High‐throughput lectin magnetic bead array‐coupled tandem mass spectrometry for glycoprotein biomarker discovery
Bibliographic record
Abstract
Alterations in protein glycosylation occur during development and progression of many diseases, hence glycomics and glycoproteomics have emerged as important tools in glycobiomarker discovery. High-throughput glycan profiling can now be achieved with the recent developments in MS-based techniques. To enable identification and rapid monitoring of glycosylation changes in serum proteins, we developed a semi-automated high-throughput glycoprotein biomarker discovery platform termed lectin magnetic bead array-coupled tandem mass spectrometry (LeMBA-MS) which includes (i) effective single-step serum glycoprotein isolation using a panel of 20 individual lectin-coated magnetic beads in microplate format, (ii) on-bead trypsin digestion, and (iii) nanoLC-MS/MS with lectin exclusion list. With use of appropriate sequence databases, LeMBA-MS can detect glycosylation changes regardless of the species. By spiking known amounts of titrated ovalbumin to a serum sample, we report nanomolar sensitivity, and linearity of response of LeMBA-MS using concanavalin A-coupled beads. Neuraminidase treatment led to reduction of binding to sialic acid-binding lectins. Interestingly, we found that desialylation caused increased binding of haptoglobin and hemopexin to mannose-specific lectins, pointing to the importance of identifying a signature of lectin-binding. High-throughput LeMBA-MS to generate glycosylation signatures will facilitate glycobiomarker discovery. LeMBA can be coupled to down-stream detection platforms for validation, making it a truly versatile platform.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".