

As noted statistician Frank Anscombe once wrote, “What is important is that we realize what the problem really is, and solve that problem as well as we can, instead of inventing a substitute problem that can be solved exactly but is irrelevant (1).”

Understanding the hypothesis and how it will be tested will lead the researcher down the path of experimental design to data analysis to statistical testing. As a bonus, by preparing for the statistical testing, the power of the experiment can be calculated so that sufficient samples are collected to provide confidence in the final results.

2.  Failure to design the proper antibody panel. 

After understanding the biological hypothesis, the process of panel design can begin. This multi-step process begins with understanding what populations will be examined, and if the end result is the change in population percentages or the expression of one or more markers.

Based on this, knowledge of the instrument, and fluorochromes’ possible panels can be developed to minimize compensation issues and loss of resolution in the channels of critical importance. When designing a panel don’t leave out a dump channel or a viability dye. At the same time, don’t give either of these two things top priority when designing your panel.

3. Failure to optimize the experiment. 

These optimization steps must include:

A.  Titration of the reagents. All reagents used in any flow cytometry experiment should be titrated and optimized. The goal with this is to get the best signal-to-noise ratio and minimize background pollution.

B.  PMT voltage. A bead-based measurement of the PMT sensitivity will ensure the experiment is run at a voltage where the PMT is most sensitive. These voltages can be further adjusted for specific cell/fluorochrome combinations, which will further optimize the sensitivity of the assay.

4.  Failure to use the right controls. 

A significant amount of time and effort goes into successfully performing a flow cytometry assay, including building a robust polychromatic panel, determining the correct controls, and performing a reproducible analysis. The last thing a researcher wants to have happen is that his or her discovery is disproven because of an instrument issue rather than a true biological change.

While Shared Resource Laboratories and Core Facilities spend a great deal of time on the QC of their instruments, the best researchers incorporate QC into their experiments as well. These QC tubes include a method for ensuring the PMT voltage is consistent between experiments, a method for ensuring the experiment is performed correctly and a method for ensuring the gating strategy is robust.

5.  Failure to provide the details of their display and gating strategies.  

It is critical for the final data presentation to be robust and reproducible. In showing the gating strategy (even in supplemental data), explain how each region was placed. Questions such as what controls were used and whether or not cutoff percentages employed should be discussed.

With digital data, make sure that any transformation of the data is revealed and how they were consistently applied. Avoid the use of univariate plots (histograms), and be sure to ensure the total amount of data on the plots is revealed–so that the magnitude of the experiment is understood.

Finally, don’t rely on just the figures. Flow cytometry data can be very quantative, so report the data as a table when possible too.

To learn more about preparing and publishing your flow cytometry data, become a Mastery Class member.