Metabolon: The Gold Standard for Actionable Metabolomics

While it is difficult to derive biological insight from unidentified metabolite features, most metabolomic service providers put a great deal of emphasis on the number of features they can detect, regardless of whether those features can be identified or not. But the number of features detected/reported per metabolite isn’t a great measure of value given that not all detected features correspond to the parent ion (molecular ion) of a known metabolite. Many features represent artifacts of the methodology used and include molecular ionization artifacts such as adducts, in-source fragments, chimeras, multimers, and background noise (Figure 1).

Users typically acquire a list of these artifacts along with all of the other detected features at the end of Step 3 with the possible assumption that all features, including the artifacts, represent parent ions and thus they will attempt to identify them. The number of misidentifications in this typical workflow became so prevalent due to these assumptions that the metabolomics community established metrics on feature characteristics that represent an identification of sufficient rigor to be used universally in metabolite identification.⁷

Figure 1. An LC-MS spectrum of glucose, demonstrating the parent ion and in-source fragments along with chlorine and formate adducts. All of these constitute “features”.

Annotation refers to the process of assigning a specific identity or name to a detected feature. A list of many features can correspond to only one metabolite (Figure 1). This is a critical step in metabolomic analysis that enables researchers to accurately identify the metabolites present in their samples and advance their studies.

To distinguish between confidence levels in annotation accuracy, the metabolomics standards initiative (MSI) in 2007 proposed four identification levels,⁷ which were later expanded to five levels (Figure 2).⁸ Within each level, the approach to annotating metabolites is different and depends on the level of confidence required by the established standards.

Figure 2. The identification levels that were established to provide confidence in annotations. Level 1 has the highest stringency requiring m/z, RT, and MS/MS of a measured feature against an authentic chemical standard.

At Metabolon, we offer the largest Level 1 library in the metabolomics industry. Our proprietary library has been built and curated over 20 years and contains over 5,400 entries. The vast majority of entries in our library are Level 1 attributing approximately 85% (~4,600 entries); however, some are Level 2 (approximately 15% accounting for around 800 entries) due to a lack of commercial standards available to qualify for Level 1. Metabolon delivers accurate, highly actionable insights for our clients’ scientific or clinical inquiries due to our unmatched library breadth and industry-leading annotation confidence levels.

Other providers claim that they can scan a large number of small molecule biomarkers or have a curated database of greater than 200,000 features. This means you may get a feature list of m/z-RT pairs with no annotations, or that the service providers’ algorithms have been developed to leverage well-known databases such as HMDB, GNPS, or KEGG. Other service providers sacrifice compound specificity and resolution for speed of acquisition. The result in each of these cases is that metabolomic spectral measurements are the sum of multiple compounds; even so, some service providers will specify a single annotation from a measurement that is the sum total of five different isomers. Using the glucose example from Figure 1, Metabolon will report the annotated metabolite, however other providers will provide the incorrect assignment to features as seen in Figure 3.

Figure 3. A comparison of how features and metabolites are reported using an LC-MS spectrum of glucose. Metabolon will report only an annotated metabolite that has been matched with an authentic standard. Other service providers claim all signals are unique compounds and will report many incorrectly.

Although some of these practices may provide sufficient metabolomic resolution for discovery-based work, there are major limitations regarding reproducibility, and they should be approached with caution considering confidence in biological interpretation. Metabolon’s platform provides by far the most accurate metabolite identification directly tied to corresponding standards, ensuring the highest quality of data reported to clients.

Why are Level 1 annotations important? The FDA recently changed the face of drug development by removing the requirement for preclinical testing on non-relevant animal models. Increasingly, drug developers are moving towards building a data package containing biomarkers supporting patient selection and best efficacy indicators. There is considerable evidence that suggests trials that use biomarkers and patient selections have higher overall success than trials without biomarkers.²⁴

Metabolon has a strong track record in supporting pharmaceutical and biopharma companies’ key decisions through the process from discovery to clinical trials.²⁵ Having executed more than 10, 000 projects over the last 20 years, Metabolon is able to leverage its reproducible and rigorous data in a regulatory setting, while providing a singular focus on understanding the client’s needs and delivering support for the success of their programs. Past and current customers include those at pharmaceutical/biotech companies, academics, and applied market segments.

In summary, Metabolon has the largest library of Level 1 annotations built off retention time and compound resolution essential for enabling meaningful, actionable biological insights. Adhering to industry standards, our library minimizes false positives, ensures scientific and clinical accuracy in biomarker discovery and supports precision medicine applications. With Metabolon, you can be confident that your results will have the maximum possible number of Level 1 annotations.