DaDIA: Hybridizing Data-Dependent and Data-Independent Acquisition Modes for Generating High-Quality Metabolomic Data

Area of Science:

• Metabolomics
• Analytical Chemistry
• Biochemistry

Background:

• Untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) is crucial for biological research.
• Existing data acquisition modes like data-dependent acquisition (DDA) and data-independent acquisition (DIA) have limitations in capturing comprehensive metabolic information.
• Challenges include limited metabolome coverage, MS2 coverage, and MS2 quality, hindering accurate metabolite identification.

Purpose of the Study:

• To introduce a novel data acquisition workflow, data-dependent-assisted data-independent acquisition (DaDIA), for untargeted metabolomics.
• To improve metabolome coverage, tandem mass spectrometry (MS2) coverage, and MS2 quality compared to conventional DDA and DIA methods.
• To develop a computational tool (DaDIA.R) for streamlined analysis and annotation of DaDIA data.

Main Methods:

• The DaDIA workflow integrates DDA and DIA acquisition modes.
• DIA mode is used for individual biological samples to maximize feature detection and MS2 coverage.
• DDA mode is applied to pooled quality control samples to enhance MS2 spectral quality.
• A custom computational program, DaDIA.R, was developed for automated feature extraction and metabolite annotation.

Main Results:

• The DaDIA workflow significantly enhanced metabolomic data quality, including increased metabolome and MS2 coverage.
• Compared to conventional DDA or DIA, DaDIA demonstrated a substantial increase in the number of detected features and annotated metabolites in human urine samples.
• A leukemia metabolomics study showed that DaDIA identified approximately four times more significant metabolites with broad MS2 coverage and high MS2 quality than the DDA workflow.

Conclusions:

• The DaDIA workflow represents a significant advancement in untargeted metabolomics data acquisition.
• This novel approach overcomes limitations of traditional DDA and DIA methods, providing superior data quality.
• DaDIA offers enhanced capabilities for statistical analysis and biological interpretation, benefiting diverse biological applications.