Abstract
Data-Dependent Acquisition (DDA) and Data-Independent Acquisition (DIA) are widely used in MS-based proteomics. However, a comprehensive evaluation of their data characteristics-including protein and peptide identification, differential expression analysis, and the performance in revealing biological insights-remains lacking. In this study, we conducted a systematic comparison of DDA and DIA across three model sample types: one disease model, two drug-treated models, and their respective controls. Our analysis extended beyond conventional metrics such as total protein and peptide counts, precision, and accuracy, to include data completeness, detection of positive control markers, reproducibility, functional annotation reliability, and sources of methodological variation. The results demonstrated that DIA outperformed DDA in terms of protein identification (disease group: 7,735 vs. 5,067; drug-treated group 1: 7,987 vs. 4,605), quantitative coverage (average quantifiable protein ratio: DIA 98-99% vs. DDA 95-96%), and reproducibility (intragroup correlation coefficients: DIA > 0.98 vs. DDA 0.93-0.98). We also found DIA exhibited lower variability (intragroup CV < 10% vs. > 15% for DDA) and improved accuracy for low-abundance and housekeeping proteins. Additionally, the functional enrichment analyses further revealed DIA's superior capability in detecting pathway activation. Finally, discrepancies between DIA and DDA were primarily attributed to proteins identified with ≤ 5 peptides, the exclusion of single-peptide proteins enhanced overall data quality. Overall, this study systematically assess the overall capabilities of DDA and DIA approaches in uncovering biologically relevant findings and driving mechanistic insights within authentic pharmacological and disease models, thereby offering practical guidance for methodological choices in future research.