Quantification and identification of differential protein isoform expression for biomarker discovery and biological systems

About the Project

Quantitative proteomics via mass spectrometry is now a cornerstone methodology for biomedical health research and bioscience. However, current methods ignore the massive disparity in evidence quality across proteins, leading to bias in downstream clinical biomarker discovery, understanding of health and disease, and applications seeking to classify or stratify patient samples. Much of this bias stems from uncertainty in measuring protein levels, which can be confounded by the multiple versions (i.e. proteoforms) of a protein expressed from a single gene in the human genome and which influence cell state, function and disease. Current methods routinely ignore this evidence, collapsing signals into one protein per gene. We will use a range of mass spectrometry-driven proteomics and bioinformatics approaches, inclusive of advanced machine learning, to develop and deploy methods to infer the presence and abundance of protein isoforms present in a sample. This will include a more considered approach to dealing with proteins that share common peptides, and using isoform-specific peptides, which can be used identify and ultimately quantify different protein isoforms. We will compare and contrast label-free proteomics methods with targeted mass spectrometry approaches to assess the utility of these approaches. We will exploit existing experimental systems human derived plasma and tissue as well as standard model cell lines to show this is a more informative and unbiased signal of benefit to biomedicine and health. Finally, we seek to show these approaches can enhance bioscience and biomedical proteomics, by co-creating a best practice workflow for incorporating isoform-resolved protein quantities and their uncertainty into downstream biomarker-based classification for diagnostics and prognostics, so that reproducible research principles and interpretability are baked in.

Candidates are expected to hold (or be about to obtain) a minimum 2:1 Bachelors Degree with Honours (or equivalent) in a related area/subject which could include any bioscience, chemistry, physics or mathematical science. Candidates with experience in proteomics, bioinformatics, computational biology and/or general interest in biomarker discovery, genomics/post-genomics and/or biotechnology, and encouraged to apply.

Before you Apply

Applicants must make direct contact with preferred supervisors before applying. It is your responsibility to make arrangements to meet with potential supervisors, prior to submitting a formal online application.

How to Apply

To be considered for this project you MUST submit a formal online application form – on the application form select PhD Bioinformatics Programme. Full details on how to apply can be found on the Website: How to apply for postgraduate research at The University of Manchester

If you have any queries regarding making an application please contact our admissions team FBMH.doctoralacademy.admissions@manchester.ac.uk

Funding Notes

Applications are invited from self-funded students. This project has a Band 1 (low) fee. Details of our different fee bands can be found on our website View Website