Neurodegenerative diseases like Alzheimer’s, Parkinson’s, and Huntington’s all share a common villain: misfolded proteins that clump together in the brain, disrupting cell function. These tiny protein aggregates form unpredictably and quickly, making them extremely difficult to spot with traditional imaging methods. Now, scientists at EPFL have developed a cutting-edge microscope that can not only detect these harmful protein clumps but also predict their formation before it even begins.
Why misfolded proteins are hard to detect
Proteins are the building blocks of life, but when they fold incorrectly, they tend to stick together and form aggregates that damage brain cells. Until now, identifying these rogue proteins was a challenge because misfolded proteins look nearly identical to healthy ones. Moreover, the rapid and random nature of their aggregation meant that by the time they were detected, much damage might already have occurred.
A smart imaging system combining AI and microscopy
The team at EPFL, combining expertise in biology, engineering, and artificial intelligence, created a smart imaging system that tracks protein aggregation in living cells in real time. This system uses deep learning algorithms alongside multiple microscopy techniques to spot protein clumps as they form and analyze their biomechanical properties, such as elasticity, without relying heavily on fluorescent labels. This is significant because fluorescent markers can interfere with the natural behavior of proteins, leading to less accurate results.
Foreseeing protein aggregation for the first time
“This is the first time we have been able to accurately foresee the formation of these protein aggregates,” says Khalid Ibrahim, recent EPFL PhD graduate and lead author of the study. Understanding how the biomechanical properties of these aggregates change as they form is key to unlocking new ways to treat and prevent neurodegenerative diseases.
This is the first time we have been able to accurately foresee the formation of these protein aggregates, unlocking new ways to treat and prevent neurodegenerative diseases.
Khalid Ibrahim, EPFL
How the AI-driven microscope works
The technology hinges on an AI-driven “self-driving” microscope system. One deep learning algorithm scans images of cells to detect mature protein aggregates. When it spots one, it activates a Brillouin microscope that uses scattered light to measure the physical characteristics of these clumps. Normally, the Brillouin microscope is too slow for capturing rapidly evolving protein structures. But by activating it only when needed, the researchers sped up the process and avoided unnecessary imaging.
Predicting aggregation onset with high accuracy
In a second step, another AI algorithm was trained to detect the very onset of aggregation, even before the clumps become mature. This algorithm, trained on fluorescently labeled images, can predict aggregation with 91% accuracy. Once the system detects the start of aggregation, it again switches on Brillouin microscopy to observe the process in unprecedented detail.
A vision realized: New biophysical insights
Aleksandra Radenovic, head of the Laboratory of Nanoscale Biology at EPFL, highlights the significance of this development: “This project was born out of a motivation to build methods that reveal new biophysical insights. It is exciting to see how this vision has now borne fruit.”
Label-free, AI-guided imaging offers new avenues for drug discovery, potentially speeding up therapies for devastating brain disorders.
Hilal Lashuel, EPFL
Implications for drug discovery and treatment
Hilal Lashuel from EPFL’s School of Life Sciences adds that the implications extend far beyond microscopy. Label-free, AI-guided imaging offers new avenues for drug discovery, particularly targeting toxic protein forms that are central to disease progression. This approach could speed up the development of therapies for devastating brain disorders.
A major step toward early diagnosis and better therapies
The breakthrough represents a new frontier in biomedical imaging and precision medicine. By seeing protein aggregation as it happens—and even before it starts—researchers are one step closer to unraveling the mysteries of neurodegenerative diseases and ultimately finding better treatments.
