Using a technique they developed for studying eye fluid, researchers in the United States have found a way to measure ocular ageing, opening avenues for treatment of numerous eye diseases.
Senior author Professor Vinit Mahajan, from Standford Medicine, said the new technique was like “holding these living cells in our hands and examining them with a magnifying glass”.
“We’re dialling in and getting to know our patients intimately at a molecular level, which will enable precision health and more informed clinical trials,” Prof Mahajan enthused.
In the study, published in Cell, 1 the scientists looked at nearly 6,000 proteins in the fluid and found that they can use 26 of them to predict ageing.
Using artificial intelligence (AI), they developed an eye-ageing ‘clock’, indicating which proteins accelerate ageing in each disease and revealing new potential targets for therapies.
Prof 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 ageing,” he said.
To glean the most information possible with small, renewable samples, Prof Mahajan and his team developed a technique — TEMPO, which stands for ‘tracing expression of multiple protein origins’. TEMPO allows the scientists to understand the cellular origin of disease-driving proteins.
“The first step in developing any kind of successful therapy is understanding the molecules,” Prof 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.”
CULPRIT CELLS BEHIND AGEING EYES
To better understand which cellular processes contribute to various eye diseases, the team analysed liquid biopsies taken from the aqueous humour during surgery. The patients had one of three eye diseases: diabetic retinopathy, retinitis pigmentosa, or uveitis.
Using eye fluid from 46 healthy patients, Prof 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.
Additionally, they discovered that some cells commonly targeted in treatment are not the ones most involved in disease, encouraging a re-evaluation of therapies. With some cells showing accelerated ageing before symptoms appeared, Prof Mahajan said treating the molecular pathway early could prevent disease damage, before it becomes irreparable.
INFORMING CLINICAL TRIALS
Targeting both ageing and disease cells could make treatment more effective, Prof Mahajan said, because the two appear to act separately but simultaneously to damage the eye.
He hopes that with these biomarkers known, researchers will have a more refined look into the cellular processes driving disease, leading to more success in clinical trials.
Reference
1. Wolf, J., Mahajan, V.B., et al., Liquid-biopsy proteomics combined with AI identifies cellular drivers of eye aging and disease in vivo. Cell. DOI: 10.1016/j.cell.2023.09.012.
