Automatic Full Glycan Structural Determination through Logically Derived Sequence Tandem Mass Spectrometry.
Shang-Ting TsaiChia Yen LiewChen HsuShih-Pei HuangWei-Chien WengYu-Hsiang KuoChi-Kung NiPublished in: Chembiochem : a European journal of chemical biology (2019)
Glycans have diverse functions and play vital roles in many biological systems, such as influenza, vaccines, and cancer biomarkers. However, full structural identification of glycans remains challenging. The glycan structure was conventionally determined by chemical methods or NMR spectroscopy, which require a large amount of sample and are not readily applicable for glycans extracted from biological samples. Although it has high sensitivity and is widely used for structural determination of molecules, current mass spectrometry can only reveal parts of the glycan structure. Herein, the full structures of glycans, including diastereomers, the anomericity of each monosaccharide, and the linkage position of each glycosidic bond, which can be determined by using tandem mass spectrometry guided by a logically derived sequence (LODES), are shown. This new method provides de novo oligosaccharide structural identification with high sensitivity and has been applied to automatic in situ structural determination of oligosaccharides eluted by means of HPLC. It is shown that the structure of a given trisaccharide from a trisaccharide mixture and bovine milk were determined from nearly 3000 isomers by using 6-7 logically selected collision-induced dissociation spectra. The entire procedure for mass spectrometry measurement guided by LODES can be programmed in a computer for automatic full glycan structure identification.
Keyphrases
- tandem mass spectrometry
- solid phase extraction
- liquid chromatography
- high performance liquid chromatography
- mass spectrometry
- gas chromatography
- ultra high performance liquid chromatography
- cell surface
- simultaneous determination
- high resolution
- molecularly imprinted
- high resolution mass spectrometry
- deep learning
- machine learning
- ms ms
- genome wide
- oxidative stress
- dna methylation
- squamous cell carcinoma
- capillary electrophoresis
- diabetic rats
- neural network
- transition metal
- stress induced
- childhood cancer