Do you have what it takes to interpret metabolomics? (Part 2|3)

Omics training usually covers experiment planning, data generation and collection, data preparation and analysis, but it almost always omits the last part that makes it all worthwhile: #datainterpretation.

Without knowing how to make sense of your results in the broader biological context, how can you pull out the actionable results you’ve been working so hard for?

My experience of data interpretation has centered on understanding molecular mechanisms. Over the years, I’ve discovered that the success of any data interpretation project requires a broad set of skills that can be honed as we go. This makes the interpretation project itself a great place for those of us who love to learn.

Over 3 posts I will discuss what I consider to be the 3 most important assets for data interpretation. Each of these assets makes data interpretation a personal exercise, as it relies on your own knowledge, and your own motivation.

Don’t hesitate to join me in discussing each of these in comments and let me know at the end if you would have chosen another top 3.

I will also discuss these 3 key assets and more about how to perform data interpretation in a webinar on data interpretation entitled The STORY principle – A 5-step guide to the biological interpretation of metabolomics. You can follow the link above to register already.

Today, we continue with: your knowledge of metabolism!

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Asset #2 Knowledge of metabolism

How much is enough to start?

If you have master’s level knowledge in a biology-related field, you have the understanding required to begin interpreting metabolomics. You don’t need to know everything, but you need to know enough about biology to make sense of the literature while you research your topic further.

A first year PhD student may have less chance of coming up with a thick plot of what’s happening in a metabolomics experiment than someone with 10 or 20 years’ experience, but that doesn’t mean that beginners shouldn’t play.

In fact, this type of work is exactly what gave the experts their experience. Inexperience can be compensated for through hard work and automated tools that help focus the work on crucial metabolites or pathways.

While proficiency in molecular biology and metabolism is certainly a plus, it can be refined along the way. You can begin a metabolomics interpretation project without an extensive knowledge of metabolism and expect to know quite a bit more by the end of it.

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Best source of knowledge

I would argue that working on a data interpretation project is possibly the best way to acquire knowledge about metabolism and biology in general. Here is an example of how that may play out in practice.

Let’s say you measured a broad panel of metabolites in blood samples from patients with Alzheimer’s disease and found that the levels of several bile acids are different compared to healthy controls. If your knowledge on this class of metabolites is a bit fuzzy, all you need to do is go to the literature to fill in the gaps with the latest knowledge on these molecules.

You may begin at the more generic level, using textbooks and web searches to get an overview of what’s known about these metabolites. You may discover that bile acids are synthesized primarily in the liver, but require the gut microbiome for certain steps. You may learn about primary and secondary bile acids, about so-called “toxic” bile acids, and about their roles in digestion and as signaling molecules. Other metabolites may be less well documented, indicating that more research is needed to understand their roles in biology.

Next, you may look into what’s known about these metabolites in relation to your disease of interest, Alzheimer’s disease. You will find several publications describing changes in bile acids in Alzheimer’s. You’ll compare levels in different sample types (blood-derived, cerebrospinal fluid, and maybe even tissues) and see what others have concluded on the topic. You will also ask the literature for answers to questions such as “do bile acids cross the blood-brain barrier, and how?” or “can bile acids be synthesized in the brain too?”

After that, you may want to look into how these metabolites have been studied in other contexts. Much of the knowledge we have on metabolites is repeated across different fields of biomedicine, and what is common knowledge to a cardiology specialist, may be groundbreaking to a neurologist. So don’t hesitate to jump around at the beginning of your search to get inspired about the possibilities of your dataset.

Someone with more experience may already know (or think they know) the answers to these questions, but there’s nothing to stop you learning about this with a thorough literature search.

Finally, you will connect these pieces of information together to build the story of what may be happening in your experiment at the level of bile acids. You’ll look at how this relates to other metabolites in your dataset or with the disease in general, including known symptoms and risk factors.

By combining a tailored literature search with your own findings, you’ll create a storyline that becomes your version of what’s happening in the experiment. This story becomes the narrative you use to explain your work to yourself and to others. It will form one of the many building blocks for your own knowledge of metabolism. That is, if you stick to your plan... (this is a hint toward asset #3)

?If you are interested in learning more about data interpretation for metabolomics, don't forget to register for my upcoming webinar next month with this link: The STORY principle – A 5-step guide to the biological interpretation of metabolomics.

Do you agree with me that we should all take a shot at interpreting metabolomics results? Or do you think that this delicate matter should remain in the hands of the experts only? Let me know in the comments