Experimental failure: Embrace troubleshooting for deeper knowledge

Brittany Carson

How often has an experiment not worked as planned? Sometimes it happens on a first attempt with a new antibody, or when using a new set of primers for your PCR. Sometimes it’s a key experiment for paper revisions that you already performed a thousand times and should be able to manage in your sleep. What do we scientists do when experiments do not work?

As in life, there is an easy way and a hard way to proceed. With the current pressures of science, it is so tempting to take the easy way out—change all the solutions for fear of contaminants, a change in pH, or solute precipitation. Or just grow new cells because the cells may have been “off,” then repeat the experiment and hope it works. Sometimes it works, and sometimes it doesn’t. Often we just shrug our shoulders and explain to ourselves or to the junior scientist that it’s just the way science goes and move on to our large pile of experiments, grant applications, and paper edits. But is this the correct approach? 

In truth, sometimes the easy way is the only way out—i.e. the antibody batch is off, the primers were faulty and so the experiment didn’t work—but this is more of an exception. If you have the luxury of not completing this specific experiment, then just aborting can be best for the sake of time and cost of reagents.

However, most experienced scientists can agree with the power of troubleshooting and optimization when things do not work. Alternatively, you can carefully construct your system in the first place to avoid common issues. These practices are all very strenuous, frustrating, and time consuming, and much less exciting than merely injecting your virus into a mouse or feeding your worms RNAi and waiting for data. However, having results that are ambiguous and unclear can be equally frustrating.

When problems arise we can appreciate how little we understand of the elements of an experiment. How easy is it to just follow a protocol without asking why? Why this incubation time? Why this frequency? Why this buffer or cell media? It is when things do not go as planned when one gets the opportunity to ask these questions and consider the deeper layers of the experiment. As one begins as a scientist, troubleshooting and optimization seem much more daunting, but like everything else, with time and practice it becomes as easy as riding a bike. You may even come to love it.

Experience argues that troubleshooting, optimization, and system set-up are essential for our training as scientists, and for a full understanding and mastery of our experiments, so that we can then utilize these systems and protocols to give the best and clearest data. If troubleshooting is done correctly, it pays off. In addition to completing the experiment with clear data, you will likely learn something new about your system or assay, whether or not it is your intention.

If you troubleshoot something to the point of mastering it, you then have the power going forward to tweak or optimize this experiment for a better result. For example, owning your experiments can allow you to bend the rules. So often as scientists we follow the protocols line-by-line, afraid to deviate—it is how we are trained. But when you understand each step, each chemical, and each action, you have the power to tweak. One student has done so to increase plasmid yield fourfold from a midi-prep kit. He knows the system well and understood which rules to break and how.

It seems now that less and less scientists understand the significance of the reagents they use or protocol steps they follow. Perhaps this is due to the improved technology we currently have, easy 1-2-3 kits, or because the optimization has already been done, rendering troubleshooting rare. Perhaps it is that now there is so much exciting science and so many new powerful techniques to learn that one does not have time to get down and appreciate the minutia. Either way, the argument for knowing your experiments and your system through and through, and owning them, is a valid one, and one every scientist should consider.

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Brittany Carson

Brittany is a postdoc at the Lunenfeld-Tanenbaum Institute in Toronto. She currently works on glioma, but has studied breast cancer, multiple myeloma and renal cancer, with a focus on cell-signaling pathways, translational regulation, the cell cycle and the cytoskeleton to develop novel biomarkers and therapeutic targets. Her science journey has taken her through cell culture, flies, worms and mice and through many different techniques. She love photography (including microscopy), traveling and non-science reading.

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