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SSSMuG: Same Sample Sequential Multi-Glycomics

delete2024-02-11
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PRE
AI
E
Edward S. X. Moh
S
Sagar Dalal
N
Nicholas J. DeBono
L
Liisa Kautto
K
Katherine Wongtrakul‐Kish
N
Nicolle H. Packer *
DOI:10.1021/acs.analchem.3c04928delete
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摘要

摘要

En 中文
The mammalian glycome is structurally complex and diverse, composed of many glycan classes such as N- and O-linked glycans, glycosaminoglycans (GAGs), glycosphingolipids (GSLs), and other distinct glycan features such as polysialic acids (PolySia), sulfation, and proteoglycan attachment stubs. Various methods are used to analyze these different components of the glycome, but they require prefractionated/partitioned samples to target each glycan class individually. To address this need for a knowledge of the relationship between the different glycan components of a biological system, we developed a sequential release workflow for analysis of multiple conjugated glycan classes (PolySia, GAGs, GSL glycans, N-glycans, and O-glycans) from the same tissue lysate, termed SSSMuG & horbar;Same Sample Sequential Multi-Glycomics. With this sequential glycan release approach, five glycan classes were characterized (or four glycan classes plus proteomics) using enzymatic or chemical release from a single sample immobilized on a polyvinylidene difluoride membrane. The various released glycan classes were then analyzed by HPLC and MS techniques using commonly available analytical setups. Compared to single glycan class release approaches, SSSMuG was able to identify more glycans and more proteins with higher-intensity analytical peaks and provide a better comparative normalization of the different glycan classes of the complex glycome. To this end, the SSSMuG technology workflow will be a foundation for a paradigm shift in the field, transforming glycoanalytics and facilitating the push toward multiglycomics and systems glycobiology.
Keyword:
PROTEIN EXTRACTION
POLYSIALIC ACID
TISSUE
SULFATE

期刊

Analytical Chemistry 封面图
Analytical Chemistry
IF:
6.7
论文数:
4.7W
被引数:
15.9W

机构

M
Macquarie University
学者数:
1.2W
论文数: 1.5W
被引数: 2.2W
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