ArticlebioRxiv : the preprint server for biology2026
Comparative Evaluation of DDA- and DIA-Based Proteomic Workflows in Beryllium-Related Lung Disease.
Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
We compared traditional data-dependent acquisition mass spectrometry (DDA-MS) with the increasingly adopted data-independent acquisition (DIA-MS) to evaluate their relative utility for large-scale quantitative biofluid proteomics of lung compartments, specifically paired bronchoalveolar lavage (BAL) cells and bronchoalveolar lavage fluid (BALF). Using beryllium-related granulomatous lung disease as a focused model, we analyzed BALF and BAL cells from beryllium-sensitized (BeS) individuals using both acquisition strategies to assess proteome depth, quantitative completeness, and analytical robustness. In BAL cells, 5,640 proteins were identified by DDA-MS and 5,227 by DIA-MS; however, DIA-MS yielded markedly improved quantitative completeness, with 5,178 proteins (~99%) quantified across all samples compared with 3,539 (~63%) quantified by DDA-MS. While 3,397 proteins were quantified by both methods, DIA-MS uniquely quantified 1,781 lower-abundance proteins. Proteins identified by both DIA and DDA-MS approaches revealed pathways associated with granulomatous inflammation, including Toll-like receptor, clathrin-mediated endocytosis, sirtuin, and C-type lectin receptor signaling, whereas DIA-MS resolved additional pathways, such as the complement cascade, coagulation system, and JAK/IL-6-type cytokine signaling. In BALF, although more proteins were identified by DDA-MS than by DIA-MS (2,069 vs 1,742), DIA-MS achieved greater quantitative completeness, with 1,695 proteins quantified across all samples compared with 1,050 using DDA-MS, underscoring its suitability for biomarker-oriented analyses in lung fluid compartments. Together, these results support DIA-MS as a robust and sensitive platform for quantitative lung proteomics and discovery of disease-relevant protein signatures.
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