Written By: Tim Bushnell, Ph.D.
Reproducibility is a key issue in science.
Massive amounts of time and money are wasted when the results of experiments are not reproducible.
For example, I was called into a lab to look at their data because they had spent thousands of dollars sorting precious human samples and were now doing genomics analysis with the isolated cells.
Unfortunately, the results of the genomics analysis made no sense based on the sorted populations. The lab was working backward through every step of the process to try to identify what might have happened and if the experiments were salvageable.
As I reviewed the sorting process, one of the striking factors was that the quality control of the cell sorting experiments was very, very poor. In fact, it was non-existent.
So whoever was running their sorter was not performing quality control on the instrument, so the sorting results were all over the place. Voltages were changed dramatically for each experiment, and the separation of the target cells ranged from barley differentiated to well separated. The compensation controls told another story about the problems with these sorting experiments.
In the end, this lab wasted tens of thousands of dollars, countless man-hours, and precious lost samples because there was not a focus on quality control and best practices.
Reproducibility is a state of mind.
It’s not one simple thing that you do that will make all your data more reproducible, it a shift in the way one thinks about and perform experiments.
With the emphasis on rigor and reproducibility in science, it’s very important that researchers start putting into place everything they can do to help improve the quality and reproducibility of there data.
Here are 3 action steps that can be taken to enhance experimental reproducibility…
1. Evaluate your quality control processes to improve reproducibility.
Quality control is an important component of reproducibility.
That includes monitoring the quality control of the instrument by making sure that quality control metrics are being run on a daily basis.
You, the end user, have a right to ask to look at that data.
Don’t be afraid to go up to whoever’s running the machine and say, “Hey, how’s the quality of the machine? “Can I look at the QC data, and see how it’s going?”
Let them talk to you about it so you understand what it means so that you can get a better feel for what’s going on with your instrument.
Figure 1: Quality control tracking using beads.
You don’t want to sit down at a machine that is not working properly and be unaware of the systems limitations.
Your data will end up looking poor or worse – you might make erroneous conclusions because the machine wasn’t performing properly.
Quality control is done to make sure the machine performs consistently on a day-to-day basis.
That means that you, as the end user, should also be thinking about quality control of your experiments, it is not just the job of the core facility.
At a minimum, researchers can include a bead tandard as a way of monitoring quality control before running the experiment.
2. Develop the assay completely before performing the assay.
When sitting down and to develop an assay, it’s important to work through the whole process.
The first part of that process is understanding what the biology is and what the experiment is trying to prove.
Next, sketch out the proposed primary analysis – what will the gating strategy look like, and what data will be extracted for secondary analysis. If the experiment is a cell sorting experiment, understand what the downstream application that cells will be used for is, and the limitations of that assay.
It is also important to decide what statistical analysis will be performed and calculate the power of the experiment to determine how many samples will be needed. These steps will go a long way to prevent p-hacking and prevent HARKing.
With those steps completed, the next step is experimental design. That includes what instrument will be used, what antigens will be needed and build an initial panel. Reviewing the diagrams for analysis that were drawn above can help identify what are the critical targets. Those should be paired with bright fluorochromes and in channels that have a low error that will allow for a more sensitive measurement. With this in place, the next steps include testing the reagents (titration and voltage optimization). From there, comes the optimization process, where the best conditions for the assay are determined.
Figure 2: Error contribution based on detector and fluorochromes.
After optimization comes validation of the assay. This includes characterization of the necessary controls that will ensure identification of the target populations, demonstrate the stability of the assay, the variation within the staining process and the like. Once the assay is validated, the process is locked down, using Standard Operating Procedures.
3. Ensure you have quality SOPs in place.
For those who are working in a regulated environment, the SOP is part of the daily routine. For others, the idea of a protocol is more common. The biggest difference between the two documents is the level of document control and the expectation of how close the document is followed. With SOPs, they must be followed exactly, and deviations have to be noted and signed. SOPs are not changed without significant discussion and demonstration of the need for change. All details about the reagents are noted, serial numbers of equipment, lot numbers of reagents and more. The exacting SOP ensures that everyone performs the experiment the same way.
A protocol, on the other hand, lists the general steps to follow. It is often changed on the fly, based on the needs of the experiment at the time. It lists recommended controls, but not all may be necessary for the specific assay. The level of detail about reagents and more are not expected.
For those performing a longitudinal study or for a long period of time, developing and implementing an SOP will improve the rigor of the experiment.
An added benefit of the SOP is that it can be used to train additional researchers to assist in the experiment, as they will know exactly what the steps are and how to perform them.
Reproducibility is the name of the game, take a few minutes to think about how you can change your activities and your research workflow to increase the quality and consistency of your data. A few action steps that you can take right now to improve your data’s reproducibility is to Evaluate your quality control processes, develop the assay completely before performing the assay, and ensure you have quality SOPs in place. Taking those few simple steps will protect you from the problems associated with non-reproducible data.
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My other passions include grilling, wine tasting, and real food. To be honest, my biggest passion is flow cytometry, which is something that Carol and I share. My personal mission is to make flow cytometry education accessible, relevant, and fun. I’ve had a long history in the field starting all the way back in graduate school.