Targeted glycomics by selected reaction monitoring for highly sensitive glycan compositional analysis
Bibliographic record
Abstract
The development of glycomics increasingly requires the detection and quantification of large numbers of glycans, which is only partially achieved by current glycomics approaches. Taking advantage of selected reaction monitoring to enhance both sensitivity and selectivity, we report here a strategy termed targeted glycomics that enables highly sensitive and consistent identification and quantification of diverse glycans across multiple samples at the same time. In this proof-of-principle study, we validated the method by analyzing global N-glycans expressed in different systems: single proteins, cancer cells, and serum samples. A dynamic range of three orders of magnitude was obtained for the detection of all five glycans released from ribonuclease B. The limit of detection of 80 attomole for Man(9)GlcNAc(2) demonstrated the excellent sensitivity of the method. The capability of the strategy to identify diverse glycans was demonstrated by identification and detection of 162 different glycans and isomers from pancreatic cancer cells. The sensitivity of the method was illustrated further by the ability to detect eight glycans from 250 cancer cells and five glycans released from 100 cancer cells. In serum obtained from rabbits fed control diet or diet enriched with 2% cholesterol, differences to 42 glycans were accurately measured and this indicates that this strategy might find use in studies of biomarker discovery and validation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".