The application of untargeted metabolic profiling (metabolomics) to large scale preclinical metabolism/toxicological, clinical, and epidemiological investigations has delivered new insights into the underlying biology of disease and toxicology. [1] Metabolomics has the advantage of being relatively simple and inexpensive to perform compared to genomics and provides both concentration and temporal data. This information can be used to track cause and effect over both short and long-time courses. Untargeted metabolomics and lipidomics also have the potential to identify new and novel markers of disease or toxicity. [2] These putative markers are often detected as a result of statistical analysis of a large data set. Identification of the analyte of interest is perhaps one of the most challenging aspects of metabolomics and lipidomics.

The identification of disease biomarkers and toxicity by liquid chromatography-mass spectrometry (LC-MS) relies on the confident detection of analytes along with the generation of high precursor and product ion spectra; this data is then used to search against databases such as LipidMaps, Metlin and Human Metabolome Database (HMDB) or for more traditional structural elucidation. Both database searching and de novo structural elucidation from MS spectra requires high quality data free from interferences. However, it is often impossible to obtain a clean MS spectrum in complex mixture analysis, such as those measured during metabolomics or lipidomics studies, due to the sheer number of analytes in the sample and the presence of isomers, fragment ions and isotopic interference. Researchers often resort to time consuming sample isolation such as L/LE, SPE or preparative LC to purify analytes of interest; this is time consuming and may not always been feasible when analytes are present in trivially small amounts.

Ion mobility-mass spectrometry (IMS-MS) provides an extra dimension of separation to the analytical process, allowing analyte ions to be separated by their size, charge and shape before detection in the MS analyser. This can be extremely powerful in simplifying the MS spectra by resolving fragment ions, geometric isomers and noise from the signal of interest. The cleaner spectra simplify data review and improve the accuracy of database searching.

The power of IMS-MS has been identified in a recent analysis of anthocyanins in red wine. Anthocyanins are phenolic pigment flavonoid compounds which are present in various plant derived foodstuffs, including red wine, associated with red, blue and violet pigmentation. These molecules contribute to the wine aging process via polymerization with tannins in the wine and are present in numerous forms due to differing degrees of glycosylation and acylation. The identification of these anthocyanin glycosides is complicated due to chromatographic coelution. By employing ion mobility it was possible to resolve the glycosides, enabling the simplification of the structural analysis process by providing simplified spectral information and removing unrelated noise. Thus, the use of ion mobility separation has been found to greatly assist in the differentiation of coeluting analytes in complex mixture analysis.

References

  1. T. Mairinger, T.J. Causon, S. Hann, The potential of ion mobility–mass spectrometry for non-targeted metabolomics, Curr. Opin. Chem. Biol. 42 (2018) 9–15. 
  2. G. Paglia, G. Astarita, Metabolomics and lipidomics using traveling-wave ion mobility mass spectrometry, Nat. Protoc. 12 (2017) 797–813.