What does this strategy enable?
DaDIA deliberately separates the strengths of two acquisition modes across the study design. Biological samples are acquired using data-independent acquisition to maximize feature and fragmentation coverage, while pooled QC samples are acquired using data-dependent acquisition to generate higher-quality, less multiplexed MS/MS spectra. Computational integration then associates the complementary DDA and DIA evidence for metabolite annotation.
Transferable workflow
Workflow
- 1
Prepare representative pooled QC material
Pool study material so the QC contains a broad representation of metabolites observed across the sample set.
- 2
Acquire individual study samples by DIA
Use DIA on individual biological samples to maximize feature detection and fragmentation coverage.
- 3
Acquire pooled QC samples by DDA
Use DDA on pooled QC material to obtain cleaner precursor-specific fragmentation spectra.
- 4
Integrate DDA and DIA information
Associate feature-level quantitative data and DIA coverage with higher-quality DDA spectra from the pooled QC.
- 5
Annotate and interpret the combined dataset
Use integrated evidence for metabolite annotation and downstream statistical interpretation.
Connected resources
Tools used in this strategy
Strategy profile
Related objectives
Molecular discovery
Components
Sample preparation, Data acquisition, Quality control, Preprocessing and feature extraction, Annotation or identification
Biological context
Broadly applicable
Ion mobility
Not specified
Scope and considerations
Designed for
- Untargeted LC-MS studies seeking broad DIA coverage without sacrificing access to cleaner DDA spectra.
- Studies already using pooled QC samples and able to dedicate additional injections to complementary acquisition.
Important requirements
- A representative pooled QC sample.
- Instrument support for both DIA and DDA acquisition.
Considerations
- The workflow increases acquisition and computational complexity relative to one acquisition mode.
- Spectra obtained from pooled QC material must be mapped appropriately back to features in individual samples.
Reusable resources
Publication
Guo J, Shen S, Xing S, Huan T. DaDIA Hybridizing Data-Dependent and Data-Independent Acquisition Modes for Generating High-Quality Metabolomic Data. Analytical Chemistry 93, 2669-2677 (2021).