MS2Planner: improved fragmentation spectra coverage in untargeted mass spectrometry by iterative optimized data...

Area of Science:

• Metabolomics
• Analytical Chemistry
• Biochemistry

Background:

• Untargeted mass spectrometry is crucial for metabolite profiling and discovery.
• Data-dependent acquisition (DDA) and data-independent acquisition (DIA) are common strategies for collecting fragmentation spectra.
• DDA yields less multiplexed spectra for easier annotation but can be redundant and suboptimal, while DIA produces complex, multiplexed spectra that are difficult to annotate.

Purpose of the Study:

• To introduce MS2Planner, a novel workflow for optimizing mass spectrometry data acquisition.
• To enhance the quality and quantity of fragmentation spectra for improved metabolite identification.
• To overcome the limitations of existing DDA and DIA strategies.

Main Methods:

• MS2Planner employs an Iterative Optimized Data Acquisition strategy.
• Topological sorting is utilized to optimize spectral acquisition across multiple experiments.
• The workflow generates an inclusion list for targeted data collection.

Main Results:

• MS2Planner significantly increased the metabolite annotation rate by 38.6%.
• The method demonstrated a 62.5% improvement in sensitivity and a 9.4% increase in specificity compared to DDA.
• Optimized data acquisition led to higher quality fragmentation spectra.

Conclusions:

• MS2Planner represents a significant advancement in mass spectrometry-based metabolomics.
• The workflow enhances metabolite discovery and identification by improving spectral quality and annotation rates.
• MS2Planner offers a more efficient and effective approach to untargeted metabolomics.