Detergent Screening With Hybrid Detergents Increases Observable Number of Protein Identities in Bottom-Up Proteomics

Jan-Simon Behnke; Andreas Hentschel; Maximilian Wolf; Virginia Wycisk; Albert Sickmann; Robert S Heyer; Leonhard H Urner

doi:10.1002/pmic.70003

Detergent Screening With Hybrid Detergents Increases Observable Number of Protein Identities in Bottom-Up Proteomics

Proteomics. 2025 Jun 20:e70003. doi: 10.1002/pmic.70003. Online ahead of print.

Authors

Jan-Simon Behnke¹, Andreas Hentschel², Maximilian Wolf³, Virginia Wycisk¹, Albert Sickmann², Robert S Heyer^{2

3}, Leonhard H Urner¹

Affiliations

¹ Department of Chemistry and Chemical Biology, TU Dortmund University, Dortmund, Germany.
² Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Dortmund, Germany.
³ Faculty of Technology, Bielefeld University, Bielefeld, Germany.

PMID: 40539229
DOI: 10.1002/pmic.70003

Abstract

Detergents are key reagents in bottom-up proteomics that create an apparent, yet underappreciated bias on observable proteomes. Maximizing the chemical diversity of detergents in parallelized screens is supposed to maximize observable proteomes if proteomics data sets of different detergents are combined. The aim of our work is to investigate the potential of fusing ionic and nonionic detergent headgroups into hybrid detergents for increasing the observable number of unique protein identities. Our data indicate that the solubilizing properties of hybrid detergents do not reflect an average of canonical detergents. The number of unique protein identities obtainable from an Escherichia coli screen increases from 1604 to 2169 when proteomics data sets from sodium dodecyl sulfate, dodecyltrimethylammonium bromide, dendritic triglycerol detergent, and related hybrid detergents are combined. Our data highlight the utility of cationic detergents and related hybrid detergents for enhancing observable proteomes. Detergent screening-based proteome reconstructions with canonical detergents and hybrid detergents present an interesting research direction towards improved proteome profiling applications.