Ion mobility-tandem mass spectrometry of mucin-type O-glycans.
Leïla BechtellaChunsheng JinKerstin FentkerGüney R ErtürkMarc SafferthalŁukasz PolewskiMichael GötzeSimon Y GraeberGaël M VosWeston B StruweMarcus Alexander MallPhilipp MertinsNiclas G KarlssonKevin PagelPublished in: Nature communications (2024)
The dense O-glycosylation of mucins plays an important role in the defensive properties of the mucus hydrogel. Aberrant glycosylation is often correlated with inflammation and pathology such as COPD, cancer, and Crohn's disease. The inherent complexity of glycans and the diversity in the O-core structure constitute fundamental challenges for the analysis of mucin-type O-glycans. Due to coexistence of multiple isomers, multidimensional workflows such as LC-MS are required. To separate the highly polar carbohydrates, porous graphitized carbon is often used as a stationary phase. However, LC-MS workflows are time-consuming and lack reproducibility. Here we present a rapid alternative for separating and identifying O-glycans released from mucins based on trapped ion mobility mass spectrometry. Compared to established LC-MS, the acquisition time is reduced from an hour to two minutes. To test the validity, the developed workflow was applied to sputum samples from cystic fibrosis patients to map O-glycosylation features associated with disease.
Keyphrases
- liquid chromatography
- tandem mass spectrometry
- cystic fibrosis
- mass spectrometry
- high performance liquid chromatography
- gas chromatography
- cell surface
- ultra high performance liquid chromatography
- end stage renal disease
- high resolution mass spectrometry
- simultaneous determination
- lung function
- newly diagnosed
- chronic obstructive pulmonary disease
- high resolution
- oxidative stress
- pseudomonas aeruginosa
- drug delivery
- papillary thyroid
- blood pressure
- mycobacterium tuberculosis
- peritoneal dialysis
- squamous cell carcinoma
- ionic liquid
- pulmonary tuberculosis
- quantum dots
- metal organic framework
- hyaluronic acid
- sensitive detection