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For a Successful Metabolomics Study, Don’t Risk Going it Alone

For a Successful Metabolomics Study, Don’t Risk Going it Alone

Metabolomics has captured the attention of many clinical researchers and principal investigators within the life sciences sector, and for good reason. The study of small molecules or metabolites can provide a snapshot of the complete physiology of a living being in its current state from an analysis of a biological sample. Metabolomics delivers a comprehensive picture of any biological system’s metabolic function, enabling a deep understanding of health, disease and treatment response, and biomarker discovery, whether influenced by genes, the environment, epigenetics, or the microbiome.

With insights like these awaiting discovery, researchers may be tempted to set up their own global metabolomics lab in-house. However, it takes much more than liquid chromatography and mass spectrometry equipment to reap the benefits from metabolomics data. A successful metabolomics study requires a comprehensive approach focused on quality and accuracy. That’s why choosing the right partner can yield many more insights than trying to go it on your own, all while avoiding expensive mistakes. Consider these four risks that come with trying to do a metabolomics analysis without support.

1. Inadequate Metabolomics Study Design

Successful metabolomics studies always start with an appropriate design, which goes far beyond the instrumentation and data or informatics approach. Researchers should ask questions like: Do I have appropriate controls? Did I collect samples into a tube type that’s not going to interfere with my sample analysis?

Each small detail has a significant impact on whether accurate, actionable insights will be generated. On the flip side, poor design will waste precious investment in time and money before the research even starts. When performing metabolomics in-house, it’s the investigator’s responsibility to ensure the design is comprehensive and that there are no confounding factors. There is a risk in designing a metabolomics analysis without working knowledge and proven experience of the potential pitfalls and limitations

While recognizing that strong study design elements are central to uncovering biologically significant results, Metabolon works with each client to design a research project with that client’s intended results. Metabolon’s approach includes everything from the desired goal of biology or multi-omics approach of the research to selecting the appropriate sample matrix and tube type and collecting adequate exposures then taking appropriate steps to minimize excess variation. With this level of support, clients can rest assured they prepared for success from day one.

For more information on setting up an optimal study design to properly power and control your research for maximum success, view our Study Design Working With Metabolon video.

2. Sample Handling Errors

A common – and costly – error in metabolomics studies is sample preparation. Hundreds of samples could be required to generate insight-driving data, and the handling of those samples could be the difference between success and failure. Researchers risk sample integrity by factors such as an illegible hand-written label or storage in the wrong kind of tube. All too often, studies are delayed or even derailed entirely due to avoidable errors, and the risk increases if inexperienced practitioners prepare samples.

To help avoid errors and ensure data quality, Metabolon provides clients with a Study Success Handling Kit. The kit reduces human error with an electronic scanning and labeling system, utilizes hand-selected tubes that offer the greatest durability and lowest levels of artifact contribution, and aims to plug a crucial gap in studies – traceability. Our Study Success Kit includes carefully curated tubes that integrate with our automated handling for the fastest turnaround times. Best of all, it’s a free feature that Metabolon provides with every project.

Watch this video to learn more about the value of Metabolon’s Study Success Sample Handling Kit to support a successful metabolomics study.

3. Incomplete Coverage of the Metabolome

An essential part of a metabolomics research project is ensuring good coverage of the metabolome and an understanding of the metabolic pathways involved, leading to a real actionable insight of what’s going on in the biological system. In-house metabolomics labs often search against publicly available databases to identify the liquid chromatography mass spectrometry (LC/MS) peaks detected. This informatics approach means they’re getting limited, poor confidence, and potentially incorrect compound identifications. Most in-house labs can identify 200-300 compounds. These limitations compromise the ability to derive biological understanding by not getting comprehensive and accurate coverage of the entire biological space.

By contrast, Metabolon clients benefit from Metabolon’s informatics approach that relies on identifying LC/MS peaks based on a search of an in-house generated database of more than 5,200 biologically relevant small molecules. This compound database enables Metabolon to provide the greatest number of identified compounds with the highest possible confidence identifications, otherwise known as Metabolomics Standards Initiative I, in the industry. Metabolon’s broad coverage and adherence to the highest level of identification standards is crucial to understanding biology and producing accurate, actionable insights.

Metabolon provides unmatched depth and breadth of metabolomics insights because no other organization has the four essential capabilities upon which truly insight-deriving metabolomics depends: Coverage, Competency, Comparability and Capacity.

For more insight into each of the 4Cs view our videos here.

4. Incomplete and Inaccurate Data Interpretation

A successful metabolomics study will generate a list of detected and identified compounds. Still, without the ability to translate this list of compounds and glean actionable insights from the metabolic pathways revealed, the investment that went into the research is wasted. The commercial software packages that support in-house metabolomics labs leave users entirely on their own with data interpretation.

At Metabolon, we provide a metabolic pathway analysis of this list of compounds for our clients, taking the guesswork out of the statistical analysis and results. Our team of leading scientists goes beyond the data, deciphering thousands of discreet chemical signals from small molecules and revealing biological pathways to deliver real-world, actionable insights.

When you look at the big picture, the cost and potential risks of setting up an in-house metabolomics lab are very high. But, there’s no reason for researchers to go it alone when they can partner with Metabolon. We have spent nearly 20 years building the best metabolomics competency globally. Today, we enable, accelerate and support drug development through biomarker discovery, understanding mechanism of action, patient stratification and more. We can inform on proper study design, generate high-quality data, derive biological insights and make actionable recommendations – all with your success in mind.

Don’t make a large investment in an in-house LC/MS metabolomics lab that won’t deliver the insights you need. Contact us today to get started on a metabolomics study that will perform for you.

Metabolon
Our team is made up of over 45 PhDs, has been published 4,000+ times, and is committed to hard work, excellence, and success through collaboration. With over 15,000 projects, Metabolon has been a trusted partner of researchers for over 25 years.

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References

1. Zgoda-Pols, J.R., et al., Metabolomics analysis reveals elevation of 3-indoxyl sulfate in plasma and brain during chemically-induced acute kidney injury in mice: investigation of nicotinic acid receptor agonists. Toxicol Appl Pharmacol, 2011. 255(1): p. 48-56.

2. Bryant, J.A., et al., The impact of an oral purified microbiome therapeutic on the gastrointestinal microbiome. Nat Med, 2026. 32(1): p. 186-196

3. McGovern, B .H., et al., SER-109, an Investigational Microbiome Drugto Reduce Recurrence After Clostridioides difficile Infection: Lessons Learned From a Phase 2 Trial. Clin Infect Dis, 2021. 72(12): p. 2132-2140.

4. Feuerstadt, P., et al., SER-109, an Oral Microbiome Therapy for Recurrent Clostridioides difficile Infection. N Engl J Med, 2022. 386(3): p. 220-229.

5. Hu, Z., et al., Targeted metabolomics reveals novel diagnostic biomarkers for colorectal cancer. Mol Oncol, 2025. 19(6): p. 1737-1750.

6. Butler, F.M., et al., Vegetarian Dietary Patterns and Diet-Related Metabolites Are Associated With Kidney Function in the Adventist Health Study-2 Cohort. J Ren Nutr, 2025.

7. Stanford, J., et al., Metabolomic Profiling and Diet Quality Scoring in a Randomized Crossover Trial of Healthy and Typical Dietary Patterns. Mol Nutr Food Res, 2025 . 69(23): p. e70271.

8. O’Connor, L.E., et al., Metabolomic Profiling of an Ultraprocessed Dietary Pattern in a Domiciled Randomized Controlled Crossover Feeding Trial. J Nutr, 2023. 153(8): p. 2181-2192.

9. Fritsch, D.A., et al., Microbiome function underpins the efficacy of a fiber-supplemented dietary intervention in dogs with chronic large bowel diarrhea. BMC Vet Res, 2022. 18(1): p. 245.

10. Leal, L.N., et al., Preweaning nutrient supply improves lactation productivity and reduces the risk of culling in Holstein cows. J Dairy Sci, 2025. 108(6): p. 5875-5888.

11. Ahsin, M., et al., Soil and pasture health underlie improved beef nutrient density determined by untargeted metabolomics in Southern US grass finished beef systems. NPJ Sci Food, 2025. 9(1): p. 151.

12. Yin, W., et al., Plasma lipid profiling across species for the identification of optimal animal models of human dyslipidemia. J Lipid Res, 2012. 53(1): p. 51-65.

13. Porter, F .D., et al., Cholesterol oxidation products are sensitive and specific blood-based biomarkers for Niemann-Pick C1 disease. Sci Transl Med, 2010. 2(56): p. 56ra81.

14. Needham, B .D., et al., Plasma and Fecal Metabolite Profiles in Autism Spectrum Disorder. Biol Psychiatry, 2021. 89(5): p. 451-462

15. Li, C., et al., Estradiol and mTORC2 cooperate to enhance prostaglandin biosynthesis and tumorigenesis in TSC2-deficient LAM cells. J Exp Med, 2014. 211(1): p. 15-28.

16. Green, P.G., et al., Metabolic flexibility and reverse remodelling of the failing human heart. Eur Heart J, 2025. 46(25): p. 2422-2433.

17. Maekawa, H., et al., SGLT2 inhibition protects kidney function by SAM-dependent epigenetic repression of inflammatory genes under metabolic stress. J Clin Invest, 2025. 135(19).

18. Wu, D., et al., Integrated screens reveal that guanine nucleotide depletion, which is irreversible via targeting IMPDH2, inhibits pancreatic cancer and potentiates KRAS inhibition. Gut, 2026.

19. Schwerdtfeger, L.A., et al., Gut microbiota and metabolites are linked to disease progression in multiple sclerosis. Cell Rep Med, 2025. 6(4): p. 102055.

20. Wu, H., et al., Microbiome-metabolome dynamics associated with impaired glucose control and responses to lifestyle changes. Nat Med, 2025. 31(7): p. 2222-2231.

21. Jacobs, J.P., et al., Cognitive behavioral therapy for irritable bowel syndrome induces bidirectional alterations in the brain-gut-microbiome axis associated with gastrointestinal symptom improvement. Microbiome, 2021. 9(1): p. 236.

22. Pietzner, M., et al., Plasma metabolites to profile pathways in noncommunicable disease multimorbidity. Nat Med, 2021. 27(3): p. 471-479.

23. Faquih, T.O., et al., Robust Metabolomic Age Prediction Based on a Wide Selection of Metabolites. J Gerontol A Biol Sci Med Sci, 2025. 80(3).

24. Scherer, N., et al., Coupling metabolomics and exome sequencing reveals graded effects of rare damaging heterozygous variants on gene function and human traits. Nat Genet, 2025. 57(1): p. 193-205.

25. Holmes, Z.C., et al., Untargeted metabolomic analysis of human milk from healthy mothers reveals drivers of metabolite variability. Sci Rep, 2024. 14(1): p. 20827.

26. Titz, B., et al., Implications of Ocular Confounding Factors for Aqueous Humor Proteomic and Metabolomic Analyses in Retinal Diseases. Transl Vis Sci Technol, 2024. 13(6): p. 17.

27. Bloom, S.M., et al., Cysteine dependence of Lactobacillus iners is a potential therapeutic target for vaginal microbiota modulation. Nat Microbiol, 2022. 7(3): p. 434-450.

28. Leimer, E.M., et al., Lipid profile of human synovial fluid following intra-articular ankle fracture. J Orthop Res, 2017. 35(3): p. 657-666.