{"doi":"10.1016/j.jmsacl.2025.10.001","title":"Tear fluid proteomics: a comparative study of DIA and DDA mass spectrometry","abstract":"Background: Mass spectrometry is a powerful technique for tear fluid proteomics, offering critical insights into its complex molecular composition. Traditional data-dependent acquisition (DDA) often favors high-abundance proteins because it selects only the most intense precursor ions within a given window during each scan cycle. A newer approach, data-independent acquisition (DIA), addresses this by fragmenting all precursor ions within defined mass windows, offering broader coverage and improved quantification. This study presents a systematic comparison of DDA and DIA workflows to assess their relative performance in detecting tear fluid proteins. 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 in a complex biological matrix. Results: DIA identified 701 unique proteins and 2,444 peptides, outperforming DDA, which identified 396 unique proteins and 1,447 peptides. Across eight replicates, DIA exhibited greater data completeness (78.7% for proteins and 78.5% for peptides) compared with DDA (42% for proteins and 48% for peptides). Reproducibility was markedly improved with DIA, with a median coefficient of variation (CV) of 9.8% for proteins and 10.6% for peptides, compared to 17.3% and 22.3%, respectively, for DDA. Quantification accuracy was also enhanced, with superior consistency across the dilution series. Conclusion: Overall, DIA provides deeper, more reproducible, and more accurate proteome profiling of tear fluid than DDA, making it well suited for biomarker discovery.","journal":"Journal of Mass Spectrometry and Advances in the Clinical Lab","year":2025,"id":514547,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":8,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9681,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1037378,"name":"Jeremy Altman","orcid":null,"position":1,"is_corresponding":false},{"id":778803,"name":"Garrett Jones","orcid":null,"position":2,"is_corresponding":false},{"id":1377802,"name":"Drew Mayernik","orcid":null,"position":3,"is_corresponding":false},{"id":1377803,"name":"Eliza Williams","orcid":null,"position":4,"is_corresponding":false},{"id":1377198,"name":"Amr S. Mahmoud","orcid":"0000-0001-9966-2888","position":5,"is_corresponding":false},{"id":264780,"name":"Tae Jin Lee","orcid":"0000-0002-5245-1140","position":6,"is_corresponding":false},{"id":367348,"name":"Wenbo Zhi","orcid":"0000-0002-7720-7048","position":7,"is_corresponding":false},{"id":286447,"name":"Shruti Sharma","orcid":"0000-0001-8200-108X","position":8,"is_corresponding":false},{"id":264781,"name":"Ashok Sharma","orcid":"0000-0001-9597-4374","position":9,"is_corresponding":false},{"id":1036799,"name":"Saleh Ahmed","orcid":"0009-0000-8271-5234","position":0,"is_corresponding":true}],"reference_count":56,"raw_metadata":null,"created_at":"2026-07-19T02:48:29.165853Z","pmid":"41158730","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}