Researchers mapped 6,000 eye proteins and developed an artificial intelligence-based "proteomic clock" to predict age. The study sheds light on accelerated aging in certain diseases and identifies proteins linked to Parkinson's disease, providing a path to early diagnosis. These findings could revolutionize precision medicine and clinical trial methods.
A research team has mapped nearly 6,000 proteins in different cell types in the eye by analyzing small eye drops routinely removed during surgery. In a recent study published in the journal Cell, researchers used artificial intelligence models to create a "proteomic clock" from this data that can predict the age of healthy people based on protein signatures.
The clock shows that diseases such as diabetic retinopathy and uveitis cause accelerated aging of specific cell types. Surprisingly, the researchers also detected proteins associated with Parkinson's disease in eye fluid, which they say may provide a way to diagnose Parkinson's disease earlier.
"The amazing thing about the eye is that we can look inside and see disease occurring in real time," said senior author Vinit Mahajan, a surgeon and professor of ophthalmology at Stanford University. "Our main focus is to correlate these anatomical changes with changes occurring at the molecular level within the patient's eye."
The eye is a difficult organ to sample in living patients because, like the brain, it is non-renewable, and taking a tissue biopsy would cause irreparable damage. Another method is to use liquid biopsy - taking a sample of fluid from near the cells or tissue in question.
While liquid biopsies can provide a snapshot of which proteins are present in a relevant area, so far they have been limited in their ability to measure large numbers of proteins in small volumes of fluid, nor can they provide information about which cells produce which proteins, which is important for diagnosing and treating disease.
Advanced protein mapping and discovery
To map the proteins produced by different types of cells in the eye, Mahajan's team used a high-resolution method to characterize proteins in 120 liquid biopsy samples taken from the eye fluid or vitreous of patients undergoing eye surgery. In total, they identified 5,953 proteins, ten times the number identified in previous similar studies. Using a software tool they developed called TEMPO, the researchers were able to trace each protein back to a specific cell type.
To study the relationship between disease and molecular aging, the researchers built an artificial intelligence machine learning model that could predict the molecular age of the eye based on a subset of 26 proteins. The model was able to accurately predict the age of healthy eyes but showed that the disease is associated with significant molecular aging. In the case of diabetic retinopathy, aging increases as the disease progresses, with patients with severe (proliferative) diabetic retinopathy aging as much as 30 years faster. These signs of aging are sometimes observed before patients develop clinical symptoms of the underlying disease and persist in patients who are successfully treated.
The researchers also detected several proteins associated with Parkinson's disease. These proteins are often discovered after death and cannot be detected by current diagnostic methods, which is one reason why diagnosing Parkinson's disease is so difficult. By screening eye fluid for these markers, Parkinson's disease can be diagnosed earlier and monitored for later treatment.
Impact and future directions
The authors say these results suggest that aging may be organ or even cell-specific, which could lead to advances in precision medicine and clinical trial design. "These findings suggest that our organs are aging at different rates," said lead author Julian Wolf, an ophthalmologist at Stanford University. "Using targeted anti-aging drugs may be the next step in preventive precision medicine."
"If we are going to use molecular therapies, we should characterize the molecules in the patient's body," Mahajan said. "I think reclassifying patients based on their molecular patterns and which cells are affected could really improve clinical trials, drug selection and drug efficacy."
Next, the researchers plan to characterize samples from more patients and a wider range of eye diseases. They also say their method could be used to characterize other difficult-to-sample tissues. For example, liquid biopsies of cerebrospinal fluid can be used to study or diagnose the brain, synovial fluid can be used to study joints, and urine can be used to study the kidneys.