Diagnosis of Primary Open-Angle Glaucoma Using Spectral Profiling of Aqueous Humor-Derived Exosomes

Area of Science:

• Ophthalmology
• Neuroscience
• Biotechnology

Background:

• Primary open-angle glaucoma (POAG) is a leading cause of irreversible optic nerve damage.
• Current diagnostic methods like intraocular pressure (IOP) and optical coherence tomography (OCT) have limitations in sensitivity and variability.
• Liquid biopsy offers a promising avenue for objective molecular diagnostics.

Purpose of the Study:

• To develop an efficient and highly sensitive diagnostic platform for POAG using aqueous humor (AH)-derived exosomes.
• To establish a method for AI-driven classification of glaucoma patients based on exosome spectral profiles.

Main Methods:

• Morphological and compositional validation of exosomes in AH samples.
• Fabrication of antibody-functionalized substrates for selective exosome capture.
• Gold nanoparticle coating for Raman signal generation and spectral acquisition.
• Training and evaluation of a convolutional neural network (CNN) model for binary classification.

Main Results:

• The diagnostic platform demonstrated high sensitivity and efficiency in analyzing AH-derived exosomes.
• The CNN model achieved an Area Under the Curve (AUC) of 0.96 and 91% prediction accuracy.
• The system successfully differentiated between healthy controls and glaucoma patients.

Conclusions:

• Ocular fluid-derived extracellular vesicles (EVs) show significant potential for diagnosing neurodegenerative diseases like POAG.
• The integrated immunoassay-based isolation and AI-driven classification system enables rapid, individualized diagnosis.
• This approach overcomes limitations of conventional clinical hallmarks and sample volume constraints.