Metabolon's Solutions and Chemo-Centric Approach

Metabolomics Challenges and Solutions

LC-MS is a favored method for conducting metabolomics studies due to its high sensitivity and wide biochemical coverage, and metabolomics is recognized for its ability to provide deep phenotypic insight beyond traditional omics sciences. However, there are challenges associated with metabolomics and traditional LC-MS approaches that can limit the insight gained from the data. These challenges include insufficient pathway coverage, a lack of reliable metabolite identification, and a shortage of context-specific data interpretability.

Metabolon’s LC-MS platform incorporates strategic innovations into the conventional analytical approach, which overcomes key limitations to deliver broader, deeper, and more precise coverage than traditional LC-MS technologies. These innovations are:

• Four chromatography methods. Samples are subjected to four methods of chromatography, each optimized to separate hydrophilic, hydrophobic, basic, and acidic compounds. Using four chromatography methods results in superior compound separation and coverage and builds analytical fail-safes into the method, so compounds with overlapping chemical characteristics are identified with the highest confidence.
• Biochemical reference library. Metabolon’s library is the largest commercial biochemically relevant reference library in the world. It contains more than 5,400 metabolites and metabolite intermediates, most of which have achieved Level 1 identification status, which denotes the highest level of confidence in a metabolite’s identity, as it confirms correct chromatographic, mass, and fragmentation properties (Figure 3). Although open-source reference libraries tend to be larger, the methods used to identify their entries are undisclosed, differ across users, and contain less data for compound annotation, which leaves room for uncertainty regarding biochemical identity. By containing the largest number of accurately annotated reference points, Metabolon’s library enables superior analytic accuracy over other LC-MS platforms. 
• Data acquisition software. Metabolon’s proprietary data acquisition software allows thousands of biochemicals from 70+ pathways to be rapidly and accurately annotated and aligned with data from scientific literature. These tools also enable metabolomics data from various studies to be merged and for metabolomics datasets to be integrated with other omics datasets to maximize biological insight and interpretability.

Figure 3. A summary of each level of metabolite identification. The confidence in a metabolite’s identity is defined by 5 levels that are categorized according to the analytical evidence of that identity. Level 1 denotes the highest confidence in identity, and the majority of biochemical entries in Metabolon’s library have achieved this level of identification.

In global metabolomics studies that use mass spectrometry, 10s to 100s of thousands of mass spectrometry signals, often referred to as ion features, are detected in a single sample. In the traditional ion-centric or feature centric approach to data analysis, only the statistically significant ion features are determined from the full dataset of 100,000+ features (Figure 4, bottom panel). This can introduce errors into data analysis because in a dataset this size many of the detected ion features are redundant and irrelevant to the study hypothesis. Keeping redundant features in the dataset can skew the statistical analysis, and irrelevant features only muddy the interpretation of the data, limiting insight. Moreover, distinguishing meaningful ion features from the rest in such a large dataset is tedious and time-consuming, and often done incorrectly. This forces the unchanging ion features (those compounds that are not affected by the study design) to be overlooked due to lack of time and leads to incorrect classification of important ion features, limiting insight further and introducing confounding data.

To address this challenge, Metabolon pioneered the chemo-centric approach, which brings the compound or biochemical to the forefront by using Metabolon’s robust biochemical reference library to appropriately account for extraneous and redundant ion features from the dataset before statistical analyses are performed (Figure 4, top panel). The chemo-centric workflow deconvolutes the ion feature data, which allows accurate identification of the statistically significant compounds (not features) as well as identification of compounds that did not change over the course of the study, extracting maximum insight from the dataset. Additionally, all entries in Metabolon’s biochemical reference library are identified by unique ChemID numbers, which enable Metabolon data to be compared across studies. The use of open-source biochemical reference libraries leads to rampant non-concordance of reported metabolite calls, which constrains the comparison of data generated by independent parties.

Figure 4. Summary of Metabolon’s chemo-centric approach. Global metabolomics typically identifies 10s to 100s of thousands of ion features per sample. Metabolon pioneered the approach of identifying compounds and accounting for redundant features using an in-house authentic standard reference library (Top). Unlike the traditional ion centric approach (Bottom) the library enables accurate compound identification based on multiple criteria including retention time, mass, and fragmentation spectra. Metabolon’s vast library contains information on the ionization products of numerous compounds, which facilitates their efficient removal from the data stream.

The innovations discussed above, coupled with Metabolon’s chemo-centric
approach set Metabolon data apart from that of other metabolomics
providers.

Chapter Takeaways

• Metabolon has developed industry leading technologies, methods, and data analysis tools that address many limitations of traditional metabolomics workflows.
• These tools impart Metabolon data with industry leading quality, reproducibility, and insightfulness.

Armed with Metabolon’s tools, numerous investigators, clinicians, and industry professionals have made discoveries that were vital to advancing their projects to the next step. Over the next three chapters we will discuss quintessential studies that show how this was done. We hope that seeing metabolomics in action across diverse scientific disciplines and sectors inspires your own research endeavors.