Don't Look: Growing Clonal Versus Nonclonal Neural Stem Cell Colonies
Bibliographic record
Abstract
Recent reports have challenged the clonality of the neurosphere assay in assessing neural stem cell (NSC) numbers quantitatively. We tested the clonality of the neurosphere assay by culturing mixtures of differently labeled neural cells, watching single neural cells proliferate using video microscopy, and encapsulating single NSCs and their progeny. The neurosphere assay gave rise to clonal colonies when using primary cells plated at 10 cells/microl or less; however, when using passaged NSCs, the spheres were clonal only if plated at 1 cell/microl. Most important, moving the plates during the growth phase (to look at cultures microscopically) greatly increased the incidence of nonclonal colonies. To ensure clonal sphere formation and investigate nonautonomous effects on clonal sphere formation frequencies, single NSCs were encapsulated in agarose and proliferated as clonal free-floating spheres. We demonstrate that clonal neurospheres can be grown by avoiding movement-induced aggregation, by single-cell tracking, and by encapsulation of single cells. Disclosure of potential conflicts of interest is found at the end of this article.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".