Enhancing glycopeptide annotation and glycan localization using electron activated dissociation through a multiplexed parallel reaction monitoring design of experiments

We present an optimized electron activated dissociation (EAD) methodology, based on hot electron capture dissociation, for liquid chromatography-tandem mass spectrometry characterization of N- and O-glycopeptides, using recombinant human erythropoietin as a model glycoprotein. Applying a full factorial design of experiments (DoE) approach, we first optimized LC-MS parameters (i.e., ion spray voltage, ion source temperature, and active gradient time) to enhance glycopeptide ionization efficiency while reducing in-source fragmentation. A second DoE was then applied to fine-tune EAD-specific parameters. Multiplexed parallel reaction monitoring was performed to efficiently and comprehensively optimize the electron beam current, reaction time, and electron kinetic energy of the EAD set-up. Finally, the optimized EAD parameters, initially determined using one glycoform per glycopeptide, were successfully applied in data-dependent acquisition mode to detect the overall glycoform composition of each studied glycopeptide. Byonic, Fragpipe and Mascot softwares, and several peak picking softwares were used to evaluate the potential of our optimized EAD set-up, and compare with collision induced dissociation (CID). The results confirmed that EAD improved confidence in glycan localization, while CID enabled the identification of a greater number of glycoforms but with less confident glycan assignments. SIGNIFICANCE: The current manuscript introduces a novel and efficient optimization strategy for electron activated dissociation (EAD) parameters, based on hot electron capture dissociation, using a synergistic combination of design of experiments (DoE) and multiplexed parallel reaction monitoring (PRM) on the ZenoTOF 7600 instrument. The PRM-DoE approach enables rapid and systematic optimization of LC-MS conditions and simultaneous evaluation of key EAD parameters across target glycopeptide glycoforms in a drastically reduced number of experimental runs, significantly saving experimental time and resources. This approach provides the glycoproteomics field with a comprehensive method for achieving improved glycan site localization confidence over conventional collision induced dissociation, as demonstrated by applying a data-dependent acquisition method. The proposed strategy advances the analytical toolkit for LC-MS/MS glycoprotein analysis and potentially other post-translational modifications.