One Eye on the Clock
Stanford University researchers develop a method to identify diseases associated with aging of the eye
Alun Evans | | 2 min read | News
Researchers at Stanford University have developed a new method to measure ocular aging (1). “TEMPO stands for Tracing Expression of Multiple Protein Origins,” says Vinit Mahajan, senior author of the study. Using this technique, the team were able to find 26 biomarkers of ocular aging out of 6,000 proteins extracted from vitreous humor and aqueous humor. “We could figure out which cells in the eye were making the proteins we found,” explains Mahajan. “We found really specific protein expression signatures for photoreceptors, retinal endothelial cells, and amacrine cells, and we could track each cell type’s health in various diseases.”
The team created an “AI proteomic clock” that enables them to see exactly which of these proteins accelerate ocular aging, as well as indicating that patients who were suffering from certain diseases, such as diabetes, also showed accelerated aging. They now plan to apply the method to other diseases. “The eye is full of neurons, so neurons in brain disease might share molecular changes,” says Mahajan.
Finally, Mahajan believes TEMPO could aid personalized medical treatment and, in turn, increase success rates for drug candidates. “TEMPO could be used to enroll patients most likely to respond to therapy. And after a trial drug was started, TEMPO could help determine if the drug started working at the molecular level – something that might be detectable a long time before clinical improvement.”
Given that around 90 percent of drug candidates currently fail in clinical trials, any improved predictive and diagnostic capability delivered by TEMPO would be welcome. “It’s as if we’re holding these living cells in our hands and examining them with a magnifying glass,” Mahajan says. “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.”
- J Wolf et al., “Liquid-biopsy proteomics combined with AI identifies cellular drivers of eye aging and disease in vivo,” Cell, 186, 4868 (2023). PMID: 37863056.