Structurally Distinct LewisX Glycans Distinguish Subpopulations of Neural Stem/Progenitor Cells
Bibliographic record
Abstract
There is increasing evidence that the stem and progenitor cell population that builds the central nervous system is very heterogeneous. Stem cell markers with the potential to divide this cell pool into subpopulations with distinct characteristics are sparse. We were looking for new cell type-specific antigens to further subdivide the progenitor pool. Here, we introduce the novel monoclonal antibody clone 5750. We show that it specifically labels cell surfaces of neural stem and progenitor cells. When 5750-expressing cells were isolated by fluorescence-activated cell sorting from embryonic mouse brains, the sorted population showed increased neurosphere forming capacity and multipotency. Neurospheres generated from 5750-positive cells could self-renew and remained multipotent even after prolonged passaging. Carbohydrate binding assays revealed that the 5750 antibody specifically binds to LewisX-related carbohydrates. Interestingly, we found that the LewisX epitope recognized by clone 5750 differs from those detected by other anti-LewisX antibody clones like 487(LeX), SSEA-1(LeX), and MMA(LeX). Our data further reveal that individual anti-LewisX clones can be successfully used to label and deplete different subpopulations of neural cells in vivo and in vitro. In conclusion, we present a new tool for the isolation and characterization of neural subpopulations and provide insights into the complexity of cell surface glycosylation.
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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 | 0.000 | 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) | 0.001 | 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 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".