Comprehensive optimization of chromatographic separation and iterative data acquisition for liquid chromatography-tandem mass spectrometry non-targeted plasma metabolomics: Enhancing feature-based molecular networking.

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) non-targeted plasma metabolomics is essential for biomarker discovery, yet the lack of standardized analytical protocols often compromises data reliability. In this study, we systematically optimized the entire metabolomics workflow, focusing on the integration of feature-based molecular networking (FBMN) to enhance metabolite annotation. Critical parameters, including protein precipitation chemistry, chromatographic selectivity, and data acquisition strategies, were comprehensively assessed. Optimal extraction was achieved using a methanol/ethanol (1:1, v/v) mixture at a 1:4(v/v) sample-to-solvent ratio. For chromatographic separation, an XBridge BEH C18 column outperformed alternatives. Superior peak capacity and ionization efficiency were obtained using 0.1% formic acid (or 10 mM ammonium acetate with 0.1% formic acid) in positive mode, and 10 mM ammonium formate with 0.1% acetic acid in negative mode. While the timing of internal standard addition did not significantly alter the metabolic profile, instrument signal stability became a critical factor for sequences exceeding one week. Notably, the transition to a 100 mm column and the implementation of iterative data-dependent acquisition (DDA) on quality control samples significantly expanded the FBMN size and the number of uniquely annotated compounds. This optimized, robust workflow provides a standardized framework for improving metabolite coverage and annotation confidence in large-scale clinical investigations.