Metabolomics is a term applied to the global metabolite profiling of biological samples (particularly biofluids such as urine and plasma). It is combined with multivariate statistical analysis in order to identify changes in response to a toxic insult, disease progression or genetic modification. [1,2] The most recent advances in the use of metabolomics employ broad profiling methods in order to generate metabolic phenotypes (or metabotypes), utilizing high-content analytical chemistry to generate global metabolite profiles. The main analytical tools employed for metabolomics are NMR and mass spectrometry (MS) interfaced with gas or liquid chromatography (GC/LC). Of these techniques, high-resolution LC combined with accurate mass MS has become the technology of choice. [3]

The general aim of metabolomics is to provide information on changes in the relative concentrations of metabolites occurring as a result of a certain biochemical state. This helps to detect biomarkers (individual metabolites, or more often “fingerprints”) that are specific to a particular condition. The discovery of these metabolites/putative biomarkers relies on the ability of the analytical system used to confidently detect and measure signals arising from as many analytes as possible in the sample. In LC-MS-based metabolomics, this process of analyte detection is often confounded by the presence of spurious chemical noise and the presence of coeluting isobaric compounds. These can mask the presence of low-level moieties, resulting in incomplete data sets.  

Ion mobility spectrometry (IMS), when interfaced with MS, provides an extra dimension of separation based upon the size, shape and charge of analyte ions in the gas phase. [4] This is achieved by the use of an ion mobility cell filled with an inert gas, through which the analyte ions pass. Ions are separated based upon their interaction with the gas in the cell – large, unfolded ions take longer to pass through the IMS cell than smaller, more compact ions do. The fact that the time domain of ion mobility separation is milliseconds allows it to be positioned between the LC separation (which has a time domain of seconds) and MS detection, which occurs in microseconds. The result is accurate peak definition and quantification. As ions are separated in the ion mobility cell based upon their charge, size and shape, unrelated isobaric analytes are detected and recorded as discrete species. Ion mobility also increases the ability to resolve and detect structural isomers – for example, Griffin et. al [5] described the use of ion mobility to resolve isobaric lipids that could not be resolved by conventional LC-MS, based on the position of the double bond in the fatty acid chain. 

A systematic study of the effect of IMS on feature detection by Rainville et. al [6] showed that the incorporation of ion mobility in urine metabolomics studies increased the number of features detected by approximately 30-50%, dependent upon the chromatographic separation time. The reason for the observed increase in feature detection is most likely due to a combination of: separation of co-eluting compounds, noise reduction, resolution of isobaric components and separation of fragment ions. 

One of the major benefits of IMS is the elimination of extraneous chemical noise from the analysis. This provides two important advantages:

  1. It allows the detection of low abundant features which would otherwise be lost.
  2. It reduces false detection of peaks.

This results in faster data processing, a greater number of “real’ ions detected and reduction in time wasted performing structural analysis on spurious peaks.

References

  1. J.K. Nicholson, J.C. Lindon, Systems biology: metabonomics, Nature 455 (2008) 1054–1056, https://doi.org/10.1038/4551054a. 
  2. J.C. Lindon, I.D. Wilson, Chapter 2 – the development of metabolic phenotyping—A historical perspective, in: E. Holmes, J.K. Nicholson, A.W. Darzi, J.C. Lindon (Eds.), Metab. Phenotyping Pers. Public Healthc, Academic Press, Boston, 2016, pp. 17–48, , https://doi.org/10.1016/B978-0-12-800344-2. 00002-1.
  3.  G.Theodoridis, H.G.Gika, I.D.Wilson. Mass spectrometry-based holistic analytical approaches for metabolite profiling in systems biology studies, Mass Spectrom. Rev. 30 (2011) 884–906, https://doi.org/10.1002/mas.20306. 
  4. https://www.waters.com/waters/en_US/Ion-Mobility-Mass-Spectrometry/nav.htm?cid=134656158&locale=en_US
  5. C. Hinz, S. Liggi, J.L. Griffin, The potential of ion mobility mass spectrometry for high-throughput and high-resolution lipidomics, Curr. Opin. Chem. Biol. 42 (2018) 42–50.
  6. Rainville, P. D., Wilson, I. D., Nicholson, J. K., Isaac, G., Mullin, L., Langridge, J. I., & Plumb, R. S. (2017). Ion mobility spectrometry combined with ultra performance liquid chromatography/ mass spectrometry for metabolic phenotyping of urine: Effects of column length, gradient duration and ion mobility spectrometry on metabolite detection. Analytica Chimica Acta, 982, 1–8.