Human tears are critical in the maintenance of ocular surface homeostasis and exhibit differential composition in states of health and disease. A healthy tear film bathes the cornea and conjunctival tissues to provide constant hydration, supply oxygen and nutrients, and maintain a smooth refractive surface. Tears also impart physical, antimicrobial, and environmental protection; their secretion and content change dynamically in response to local, systemic, and even emotional stimulation. Tear fluid has a relatively high protein content and proteins within the tear fluid arise primarily from the main lacrimal gland, but proteins have also been traced to serum and infiltrating immune cells [1]. With advancements in proteomic analysis techniques, patterns of change within the tear proteome have been identified in association with conditions ranging from dry eye disease and contact lens use to diabetes and Alzheimer's disease [[2], [3], [4], [5]].

The non-invasive nature of sample collection and insight into physiologic and pathologic processes make tears an attractive source for biomarker discovery [6]. However, proteomic analysis of tear fluid presents challenges. Individual sample volumes are small, particularly when collected from participants with aqueous-deficient dry eye disease. Consequently, many early tear proteome studies relied on sample pooling from multiple individuals with particular conditions or attributes. Given the wide range of factors influencing the proteome, pooling participant samples based on one factor of interest may confound data analysis. In addition, high abundance proteins predominate in the composition of human tear fluid and can suppress detection of lower abundance proteins, which may also vary in expression levels between individuals and conditions [7]. Recent technical advances have increased the capacity for rapid protein identification and quantification [8]. However, in the absence of standardized protocols to improve reproducibility in proteomic techniques and data analysis, investigators face challenges in interpreting results across studies.

In the present study, we used isobaric labeling of samples with tandem mass tags (TMT) and subsequent fractionation to allow for parallel processing of individual tear samples and deeper sampling of the proteome, producing quantitative tear protein data with minimal missing values. Differential abundances of tear proteins are elucidated using data analytic pipelines for protein identification and quantification, extensive quality control analysis, and robust statistical comparisons. These methods were applied to compare the degree of influence of three different factors on human tear proteomes: Schirmer strip wetted length, geographic site of tear collection, and contact lens (CL) use.