ABSTRACT: Background: Mass spectrometry is a powerful technique for tear fluid proteomics, offering critical insights into its complex molecular composition. Data-dependent acquisition (DDA), the most commonly used approach, preferentially selects high-abundance proteins, limiting reproducibility and the quantification of low-abundance proteins. In contrast, data-independent acquisition (DIA) provides an unbiased and comprehensive proteomic profile by fragmenting all precursor ions, enhancing protein coverage, quantification accuracy and reproducibility. This study presents a comparative analysis of DDA and DIA approaches for tear fluid proteomics to improve detection of low-abundance proteins and facilitate biomarker discovery. Methods: Tear fluid samples were collected from healthy individuals using Schirmer strips, processed using in-strip protein digestion, and analyzed via liquid chromatography-tandem mass spectrometry (LC-MS/MS). DDA and DIA workflows were compared for proteomic depth, reproducibility, and data completeness. Quantification accuracy was assessed using serial dilutions of tear fluid samples in a complex biological matrix. Results: DIA identified 701 unique proteins and 2,444 peptides, significantly outperforming DDA, which identified 396 unique proteins and 1,447 peptides. DIA exhibited greater data completeness (78.7% proteins, 78.5% peptides) compared to DDA (42% proteins, 48% peptides) across replicates. Reproducibility was markedly improved in DIA, with median coefficients of variation (CVs) of 9.8% for proteins and 10.6% for peptides, compared to 17.3% and 22.3% in DDA, respectively. Quantification accuracy was also enhanced, demonstrating superior consistency across dilution series. Conclusion: This study demonstrates that DIA is a robust and reliable method for proteomic analysis of complex tear fluid samples, offering significantly greater depth, reproducibility, and accuracy compared to DDA.
Ashok Sharma 