Easy-To-Forget Flow Fundamentals That Thwart Bad Science

No matter how advanced or cutting-edge your study is, good science is profoundly dependent on the fundamentals. In fact, no matter your experience level, it is always good to revisit the fundamentals now and again. After all, if flow cytometry were easy, anyone could don a lab coat and get published. In reality, science is as challenging to conduct as it is exciting – that means it’s beneficial for virtually all scientists to take some time and refresh themselves on best practices. You might be surprised to realize what an impact they have on experimental quality… 

Let’s do a simple overview of “ground-level” research considerations. We’ll touch on the basic phases of a high-quality flow cytometry experiment and the steps you need to follow to build reproducibility. From bright-eyed novices to hardened flow cytometry veterans, there’s something here for everyone.

Experiment Phases

Preparation fundamentals

For those starting with a liquid sample (pond water, blood, suspension culture cells), it’s pretty easy to get to the single-cell suspension. When working with solid tissues, dissociation techniques become critical, and references like the Worthington Tissue Dissociation Guide are excellent. Of course, don’t forget to filter samples before running them through the flow cytometer, especially if you are planning a sorting experiment. Finally, make sure to take a look at your cells under a microscope to assess the quality of your single-cell suspension.

Then you’ll want to gather and validate reagents. Make sure you have everything you will need – ideally, always work with previously tested reagents. You don’t want to rely on a new technique using a reagent of undetermined efficacy. You should be familiar with any special issues your reagents might have — for example, don’t leave tandem dyes out in the light or on the bench for any length of time. Otherwise, they can degrade and become useless.

Beginning the experiment

As you begin your experiment, double-check the necessary controls. Flow cytometry experiments require a large number of controls for successful interpretation. These include: 

• Compensation controls — for setting compensation
• Fluorescence Minus One (FMO) controls — for assisting in gating
• Biological controls — for addressing variation in the assay
• QC controls — usually beads that will be used to monitor the QC of the assay over time

If you forget a control, the downstream analysis will be harder. Another thing to remember is counting your cells. You don’t want to end up with fewer cells than you need.

And while it’s easy to skip the step of annotating your data, this is a mistake. In many acquisition packages, you can add keywords at the experiment level, the sample level, and the tube level. Get into the habit of using keywords – if you don’t know this already, this little habit is its own reward if you find yourself short on time. Make sure you collect enough events too. Some packages default to a very low number of events, so check to be sure you are collecting enough data. 

Post-experiment fundamentals

Once the experiment is complete, clean the machine. It’s probably not your favorite part of the process, but it helps a core facility run smoother. Small things like leaving bleach on the sample injection port (SIP) can kill the next user’s experiment. 

Reproducibility

If it’s not reproducible, it’s not good science. One of the most important reasons to publish results is that it gives other labs the chance to reproduce them. This can also lead the investigation into new areas once research from around the world begins to contribute. From the Begley and Ellis commentary in Nature to the development of the Rigor and Reproducibility initiative at NIH, reproducibility is on everyone’s mind. Following the best practices in flow cytometry provides investigators, peer reviewers, and colleagues with more confidence in your data.

Here are a few fundamentals you should pay attention to enhance reproducibility.

File maintenance and organization

Before you even start a project, you need a good file directory structure. This begins with the project name. Then you would have subfolders for the methods that you used, all your raw data, the analysis that you did on these, and any manuscript or other scripts you developed to be able to analyze these data. Experiments may take months—or years—to complete. You need to know where the data and analysis are located in order to avoid repeating an experiment unnecessarily.

Think carefully about your file names. These names should describe the exact experiment without requiring a lab notebook to determine what you did that particular day. And if you’re doing batch analysis, those tags can be valuable data for grouping your experiments and getting a meaningful output in software such as FlowJo and FCSExpress. 

Electronic notebooks

To improve reproducibility, you can also move your work to an electronic notebook or “ELN.” This allows you to maintain a repository of SOPs for the lab that students can pull down for an experiment. That way, anyone can see what students have been working on and search within the notebook to find specific experiments. SOPs kept on ELNs can be passed to other labs with ease, giving access to collaborators across the country. Easy sharing of SOPs improves reproducibility and can even accelerate science. 

Digital analysis and good figures

It might seem like figures are obvious – How hard could it be? 

Weissgerber et al. suggest that an effective figure had 3 main features: 

1. It must be easy to understand the study design using the figure. Using the figure and its legend, can readers understand what you did without reading the main text?
2. The figure must illustrate important findings – only the important ones. Additional supportive data can be moved to the supplementary figures.
3. The figure needs to allow readers to critically evaluate the data.

When I’m reading a paper, go through the figures and the figure legends. Try to determine what conclusions you can reach, and check to see if you came to the same conclusions that the authors did. This is how you can quickly evaluate whether a study is rigorously conducted as well as whether you’ll be able to easily understand it. Therefore, carefully constructed figures are another way to ensure reproducible science.

Fundamentals of data hosting

You can improve reproducibility by openly sharing data and code. If you publish in a journal like Nature, they state, “authors are required to make materials, data, code, and associated protocols promptly available to readers without undue qualifications.” If you produced custom analysis for the data, here are some fundamentals on how to make your data and code publicly available.

1. Find a place to host your raw data.  For flow cytometry data, consider the Flow Repository.  Imaging data can be uploaded to the Image Data Repository, where you can easily submit a 100GB data set with included metadata. Other popular options are Dropbox or Google Drive with links to the dataset on your lab’s webpage. This helps with reproducibility and allows other researchers to mine data from experiments that have already been performed.
2. Post your code in a repository like Github. Github is one of the most popular platforms for labs to post image analysis code, and it’s a great way to maintain version control. How many times have you tried to “improve” something only to make it worse?

If you are not striving to add quality data to a body of knowledge, you are wasting time in the lab. If no one can redo your experiment and get the same results, it will not add to the current hypotheses. So remember the basics, from experimental outline and procedure to post-experiment maintenance and reproducibility enhancers.

To learn more about important control measures for your flow cytometry lab, and to get access to all of our advanced materials including 20 training videos, presentations, workbooks, and private group membership, get on the Flow Cytometry Mastery Class wait list.