Fecal Microbiota Transplantation (FMT) Explained | Multidimensional Chromatography & Gut Health

Join us for an insightful conversation with Dr. Ryland Giebelhaus a chromatographer and metabolomics researcher from the University of Victoria, as he breaks down fecal microbiota transplantation (FMT)—a revolutionary treatment changing lives by restoring healthy gut bacteria. 

In this episode, you’ll learn:

• What FMT is and why it’s a game-changer for treating recurrent Clostridioides difficile infections and other gut-related diseases  
• How cutting-edge techniques like comprehensive two-dimensional gas chromatography (GC×GC-TOFMS) enable detection of key metabolites such as short-chain fatty acids, linking gut microbiome function to health  
• Practical challenges of analyzing biological samples, including sample prep and maintaining bacterial viability for research and clinical use  
• The role of advanced chromatography and mass spectrometry in monitoring FMT product stability, efficacy, and future therapeutic design  
• Why data science and coding skills are essential for next-generation metabolomics and microbiome research  
• The exciting future of multidimensional separations (GC×GC, LC×LC) in expanding our understanding of complex biological systems  

Whether you’re an undergraduate entering analytical chemistry or a researcher curious about the intersection of microbiology and metabolomics, this episode offers valuable perspectives on innovative science improving patient health.

Video Transcription

I’m sure you get asked this question a lot. Can you introduce that concept of FMT to an unfamiliar audience?

So a fecal microbiota transplantation, or FMT, is essentially the transplantation of living bacteria from a donor who has a healthy gut microbiome, and it’s transplanted into a recipient. These have been researched quite extensively—especially recently—and they’re growing in popularity for treating recurrent C. diff infections. So these are patients that had a major GI surgery and they have a recurring bacterial infection in their intestines that is not being resolved with antibiotics.

These patients are reporting really, really low quality of life. They have diarrhea that will not resolve. It really is just awful for the patients, and often providing antibiotics does not fully treat or resolve these recurrent infections. So the FMTs are then given to patients in order to change what bacteria are present in their gut microbiome—living in their gut—and to resolve the recurrent C. diff infection and improve their quality of life. It’s also been kind of the “it” thing for now for treating different diseases as well, both GI diseases and even some research exploring mental health and the links that has—you know, that the gut is linked to mental health as well as overall physical health and our well-being. So these are very important not only for C. diff infections but might be important toward improving the quality of life for patients affected by multiple illnesses.

Before we kicked off the discussion, you had mentioned that this was a very large study—a piece of research that you participated in. So there were certainly a number of different analytical platforms chosen, and luckily for us this podcast, this interview series focuses on chromatography and mass spec. And you noted that GC×GC-TOFMS was one of the approaches. So I was hoping you could talk to us about how that platform was used and the unique insight that it provided.

I have my training as a chromatographer, and my research interest is in metabolomics and multi-dimensional chromatography. My PhD—which is where this research was done—was in comprehensive two-dimensional gas chromatography and TOFMS for analyzing complex matrices that are often used for metabolomic studies. So the motivation for using GC×GC-MS for this was because bacteria in fecal microbiome transplants and stool as well, they produce a lot of volatile compounds that are biologically relevant. These include short-chain fatty acids. Short-chain fatty acids are produced by bacteria, they’re quite volatile, and they’re difficult to detect using gas chromatography.

They also have been linked to gut health as well. So they seem to be important in mediating gut health and potentially interacting and having a positive effect on gut health.

I developed a method using solid-phase microextraction—SPME—and just straight from the FMT cultures you’re able to detect the short-chain fatty acids and other volatiles as well without having to derivatize them. So not changing them— not actually going in and altering them chemically—just detecting them by heating up the sample, absorbing them onto a fiber, and then putting them onto the column.

In the work I shared—two papers—and with the other paper as well, we were looking at polar metabolites too. So trying to look at polar, slightly larger small molecules as well. This was achieved again by using GC×GC-MS to look at these metabolites—derivatizing, adding a TMS group onto the metabolites before analyzing.

And I think GC×GC-MS and multi-dimensional separations will play a really large role in metabolomics and metabolite profiling going forward in the future because of how many metabolites you can see in a single separation. We can routinely detect thousands of individual analytes in a single GC–time-of-flight MS separation. This is incredibly powerful for these sorts of untargeted studies. Okay, we know short-chain fatty acids are important, but what other small, volatile molecules are present that may be contributing positively or negatively to the effect that we’re trying to observe?

I think this is a very important analytical platform and going forward will be used more in clinical research where researchers want to detect as many small molecules as possible.

It’s been really interesting for me to learn more about GC×GC—part of it through this interview series, but also my company just has more people using our evaporators to concentrate samples ahead of that analysis. I was hoping you could walk us through any sample prep challenges you faced, and there’s certainly differences between the live versus the dead bacterial fractions I think would be interesting to discuss.

So of course one challenge is dealing with biological samples and getting them into the format where we can analyze them by GC. So we can’t just take a live bacterial fraction that might be in water and inject it straight onto a GC—it’ll destroy the instrument. So I was worried about trying a derivatized method for short-chain fatty acids. So instead, that was why just looking at the FMT culture fractions that were in solution, we just added salt to them and then stuck an SPME fiber in the headspace, heated them up a little bit, and I was able to absorb all these volatiles relatively quickly within a few minutes on the SPME fiber.

This was minimal sample preparation, which is good for analysis, and also it’s not destructive. So we’re not potentially losing short-chain fatty acids and introducing false negatives into the analysis. So that was really powerful for looking at the polar metabolome. It’s also more “green” too—so the only reagent used was sodium chloride versus other derivatization agents that can be quite environmentally toxic.

Looking at the polar metabolites—that is trickier. Fecal samples are solid samples, so you have to get them into the liquid state before you can derivatize and analyze them. The other FMT samples were already liquid, which made it a little bit easier to work with. We still had to extract the metabolites and then dry them down and derivatize them—dry them down using one of your devices. I think that one’s quite old—well, not old enough to be in the competition that I’ve seen advertised on LinkedIn—but we did have to dry them down and then derivatize them, push water out of the system.

One benefit of GC×GC as well is you resolve a lot of the analytes away from the derivatization reagents. So when you add derivatization you get a huge band of TMS compounds that streak across the chromatogram, but your analytes are resolved from that in the second dimension. So you can achieve more limited detection and more pure mass spectra, which is important when you’re doing a complicated sample preparation like this versus 1D GC-MS, where the background is going to be all these difficult, low-abundance molecules that might be relevant.

Well, I’m always happy to hear about a happy user of our equipment, certainly, but also the idea that in general there were many challenges with your sample prep and that it seemed like a pretty streamlined process, which is great to hear.

What was the most difficult aspect of measuring short-chain fatty acid production in vitro and how were you able to help overcome that with the GC×GC?

So one big challenge is they’re volatile, of course—so we put them on the instrument and try to make the analysis go as quick as possible. So samples are prepared on the fly rather than preparing a whole batch of 100 samples and then loading them on the instrument and leaving them to sit overnight. So that was the biggest challenge, and that just meant I had an undergrad technician working with me and we were just preparing samples kind of on the fly—preparing one or two samples as we go.

Another difficulty as well is the quality control side. When you’re analyzing samples on the fly like this, you can’t just go and make a pooled sample of all the samples you’re analyzing because then you’d have to thaw the samples and then refreeze them. So the freeze–thaw cycle is an important consideration when looking at biological samples. So those are both important considerations.

I wouldn’t necessarily say that GC×GC made it any easier, but having a really reproducible method with SPME that didn’t involve complex sample cleanup for looking at short-chain fatty acids made doing the sample preparation a lot more accessible.

You found that bacterial viability was fairly stable across frozen formulations, but that storage duration reduced the diversity. Let’s say from a therapeutic perspective, how important is this finding for defining shelf-life guidelines?

Yeah. So with any live biological sample or treatment being given, one challenge to overcome is these are live cultures. They change with time. You have to preserve them to keep them alive so they still have efficacy. It’s not like a small-molecule compound that is in a pharmaceutical pill that’s shelf stable at room temperature for 10 years. Unfortunately, there are other considerations. Clinically, there’s a trade-off for the convenience that comes with a reduced shelf life, and ultimately we need to monitor these FMT products closely to ensure that they’re not degrading over time.

And this is something I think GC-MS actually has an important role to play in: you could use an analytical technique like this to monitor chemical quality to determine if these products are degrading or not, and then correlate this back with clinical efficacy in future studies. So I think linking it back to the analytical side—which is where my interests are—analytical chemistry plays an important role in our day-to-day life, but I think it’s a particularly important part of findings on the analytical side, because you can gauge the efficacy and monitor FMT products before clinical use.

Now, I know this is a pretty broad question, but I’m sure you have some thoughts on it. Purely from that analytical chemistry standpoint, what tools or methods do you think will be most important for next-generation microbiome therapeutics?

Yeah, so of course I have a bit of bias here. I think that chromatography coupled to mass spec is going to be the most important when looking at small molecules. Not only are small molecules produced by bacteria and they can interact beneficially with human health, but additives may also change formulations of FMT products. We can use them—as I mentioned in the previous section—to monitor: are these products shelf stable, are they degrading, on-the-fly QA/QC for these products before they’re being used in a therapeutic setting.

Of course, though, it’s not just one analytical method that’s going to be important going forward. So genomics perhaps are important questions that people interested in proteomics may have. I think these are all going to be a team effort across all fields of analytical and bioanalytical sciences to work with these living biological samples.

How might findings from your research inform the design of synthetic microbial consortia?

On the side of GC×GC we know that short-chain fatty acids are important. So perhaps one obvious use of these results in designing synthetic or defined therapeutics would be trying to mirror what the metabolome looks like—both the volatile metabolome and the nonvolatile metabolome. So what does the global metabolome look like to ensure that we are preserving what small molecules are actually present in these samples as we give these to patients?

So not only ensuring that the beneficial microbes are there, but what small molecules are there, and are the bacteria producing these small molecules such as short-chain fatty acids, so these products continue to have high clinical efficacy and therapeutic use. So that’s where I think the findings of this research should help going forward—as you mentioned—with synthetic microbial products, trying to replicate the metabolome.

You just touched on the importance of chromatography mass spec moving forward, but just in general terms, what excites you about microbiome science in the coming years in terms of progress that’ll be made?

I would say I think just all the unique applications that researchers continue to find for our technologies. I’m really on the technology development and the data analysis side. I mean, I do have biological questions that interest me, but my research program isn’t looking at one pathway or one bacterium. I’m always excited when I see some cool problem that involves microbes or other biological samples, and someone has taken GC-MS or GC×GC-MS and gone and analyzed a really cool sample.

At least in my own research, since I started as a professor at the University of Victoria in Canada a couple months ago, comprehensive two-dimensional liquid chromatography—LC×LC-MS—for metabolomics is an area that really excites me. Right now with one-dimensional LC-MS we get a lot of powerful results—we can see lots of biologically relevant compounds—but I suspect that similar to how GC×GC has become very important and powerful, we’ll be able to cover more of the metabolome in a single analysis with LC×LC-MS, and also be able to get more valuable insight from fewer samples.

With LC×LC-MS you can look at what compounds are present and then make some biological decisions or discover new compounds or new metabolites that might be clinically relevant, that might inspire new drugs or pharmaceuticals, and then use these in future studies. So these are the areas that get me really excited. And of course, like I said, I have a bit of bias—I’m really excited about comprehensive multi-dimensional separations, both GC×GC and LC×LC. I just like seeing the applications. So yeah, that’s where my excitement lies. And I’m always looking for collaborators as well that are looking to study and solve some unique problems with small molecules, and I’m trying to get an LC×LC-MS system in the next year.

I should have mentioned earlier to you that the audience for this interview series is undergraduate researchers or people in that spot just beginning their career. What else should they know about this research from your perspective?

I would say the most important thing to be aware of is the data processing and data handling that’s required to work with metabolomics data. If you’re really interested in metabolomics, you have to have a really strong data analysis skill set. So that includes understanding statistics, understanding how to code, being able to use Excel proficiently. These are all really important skills—and I think a lot of undergraduate chemistry degrees’ skills that are often not taught to undergrads. So they either have to do a minor in computer science or learn these skills on their own.

A lot of people think of analytical chemistry—they think of just measuring one or two molecules and maybe doing a p-test and that’s it. But in targeted metabolomics and compound discovery, there’s a lot of multivariate statistics that’s required. So having a really strong background in data analysis is a really important skill that complements all of the other technical skills that you learn in a laboratory—an analytical laboratory—or in an undergrad degree.

Knowing data science never hurt, and I think pairing a strong background in computer science or data science with another physical science—be it analytical chemistry or organic chemistry or physics or biology—opens a lot of doors. It creates a lot of opportunities for undergraduate researchers going into graduate school.

I know in my research group I’m always looking for people that are able to code and understand data and do statistics, because there are a lot of powerful insights we’ve obtained from this, and if you don’t have that, it’s a lot more challenging to process and analyze complex untargeted data.

Every analytical chemist I’ve spoken with has been very quick to point to the fact that to really excel in the field, you do need skills from different areas and it’s not just the chemistry focus. It could be the math or the physics, and certainly the data work, as you mentioned, is critically important. I think that’s a great thing for undergraduate students to keep in mind—that it’s really how well you can bring together your experience and skills from different areas and combine them to help your chemistry research.

Yeah. And I also see now a lot of discussion around concern that a computer science degree is not as employable as it was five years ago or ten years ago. And while I think that’s true, I think having other skills that complement computer science still makes people employable. So it’s important to have other skills that complement this as well, because people aren’t just coding for the sake of coding. They’re using computer science and coding to make software that enables more exciting future research, that enables discoveries in research, and also enables partners and people in industry to achieve their goals as well.

Just my two cents, but I certainly think that the creation of AI and the concept that it has destroyed the need for knowing coding is pretty exaggerated.

Yes, I agree. It’s still important. While AI can write code, if you don’t understand how to implement that—or even how to execute a line of code—there’s nothing you can do, right? Sure, AI tools can write code, but if you don’t understand how coding works or the types of problems that coding is really good at solving, then I don’t think it’s that useful.

So that’s why having a really strong background that complements this is important. You can use these AI tools to help write code faster, but you can’t necessarily excuse yourself from learning data science altogether.

This text has been automatically transcribed from a video presentation using AI technology. It may contain inaccuracies and is not guaranteed to be 100% correct.

Concentrating on Chromatography Podcast

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