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Ion Mobility as a tool for lipidomics to understand lipid remodelling in obesity

  • Full or part time
    Prof J Griffin
  • Application Deadline
    Thursday, January 24, 2019
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

This BBSRC iCASE studentship represents an exciting opportunity to use state of the art mass spectrometry in conjunction with Agilent Corp. to improve our understanding of why the accumulation of lipids inside cells induces lipotoxcity and subsequent disease.

Lipids are of fundamental importance to the cell, acting as major components of cell membranes, important energy storage sources, signalling molecules and precursors for a wide range of biomolecules. Thus, it is of fundamental importance for many biological systems to be able to measure the diverse range of lipids that make up the cellular lipidome. However, a major challenge of the mass spectrometry of complex lipid mixtures from biological material is the separation of isobaric compounds, which often have similar chromatographic properties limiting the use of liquid chromatography for separation, while fragmentation data can be inconclusive in determining chemical structure. Ion mobility allows the separation of isobaric ions based on their mobilities through an inert gas, with mobility correlated to the collisional cross sections (CCSs) of molecules. We have been applying this approach in conjunction with high resolution mass spectrometry and chromatography to maximise the resolving power of different lipid classes. As part of the method development we have also developed a data pipeline within the software package KNIME to process the three dimensional data that is produced by the workflow as well as developing an in-house database based on mass-to-charge (m/z) ratios, retention times, MS/MS spectra, and CCS values of non-labelled phospholipid and fatty acid standards. This studentship will use these developments in ion mobility based lipidomics to study diet induced obesity in a mouse model and human tissue from volunteer studies. Specifically, we will look at how diets high in saturated fats affect the composition of cell membranes within key tissues across the body, using ion mobility to increase our coverage of the lipidome. We will also investigate whether CCS values can be used to model changes in cell membrane composition and fluidity as is thought to occur when dietary saturated fats begin to displace mono- and polyunsaturated fats in cell membranes.

Applicants should have a strong background in analytical chemistry, with those with experience in LC-MS being particularly welcome.

Funding Notes

The PI is part of the MRC., BBSRC, BHF and Wellcome Trust funded PhD Departmental Training Programmes. Students eligable for these schemes (UK and EU nationals) are encouraged to apply to these programmes. For overseas students they are encouraged to apply through the Cambridge Overseas Trust and related funding schemes through the University of Cambridge.


1. Wang X, West JA, Murray AJ, Griffin JL. Comprehensive Metabolic Profiling of Age-Related Mitochondrial Dysfunction in the High-Fat-Fed ob/ob Mouse Heart. J Proteome Res. 2015;14(7):2849-62.
2. Hall Z, Bond NJ, Ashmore T, Sanders F, Ament Z, Wang X, Murray AJ, Bellafante E, Virtue S, Vidal-Puig A, Allison M, Davies SE, Koulman A, Vacca M, Griffin JL. Lipid zonation and phospholipid remodeling in nonalcoholic fatty liver disease. Hepatology. 2017 Apr;65(4):1165-1180.
3. Sanders FWB, Acharjee A, Walker C, Marney L, Roberts LD, Imamura F, Jenkins B, Case J, Ray S, Virtue S, Vidal-Puig A, Kuh D, Hardy R, Allison M, Forouhi N, Murray AJ, Wareham N, Vacca M, Koulman A, Griffin JL. Hepatic steatosis risk is partly driven by increased de novo lipogenesis following carbohydrate consumption. Genome Biol. 2018 Jun 20;19(1):79. doi: 10.1186/s13059-018-1439-8.
4. Liggi S, Hinz C, Hall Z, Santoru ML, Poddighe S, Fjeldsted J, Atzori L, Griffin JL. KniMet: a pipeline for the processing of chromatography-mass spectrometry metabolomics data. Metabolomics. 2018;14(4):52. doi: 10.1007/s11306-018-1349-5.

How good is research at University of Cambridge in Biological Sciences?

FTE Category A staff submitted: 189.63

Research output data provided by the Research Excellence Framework (REF)

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