Researchers Discover the Molecular Age of the Eye for the First Time

A group of researchers has charted nearly 6,000 proteins derived from various cell types in the eye through the analysis of small samples of eye fluid routinely collected during surgical procedures.

Published on October 19 in the journal Cell, the scientists used an AI model to generate a "proteomic clock" from this data, capable of predicting the age of a healthy individual based on their protein profile.

The clock unveiled that conditions such as diabetic retinopathy and uveitis accelerate aging in specific cell types. Interestingly, the researchers also identified proteins associated with Parkinson's disease within the eye fluid, potentially offering a route to early Parkinson's disease diagnoses.

"What's amazing about the eye is we can look inside and see diseases happening in real time," says senior author Vinit Mahajan, a surgeon and professor of ophthalmology at Stanford University. "Our primary focus was to connect those anatomical changes to what's happening at the molecular level inside the eyes of our patients."

Sampling the eye in living patients presents a considerable challenge due to its non-regenerative nature, much like the brain, where taking a tissue biopsy could result in irreparable damage. An alternative approach involves liquid biopsies, which entail collecting fluid samples from areas close to the cells or tissues of interest.

While liquid biopsies can offer insights into the presence of proteins within the targeted region, they have faced limitations in quantifying numerous proteins in the relatively small volumes of fluid. Additionally, they have not been able to provide information regarding the cellular origins of these proteins, a crucial factor in disease diagnosis and treatment.

TEMPO Software Sheds Light on Specific Eye Cell Protein Origin

To comprehensively chart protein production among distinct cell types within the eye, Mahajan's research team employed a high-resolution method to characterize proteins in 120 liquid biopsies derived from the aqueous or vitreous humor of patients undergoing eye surgery. In total, they identified 5,953 proteins, which is tenfold the number of proteins previously documented in analogous studies. Using their custom software tool, TEMPO, the researchers successfully traced each protein back to specific cell types.

In an exploration of the connection between disease and molecular aging, the scientists developed an AI machine learning model that can forecast the molecular age of the eye based on a subset of 26 proteins. Notably, this model accurately predicted the age of healthy eyes, while revealing a noteworthy association between diseases and significant molecular aging.

In the case of diabetic retinopathy, the extent of aging became more pronounced with disease progression, with acceleration of aging by up to 30 years observed in individuals with severe (proliferative) diabetic retinopathy. Remarkably, these indications of aging sometimes manifested prior to the appearance of clinical symptoms related to the underlying disease and persisted in patients who had received successful treatment.

Furthermore, the researchers identified several proteins linked to Parkinson's disease. Typically, these proteins are identified postmortem, and current diagnostic methodologies are incapable of detecting them, contributing to the challenge of diagnosing Parkinson's disease. The screening of these markers in eye fluid could potentially enable earlier diagnosis of Parkinson's disease and facilitate subsequent therapeutic monitoring.

The authors say that these findings imply that aging might exhibit organ- or even cell-specific patterns, offering potential advancements in precision medicine and the design of clinical trials. Julian Wolf, the first author and an ophthalmologist at Stanford University, emphasizes, " These discoveries underscore the varying rates of aging in our organs. The potential utilization of targeted anti-aging medications may represent the next phase in preventive, precision medicine."

"If we're going to use molecular therapies, we should be characterizing the molecules in our patients," says Mahajan. "I think reclassifying patients based on their molecular patterns and which cells are being affected can really improve clinical trials, drug selection, and drug outcomes."

Next, the researchers plan to analyze samples from a more extensive pool of patients including a wider spectrum of eye-related conditions. They also highlight the potential applicability of their method in characterizing challenging-to-sample tissues. For instance, the approach could extend to liquid biopsies of cerebrospinal fluid for brain analysis and diagnosis, synovial fluid for joint studies, and urine for kidney investigations.

Reference

Liquid biopsy proteomics combined with AI identifies cellular drivers of eye aging and disease in vivo, Cell (2023). DOI: 10.1016/j.cell.2023.09.012. www.cell.com/cell/fulltext/S0092-8674(23)01033-4