Ensuring the Identified Metabolite is Correct

January 15, 2018

In science, as in most things, doing the bare minimum rarely results in excellence. While it’s important to follow all applicable guidelines in the performance of sediment metabolism studies, I believe it is also the scientific team’s responsibility to understand if those guidelines go far enough. Aerobic and anaerobic sediment metabolism (e.g., OECD 308 test) studies typically require analysis in at least two matrices (differing in organic carbon content and texture) and produce two “compartments” for each matrix. The industry standard practice is to analyze the one sample from the one matrix that contains the transformation product (metabolite) at the highest concentration observed over the course of the study using RAM (RadioActivity Monitor) detection. It turns out that, in some cases, analyzing only one compartment (only the water or only the sediment extract) can lead to false positives.

Recently in our labs we performed a study with a relatively well-characterized compound using two different matrices—Taunton River sediment (finely textured with high organic carbon content) and Weweantic River sediment (coarsely textured with low organic carbon content). This resulted in four “compartments:” 1) Taunton River water phase, 2), Taunton River sediment extract, 3) Weweantic River water, and 4) Weweantic River sediment extract. Since there were observed differences in retention times for some of the metabolites in the four compartments it was decided that all four compartments would be analyzed for all potential metabolites. This was made simpler since the analyses were done using a quadrupole, time-of-flight (QqTOF) mass spectrometer with information-dependent data acquisition. This instrument can automatically acquire data for multiple analytes with one injection. Known metabolites can be given preference, but typically the data can be “re-mined” for data from other, unexpected analytes for which data was automatically collected anyway.

Analysis of all four compartments showed that for two of the four expected metabolites, if the only compartment where each of the proposed metabolites was most abundant had been analyzed, the proposed metabolites would have been confirmed. However, since these metabolites had been detected by RAM analysis in all four compartments (at different concentrations) they should also have been found by LC-MS. The LC-MS analysis for one metabolite showed its presence in three of the four compartments (just barely found in the third and not there in the fourth) and the other metabolite was found by LC-MS in only two of the four compartments. Metabolite ID work was then done that discovered new, unexpected compounds that were found in relative ratios that corresponded to the RAM data.

Conclusion: going beyond the bare minimum sometimes results in more accurate data. Having a detection system that is relatively data independent such as our QqTOF system often allows detection of unexpected metabolites without requiring more LC-MS sample analysis or more-involved sample preparation steps.


RAM-Data.png LC-MC-Peaks.png LC-MC-Peaks-2.png
RAM Data LC-MS peaks for the originally proposed Met 3 LC-MS peaks for the
newly identified Met 3
 To discuss this topic further, contact:

Dr. James Ferguson
+1 508-295-2550


About The Author | Dr. James Ferguson

Dr. James Ferguson has degrees from the University of Rochester, Texas A&M U. and the University of Florida. He’s been working in the field of mass spectrometry for over 25 years and on metabolism studies for over 15. He previously worked for the States of Texas and Florida, for Thermo and Sciex, and now works for Smithers Viscient doing LC-MS in the Environmental Fate and Metabolism Department.

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