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Updated: Oct 24, 2025

A Quantitative Glycomics and Proteomics Combined Purification Strategy
11:38

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gQuant, an Automated Tool for Quantitative Glycomic Data Analysis.

Jiangming Huang1,2, Biyun Jiang1, Mingqi Liu1

  • 1The Fifth People's Hospital, Fudan University, and the Shanghai Key Laboratory of Medical Epigenetics, The International Co-laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Institutes of Biomedical Sciences, Fudan University, Shanghai, China.

Frontiers in Chemistry
|August 16, 2021
PubMed
Summary
This summary is machine-generated.

We developed gQuant, an automated tool for analyzing MALDI-MS glycan isotope labeling data. This open-source software improves quantitation accuracy and speed for glycomics research, aiding clinical sample analysis.

Keywords:
MALDI-MS analysisautomated processingglycomic quantitationquantitative toolstable isotope labeling

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Area of Science:

  • Glycomics
  • Mass Spectrometry
  • Bioinformatics

Background:

  • Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) with glycan isotope labeling is crucial for quantitative glycomics.
  • Current bioinformatic tools for MALDI-MS data analysis are inadequate, leading to inaccurate and time-consuming interpretations.

Purpose of the Study:

  • To present gQuant, an automated, user-friendly software tool designed to enhance the efficiency, accuracy, and convenience of processing MALDI-MS-based glycan isotope labeling data.

Main Methods:

  • gQuant utilizes dedicated algorithms for rapid and precise quantitation of glycan data from MALDI-MS.
  • The software was tested on reference datasets and model glycoproteins, demonstrating significant improvements over manual analysis.

Main Results:

  • gQuant achieved fast processing speeds, analyzing complex glycan data in minutes and yielding more comprehensive results than manual methods.
  • Quantitation ratios reported by gQuant closely matched experimental ratios across a wide range (1:10 to 10:1).
  • The tool is open-source, Python-based, cross-platform compatible, and offers a user-friendly interface.

Conclusions:

  • gQuant significantly improves the processing of MALDI-MS glycan isotope labeling data, offering enhanced speed and accuracy.
  • Its adaptability for various experimental designs facilitates efficient glycomic quantitation, particularly for clinical sample analysis.