Cellular O‐Glycome Reporter/Amplification (CORA) to Explore O‐Glycans of Living Cells
Bibliographic record
Abstract
O‐glycosylation is present on over 80% of proteins that traverse the secretory apparatus and plays key roles in many biological processes. However, the repertoire of O‐glycans synthesized by cells and thus their function are difficult to determine. Current strategies to evaluate O‐glycans from cells utilize chemical release, such as alkaline β‐elimination, prior to analysis by mass spectrometry or other technologies. However, β‐elimination is inefficient, potentially biased, and results in O‐glycan degradation. To address this challenge, we developed a technology to amplify and profile mucin‐type O‐glycans synthesized by living cells, termed Cellular O‐Glycome Reporter/Amplification (CORA). We developed a chemical O‐glycan precursor that when incubated with live cells crosses the plasma membrane, is taken up by the Golgi Apparatus, and is modified by glycosyltransferases in situ , before being secreted from cells as a variety of modified O‐glycan derivatives, allowing easy purification for analysis by HPLC and mass spectrometry (MS). CORA detected O‐glycans observed by β‐elimination as well as many additional complex structures with ~100–1000‐fold increase in sensitivity over conventional O‐glycan analyses. Furthermore, CORA coupled with computational modeling allowed us to predict the diversity of the human O‐glycome. To our knowledge, CORA is the first technology for glycome amplification and thus could offer new opportunities for understanding the role of glycans in health and disease. Support or Funding Information This work was supported by National Institutes of Health Grant U01CA168930 to TJ and RDC, P41GM103694 to RDC, Georgia Cancer Coalition (now Georgia Research Alliance, GRA) Award to TJ, and by Biotechnology and Biological Sciences Research Council grant BB/K016164/1 (AD and SMH for Core Support for Collaborative Research). AD is supported by a Wellcome Trust Senior Investigator Award. We also acknowledge support from the Emory Integrated Proteomics Core.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".