IMS–MS potential for untargeted metabolomics

In this article, Professor Zheng-Zhu (Principal Investigator and Director of Metabolomics Research Center at the Shanghai Institute of Organic Chemistry) and Lucy Woods (Business Unit Manager for Phenomics and Metabolomics at Bruker Daltonics), summarise the most important configurations and options for partnering IMS–MS with liquid chromatography (LC) and show how this approach could revolutionise metabolome studies by allowing a far greater breadth of analytes to be separated and confidently identified.

Combining ion mobility spectrometry with mass spectrometry (IMS–MS) has only recently become popular as a tool for biochemical analysis and it has special potential for enhancing the clinical applications of untargeted metabolomics.

IMS has a long history, with the foundations laid in the late 1800s, and the technique having been popular for decades for the rapid atmospheric-pressure detection of hazardous chemicals in the defence sector.1 However, it is now witnessing a strong resurgence, following the demonstration in the mid-1990s that coupling IMS with mass spectrometry (MS) could allow protein conformations to be separated in the gas phase,2 followed by a study showing the value of the newly-developed ‘travelling-wave’ IMS–MS configuration for biological applications in 2007.3

How IMS–MS works

Ion mobility methods work by subjecting ions to an electric field in a neutral buffer gas (usually nitrogen), which allows them to be separated on the basis of their structures and conformations. The metric used to define ion mobility – the collision cross-section (CCS) – represents the effective area of interaction between an ion and the buffer gas, and is both highly reproducible between laboratories, and able to distinguish between isomers and even conformers.

Early IMS–MS configurations worked on the basis of separating ions along different paths in the drift cell and are popular for targeted metabolite analysis due to their high selectivity. They are rarely used for untargeted metabolomics because it is not possible to generate CCS values. Commercially available IMS–MS instruments suitable for untargeted metabolomics applications can be classified into three types: drift tube, travelling-wave, and trapped-ion (also called confinement and selective release).4 The principle of each is illustrated in Figure 1.

Drift tube ion mobility–mass spectrometry (DTIM–MS) is widely used for untargeted metabolomics. In this method, ring electrodes are stacked to form the gas-filled drift cell, and a weak uniform electric field is applied to drive the movement of ions through it (Figure 1A). Ions with different structures have different drift times, enabling them to be separated, and CCS values can be measured using either the ‘single-field’ method or the ‘stepped-field’ method (the latter providing instrument-independent ‘gold-standard’ CCS values). A further benefit of DTIM-MS is that collision-induced dissociation can be performed to generate mobility-correlated MS/MS spectra through all-ion fragmentation. The integration of m/z, CCS and MS/MS facilitates the metabolite identification in untargeted metabolomics. However, one drawback of DTIM–MS is that resolution is relatively low, which severely limits its application for isomer separation.

Travelling-wave ion mobility–mass spectrometry (TWIM–MS) is currently the most popular ion mobility instrument for untargeted metabolomics, and voltage waves to drive the ions through the drift cell (Figure 1B). In contrast to DTIM–MS, the drift cell is placed between the quadrupole (Q) and Tandem time-of-flight (TOF), meaning that collision-induced dissociation can take place before or after the ion mobility separation. The resolution of TWIM–MS is usually rather low, but this can be improved by cyclic methods (albeit at the expense of analysis time) and ‘lossless’ ion manipulations.

Trapped-ion mobility spectrometry–mass spectrometry (TIMS–MS) is one of the newest commercial IMS–MS configurations. In this method, the force an ion experiences from the gas flow is initially set to match the opposing force from the electrical field. Ramping down the electrical field allows selective release of ions from the drift tunnel according to their mobility (Figure 1C), thereby separating ions with the same charge but different CCS. The duty cycle of early trapped-ion instruments was low because of the accumulation procedure, but has been improved to nearly 100% by using two stages – ion accumulation, followed by separation and measurement. Importantly, resolution is considerably higher for this method than time-dispersive methods, and MS/MS spectra can be acquired too.

Metabolite separation and identification using IMS–MS with LC

In IMS–MS-based untargeted metabolomics, the ion mobility component of the system enables separation of ions according to their structures and conformations, while the mass spectrometry instrument (usually a TOF or QTOF) enables separation according to their mass/charge ratio. However, IMS–MS techniques have most utility for untargeted metabolomics when used to enhance conventional front-end liquid chromatography (LC) separations.

This is important because LC–MS, although popular for biological applications, struggles to deal with the mixtures of isomers encountered in metabolomics studies. Because they tend to produce similar fragments in their mass spectra, they are often very difficult to distinguish on the basis of conventional LC–MS alone. However, the resolving power of the higher-performing LC–IMS–MS configurations is such that it enables both constitutional isomers and stereoisomers to be separated. For example, LC–TIMS–MS has been used to separate cis and trans isomers of phosphatidylcholines and diacylglycerols from human plasma with an ion mobility resolution of more than 300.5

In addition to its benefit for ion separation, IMS–MS is also valuable because of the high reproducibility of CCS values. In this regard, the availability of well-curated sets of CCS values has become a very important aspect, with databases of experimental and predicted CCS values having been developed for small molecules including metabolites6 and lipids.7 Combining datasets is also important, and Zhou et al. have developed a software tool to integrate data on ion fragmentation (MS and MS/MS), LC retention time and CCS values for lipid identification.8

A final benefit of the latest IMS–MS methodologies is the speed with which ion mobility separations can be run. A good example of this is the latest TIMS–MS system from Bruker, which offers speeds down to 6 minutes per sample, and potentially as low as 2 minutes per sample.9 Potentially, in situations where the front-end LC separation step could be dispensed with or replaced, this could allow even higher-throughput metabolomics analyses to be conducted.

Applying IMS–MS to clinical metabolomics

Although research into metabolomics applications of LC–IMS–MS is at an early stage, the technology has been adopted worldwide. For example, LC–IMS–MS-based approaches have been used to identify lipids from the mouse brain,10 human plasma (Figure 2),11 and the nematode Caenorhabditis elegans.12

With regard to making an initial foray into clinical applications, the Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, has developed a collaboration with hospitals in Shanghai to test for metabolomic differences in colorectal cancer patients, with a view to uncovering differences in the metabolome between those who respond well to chemotherapy, and those who do not. Another area of interest enabled by new LC–IMS–MS techniques is the study of brain-to-liver communication in mice. Such communication is known to be very good in young animals, but to fall off drastically with age, with negative consequences for metabolic homeostasis. Understanding the role of metabolites in such inter-organ communications opens the possibility of using supplements to improve metabolic health and even expand the lifespan for these animal models.

Looking to the future

The efforts mentioned above represent the opening of a new chapter in clinical metabolomics, and one can envisage that within 10 years, a much higher proportion of research groups will be taking advantage of the added performance achievable using IMS–MS approaches.

One of the main drivers of improved uptake is the latest improvements in resolution that are now achievable using trapped-ion methods and variants of the travelling-wave approach. This should enable even the most challenging mixtures of isobaric and isomeric compounds to be separated and identified on a routine basis.

Accompanying such resolution improvements – and arguably at least as important – are developments in databases and software for interrogation of results. In particular, the availability of open-source and freely available databases of CCS values is a major step in supporting a variety of untargeted metabolomics applications.

Aided by these developments, IMS–MS-based techniques should allow us to deepen our understanding of biological mechanisms, and ultimately improve our understanding of cell biology, physiology and medicine. In due course, this offers the prospect of improving clinical treatments based on the patient’s metabolic profile and making the vision of ‘personalised medicine’ a reality.

Figure 1: The principal configurations of ion mobility–mass spectrometry (IMS–MS) instruments used for untargeted metabolomics: (A) Drift tube, (B) travelling wave, (C) trapped ion.
Figure 2: Analysis of lipids from NIST SRM 1950 reference plasma using LC with trapped-ion IMS–MS. The main image shows the detection of 102 precursors on the basis of m/z and mobility data. The inset shows two co-eluting isobaric lipids that were separated on the basis of their ion mobility.

Biography

Professor Zheng-Zhu Principal Investigator and Director of Metabolomics Research Center, Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, P. R. China.

Prior to working for Bruker Daltonics, Lucy Woods worked for pharma companies GlaxoSmithKline and AstraZeneca, developing workflows to identify unknown impurities using mass spectrometry. Woods’ background in mass spectrometry led her to join Bruker and take the role of Product Manager for the timsTOF series.

References

  1. A. Mäkinen, O.A. Anttalainen and M.E.T. Sillanpää, Ion mobility spectrometry and its applications in detection of chemical warfare agents, Analytical Chemistry, 2010, 82: 9594–9600.
  2. E. Clemmer, R.R. Hudgins and M.F. Jarrold, Naked protein conformations: Cytochrome c in the gas phase, Journal of the American Society for Mass Spectrometry, 1995, 117: 10141.
  3. D. Pringle, K. Giles, J.L. Wildgoose, J.P. Williams, S.E. Slade, K. Thalassinos, R.H. Bateman, M.T. Bowers and J.H. Scrivens, An investigation of the mobility separation of some peptide and protein ions using a new hybrid quadrupole/travelling wave IMS/oa-ToF instrument, International Journal of Mass Spectrometry, 2007, 261: 1–12.
  4. Luo, Z.-W. Zhou and Z.-J. Zhu, The application of ion mobility‑mass spectrometry in untargeted metabolomics: From separation to identification, Journal of Analysis and Testing, 2020, 4: 163–174.
  5. Jeanne Dit Fouque, C.E. Ramirez, R.L. Lewis, J.P. Koelmel, T.J. Garrett, R.A. Yost and F. Fernandez-Lima, Effective liquid chromatography trapped ion mobility spectrometry-mass spectrometry separation of isomeric lipid species, Analytical Chemistry, 2019, 91: 5021–5027.
  6. Zhou, X. Shen, J. Tu and Z.-J. Zhu, Large-scale prediction of collision cross-section values for metabolites in ion mobility-mass spectrometry, Analytical Chemistry, 2016, 88: 11084–11091.
  7. Zhou, J. Tu, X. Xiong, X. Shen and Z.-J. Zhu, LipidCCS: Prediction of collision cross-section values for lipids with high precision to support ion mobility–mass spectrometry-based lipidomics, Analytical Chemistry, 2017, 89: 9559–9666.
  8. Zhou, X. Shen, X. Chen, J. Tu, X. Xiong and Z.-J. Zhu ZJ. LipidIMMS Analyzer: Integrating multi-dimensional information to support lipid identification in ion mobility–mass spectrometry based lipidomics, Bioinformatics, 2019, 35: 698–700.
  9. Bruker launches the timsTOF™ Pro mass spectrometer to enable the revolutionary PASEF method for next-generation proteomics, CSIon PR Newswire, 2017, https://www.prnewswire.com/news-releases/bruker-launches-the-timstof-pro-mass-spectrometer-to-enable-the-revolutionary-pasef-method-for-next-generation-proteomics-300520791.html.
  10. Li, Y. Yin, Z. Zhou, J. Qiu, W. Liu, X. Zhang, K. He, Y. Cai and Z.-J. Zhu, Ion mobility-based sterolomics reveals spatially and temporally distinctive sterol lipids in the mouse brain, Nature Communications, 2021, 12: 4343.
  11. W. Meyer, A. Barsch and U. Schweiger-Hufnagel, Using parallel accumulation serial fragmentation (PASEF) to speed up untargeted 4D lipidomics LC-MS/MS workflows [White Paper], Bruker Daltonics, 2021.
  12. Witting, A. Barsch, S.W. Meyer, U. Schweiger-Hufnagel, N. Kessler, P. Schmitt-Kopplin, Investigating the increased lifespan in C. elegans daf-2 mutants by 4D-Lipidomics™ [White Paper], Bruker Daltonics, 2021.

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