Study Rationale:
Neurodegenerative disorders like Alzheimer's and Parkinson's disease often have overlapping symptoms, which can make it difficult to differentiate them from one another in the clinic. Biomarker-based (track disease activity) methods that detect disease-specific protein aggregates (clumps) in body fluids could be the key to an earlier and more reliable diagnosis - an improvement that would also help in the search of a cure. However, the symptom overlap may itself be the result of molecular interactions between these aggregates, which have not yet been explored.
Hypothesis:
Hetero-aggregates, which are composed of two or more species of disease-related proteins, may contribute to the the clinical overlap between neurodegenerative diseases and should be taken into consideration when designing biomarker-based diagnostic methods.
Study Design:
We have established a method called surface-based fluorescence intensity distribution analysis (sFIDA) for the diagnosis of Alzheimer's disease, which uses fluorescent markers to label aggregates, thus making them visible under a fluorescence microscope. In this project, we aim to apply our sFIDA technology to measure several types of protein aggregates that occur in various neurodegenerative diseases. A unique feature of the sFIDA method is that it allows us to not only count aggregates but also to gather information about their composition. Thus, we will screen biosamples for the presence of hetero-aggregates and determine if these types of aggregates are valid biomarkers.
Impact on Diagnosis/Treatment of Parkinson's Disease:
The results from this study could provide insights into the molecular links between Parkinson's and other neurodegenerative diseases, bringing us closer to a well-defined molecular biomarker for Parkinson's to support clinical diagnosis, as well as potentially provide a way of tracking the benefits of a drug in clinical trials.
Next Steps for Development:
In the future, we plan to further validate the biomarkers from this study in larger groups, while optimizing sFIDA for application in routine clinical diagnostics.