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Sebomics analysis for disease diagnosis using mass spectrometric approaches in health and disease


Department of Chemistry

About the Project

In our ongoing work with collaborators we have demonstrated that sebum is an excellent, unexplored biofluid for disease diagnosis. We have demonstrated that sebum and related odourous compounds can be useful for diagnosing diseases such as Parkinson’s disease [1,2], tuberculosis and cancer. Joy Milne, a super-smeller has collaborated on projects in the past to demonstrate that smell can be associated with disease state [3]. Not much is known about how sebaceous glands are able to trap the change in metabolome and volatilome in these diseases.

This project will employ state of the art, high resolution mass spectrometry along with olfactometry analysis to study sebum, as a diagnostic biofluid. The physico-chemical properties of sebum will be measured in longitudinal studies to ascertain factors such as stability, viability and recovery of compounds from sebum. Profiling as well as targeted metabolomics approaches will be employed to build a baseline profile of sebum in health and disease. Microbiome on skin will also be measured and data will be integrated using chemometrics approaches. The study will employ multivariate analysis as well as machine learning to build data driven hypothesis about the role of sebum in health and disease.

Understanding the relationship between host-microbiome on skin will provide valuable insight in changing landscape of skin metabolome. Mass spectrometry and 16s-RNA data will be integrated to understand the relationship between host metabolome and microbiome in health and disease. Along with longitudinal sampling, multi-site sampling will also be undertaken during this study to understand the effect of exposome on sebum metabolome. Understanding cofounding effects on sebum metabolome will improve possibilities of using sebum as an excellent diagnostic biofluid for personalised medicine [4].

Training will be provided in analytical mass spectrometry data acquisition, analysis as well as processing. Basic knowledge of statistical approaches is desirable but not essential.

Academic background of candidates
Eligible candidates must hold at least a 2:1 BSc in chemistry, biomedical sciences, analytical sciences or related area. Candidates with experience in mass spectrometry analysis, data analysis or microbiology are encouraged to apply. Candidates with interest in biomarker discovery and personalised medicine should also apply. Training will be provided in analytical mass spectrometry data acquisition, analysis as well as processing. Basic knowledge of statistical approaches is desirable but not essential.

Contact for further Information
Drupad K Trivedi

https://www.research.manchester.ac.uk/portal/drupad.trivedi.html


Funding Notes

Applications are invited from self-funded students. For UK tuition fees are £9,000 and International are £26,000 for 2020/21 academic year.

References

1. Trivedi DK et al (2019), ‘Discovery of Volatile Biomarkers of Parkinson’s Disease from Sebum’, ACS Central Science, 5 (4), 599-606
2. Sinclair et al (2020), ‘Sebum: A Window into Dysregulation of Mitochondrial Metabolism in Parkinson’s Disease’, ChemRxiv. Preprint. https://doi.org/10.26434/chemrxiv.11603613.v1
3. Morgan J (2016), ‘Joy of super smeller: sebum clues for PD diagnostics’, Lancet Neurology, DOI: 10.1016/S1474-4422(15)00396-8
4. Trivedi DK et al (2017), ‘Metabolomics for the masses: The future of metabolomics in a personalised world’, New Horizons in Translational Medicine, 3 (6), 294-305

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