University of York
Applications accepted all year round
About the project
Alzheimer’s disease is a global health concern and leading cause of mortality. Improving patient outcomes necessitates early-stage diagnosis, but the lack of specific biomarkers hinders this. Detecting extracellular vesicles (exosomes) could accelerate early detection and significantly improve survival rates.
Exosomes are produced by cells and carry molecular information including proteins, DNA and RNA, reflecting disease pathology more comprehensively than single biomarkers. They are crucial in the development of Alzheimer’s disease, serve as early disease indicators of dementia and can help differentiate between dementia forms based on their contents. Detecting exosomes is appealing because they can be non-invasively extracted from blood, urine and saliva, and there is a convincing correlation between exosome levels and disease pathology. However, current detection methods lack sensitivity and specificity, motivating the need for new techniques to reliably identify and quantify exosomes early in disease.
This project will take advantage of cutting-edge fluorescence sensing techniques, including single-vesicle spectroscopy and imaging methods, to detect, quantify and differentiate between exosomes of various composition and content. The objectives are:
1) Characterize the fluorescence properties of lipophilic dye molecules in model exosomes with varying compositions and content to establish a comprehensive reference dataset.
2) Develop and validate robust analytical techniques based on fluorescence to effectively and reliably differentiate between exosomes based on their unique physical and biochemical properties.
3) Extend the analysis methods to detect and discriminate patient-derived exosomes, aiming to identify and quantify specific biomarkers for early-stage diagnosis and monitoring.
The experimental strategies involve state-of-the-art spectroscopy and biophysical methods that build on the interdisciplinary synergies between established research groups from the School of Physics, Engineering and Technology and the Department of Biology. This successful candidate will receive unique multidisciplinary scientific training at the life-sciences interface, develop key quantitative and interdisciplinary skills and work on world-class research of immediate relevance to medical diagnosis.