State-of-the-art glycopeptide identification using an Orbitral(TM)-Astral(TM) mass spectrometer

Introduction

Glycoproteomics leverages powerful new tandem mass spectrometry (MS/MS) tools for exploring glycosylation at a granular level. While improvements in instrumentation, computational tools, and sample preparation methodologies have made glycoproteomics attainable by many labs, consistently detecting glycopeptides in clinical samples such as human plasma remains a challenging endeavor due to its complexity. One way to improve the ability to detect glycopeptides in human plasma is to use a redefined electron-transfer/higher-energy collision dissociation (ET/HCD) to fragment peptides and glycan in the same spectrum. Recent reports have suggested that using two different collision energies (CE) improves glycopeptide detection in complex samples such as human plasma. In this work, we sought to demonstrate that an Orbitrap(TM) Astral(TM) mass spectrometer is a suitable platform for glycoproteomics analysis

Methods

We configured an Orbitrap Astral mass spectrometer to collect data-independent acquisition (DIA) MS/MS from 500 ng human plasma using two different normalized collision energies known as "stepped collision energies" per precursor,. The raw files from this study were processed using MSFragger Glyco.

Preliminary Data

By analyzing enriched plasma samples on an Orbitrap Astral mass spectrometer coupled with the Vanquish(TM) Neo UHPLC system, we can identify 1774 unique glycopeptides from 5988 glycopeptide-spectrum matches with two CEs and 1073 unique glycopeptides from 4016 glycopeptide-spectrum matches with single CE. This study demonstrates that using stepped collision energy in an HCD device at a relatively slow scan rate of 75 Hz, but coupled with the fast and accurate Orbitrap Astral allows highly sensitive, state-of-the-art identification of glycopeptides without a fully optimized routine. These results pave the way for even faster stepped CE scans with more optimized routines to provide ever-improved sensitivity of glycopeptide identification in the field of glycoproteomics.