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Metabolomics using anion-exchange chromatography mass spectrometry for the analysis of cells, tissues and biofluids

Nature Protocols (2025)Cite this article

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Abstract

The direct coupling of ion-exchange chromatography with mass spectrometry using electrochemical ion suppression creates a hyphenated technique with selectivity and specificity for the analysis of highly polar and ionic compounds. The technique has enabled new applications in environmental chemistry, food chemistry, forensics, cell biology and, more recently, metabolomics. Robust, reproducible and quantitative methods for the analysis of highly polar and ionic metabolites help meet a longstanding analytical need in metabolomics. Here, we provide step-by-step instructions for both untargeted and semi-targeted metabolite analysis from cell, tissue or biofluid samples by using anion-exchange chromatography–high-resolution tandem mass spectrometry (AEC-MS/MS). The method requires minimal sample preparation and is robust, sensitive and selective. It provides comprehensive coverage of hundreds of metabolites found in primary and secondary metabolic pathways, including glycolysis, the pentose phosphate pathway, the tricarboxylic acid cycle, purine and pyrimidine metabolism, amino acid degradation and redox metabolism. An inline electrolytic ion suppressor is used to quantitatively neutralize OH− ions in the eluent stream, after chromatographic separation, enabling AEC to be directly coupled with MS. Counter ions are also removed during this process, creating a neutral pH, aqueous eluent with a simplified matrix optimal for negative ion MS analysis. Sample preparation through to data analysis and interpretation is described in the protocol, including a guide to which metabolites and metabolic pathways are suitable for analysis by using AEC-MS/MS.

Key points

  • This protocol uses an integrated ion-chromatography-mass spectrometry system that incorporates in-line eluent generation and electrochemical ion suppression for the analysis of metabolites in cells, tissues and biofluids.

  • Highly sensitive, selective, reproducible and robust, this protocol provides an alternative to methods such as HILIC-MS and ion-pairing-MS, which can be more limited by analytical conditions and provide different metabolite coverage.

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Fig. 1: Benchmarking method validation parameters between HILIC-MS/MS and AEC-MS/MS for untargeted metabolomics analysis of authentic metabolite standards and cell extracts.
Fig. 2: AEC-MS/MS metabolomics protocol.
Fig. 3: Anticipated results for untargeted studies.

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Data availability

The main data discussed in this protocol are available in the supporting primary research papers (https://doi.org/10.1038/s42003-020-0957-6 and https://doi.org/10.1038/s41467-022-34095-x). The raw datasets have also been deposited in publicly available repositories for research purposes, and any further data are available from the corresponding author upon reasonable request. Source data for Fig. 1 is available in the Oxford University Research Archive with the identifiers https://doi.org/10.5287/bodleian:2abVOAvrg and https://doi.org/10.5287/bodleian:eyq4Qj8AR (ref. 19). Source data for Fig. 3 are available in Supplementary Data 6.

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Acknowledgements

We thank all those who have been involved and contributed to establishing IC-MS in the McCullagh Lab, in particular, J. Harvey, E. Smith, A. Nazeer and J. Gannon. We are grateful to all the staff at the Mass Spectrometry Research Facility, Department of Chemistry, for their support. We also thank E. Haythorne (University of Edinburgh) and F. Ashcroft (University of Oxford) for their collaboration exploring pancreatic islet beta-cell function using AEC-MS/MS. J.S.O.M. acknowledges funding from the University of Oxford John Fell Fund (JFF142/116) to develop a metabolites standards database; a Wellcome Trust Seed Award in Science (204483/Z/16/Z), which helped establish the method; and BBSRC funding (BB/R013829/1), which provided equipment that supported this project. J.S.O.M. and R.W. acknowledge a Thermo Fisher Scientific Industry Collaboration, which provided funding and equipment that have supported recent parts of this work. I.C.H. thanks the Anne Grete Eidsvig and Kjell Inge Røkke Foundation for Education for an Aker Scholarship, which funded her D.Phil. at Oxford, where this protocol was developed. T.K. acknowledges an EPSRC Doctoral Training Partnership (EP/W524311/1) and Numares AG (Am Biopark 9, 93053 Regensburg-Graß, Germany) for funding her D. Phil. We thank T. Christison, W. C. Man, N. Rumachik, B. Amer, R. R. Deshpande, V. Jespers and S. S. Bird at Thermo Fisher Scientific for their continued help and support. Special thanks to A. Huhmer, who helped get the ball rolling!

Author information

Author notes

  1. Ingvild Comfort Hvinden

    Present address: Norwegian Defence Research Establishment (FFI), Kjeller, Norway

  2. Mariya Misheva

    Present address: Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, Oxford, UK

  3. David Hauton

    Present address: School of Chemistry & Biosciences, Faculty of Life Sciences, University of Bradford, Bradford, UK

  4. Judith B. Ngere

    Present address: Thermo Fisher Scientific, Hemel Hempstead, UK

Authors and Affiliations

  1. Department of Chemistry, University of Oxford, Oxford, UK

    Rachel Williams, John Walsby-Tickle, Ingvild Comfort Hvinden, Isabelle Legge, Tereza Kacerova, KyoungEun Vicky Lee, Mariya Misheva, David Hauton, Judith B. Ngere, John D. Sidda, Elisabete Pires, Tom Cadoux-Hudson & James S. O. McCullagh

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Contributions

J.S.O.M. and J.W.-T. conceived and developed the original method. R.W. provided major updates. R.W., J.W.-T., I.C.H., J.B.N., T.K., J.S. and J.S.O.M. performed laboratory experiments. R.W., J.W.-T., I.C.H., M.M., K.V.L., E.P., T.K., D.H. and J.S. developed methods and tested workflows. J.W.-T., I.C.H., I.L., D.H., J.S., T.K., R.W., T.C.-H. and J.S.O.M. worked on the data-analysis pipeline. J.S.O.M. supervised the project and co-wrote the manuscript with R.W. All authors edited the manuscript.

Corresponding author

Correspondence to James S. O. McCullagh.

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Competing interests

J.S.O.M. has a research collaboration with Thermo Fisher Scientific, and R.W.’s D.Phil. is supported by funding from Thermo Fisher Scientific. J.B.N. contributed to this research while a postdoc in the McCullagh group. She currently works for Thermo Fisher Scientific.

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Nature Protocols thanks Gary Williamson, Heidi Schwartz-Zimmermann and Markus Aigensberger for their contribution to the peer review of this work.

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Key references

Walsby-Tickle, J. et al. Commun. Biol. 3, 247 (2020): https://doi.org/10.1038/s42003-020-0957-6

Haythorne, E. et al. Nat. Commun. 13, 6754 (2022): https://doi.org/10.1038/s41467-022-34095-x

Schulthess, J. et al. Immunity 50, 432–445.e7 (2019): https://doi.org/10.1016/j.immuni.2018.12.018

Then, C. et al. Microbiome 12, 89 (2024): https://doi.org/10.1186/s40168-024-01804-1

Ngere, J. et al. Anal. Chem. 95, 152–166 (2023): https://doi.org/10.1021/acs.analchem.2c04298

Supplementary information

Supplementary Information

Supplementary Figures 1 and 2 and Supplementary Method 1

Reporting Summary

Supplementary Table 1

Validation metrics for a selection of metabolite standards analyzed on the AEC-MS/MS method including sensitivity, linearity and RT stability. Supplementary Table 2 Selection of metrics to assess system suitability for citrate, lactose, fructose-1,6-bisphosphate and 2-hydroxyglutarate from analysis of our metabolite standard mixes. Supplementary Data 1 Chemical compositions of our metabolite standard mixes. Supplementary Data 2 Additional autosampler parameters for the AEC-MS/MS method Supplementary Data 3 AEC-MS/MS parameters for an ICS-6000 coupled to an Exploris 240; AEC-MS/MS RT database for metabolite identification. Supplementary Data 4 Example output from Progenesis QI after metabolite identification Supplementary Data 5 Example of the modified Progenesis QI output for use with Statistical Analysis within MetaboAnalyst. Supplementary Data 6 Example of the modified Progenesis QI output for use with Functional Analysis within MetaboAnalyst

Supplementary Method 2

Workflow template for use with AEC-MS/MS data in Compound Discoverer 3.3

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Williams, R., Walsby-Tickle, J., Hvinden, I.C. et al. Metabolomics using anion-exchange chromatography mass spectrometry for the analysis of cells, tissues and biofluids. Nat Protoc (2025). https://doi.org/10.1038/s41596-025-01222-z

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  • Published:

  • DOI: https://doi.org/10.1038/s41596-025-01222-z

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