Dataset Information

SSSMuG: Same Sample Sequential Multi-Glycomics

ABSTRACT: 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 analyse these different components of the glycome, but they require pre-fractionated/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 have developed a sequential release workflow for analysis of multiple conjugated glycan classes (PolySia, GAGs, GSL glycans, N-glycans O-glycans) from the same tissue lysate, termed SSSMuG: Same Sample Sequential Multi-Glycomics. With this sequential glycan release approach, five glycan classes were characterized (or four glycan classes plus proteomics) from a single sample using enzymatic or chemical release from the same spot immobilized on a polyvinylidene difluoride membrane. The various released glycan classes were then analysed by HPLC and/or MS techniques. Compared to single glycan class release approaches, SSSMuG is able to identify more glycans and more proteins with higher intensity analytical peaks and provides a better comparative normalisation of the different glycan classes of the complex glycome.

INSTRUMENT(S): Q Exactive HF

ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Brain

SUBMITTER: Edward Moh

LAB HEAD: Nicki Packer

PROVIDER: PXD046012 | Pride | 2024-02-13

REPOSITORIES: Pride

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Publications

SSSMuG: Same Sample Sequential Multi-Glycomics.

Moh Edward S X ESX Dalal Sagar S DeBono Nicholas J NJ Kautto Liisa L Wongtrakul-Kish Katherine K Packer Nicolle H NH

Analytical chemistry 20240211

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 know ...[more]

PMID: 38343116

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Project description:BACKGROUND: Novel biomarkers are urgently needed to distinguish between benign and malignant thyroid nodules and detect thyroid cancer in the early stage. The associations between serum IgG N-glycosylation and thyroid cancer risk have been revealed. We aimed to explore the potential of IgG N-glycan traits as biomarkers in the differential diagnosis of thyroid cancer.METHODS: Plasma IgG N-glycome analysis was applied to a discovery cohort followed by independent validation. IgG N-glycan profiles were obtained using a robust quantitative strategy based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. IgG N-glycans were relatively quantified, and classification performance was evaluated based on directly detected and derived glycan traits.RESULTS: Four directly detected glycans were significantly changed in thyroid cancer patients compared to that in non-cancer controls. Derived glycan traits and a classification glycol-panel were generated based on the directly detected glycan traits. In the discovery cohort, derived trait BN (bisecting type neutral N-glycans) and the glyco-panel showed potential in distinguishing between thyroid cancer and non-cancer controls with AUCs of 0.920 and 0.917, respectively. The diagnostic potential was further validated. Derived trait BN and the glycol-panel displayed “accurate” performance (AUC>0.8) in discriminating thyroid cancer from benign thyroid nodules and healthy controls in the validation cohort. Meanwhile, derived trait BN and the glycol-panel also showed diagnostic potential in detecting early-stage thyroid cancer.CONCLUSIONS: IgG N-glycome analysis revealed N-glycomic differences that allow classification of thyroid cancer from non-cancer controls. Our results suggested that derived trait BN and the classification glyco-panel rather than single N-glycans may serve as candidate biomarkers for further validation.

Dataset Information

SSSMuG: Same Sample Sequential Multi-Glycomics

Publications

SSSMuG: Same Sample Sequential Multi-Glycomics.

Similar Datasets

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