Tear fluid is rich in molecules such as proteins, lipids, electrolytes, and metabolites, making its metabolomic profile a valuable indicator of overall health. This project aims to develop technologies to detect various biomarkers in tear fluid. The first objective focuses on a multi-omics tear biomarker discovery and validation platform, using high-resolution mass spectrometry combined with bioinformatics and machine learning to identify and quantify clinically relevant proteomic and metabolomic markers. The second objective targets the development of tear based point of care tests, translating validated biomarkers into practical diagnostic kits for conditions such as dry eye disease, glaucoma, retinopathy of prematurity, and contact lens discomfort.