Comparative Analysis of the Frequency and Distribution of Stem and Progenitor Cells in the Adult Mouse Brain
Bibliographic record
Abstract
The neurosphere assay can detect and expand neural stem cells (NSCs) and progenitor cells, but it cannot discriminate between these two populations. Given two assays have purported to overcome this shortfall, we performed a comparative analysis of the distribution and frequency of NSCs and progenitor cells detected in 400 mum coronal segments along the ventricular neuraxis of the adult mouse brain using the neurosphere assay, the neural colony forming cell assay (N-CFCA), and label-retaining cell (LRC) approach. We observed a large variation in the number of progenitor/stem cells detected in serial sections along the neuraxis, with the number of neurosphere-forming cells detected in individual 400 mum sections varying from a minimum of eight to a maximum of 891 depending upon the rostral-caudal coordinate assayed. Moreover, the greatest variability occurred in the rostral portion of the lateral ventricles, thereby explaining the large variation in neurosphere frequency previously reported. Whereas the overall number of neurospheres (3730 +/- 276) or colonies (4275 +/- 124) we detected along the neuraxis did not differ significantly, LRC numbers were significantly reduced (1186 +/- 188, 7 month chase) in comparison to both total colonies and neurospheres. Moreover, approximately two orders of magnitude fewer NSC-derived colonies (50 +/- 10) were detected using the N-CFCA as compared to LRCs. Given only 5% of the LRCs are cycling (BrdU+/Ki-67+) or competent to divide (BrdU+/Mcm-2+), and proliferate upon transfer to culture, it is unclear whether this technique selectively detects endogenous NSCs. Overall, caution should be taken with the interpretation and employment of all these techniques.
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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 | 0.002 | 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".