In-depth analysis of data characteristics and comparative evaluation of dda and dia accuracy in label-free quantitative proteomics of biological samples

作者：Xun Zou, Lulu Wang, Yulu Chen, Hang Fu, Yuan Gao, Bin Liu, Minjia Tan, Linhui Zhai · 发表于：Clinical Proteomics · 年份：2025 · DOI：10.1186/s12014-025-09572-2 · 被引用次数：8 · 研究领域：Advanced Proteomics Techniques and Applications、vaccines and immunoinformatics approaches、Advanced Biosensing Techniques and Applications

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 pept...