Comprehensive glyco-analytics involves extensive analysis including distinguishing structural differences such as monosaccharide linkages, branching and anomericity of oligosaccharides. Experimentally these challenges have been addressed by significant improvements to analytical technologies such as ion-mobility mass spectrometry. Despite this, data analysis remains one of the largest bottlenecks in this workflow. Here we present an automated glyco-informatics pipeline that improves this situation by accurately identifying and quantifying the glycans released from glycoproteins and glycospingolipids.
Our pipeline called Multi-Attribute Glycomic Map (MAGMap), leverages the multiple attributes such as normalized retention time (expressed in Glucose Units), m/z and Collision Cross Sectional values all of which can be derived after fluorescently labelled glycans released from GSL glycan head groups or are analysed from a coupled HILIC-UPLC-IMS-MS setup (Waters H-Class UPLC-SYNAPT® G2-S MS). MAGMap utilizes a statistical transformation of these attributes measured from any labelled glycans and performs distance metrics to accurately characterize the glycans.
Our method and algorithm is automated and improves upon conventional approaches by 20% increase in the precision of the glycomic characterization. This approach has potential to democratize complicated glyco-analytics as well as provide the platform for predictive glycan structural characterisation.