How do you help partners understand the value of metabolomics within multiomic studies?
I usually start by meeting partners where they are. Most already have a strong foundation in genomics, transcriptomics, or proteomics, so the conversation is really about how metabolomics fits into that bigger picture and helps them see what might be missing. For me, metabolomics is that missing layer. It reflects what is actually happening in real time, which is often what researchers ultimately aim to capture.
A lot of what I do is sit down with researchers early in the study design phase and work through those questions together. We talk through controls, study power, and sampling strategy. Once those pieces are in place, the value becomes clear.
When I look back at my own research, I realize I was asking many of these same questions. I just did not have access to this type of data. I can only imagine how much more insight I could have gained, and how much further it could have pushed the work. That perspective is what I bring into every conversation now.
What biological questions are best answered when metabolomics is included?
The questions that come to mind are the ones that start with “what is actually happening here?”
- What is changing in this system right now?
- How is a disease or treatment shifting pathways in a meaningful way?
- Which molecular changes are truly tied to phenotype?
Those are the kinds of questions where metabolomics really adds value.
My own research focused on complex systems, including evolutionary biology, venom resistance, and immuno-oncology. From this, I know and understand that biology is not static. It is constantly changing, and metabolomics captures that movement. It helps connect upstream signals to downstream outcomes more closely to biological truth, especially when paired with other -omic layers. Looking back, this is where metabolomics would have had the biggest impact on my own work. It would have given me a much clearer view of how all the pieces I was studying were actually functioning together.
How does integrated omics change the types of conversations you have with researchers?
It shifts the conversation from “what data do you want?” to “what story are you trying to tell?”
When metabolomics is part of the strategy, discussions become more systems-focused. We are no longer looking at isolated datasets but rather are thinking about how different layers of biology connect. That usually means getting involved earlier and developing a strategy not just for study design but also for data analysis. It’s truly a collaborative process in which we align the different omics layers with the biological question from the start and determine how to get the most out of these datasets. It also means translating across disciplines and perspectives.
Some collaborators are thinking computationally, others are thinking clinically, and others are deeply rooted in wet lab biology. My role is to help connect those viewpoints so that everything points back to answering a clear biological question. We (Metabolon) are not just delivering data or providing a service. We are invested in progressing the science together.
What trends are you seeing in multiomic study design?
One of the biggest changes I have seen is that metabolomics is now included earlier in the initial study design. There is also more focus on studies that capture change over time, and on research that connects more directly to real-world or clinical outcomes.
But the biggest shift, to me, is in how people think. There is more intention behind study design and thought behind how to push further for more complete answers to their biological questions, and moving away from the “this is how we’ve always done it” mindset.
A question I am hearing more often is, “Metabolites are so dynamic and sensitive to change. How do we know we are capturing something real?”
This is such an important question. The answer is that this is both the challenge and the strength of metabolomics. That same dynamic nature is what makes metabolomics so powerful. It captures biology as it unfolds, not just what is predicted.
Overcoming this challenge is something I really enjoy working through with researchers. With the right study design, a lot of that variability can be managed. Things like study power, controls, and sampling strategy make a huge difference. At the same time, we see strong biological signals come through again and again, even when sampling resources are more limited. Further, when metabolomics is combined with other omics data, those signals become even more meaningful because they can be seen in context.
I often work with researchers who already have other -omics data they are trying to understand. The transcriptomics and proteomics just don’t match up, and they are unsure whether the data they already have is even useful. Enter metabolomics. Once they have this added phenotypic layer, the others start to make sense, and the data fit together. For me, that is where things get exciting. Instead of trying to simplify biology, we are starting to embrace its complexity and use it to our advantage.
