Five costly mistakes in metabolomics and how to avoid them

Multiomics has been on an exponential growth curve and has been a hot topic among many researchers recently. Metabolomics is one of the most promising ‘omics’ sciences for implementation in medicine, specifically concerning diagnostic tests and therapeutic intervention monitoring. However, being a business developer, I have a peek behind the curtain that most don’t see. I’ve seen researchers have the discovery of a lifetime on a side project and others dump 10s of thousands of dollars into an experiment that was destined to fail from the start.?Because of this view behind the curtain, I wanted to share with you the top five ways researchers new and old in the field overspend on their metabolomics studies.

1. Lacking Clarity on the End Goal

Have you ever heard the phrase, “Clarity is kindness”? In this case, clarity is time and time is money.

When I was younger, I loved starting crochet projects. When I began it was winter and I wanted a blanket, but about halfway through I would always change my mind. I decided I didn’t want the blanket to be a blanket but rather a scarf or a bag. This led to many unfinished crochet projects and some really “unique” finished products. Starting an experiment without a clear understanding of the research objectives or hypothesis sets you up for a long grueling experience with metabolomics. You’ll end up with many projects that seem to never be finished or, to put it nicely, “unique” datasets. Sure, you can generate data, but is that data useful, publishable, and actionable to help others? Probably not.

Solution: Plan for a scarf and stick to it.

Define the research objectives with expected outcomes clearly before initiating the project. Identify your research questions, hypotheses, and potential pitfalls to guide decision-making throughout the experimental design and execution phases. Also, refer to these questions and objectives as you collect data and analyze it. This way you don’t end up generating data for data's sake. Without a clear understanding of the research objectives, researchers may struggle to identify the most appropriate analytical techniques or collaboration opportunities, leading to inefficiencies in project planning and execution.

2. Choosing the Wrong Analytical Approach

I recently watched my 1-year-old son carry around a hammer all day, all night, and use it for EVERYTHING. He loves that hammer, but let me tell you, that hammer doesn’t make a good spoon, or a soft pillow for his head. Just like my son, we all have a favorite tool. In metabolomics, though, it is important to select the right tool for the job. Sometimes researchers come to me and say, “I want to see everything.” But my next question is always, “What if you do see everything; what will you do with it?”

It's tempting to run the untargeted metabolomics experiment with wanderlust in our eyes. Pursuing the Eureka! moment with the thought of discovering a new metabolite. Or run the $20 assay at the core that isn’t quantitative or validated. However, experience has shown me that picking the wrong tool for the job just means doing another experiment later with the right tool. Sometimes an overly sophisticated or costly analytical technique appears shiny or groundbreaking, but the simpler, more cost-effective alternative aligns with the experimental objective. Other times the budget is tight, and the assay isn’t quite right, but we think, “At least I’ll have some data.” This always leads to a longer more drawn-out process to get to our end goal.

Solution: Don’t use the hammer as a spoon.

Evaluate the analytical requirements carefully and choose the method that strikes the balance between complexity, cost, and suitability for your research goals.

3. Neglecting Preanalytical Considerations

I was recently cooking, which I don’t often do as my husband is typically the cook, but I saw a recipe I wanted to try. However, the meat that I was going to use was just a day over the expiration date. “Surely it will be fine”, I thought. BOY was I wrong.

The same is true for metabolomics. Ignoring critical preanalytical factors such as sample handling, processing, and storage procedures can introduce big problems. Just as I learned that expired meat changes a recipe, small changes can have a big impact in metabolomics. such as bias and variability in the data, compromising its reliability. I’ve seen researchers come to me and say, “These samples were collected in hospitals”, but when I ask them, how long they remained at room temperature, or if any other buffers or fluids were introduced to the sample, the answer is often, “I don’t know.” When planning a metabolomics experiment, it is important to remember that metabolomics strength is a double-edged sword. Metabolomics is a very sensitive and dynamic science. Some metabolites degrade in seconds while others are roughly shelf-stable.

Solution: Don’t use expired ingredients.

Prioritize rigorous preanalytical practices, including standardized sample collection, extraction, and storage protocols, to maintain data integrity and minimize potential sources of error. Regular validation and quality control checks are essential to uphold data quality standards.

4. Overlooking Collaboration Opportunities

When I was in graduate school, I spent hours and hours reading papers to find different ways to prepare tissue. Frozen, fixed, formalin embedded, the whole lot. I tried many experiments where I got it wrong and even when I got it right, I wasn’t sure. One day I was talking to another scientist, and they mentioned that we have a tissue fixation core in the same building as my research lab and I should reach out to them to see if I can shadow them. I was so lost in the way I expected to solve the problem and in the way that I was used to solving things that I didn’t see the solution someone else could provide.

The same is true in metabolomics. Failure to engage with potential collaborators from complementary fields deprives researchers of valuable expertise and resources, hindering the project's success and perpetuating research silos.

Solution: Don’t forget your neighbors.

Go to conferences and share your successes as well as lingering questions. Actively seek collaboration with experts in bioinformatics, statistics, and related disciplines to enhance the study design, data analysis, and interpretation processes.

5. Inadequate Data Planning

I was recently watching my family build a balloon arch for a party. Instead of counting the number of balloons we needed for the arch, we assumed that the number that came in the kit would be correct and blew up all the balloons before attempting the arch. We ended up with a lot of leftover balloons and had a really hard time building that arch the way we wanted. The balloons weren’t all the right size, and the shape was off.

Like blowing up all the balloons before building the balloon arch, I see researchers just plan to “run some metabolomics on it,” without considering their data management plan. Insufficient preparation for data management, storage, and analysis can lead to challenges in handling metabolomics data effectively. Also, bioinformaticists are highly valued, trained, and busy individuals, so never assume you will “just get someone to analyze it.”

Solution: Plan the balloon arch first.

Develop a robust data management plan encompassing data acquisition, storage, backup, and analysis protocols to ensure efficient workflow and reproducible results. Additionally, budget for data analysis expertise. This can range widely depending on the comparisons and your biological or experimental goals.

Regardless of your background, we all want our science to be impactful and not cost us an arm and a leg. I hope that these few stories and suggestions can help you in your journey in metabolomics. If you are new to metabolomics and would like to discuss your potential project, I’d love to connect with you and learn more. Please feel free to email me at [email protected]. If you have been in metabolomics for a while and have some advice to share as well, please do leave a comment, like and share.