Dry Eye Disease (DED) stands as one of the most prevalent ocular surface disorders globally, with an increasing incidence that significantly impairs patients’ visual function and quality of life. Conventional diagnostic approaches, which rely on subjective symptom questionnaires and tests of tear film stability, often lack sufficient sensitivity and specificity and fail to accurately delineate underlying etiologies. Tear fluid, a non-invasive biological sample that directly mirrors the ocular surface microenvironment, is rich in proteins, lipids, and metabolites, offering an ideal resource for investigating disease mechanisms and identifying biomarkers. Driven by advances in mass spectrometry (MS) and quantitative proteomics, tear proteomics has emerged as a pivotal tool for elucidating the molecular mechanisms of DED and discovering potential biomarkers. This article provides a systematic review of current methodologies for tear sample processing and proteomic analysis. It focuses on the application of tear proteomics in uncovering key molecular mechanisms involved in DED, including inflammatory responses, oxidative stress, tear film instability, and neurofunctional dysregulation. Furthermore, it summarizes the current landscape of potential biomarker candidates and their validation status. The review also critically examines prevailing challenges in the field, such as sample heterogeneity, technical standardization issues, and barriers to clinical translation. Finally, it outlines promising future directions, aiming to provide a foundation for the precision diagnosis and targeted therapy of DED. Tear proteomics enables non-invasive exploration of dry eye disease (DED) pathogenesis and biomarker discovery, starting with capillary/filter paper strip sample collection and LC-MS/MS-based profiling (DDA/DIA/MRM). Bioinformatic analysis (GO/KEGG/PPI) deciphers core DED mechanisms (inflammation, oxidative stress, tear film instability, neuroregulation), while multi-omics integration (proteomics+transcriptomics+metabolomics+lipidomics) achieves systematic disease understanding. Translational applications include DED diagnosis, subtyping, severity assessment and treatment response prediction, paving the way for personalized precision medicine. Key challenges (sample/technical/validation) and future directions (standardization, POCT, AI integration) are also highlighted to accelerate clinical translation of tear proteomic findings.
Hao et al. (Sun,) studied this question.