Design of experiments for the optimization of sample preparation for bottom-up targeted protein LC-MS/MS workflows.

Aims: Design of experiments (DOE) is a versatile and efficient approach to tackle complex scientific problems. We aimed to assess its feasibility in the optimization of the multistep, involved bottom-up sample preparation for the UPLC-MS/MS analysis of proteins.

Materials and methods: The model analyte was a human IgG1 monoclonal antibody which was spiked into rat plasma and processed further by reduction, alkylation, and digestion for the subsequent UPLC-MS/MS analysis. The Modde Go software was used for the generation of experimental designs and for processing, analyzing and the interpretation of the data.

Results: DOE screening revealed that urea made the biggest improvement on the surrogate peptide responses, while guanidine significantly suppressed them. DOE optimization resulted in a 2-, 10-, 10- and 50-fold response increase for the respective DTLM, FNWY, TPEV and VVSV surrogate peptide even after a short, <3-h sample preparation, when compared to a legacy method that required 2-day preparation.

Conclusions: The DOE approach was applied successfully for the comprehensive optimization of eight denaturation, reduction and digestion parameters. DOE was found to be an efficient tool for protein sample preparation optimization, and the predictive power of the DOE models was successfully demonstrated.