Integrating full scan and data-dependent acquisition (IFSDDA): advancing quantitative precision and metabolic coverage in untargeted metabolomics for discovering anti-methicillin-resistant Staphylococcus aureus (MRSA) compounds

• IFSDDA links full-scan MS1 peak picking with DDA MS/MS spectra • Combines full-scan quantification with DDA-based metabolite annotation • IFSDDA expands metabolite coverage and improves quantitative precision • The anti-MRSA metabolites in P. aeruginosa cultures were identified with IFSDDA. • IFSDDA enables deeper insights into biological samples In untargeted metabolomics, high-resolution liquid chromatography–tandem mass spectrometry (HR-LC-MS/MS) is widely applied to achieve broad metabolite coverage. However, a single acquisition mode often presents trade-offs between coverage and quantification. To overcome this, we propose a dual-injection strategy combining full scan (FS) and data-dependent acquisition (DDA) modes in consecutive runs of the same sample. The resulting datasets are processed by an in-house developed algorithm that integrates feature lists from both FS and DDA, enhancing both the number of detected features and the quantification precision. This integrated approach, named Integrated Feature Set from DDA and FS (IFSDDA), improves peak picking robustness and captures features otherwise missed by conventional methods. A total of 32,830 metabolic features were detected using IFSDDA, markedly surpassing those detected by traditional approaches. Quantitative precision was ensured through QC-based support vector regression (SVR) normalization, with 94.2% of features achieving a relative standard deviation (RSD) below 20%. For metabolite annotation, 2,845 features were structurally annotated, indicating superior annotation efficiency. Notably, the IFSDDA strategy facilitated the identification of six anti-MRSA metabolites from Pseudomonas aeruginosa , including 1-carboxamide (PCN), pyocyanin (PYO), 1-hydroxyphenazine (1-HP), PQS, and 2-heptyl-4-hydroxyquinoline N-oxide (HQNO). These metabolites were subsequently validated through multiple reaction monitoring (MRM) and biological activity assays. Overall, IFSDDA effectively addresses key limitations in untargeted metabolomics by enhancing data quality, reproducibility, and metabolite annotation. This strategy represents a significant advancement for comprehensive metabolic profiling and holds strong potential for biomarker discovery in complex biological systems.