GlyQ-IQ: glycomics quintavariate-informed quantification with high-performance computing and GlycoGrid 4D...

Area of Science:

• Glycomics
• Mass Spectrometry
• Biochemistry

Background:

• Glycomics analysis of N-glycans using LC-MS data presents challenges due to convoluted extracted ion chromatograms.
• Accurate deconvolution of LC peaks is crucial for distinguishing between glycan structural isomers.
• Existing software tools struggle with the complexity of glycomics data deconvolution.

Purpose of the Study:

• To develop and evaluate a novel biologically guided glycomics analysis tool, GlyQ-IQ, for identifying N-glycans in LC-MS data.
• To improve the sensitivity and specificity of N-glycan analysis in complex biological samples.
• To demonstrate the capability of GlyQ-IQ in deconvoluting complex glycan isomers and profiling N-glycan compositions.

Main Methods:

• Development of GlyQ-IQ software utilizing a biologically targeted analysis approach.
• Leveraging a priori glycan information, including elemental composition and isotope profiles, for enhanced sensitivity.
• Employing glycan family relationships and in-source fragmentation for improved specificity in LC-MS feature detection.

Main Results:

• GlyQ-IQ successfully identified and removed confounding chromatographic peaks from high mannose glycan isomers in human blood serum.
• A broad N-glycan profile of human serum was generated, detecting 156 glycan compositions and 640 glycan isomers from a single sample.
• Over 99% of GlyQ-IQ glycan-feature assignments were validated manually and supported by high-resolution mass spectra.

Conclusions:

• GlyQ-IQ is an effective tool for accurate N-glycan identification and quantification in complex LC-MS data.
• The biologically guided approach significantly enhances the deconvolution of glycan isomers and specificity of analysis.
• GlyQ-IQ provides a robust method for comprehensive glycomics profiling of biological samples like human serum.