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Now you see me: Preserving vision through RNA

The University of Sydney

Grant:
  • RNA Future Leaders Program
Date Funded:
  • 3 August, 2021
Chief Investigator/s:
  • Dr. Ulrike Schumann

Project Summary

This research will identify serum-derived RNA molecules that diagnose early-stage retinal degeneration and develop biosensors for their detection.

The main researcher for this project is Dr. Ulrike Schumann.

What is the issue for NSW?

Vision loss has devastating effects on the affected individuals’ quality of life and mental health. In retinal degenerative diseases such as age-related macular degeneration, retinitis pigmentosa, diabetic retinopathy or glaucoma, neuron degeneration within the retina leads to irreversible and often untreatable vision loss. Early diagnosis before vision loss occurs is essential to develop treatments that slow or halt the disease and preserve vision.

RNA biomarker molecules within the blood circulation are indicators of many diseases, including retinal degeneration. However, how reliable these are to facilitate clinical diagnosis is unclear. Identifying which RNA biomarkers are specific for early stages of retinal degeneration, will help develop specialised treatment and new therapies. Developing biosensors, a method to specifically detect these molecules in the blood, will provide a tool to enable rapid, easy access, minimally invasive diagnosis.

What does the research aim to do and how?

Aim 1: Identify changes to circulating RNA biomarkers due to retinal degeneration. Using serum samples, we will identify all RNAs that are present at different levels in patients compared to healthy individuals.

Aim 2: Manufacture biosensors that specifically detect RNA biomarkers. Improving on our existing design, we will manufacture a robust and reusable biosensor that is compatible with direct application in human serum samples.

Aim 3: Use this biosensor to determine whether RNA biomarkers can predict diagnosis. We will measure RNA biomarkers directly in patient serum samples and attempt to predict the patients’ diagnosis.