Methods to Study Glycoproteins at the Blood-Brain Barrier Using Mass Spectrometry
Bibliographic record
Abstract
Glycosylation is the most common posttranslational modification of proteins in mammalian cells and is limited mainly to membrane and secreted proteins. Glycoproteins play several key roles in the physiology and pathophysiology of the blood-brain barrier (BBB) and are attractive as diagnostic markers and therapeutic targets for many neurological diseases. However, large-scale glycoproteomic studies of the BBB have been lacking, largely due to the complexity of analyzing glycoproteins and a lack of available tools for this analysis. Recent development of the hydrazide capture method and significant advances in mass spectrometry (MS)-based proteomics over the last few years have enabled selective enrichment of glycoproteins from complex biological samples and their quantitative comparisons in multiple conditions. In this chapter, we describe methods for: (1) isolating membrane and secreted proteins from BEC and other cells of the neurovascular unit, (2) enriching glycoproteins using hydrazide capture, and (3) performing label-free quantitative proteomics to identify differential glycoprotein expression in various biological conditions. Hydrazide capture, when coupled with label-free quantitative proteomics, is a reproducible and sensitive method that allows for quantitative profiling of a large number of glycoproteins from biological samples for the purposes of differential expression measurements and biomarker discovery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".