Detection of Tear Components Using Matrix-Assisted Laser Desorption Ionization/Time-of-Flight Mass Spectrometry for Rapid Dry Eye Diagnosis

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Abstract
The tear is a biological fluid that has the diagnostic potential for ocular diseases. Extracellular vesicles (EVs), wildly detected in various biofluids including tears, are nanoparticles released by living cells and considered promising detection sources for non-invasive liquid biopsy. Understanding the roles of tears and tear-EVs in ocular diseases such as dry eye can facilitate the studies of clinical diagnosis, which usually entails detecting such liquid objects with a rapid and effective method. In this study, we utilized a mass spectrometry-based strategy to analyze peptidome/proteome profiles of tears and EVs for rapid dry eye diagnosis. Nano-sized EVs were isolated from tears of both healthy control (HC) individuals and dry eye syndrome (DES) patients, and the tear compositions were further analyzed by tracking their fingerprints with matrix-assisted laser desorption ionization/time-of-flight mass spectrometry (MALDI-TOF-MS). The fingerprints of tear-EVs could be observed in a dose-dependent manner as well as tears, allowing comparing their discriminant peaks between tears and EVs. By analyzing these peaks, the fingerprints of both tear and tear-EVs were showed to have the capability of distinguishing DES patients from HC donors and providing an efficient way for screening potential DES biomarkers. The proposed tear and EV fingerprinting approach is expected to be a potential tool in the rapid diagnosis of ocular disease and in-depth researches on pathogenesis. Data are available via ProteomeXchange with identifier PXD020217.
Funding Information
  • Wenzhou Medical University (89218012, 89219012)
  • National Health and Family Planning Commission of the People's Republic of China (WKJ-ZJ-1707, WKJ-ZJ-1910)
  • National Natural Science Foundation of China (21904098, 31741036, 81771984)
  • Zhejiang Province (2017R52050)
  • Wenzhou Institute of Biomaterials and Engineering (WIBEZD2017006-05)

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