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Stanford Medicine researchers find new way to measure ocular aging

Using a technique they developed for studying eye fluid, Stanford Medicine researchers and their collaborators have found a way to measure ocular aging, opening avenues for treatment of numerous eye diseases.

The scientists looked at nearly 6,000 proteins in the fluid and found that they can use 26 of them to predict aging. Using artificial intelligence, they developed an eyeaging “clock,” indicating which proteins accelerate aging in each disease and revealing new potential targets for therapies.

The study was published Oct. 19 in Cell. Vinit Mahajan, MD, PhD, a professor of ophthalmology, is the senior author, and Julian Wolf, MD, a postdoctoral scholar in Mahajan’s lab, is the lead author of the paper.

Mahajan and his colleagues intend to apply the clock method to other bodily fluids to develop more effective drugs for a variety of diseases.

This is one of the best connections ever made that suggests disease triggers accelerated aging.”

Vinit Mahajan, MD, PhD, professor of ophthalmology

To glean the most information possible with small, renewable samples, Mahajan and his team developed a technique -; TEMPO, or tracing expression of multiple protein origins. By tracing proteins to a type of cell where the RNA that creates the proteins resides, TEMPO allows the scientists to understand the cellular origin of disease-driving proteins with the hope that eventually they can target the cells with personalized medical treatments.

“The first step in developing any kind of successful therapy is understanding the molecules,” Mahajan said. “At the molecular level, patients present different manifestations even with the same disease. With a molecular fingerprint like we’ve developed, we could pick drugs that work for each patient.”

The culprit cells behind aging eyes

To better understand which cellular processes contribute to various eye diseases, the team analyzed liquid biopsies taken from the aqueous humor -; fluid between the lens and the cornea -; while patients were locally anesthetized during surgery. The fluid was collected in patients with three types of eye diseases: diabetic retinopathy, which causes blood vessels in the eye to leak, leading to vision loss; retinitis pigmentosa, which causes light-sensitive cells in the back of the eye to break down; and uveitis, inflammation inside the eye.

Using eye fluid from 46 healthy patients, Mahajan and his team trained an AI algorithm to predict the age of the patient. They then fed the algorithm the nearly 6,000 proteins present in the fluid to see if a subset of these proteins could predict the patient’s age. They found 26 that could do so when used as a group.

Comparing the diseased eye fluid with the healthy fluid, they found that patients with diseased eyes had proteins that indicated a higher age: 12 years older in patients with early-stage diabetic retinopathy, 31 years in those with late-stage diabetic retinopathy, 16 years in retinitis pigmentosa patients and 29 years in uveitis patients.

The model also found that the cells responsible for indicating increased age were different with each disease: vascular cells in late-stage diabetic retinopathy, retinal cells in retinitis pigmentosa and immune cells in uveitis.

They also found that some cells commonly targeted in treatment are not the ones most involved in disease, encouraging a reevaluation of therapies. For example, diabetes drugs commonly target blood vessel cells because they become leaky with disease, but they found a big increase in proteins from healthy to late-stage diabetic retinopathy is in macrophages, an immune cell that removes dead cells.

The researchers found that some cells had showed accelerated aging before symptoms appeared. Treating the molecular pathway early, Mahajan said, could prevent disease damage before it becomes irreparable.

Informing clinical trials

Targeting both aging and disease cells could make treatment more effective, Mahajan said, because the two appear to act separately but simultaneously to damage the eye.

Mahajan anticipates that researchers will apply the TEMPO technique and aging clock to other organ fluids such as liver bile and joint fluid.

Mahajan hopes that by knowing these biomarkers, researchers will run more successful clinical trials because they will have a more refined look into the cellular processes driving disease. Currently, 90% of drug candidates tested in mice models or human cells fail in clinical trials. Knowing the cells driving disease and aging may increase chances of success, Mahajan said.

“It’s as if we’re holding these living cells in our hands and examining them with a magnifying glass,” Mahajan said. “We’re dialing in and getting to know our patients intimately at a molecular level, which will enable precision health and more informed clinical trials.”

Researchers from the Aarhus University in Denmark, University of Minnesota, Retina Consultants of Minnesota, University of Calgary, University of Iowa and Veterans Affairs Palo Alto Health Care System contributed to the work.

The research team was supported by NIH grants (R01EY031952, R01EY031360, R01EY030151, P30EY026877, R01EY030151, R01EY031952 and R35GM138353), Stanford University, Research to Prevent Blindness, VitreoRetinal Surgery Foundation, Lundbeck Foundation’s DARE fellowship and BrightFocus Foundation’s Macular Degeneration Research program.

The study was made possible by Stanford researchers affiliated with the Byers Eye Institute who created a biobank of eye fluid collected in the operating room.

Source:

Journal reference:

Wolf, J., et al. (2023) Liquid-biopsy proteomics combined with AI identifies cellular drivers of eye aging and disease in vivo. Cell. doi.org/10.1016/j.cell.2023.09.012.

Leah Sirama
Leah Sirama
Leah Sirama, a lifelong enthusiast of Artificial Intelligence, has been exploring technology and the digital realm since childhood. Known for his creative thinking, he's dedicated to improving AI experiences for all, making him a respected figure in the field. His passion, curiosity, and creativity drive advancements in the AI world.
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