Chemical compound and method that enables detection of O-glycans with higher sensitivity than conventional approaches.
- Higher accuracy and sensitivity than current β-elimination method.
- Smaller sample quantities required compared to traditional mass spectrometry.
- Method promotes the discovery of novel glycans and study of cellular glycome.
- Method can be useful to identify novel disease biomarkers.
Protein glycosylation is a common post-translational modification in all animals. The field of glycomics currently lacks simple and sensitive technology that can be used to analyze all glycans synthesized by cells. This type of analysis has been challenging because of the diversity and complexity of glycans, the low abundance of certain glycan species, the poor sensitivity of existing glycomics approaches and the lack of efficient and unbiased strategies for releasing glycans from complex samples. Current technologies for evaluating glycans require relatively large amounts of biological samples for detailed structural analyses, which limits their widespread application.
Emory University researchers have developed a novel method for profiling and amplifying mucin-type O-glycans from living cells with enhanced sensitivity and accuracy. This technology is a chemical O-glycan precursor, which is taken up by living cells, modified inside the cell by glycosyltransferases, and then secreted into the media. This novel method for profiling and amplifying mucin-type O-glycans from living cells, is termed Cellular O-Glycome Reporter/Amplification (CORA). CORA provides ~100–1,000-fold enhanced sensitivity and much cleaner mass spectroscopy profiles of O-glycans compared to conventional analyses, and it revealed O-glycans not seen by classic approaches in a variety of cancer and primary cells. This methodology can be useful for glycan arrays to identify novel disease biomarkers including those related to cancer.
Inventors analyzed 18 cell lines and identified 57 distinct O-glycan compositions.
Publication: Kudelka, MR et al. Nature methods. 2016;13(1):81-86.