Enhanced biomarker discovery through narrow-range data-dependent acquisition in untargeted metabolomics

Metabolomics involves the comprehensive analysis of small molecules in biological systems and is critical for clinical research and precision medicine. Liquid chromatography-mass spectrometry (LC-MS) is important in metabolomics due to its extensive metabolite coverage and high sensitivity. The increasing demand for disease biomarkers has increased LC-MS use, particularly untargeted metabolomics, which provides a complete view of the metabolome. However, the choice of MS acquisition method in untargeted metabolomics is crucial and challenging, as it impacts analysis coverage, sensitivity, and reproducibility. This work addresses the critical issue of selecting appropriate acquisition modes for untargeted metabolomics studies. We evaluated All Ion Fragmentation (AIF), Data-Dependent Acquisition (DDA), and Data-Independent Acquisition using Sequential Window Acquisition of All Theoretical Fragment Ion Spectra (DIA-SWATH) in untargeted metabolomics. Additionally, we introduced a novel modified DDA method with narrow mass ranges (DDA-NR). Using a standard mixture of 206 metabolites, we compared these methods in terms of metabolite identification, quantification, and spectra quality. Both DDA methods, particularly the modified DDA-NR, exhibited superior performance, identifying over 90 % of metabolites with high spectral quality and precision. We applied these methodologies to analysis of apheresis samples from CAR-T therapy patients, effectively identifying significant metabolic differences between complete response and progressive disease groups. The DDA-NR method particularly excelled in identifying metabolites at higher mass ranges, demonstrating its effectiveness in comprehensive metabolic profiling that can potentially inform and enhance therapeutic strategies. This study underscores the efficacy of DDA methods, particularly DDA-NR, in enhancing metabolite identification and spectral quality in untargeted metabolomics. These findings are significant for clinical applications, as they improve the reliability of metabolic profiling, aiding in better understanding and monitoring of metabolic changes in therapies such as CAR-T. This work contributes to establishing best practices in LC-MS-based metabolomics, facilitating advancements in analytical methodologies, and contributing to more effective and personalized cancer treatment strategies. • The modified DDA method with narrow mass ranges improved the coverage and spectral quality. • Method selection is important in capturing comprehensive metabolic signatures. • The DDA-NR approach has beneficial in detecting metabolites at higher mass ranges.