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Glycomics-Guided Glycoproteomics Facilitates Comprehensive Profiling of the Glycoproteome in Complex Tumor Microenvironments

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Autonomous Dissociation-type Selection for Glycoproteomics Using a Real-Time Library Search.

Emmajay Sutherland¹, Tim S Veth¹, William D Barshop²

¹Department of Chemistry, University of Washington, Seattle, Washington 98195, United States.

Journal of Proteome Research

|November 12, 2024

Summary

Intelligent data acquisition using autonomous dissociation-type selection (ADS) improves glycoproteomics. This method enhances the identification and site-specific characterization of both N- and O-glycopeptides in a single LC-MS/MS analysis.

Keywords:

Glycoproteomics N-glycopeptides O-glycopeptides beam-type collisional dissociation electron transfer dissociation intelligent data acquisition product-dependent triggering real-time library search tandem mass spectrometry

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Last Updated: Jun 7, 2025

Glycomics-Guided Glycoproteomics Facilitates Comprehensive Profiling of the Glycoproteome in Complex Tumor Microenvironments

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Published on: February 7, 2025

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Published on: March 8, 2016

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Published on: June 28, 2024

Area of Science:

Biochemistry
Analytical Chemistry
Proteomics

Background:

Tandem mass spectrometry (MS/MS) is vital for glycopeptide identification and site localization.
Different glycopeptide types (N- and O-linked) require distinct dissociation methods for optimal analysis.
Current methods using multiple dissociation techniques in one analysis often reduce sensitivity.

Purpose of the Study:

To investigate the use of intelligent data acquisition for glycoproteomics.
To develop a method for on-the-fly selection of appropriate dissociation techniques.
To enhance the throughput and efficiency of glycopeptide analysis.

Main Methods:

Real-time library searching (RTLS) to match oxonium ion patterns.
Autonomous dissociation-type selection (ADS) to match dissociation method with glycopeptide class.
Integration of RTLS and ADS within a single LC-MS/MS acquisition.

Main Results:

ADS achieved comparable N-glycopeptide identifications to traditional beam-type collisional activation.
ADS yielded similar site-localized O-glycopeptide identifications compared to electron transfer dissociation.
The method enabled simultaneous site-specific characterization of both N- and O-glycopeptides.

Conclusions:

Autonomous dissociation-type selection (ADS) significantly advances glycoproteomics throughput.
This intelligent data acquisition strategy optimizes dissociation methods for diverse glycopeptide classes.
ADS facilitates comprehensive glycopeptide analysis within a single LC-MS/MS run.