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New methods for ultra-high throughput proteomics for biomarker discovery


Project Description

The ability to reliably measure protein concentrations in blood samples is critical for the identification of disease biomarkers to allow patients’ stratification and improve patient care. Most protein biomarker discovery platforms revolve around mass spectrometry for protein detection and quantification. However, existing methods are relatively time consuming, such that most biomarker discovery projects are performed in relative small samples sizes. Such studies are almost certainly underpowered – limiting the ability of these methods to discovery new biomarkers. Recent advances in mass spectrometry and liquid chromatography are enabling ever faster and more sensitive proteome analysis, bringing the possibility of performing biomarker discovery experiments on thousands or tens of thousands of samples possible. Such a scale-up has revolutionised clinical genomics approaches, and it will do the same for clinical proteomics. This project, therefore is concerned with developing high throughput methodologies for plasma sample preparation, using plate-based methods and robotics, alongside optimising high flow, high speed LC-MS approaches to increase throughput and enable such large-scale biology projects to become tractable. The student will therefore develop an in-depth understanding of protein handling and analytical methodologies, of building new LC-MS/MS methods for rapid data acquisition, and skill in bioinformatics for data analysis and interpretation, along with a thorough understanding of the requirements for developing of high quality protein quantitation in a biological context.
The method will be tested using an existing sample set to identify plasma biomarkers associated with malignant disease and inflammatory disease.

Training/techniques to be provided:
Analytical biochemistry, including assay building with a view to optimising key analytical parameters such as sensitivity, accuracy, precision and reproducibility.
Proteomics, including protein sample preparation methods and developing new workflows.
Mass spectrometry, including novel method development and optimisation.
Liquid chromatography, including novel method development and optimisation.
Proteome bioinformatics, mass spectrometry data handling and processing from raw MS data through design and execution of custom scripts and workflows.
Biomarker discovery and validation, in the final phase of the project the student will be trained in assessment and validation of clinical markers, including development of targeted MS asays or immunoassays, as appropriate.

Entry Requirements:
Candidates are expected to have at least a 2.1 undergraduate degree in biochemistry. A masters-level degree in biochemistry or a related subject would be an advantage. Some experience in projects concerning quantitation of biomolecules in patient samples would be an advantage, although full training in all required methods will be given.

For international students we also offer a unique 4 year PhD programme that gives you the opportunity to undertake an accredited Teaching Certificate whilst carrying out an independent research project across a range of biological, medical and health sciences. For more information please visit http://www.internationalphd.manchester.ac.uk

Funding Notes

Applications are invited from self-funded students. This project has a Band 3 fee. Details of our different fee bands can be found on our website (View Website). For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (View Website).

As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.

References

Xu J, Patassini S, Rustogi N, Riba-Garcia I, Hale BD, Phillips AM, Waldvogel H, Haines R, Bradbury P, Stevens A, Faull RLM, Dowsey AW, Cooper GJS, Unwin RD. (2019) Regional protein expression in human Alzheimer’s brain correlates with disease severity. Communications Biology. 2:43.

Geary B, Walker MJ, Snow JT, Lee DCH, Pernemalm M, Maleki-Dizaji S, Azadbakht N, Apostolidou S, Barnes J, Krysiak P, Shah R, Booton R, Dive C, Crosbie PA, Whetton AD. (2019) Identification of a Biomarker Panel for Early Detection of Lung Cancer Patients. J Proteome Res. 2019 Sep 6;18(9):3369-3382.

Russell MR, Graham C, D'Amato A, Gentry-Maharaj A, Ryan A, Kalsi JK, Ainley C, Whetton AD, Menon U, Jacobs I, Graham RLJ. A combined biomarker panel shows improved sensitivity for the early detection of ovarian cancer allowing the identification of the most aggressive type II tumours. Br J Cancer. 2017 Aug 22;117(5):666-674.

Blankley RT, Fisher C, Westwood M, North R, Baker PN, Walker MJ, Williamson AJ, Whetton AD, Lin W, McCowan L, Roberts CT, Cooper GJS, Unwin RD and Myers JE (2013) A label-free SRM workflow identifies a subset of pregnancy specific glycoproteins as novel putative predictive markers of early-onset pre-eclampsia. Molecular and Cellular Proteomics. 12:3148-59.

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