Prospective identification of functionally distinct stem cells and neurosphere-initiating cells in adult mouse forebrain
Bibliographic record
Abstract
Neurosphere formation is commonly used as a surrogate for neural stem cell (NSC) function but the relationship between neurosphere-initiating cells (NICs) and NSCs remains unclear. We prospectively identified, and isolated by flow cytometry, adult mouse lateral ventricle subventricular zone (SVZ) NICs as Glast(mid)EGFR(high)PlexinB2(high)CD24(-/low)O4/PSA-NCAM(-/low)Ter119/CD45(-) (GEPCOT) cells. They were highly mitotic and short-lived in vivo based on fate-mapping with Ascl1(CreERT2) and Dlx1(CreERT2). In contrast, pre-GEPCOT cells were quiescent, expressed higher Glast, and lower EGFR and PlexinB2. Pre-GEPCOT cells could not form neurospheres but expressed the stem cell markers Slc1a3-CreER(T), GFAP-CreER(T2), Sox2(CreERT2), and Gli1(CreERT2) and were long-lived in vivo. While GEPCOT NICs were ablated by temozolomide, pre-GEPCOT cells survived and repopulated the SVZ. Conditional deletion of the Bmi-1 polycomb protein depleted pre-GEPCOT and GEPCOT cells, though pre-GEPCOT cells were more dependent upon Bmi-1 for Cdkn2a (p16(Ink4a)) repression. Our data distinguish quiescent NSCs from NICs and make it possible to study their properties in vivo.DOI: http://dx.doi.org/10.7554/eLife.02669.001.
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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 | 0.001 | 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 | 0.001 |
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".