Researchers from Tel Aviv University, led by Professor Uri Ashery and PhD student Ofir Sade, in collaboration with three major Israeli hospitals, have developed a groundbreaking method to detect protein aggregation in cells. Protein aggregation is an early marker of Parkinson's disease. With this innovative technology, it is now possible to diagnose Parkinson’s up to 20 years before the first motor symptoms appear, offering new hope for better treatment and even prevention. The findings of the research have been published in Frontiers in Molecular Neuroscience.
Parkinson’s Disease: A Global Challenge
Parkinson’s disease is the second most common neurodegenerative disease worldwide, following Alzheimer’s. It affects around 8.5 million people globally, as brain cells responsible for producing dopamine slowly deteriorate. This leads to various symptoms, such as difficulty with movement and cognitive challenges.
Early diagnosis of Parkinson's has long been a challenge, as initial symptoms are often mild and non-specific, and the disease cannot be detected on a brain scan. By the time motor symptoms become apparent, the disease has usually progressed significantly, with up to 80% of dopamine-producing cells already damaged. Current treatments are therefore limited, focusing primarily on symptom management rather than halting disease progression.
Early Diagnosis and Prevention
The new method developed by the Israeli team can detect early signs of Parkinson’s up to 20 years before the onset of motor symptoms. This could enable preventive treatments for individuals at risk of developing the disease later in life. The technology may also be adapted in the future to diagnose other neurodegenerative diseases, such as Alzheimer’s, earlier.
PhD student Ofir Sade of Tel Aviv University explained: “A hallmark of Parkinson’s is cell death caused by aggregates of the protein alpha-synuclein. These aggregates begin forming up to 15 years before the first symptoms appear, and a few years later, the first cells start dying. This gives us a window of up to 20 years before symptoms emerge for diagnosis and prevention. If we can detect this process early, we may be able to prevent the ongoing formation of protein aggregates and the subsequent cell death.”
The Method: Super-Resolution Microscopy
Previous research has shown that alpha-synuclein aggregates also form in other parts of the body, such as the skin and digestive system. To explore this, the researchers examined skin biopsies from seven people with Parkinson’s and seven without. Using super-resolution microscopy combined with computational analysis, they mapped the protein aggregates with high precision. Sade noted: “As expected, we found more protein aggregates in Parkinson’s patients compared to those without the disease. We also discovered that nerve cells in the skin were damaged in areas where the protein aggregates were present.”
Future Research and Machine Learning
In the next phase, the researchers aim to increase the sample size to 90 (45 from healthy individuals and 45 from Parkinson’s patients) to better understand the differences between the two groups. The goal is to pinpoint the exact stage at which normal protein levels turn into pathological aggregates. Additionally, the team will collaborate with Professor Lior Wolf from Tel Aviv University's School of Computer Science to develop a machine learning algorithm that links motor and cognitive test results with microscopic findings.
Ashery added: “We are focusing our method primarily on family members of Parkinson’s patients who carry genetic mutations that increase their risk of developing the disease. A clinical trial is already underway to test a drug that is expected to prevent the formation of aggregates that cause Parkinson’s. We hope that in the coming years, it will be possible to treat at-risk patients preventively.”
AI Technology for Parkinson’s Diagnosis
The U.S. Food and Drug Administration (FDA) has also expressed confidence in the potential of AI to help better monitor the health of individuals at risk of developing Parkinson’s. As such, the FDA has called on developers to submit AI models for testing and approval. By trialing artificial intelligence and machine learning models, the FDA aims to establish best practices for the early diagnosis of Parkinson’s using smartphones and wearable devices. The agency has already approved several wearable technologies capable of monitoring Parkinson’s symptoms.
