High resolution untargeted metabolomics
Profile your samples for known and unknown metabolites.
Sample profiling using untargeted metabolomics is the best available technique to identify unknown changes in small molecules between conditions. Since it is untargeted, this method records every ionizable molecule in the sample. Metabolite identification is performed post-acquisition with the mapping of the recorded data on an ion library. As always, untargeted workflows are less precise and sensitive than targeted workflows, but have a far superior profiling capacity. This is why this kind of experiment is better suited to explore all the different metabolic pathways at once, rather than only one specific pathway.
The chemical nature of the small molecules that are analyzed in an untargeted metabolomics workflow is incredibly diverse. This heterogeneity means that the molecules will behave differently both on the chromatography column and in the mass spectrometer. We generally perform 4 different acquisitions, using two different chromatography and two different ionization modes to optimize the number of identification and quantification:
|RP chromatography - Positive ionisation||HILIC chromatography - Positive ionisation|
|RP chromatography - Negative ionisation||HILIC chromatography - Negative ionisation|
The reversed phase (RP) chromatography separates molecules based on their hydrophobicity whereas the HILIC works the opposite way and separates molecules based on their hydrophilicity. Having two different chromatographic methods enables a better separation of the molecules, which translates into a better identification. Also, a molecule that ionizes in positive mode will not necessarily ionize in negative mode. Therefore, by using a combination of different chromatography and different ionization mode, we maximize the number of positive identification and can explore a bigger portion of the metabolite world.
The data report for untargeted metabolomics can be customized to your needs. However, it will always include the quantification for all the detected features whether or not they were correctly identified. Basic statistics such as average, standard deviation and %CV will also be presented in the report when needed. Additionally, more advanced statistical analyzes like Principal Component Analysis, pathway enrichment and heatmap clustering can be provided.
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