Imaging Protein Aggregates for Early Diagnosis and Monitoring of Parkinson's Disease
Lead Research Organisation:
University of Cambridge
Department Name: Chemistry
Abstract
Neurogenerative diseases such as Alzheimer's and Parkinson's diseases are major health problems whose burden is increasing as we live for longer, and cost the UK about £30 billion per year in health and social care, informal care costs and productivity losses. An important limiting factor in studying and treating these disorders is the lack of simple tests for diagnosis and monitoring. For this project we will focus on Parkinson's disease, building on very encouraging preliminary results that we have obtained. We aim to develop a method for the early diagnosis of Parkinson's disease that can be performed on easily available samples such as blood or saliva and so could be used to screen people in 'at risk' groups. If this is possible it would allow patients who are developing disease to be identified before they develop symptoms and hence any available treatment is likely to be much more effective. We also aim to establish whether our new test can pick up change in the disease over time by comparing samples collected from patients at different disease stages, as well as repeated samples collected from the same individuals over time. If so, this would provide a much needed marker for monitoring the disease in clinical trials.
During the development of Parkinson's disease and other neurodegenerative diseases, proteins clump together and form larger clumps in the brain. These clumps are secreted into the fluid which bathes the brain and spinal cord (cerebrospinal fluid) and into the blood. We have found that a higher proportion of larger clumps of a protein called a-synuclein are present in the blood of patients within 1-2 years of being diagnosed with the disease. We detect these clumps by capturing them selectively on a surface and then imaging them by adding antibodies to make them visible and using a sensitive fluorescence microscope. We will build on our preliminary data to develop an automated way to perform these experiments so we can detect clumps of all the key proteins in blood, in hundreds of samples at the same time. We will then test to see whether we can find the changes in the aggregates what best distinguish between patients with the disease and healthy 'controls' without the disease. We will also test whether blood is the best sample to use or if saliva or nasal swabs alone or in combination with blood provide better discrimination between people with Parkinson's disease and controls. We will collect a new set of samples from a large group Parkinson's patients at different disease stages, as well as controls, to test how well the optimised method works, and whether it is linked to disease severity. We will also repeat the test in samples collected from the same individuals at a later timepoint to see whether it changes over time. We will also analyse blood samples from patients with a sleep problem called REM Sleep Behaviour Disorder (RBD), many of whom are likely to go on to develop Parkinson's disease, to see whether our method also can identify those at particularly high risk of conversion to Parkinson's. This would demonstrate the potential of the method for early disease diagnosis.
By the end of the project we hope to have developed a blood or saliva based method for early detection and monitoring of Parkinson's disease that has been validated on clinical samples. This could then be used to screen people who are 'at risk' of the disease (e.g. due to RBD, carrying genetic risk variants, or having subtle neurological abnormalities) to see whether they are in early stages of developing Parkinson's. The same method might then be applicable to other diseases such as Alzheimer's disease or traumatic brain injury, as experienced by footballers from heading footballs repetitively, for early disease diagnosis. We hope the method will also be useful to monitor changes in the disease process over time which is critically important for monitoring the effectiveness of new therapies in clinical trials.
During the development of Parkinson's disease and other neurodegenerative diseases, proteins clump together and form larger clumps in the brain. These clumps are secreted into the fluid which bathes the brain and spinal cord (cerebrospinal fluid) and into the blood. We have found that a higher proportion of larger clumps of a protein called a-synuclein are present in the blood of patients within 1-2 years of being diagnosed with the disease. We detect these clumps by capturing them selectively on a surface and then imaging them by adding antibodies to make them visible and using a sensitive fluorescence microscope. We will build on our preliminary data to develop an automated way to perform these experiments so we can detect clumps of all the key proteins in blood, in hundreds of samples at the same time. We will then test to see whether we can find the changes in the aggregates what best distinguish between patients with the disease and healthy 'controls' without the disease. We will also test whether blood is the best sample to use or if saliva or nasal swabs alone or in combination with blood provide better discrimination between people with Parkinson's disease and controls. We will collect a new set of samples from a large group Parkinson's patients at different disease stages, as well as controls, to test how well the optimised method works, and whether it is linked to disease severity. We will also repeat the test in samples collected from the same individuals at a later timepoint to see whether it changes over time. We will also analyse blood samples from patients with a sleep problem called REM Sleep Behaviour Disorder (RBD), many of whom are likely to go on to develop Parkinson's disease, to see whether our method also can identify those at particularly high risk of conversion to Parkinson's. This would demonstrate the potential of the method for early disease diagnosis.
By the end of the project we hope to have developed a blood or saliva based method for early detection and monitoring of Parkinson's disease that has been validated on clinical samples. This could then be used to screen people who are 'at risk' of the disease (e.g. due to RBD, carrying genetic risk variants, or having subtle neurological abnormalities) to see whether they are in early stages of developing Parkinson's. The same method might then be applicable to other diseases such as Alzheimer's disease or traumatic brain injury, as experienced by footballers from heading footballs repetitively, for early disease diagnosis. We hope the method will also be useful to monitor changes in the disease process over time which is critically important for monitoring the effectiveness of new therapies in clinical trials.
Technical Summary
We have developed new biophysical methods to image individual soluble protein aggregates of amyloid-beta, tau and alpha-synuclein present in human serum and saliva and determine their number and size, down to 30 nm resolution. We can also detect ASC specks which are formed during inflammasome formation and hence are a marker of inflammation. Building on our work that shows there is an increased fraction of larger alpha-synuclein aggregates in the serum of Parkinson's disease (PD) patients compared to healthy controls, we aim to find the best combination of measurements of aggregates in patient serum, saliva or nasal swabs for early diagnosis of PD. We will first automate these measurements to increase throughput allowing us to study hundreds of samples in a more reproducible fashion. We will then run our existing bank of more than 200 stored serum samples from PD patients and healthy controls to find the best biomarkers. We will also study stored serum samples from 100 people with Rapid Eye Movement Sleep Behaviour Disorder (RBD), who are known to be at high risk of PD, to see if there are detectable differences from control samples. In addition, we will collect a large new sample set of matched serum/plasma/saliva/nasal swabs and clinical phenotypic data from prodromal (RBD) cases , established PD cases (early stage and late stage), and healthy aged controls (n=50 per group), with repeat longitudinal sampling. We will analyse these samples to determine the optimal biofluid and marker combination for early PD diagnosis, and establish the sensitivity of the method for disease stage and change over time. This methodology should be broadly applicable to early diagnosis of other neurodegenerative diseases such as Alzheimer's disease and traumatic brain injury.