5 Important Peer Review Questions To Answer Before Submitting Your Flow Cytometry Data

Publish or perish remains the mantra of science.

All the experiments and experience in the world do not count if you are unable to communicate your results to the scientific community.

As part of that communication process, your paper will undergo the dreaded ‘Peer Review’ process. If you wish your paper to survive the peer review process, you must collect, analyze, and present your data properly—before you submit your paper.

A review of the following peer review questions, as well as how to answer these questions, will help ensure your paper is not rejected.

5 Questions Peer Reviewers Will Ask

As one who is asked to peer review papers regularly, especially those with flow cytometry data, there are 5 specific questions I ask when reading a manuscript.

If these questions are not adequately answered or explained in the paper, it raises red flags and ultimately leads to rejection.

1. What is the scientific hypothesis?

It all begins with the scientific hypothesis being tested. One of the critical parts of the paper is a clear statement of the reason for the study.

While it may seem obvious, answering this question should be the focus of the paper. However, many scientists make the mistake of forming one hypothesis and answering another.

To paraphrase the statistician Francis Anscombe…

“It is better to 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.”

There are many scientific papers that state one hypothesis and end up proving a second hypothesis because the second one was easier. This is especially true for papers with flow cytometry data. The truth is some questions are better suited to be answered by flow cytometry than others. If you’re using flow, make sure you are using it to answer the right hypothesis.

2. Are the correct statistics applied to the data?

If you want your flow cytometry data published, be sure to apply the correct statistics to your data.

Flow cytometry data is very numbers rich—you have percent positive, median fluorescent intensities, and much more. All of these numbers make up a critical part of the information that you must extract from your flow cytometry data, and the statistical tests to be used for your analysis will dictate which values are needed.

For questions concerning expression levels, the median is a better measure of the central tendency of the data than the mean is. If you’re using the term MFI in the paper—make sure it is properly defined and justified.

Using the mean implies a Gaussian distribution, which means that all the values are known and the distribution returns to zero. It also means that outliers can skew the results, as well as the interpretation of the results. Avoid this mistake and your paper will have a much better chance of being published.

3. How was the data compensated?

In reviewing the methods section of a paper, I always pay attention to how the data was compensated.

There are three rules that should be followed for proper compensation:

• Controls must be at least as bright as the samples they will be applied to. Brighter is better, but not off scale.
• Background fluorescence should be the same between the negative and positive population. Avoid using the universal negative for compensation.
• The compensation color must be the same as the experimental color. For example, don’t use Alexa488 to compensate for FITC.

Automatic compensation programs are available with almost all of the major digital instruments, as well as third party software—so no one should be trying to manually compensate their flow cytometry data anymore.

Manual compensation is what we call cowboy compensation (see below figure)…

If the methods section says the data was manually compensated, this is clear reason to be suspicious of the data and often leads to rejection.

4. How were the antibody panels and gates designed?

One of the fundamental processes in flow cytometry data is the processing, or gating of the data, to identify the cells of interest. This gating process requires proper controls and application of these controls. It also relies on the proper polychromatic panel design.

In evaluating a polychromatic panel and the experimental protocols used to process the cells, some secondary questions that are important to ask include…

• Was a dead cell marker included in the panel? Dead cells can indiscriminately take up all the antibodies in the panel, thus mask as positive cells. With the advent of live/dead fixable cells, there is no reason that one cannot be used in the assay.
• Were the antibodies titrated? High antibody concentration can lead to non-specific binding, and thus reduce the sensitivity of the measurement because of the increase in background staining.
• Were isotype controls used to set gates? There is a lot of contradictory information about the use of isotype controls on the web. Some say yes, some say no. The reality is that an isotype control can show if there is poor blocking of the cells, but isotype controls should not be used to set the gates. If the paper discusses the use of the isotype control to set gates, this is another strike against the paper and can lead to rejection.
• Were the FMO controls used? The Fluorescence Minus One control is a gating control where every fluorochrome but one is used. The missing (minus one) fluorochrome will reveal the spread of data in the channel of interest, and help define where the gate should be placed (see below figure).

5. Which instrument and instrument settings did you use?

With a few exceptions, modern flow cytometers are very customizable. This means that it is critical to communicate the important features of the instrument used in the studies—specifically excitation wavelengths and laser power, and the arrangement of the detectors and filters on the emission side.

These two pieces of information help the reviewer understand where, if any, problems with the fluorescent panel may reside. For example, if there is a 488 nm laser of 50 mW power and the researchers used PerCP as a fluorochrome, that should raise some significant issues.

Likewise, having a 633, 561, 488 and 405 laser on a system, and having a 700/50 type filter on the emission side when using a fluorochrome like QDot705 would be a cause for concern (see below figure).

At the end of the day, there are many small errors that can easily be committed which doom peer reviewed papers to rejection. Avoiding these errors and ensuring that your flow data stands out for all the right reasons is the path to surviving peer review questions.

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