Guide to Metabolomics, 3rd Edition

This is the third edition of our Guide to Metabolomics, a comprehensive resource covering the biochemical foundations, analytical workflows, and biological applications of metabolomics across broad applications. This edition includes an expanded focus on alternative matrices and translational science, reflecting the growing role of metabolomics in bridging discovery research and clinical application.

Chapter guide

By the time you complete this series, you’ll have a deep understanding of metabolomics and how you can start implementing it in your own research. Each chapter in this series focuses on a specific topic, delivering everything you need to know about metabolomics in manageable bite-sized chunks.

Chapter 1: Introduction to Metabolomics

This chapter introduces the concept of metabolomics, discusses why it is important to scientific discovery, and provides an overview of how metabolomics is performed, which profiling methods are used, and how sample matrix selections impact results and interpretation of metabolomics data.

Chapter 2: Metabolon’s Solutions and Chemo-centric Approach

Here, we discuss some common challenges associated with metabolomics studies, the technical solutions Metabolon has developed to address them, and how those solutions elevate the quality of metabolomics data above other metabolomics providers.

Chapter 3: Metabolomics for Commercial Applications

In this chapter, we explore peer-reviewed scientific studies that demonstrate the importance of using metabolomics in various applied markets. We discuss how metabolomics can identify high performing diagnostic biomarkers that are invisible to other omics sciences, how metabolomics saved a promising drug from being shelved due to a failed phase 2 clinical trial, how metabolomics can inform human nutrition to mitigate disease and improve overall health, and more.

Chapter 4: Metabolomics for Translational Research

In this chapter, we discuss peer reviewed studies that validate the crucial role of metabolomics in translational research. We show how metabolomics can help develop disease models that most accurately reflect human biology, can be used to characterize novel biological mechanisms that translate remarkably well to humans.

Chapter 5: Metabolomics for Basic Science

This chapter is dedicated to studies that show how metabolomics can reveal crucial insights beyond other omics sciences to propel basic science research forward. The examples covered in this chapter show how metabolomics can elucidate mechanisms that are invisible to other omics sciences to transform future therapeutic strategies, reveal key insights that have significantly advanced our understanding of the microbiome-brain axis and population health, generate meaningful data from sample matrices that are not commonly studied.

Chapter 6: How to Design a Metabolomics Study

In this final chapter we discuss the principles that one must consider when designing a metabolomics study and how Metabolon’s technologies, service offerings, and scientific support can aid in this process to ensure that maximum insight will be gleaned from your study’s findings.

Download the complete PDF guide

Download this comprehensive guide developed to teach you the ins and outs of one of the most powerful omics tools in any scientist’s toolbox: metabolomics.

Download now

Why Metabolon?

Once you see the full value of metabolomics, the only remaining question is who does it best? While many laboratories have metabolite profiling or analytical chemistry capabilities, comprehensive metabolomics technologies are extremely rare. Accurate, unbiased metabolite identification across the entire metabolome introduces signal-to-noise challenges that very few labs are equipped to handle. Also, translating massive quantities of data into actionable information is slow, if not impossible, for most because proper interpretation takes two things that are in short supply: experience and a comprehensive database.

Only Metabolon has all four core metabolomics capabilities

Coverage

Ability to interrogate thousands of metabolites across diverse biochemical space, revealing new insights and opportunities

Comparability

Ability to integrate the data from different studies into the same dataset, in different geographies, among different patients over time

Competency

Ability to inform on proper study design, generate high‐quality data, derive biological insights, and make actionable recommendations

Capacity

Ability to process hundreds of thousands of samples quickly and cost‐efficiently to service rapidly growing demand

Partner with Metabolon to access:

A library of 5,400+ known metabolites, 2,000 in human plasma, all referenced in the context of biochemical pathways

That’s 5x the metabolites of the closest competitor

Unparalleled depth and breadth of experience analyzing and interpreting metabolomic data to find meaningful results

10,000+ projects with hundreds of clients
3,500+ publications covering 500 diseases, including numerous peer-reviewed journals such as Cell, Nature and Science
Nearly 40 PhDs in data science, molecular biology, and biochemistry

Using our robust platform and visualization tools, our experts are uniquely able to tell you more about your molecule and develop assay panels to help you zero in on the results you need.

Related Resources

See all resources

E-Book

Metabolon's Global Discovery Panel

With more than 25 years of experience and continuous innovation, Metabolon has created the industry’s most advanced system for measuring and understanding the metabolome.

Learn more →

Case Study

UPF Biomarkers Case Study

Metabolon’s involvement in this study helped identify biomarkers of Ultra-Processed Food (UPF) intake and metabolic responses, shedding light on the effects of UPFs on human metabolism and their potential role in disease.

Read Case Study →

Product Brochure

Integrating Global Untargeted Metabolomics and GWAS

This study shows how integrating global untargeted metabolomics and genome-wide association studies (GWAS) can yield new insights into genotype-phenotype associations in disease.

Download PDF →

Talk with an expert

Request a quote for our services, get more information on sample types and handling procedures, request a letter of support, or submit a question about how metabolomics can advance your research.



Corporate Headquarters

617 Davis Drive, Suite 100
Morrisville, NC 27560

Mailing Address:
P.O. Box 110407
Research Triangle Park, NC 27709



+1 (919) 572-1711



+1 (919) 572-1721



International Headquarters

Metabolon GmbH

Zeppelinstraße 3
85399 Hallbergmoos
Germany



+49 89 99017752

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.