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Glycosaminoglycans in biological samples: towards identification of novel biomarkers
Natl Inst Metrol, Div Chem & Analyt Sci, Beijing, Peoples R China;Beijing Univ Chem Technol, Coll Life Sci & Technol, Beijing, Peoples R China.
Beijing Univ Chem Technol, Coll Life Sci & Technol, Beijing, Peoples R China.
Beijing Univ Chem Technol, Coll Life Sci & Technol, Beijing, Peoples R China.
Beijing Univ Chem Technol, Coll Life Sci & Technol, Beijing, Peoples R China.
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2020 (English)In: TrAC. Trends in analytical chemistry, ISSN 0165-9936, E-ISSN 1879-3142, Vol. 122, article id 115732Article, review/survey (Refereed) Published
Abstract [en]

Glycosaminoglycans (GAGs) are unbranched polyanionic polysaccharides involved in a wide spectrum of biological activities. Accumulation and/or structural alteration of GAGs in biological samples have been found correlated with diseases, making these molecules potential biomarkers for the diagnosis of these diseases and monitoring treatment effects. Detection and structural characterization of GAGs in biological samples have been challenging mainly due to their low abundance, structural complexity and heterogeneity. It is highly demanding to develop robust and reliable methodologies for structural characterization and quantification of GAGs, not only for research purposes, but most importantly for pharmaceutical and potential clinical applications, including pharmacokinetic studies for GAGs based drugs and identification of novel biomarkers. In this review, we have collected currently available techniques for detection and analysis of GAGs in biological samples, advantages and drawbacks of these techniques are discussed. Specially, perspectives of the developing methods for GAGs are reviewed.

Place, publisher, year, edition, pages
2020. Vol. 122, article id 115732
Keywords [en]
Glycosaminoglycans, Oligosaccharides, Mass spectrometry, Biological samples
National Category
Cell and Molecular Biology
Identifiers
URN: urn:nbn:se:uu:diva-407177DOI: 10.1016/j.trac.2019.115732ISI: 000508387300024OAI: oai:DiVA.org:uu-407177DiVA, id: diva2:1416228
Funder
Swedish Research Council, 2018-06016Available from: 2020-03-23 Created: 2020-03-23 Last updated: 2020-03-23Bibliographically approved

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Li, Jin-Ping

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