Optimizing MS Parameters for Data-Independent Acquisition (DIA) to Enhance Untargeted Metabolomics.

Data-Independent Acquisition (DIA) has emerged as a powerful mass spectrometry (MS) strategy for comprehensive metabolomics. This study presents a novel short gradient (13 min) nanosensitive analytical method for human plasma analysis using DIA LC-MS/MS, focusing on in-depth optimization of MS parameters to maximize data quality and metabolite coverage. Key MS parameters, including scan speed, isolation window width, resolution, automatic gain control, and collision energy, were systematically tuned to balance the sensitivity and specificity while minimizing interferences. The optimized method enabled the detection of 2,907 features with 675 annotated compounds, leveraging recent progress in nano-LC-MS/MS for multiomics applications and showcasing the possibility of combining proteomics and metabolomics within a single chromatographic system. Ultimately, a comparison was performed between the data acquired through the DIA and DDA MS approaches in the context of untargeted metabolomics. This optimized analytical method yields more robust and reproducible results, thereby expanding the potential for meaningful discoveries across diverse biological fields.

• Publication year

  2025

• Venue

  Journal of Proteome Research

• Publication date

  2025-11-12

• Fields of study

  Medicine, Chemistry

• Identifiers
  DOI 10.1021/acs.jproteome.5c00622PMID 41220336
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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