Automated precursor ion exclusion during LC-MS/MS data acquisition for optimal ion identification

Area of Science:

• Analytical Chemistry
• Biochemistry
• Proteomics

Background:

• Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a key technique for analyzing complex mixtures.
• Characterizing multiple samples with similar compositions presents challenges in maximizing unique data acquisition.
• Existing methods may not efficiently capture all relevant MS/MS spectra across a sample set.

Purpose of the Study:

• To develop and evaluate an automated intersample data acquisition strategy for LC-MS/MS.
• To maximize the number of unique, high-quality MS/MS spectra obtained from multiple samples.
• To improve the efficiency and comprehensiveness of LC-MS/MS analyses.

Main Methods:

• An automated intersample data-dependent acquisition strategy using precursor ion exclusion (PIE) was developed.
• The MassAnalyzer software was utilized for automated implementation on Thermo Scientific mass spectrometers.
• A PIE list, including m/z and time window, was generated and updated iteratively across sample runs.

Main Results:

• The automated PIE strategy significantly increased the number of identified ions.
• A 52% increase in identified ions was observed for an antibody tryptic digest.
• A 93% increase in identified ions was observed for a soy hydrolysate sample.

Conclusions:

• The automated PIE strategy effectively maximizes both the quality and quantity of unique MS/MS spectra.
• This approach enhances the identification of components in multiple, compositionally similar samples.
• The method offers a significant improvement over traditional data acquisition techniques in LC-MS/MS.