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Updated: Feb 9, 2026

Quantitative Metabolomics of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry
Published on: January 5, 2021
Comprehensive Tandem-Mass-Spectrometry Coverage of Complex Samples Enabled by Data-Set-Dependent Acquisition.
Corey D Broeckling1, Emmy Hoyes2, Keith Richardson2
1Proteomics and Metabolomics Facility , Colorado State University , C-121 Microbiology Building 2021 Campus Delivery , Fort Collins , Colorado 80523 , United States.
Data-dependent MS/MS (DDA) struggles with comprehensive molecule coverage in complex samples. A new data-set-dependent MS/MS (DsDA) approach improves MS/MS sampling by integrating MS1 data processing and target prioritization for omics experiments.
Area of Science:
- Analytical Chemistry
- Biochemistry
- Proteomics
Background:
- Tandem mass spectrometry (MS/MS) is crucial for identifying small molecules and peptides in omics studies.
- Data-dependent MS/MS (DDA) acquisition, while responsive, often lacks comprehensive MS/MS coverage in complex samples due to speed limitations.
- Data-independent approaches (DIA) improve MS/MS coverage but can compromise selectivity or sensitivity.
Purpose of the Study:
- To introduce data-set-dependent MS/MS (DsDA), a novel strategy for comprehensive MS/MS sampling.
- To leverage MS1 data processing and target prioritization for optimizing MS/MS acquisition in real-time.
- To enhance MS/MS coverage in omics experiments by integrating data processing feedback with acquisition.
Main Methods:
- Developed and implemented data-set-dependent MS/MS (DsDA) by integrating MS1 data processing and target prioritization.
- Utilized a combination of R, Proteowizard, XCMS, and WRENS software for the DsDA workflow.
- Applied the DsDA approach on a liquid-chromatograph-coupled quadrupole time-of-flight mass spectrometer.
Main Results:
- Achieved comprehensive MS/MS coverage for complex small-molecule samples.
- Demonstrated significant improvements in MS/MS sampling compared to traditional DDA.
- Showcased the effectiveness of real-time optimization of MS/MS acquisition parameters.
Conclusions:
- Data-set-dependent MS/MS (DsDA) offers a powerful solution for achieving comprehensive MS/MS coverage in omics experiments.
- The integration of data processing feedback and acquisition optimizes MS/MS sampling efficiency.
- DsDA represents a substantial advancement over traditional DDA for complex sample analysis.
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