Poster Presentation 31st Annual Lorne Proteomics Symposium 2026

Improved Detection of Cross-Linked Peptides with Orbitrap Astral (#108)

Han-Chung Lee 1 , Chaozhen Chen 2 3 , Joel R Steele 1 , Scott Blundell 1 , Wilson Wong 2 3 , Ralf Schittenhelm 1
  1. Monash Proteomics & Metabolomics Platform, Monash Biomedicine Discovery Institute & Department of Biochemistry and Molecular Biology, Monash University, CLAYTON, VIC, Australia
  2. Molecular and Translational Science, Monash University, Clayton, VIC, Australia
  3. Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, CLAYTON, VIC, Australia

Cross-linking mass spectrometry (XL-MS) is a powerful technique for characterising protein structure, topology, and protein–protein interactions. By covalently linking spatially adjacent residues, XL-MS provides geometric constraints that support structural modelling and identify interaction interfaces within complexes. Despite its utility, the approach is often constrained by cross-linking efficiency and the limited detectability of cross-linked peptides, which typically appear at much lower ion intensities and representation compared to linear peptides within the same digest.

In this study, we benchmarked the Thermo Scientific™ Orbitrap Astral™ against the Thermo Scientific™ Orbitrap Exploris™ 480 using a BS3-based XL-MS workflow, with matched sample preparation and LC conditions. Acquisition on the Exploris served as the reference platform for standard performance. When analysed on the Astral, we observed a remarkable increase in identification depth: over 2.3-fold more cross-linked spectra, threefold greater total spectra, and importantly, four cross-linked peptides not detected on Exploris. These additional identifications likely reflect low-abundance or conformationally constrained sites that were previously undetectable. Notably, these gains were achieved using only one-quarter of the sample input. Furthermore, the identified cross-links were consistent with BS3 spacer constraints and matched predicted structural models, supporting their validity.

Together, these results demonstrate that Orbitrap Astral acquisition substantially improves the sensitivity and depth of XL-MS workflows, enabling more comprehensive structural insights even from limited material.