Research is like a treasure hunt. Without the right navigation tools, precious time is spent on trial and error exploration to find the right path, increasing the cost as well as the risk of program failure. Metabolomics provides uniquely valuable guidance for navigating a biological system, but the signal-to-noise ratio from vast amounts of data can cloud your ability to see the actionable insight.  

Tier Metabolomics.pngIn untargeted metabolomics, the critical step to separating noise from insight is accurate metabolite identification (often also called annotation). As pointed out by Schrimpe-Rutledge et al, “metabolite annotation is the crucial link between acquired data and meaningful biological information.” 

Phrased another way, the biological insight from a study is only as good as the metabolite annotation – if you want the highest quality metabolomic insight you need the highest quality annotation. In recognition of this fact, Sumner et al proposed a schema for stratifying the quality of metabolomic annotations. Tiers, or levels, ranging from 1 to 5 are commonly used to convey metabolite identification confidence. Tiers refer to the level of detail for each metabolite, and thus the assigned confidence for accurate identification. Tier 5, the lowest level of identification, offers a unique feature in the metabolite, but lacks the information required for confirmation. Through increasing precision measurement, additional unique characteristics are discoverable enabling a definitive identification of the molecule to be made. It is not until the appropriate level of detail is reached in Tier 1 that definitive compound identification based on multiple orthogonal measurements and comparison to data from an authentic standard can be achieved. Therefore, Tier 1 identifications represent the highest level of confidence in the annotation. Tiers 2 through 5, on the other hand, represent decreasing levels of confidence based on less rigorous or more ambiguous criteria.

While most metabolomic practitioners operate primarily with annotations that only meet the standards of Tiers 3-5, Metabolon’s Precision Metabolomics™ workflow is uniquely designed to deliver Tier 1-2 identifications for detected metabolites. This unique level of confidence in the annotations is made possible by Metabolon’s use of a chemocentric approach to metabolomics that uniquely detects metabolite features and matches them against Metabolon’s vast in-house library. Metabolon built this library through the analysis of >5,000 authentic standards run in-house using our methods and instruments. This approach to untargeted metabolomics means that all metabolite annotations meet the stringent criteria required for a Tier 1 or 2 identification.

Our method stands in stark contrast to the more traditional metabolomics workflow in which the individual ion features, the instrument signal from a mass spectrometer, undergo statistical analysis. Only the most significant are subjected to an attempt at a high Tier identification. Metabolon’s approach leverages this vast library to ensure accurate annotation of not only the metabolites, which show statistically significant changes in a study, but also those which remain unchanged, and therefore add crucial insight into the underlying biology.

The field of untargeted metabolomics continues to expand due to its growing track record of providing a crucial understanding of biological processes including aging, disease, and the role of the microbiome in health. Metabolon sets the standard in delivering high-quality metabolomic data. By harnessing the power of our extensive library and delivering metabolite measurements with Tier 1 identifications, Metabolon leads the way to unlocking the information stored in the metabolome and revealing the contained biological story. 

To learn more about how metabolomics can help you uncover actionable insights with our Precision Metabolomics platform, contact us at


Schrimpe-Rutledge AC, Codreanu SG, Sherrod SD, Mclean JA. Untargeted Metabolomics Strategies—Challenges and Emerging Directions. Journal of The American Society for Mass Spectrometry. 2016;27(12):1897–905.
Sumner LW, Amberg A, Barrett D, Beale MH, Beger R, Daykin CA, et al. Proposed minimum reporting standards for chemical analysis. Metabolomics. 2007Dec;3(3):211–21.