Neural-Colony Forming Cell Assay: An Assay To Discriminate Bona Fide Neural Stem Cells from Neural Progenitor Cells
Bibliographic record
Abstract
The neurosphere assay (NSA) is one of the most frequently used methods to isolate, expand and also calculate the frequency of neural stem cells (NSCs). Furthermore, this serum-free culture system has also been employed to expand stem cells and determine their frequency from a variety of tumors and normal tissues. It has been shown recently that a one-to-one relationship does not exist between neurosphere formation and NSCs. This suggests that the NSA as currently applied, overestimates the frequency of NSCs in a mixed population of neural precursor cells isolated from both the embryonic and adult mammalian brain. This video practically demonstrates a novel collagen based semi- solid assay, the neural-colony forming cell assay (N-CFCA), which has the ability to discriminate stem from progenitor cells based on their long-term proliferative potential, and thus provides a method to enumerate NSC frequency. In the N-CFCA, colonies ≥2 mm in diameter are derived from cells that meet all the functional criteria of a NSC, while colonies < 2mm are derived from progenitors. The N-CFCA procedure can be used for cells prepared from different sources including primary and cultured adult or embryonic mouse CNS cells. Here we use cells prepared from passage one neurospheres generated from embryonic day 14 mice brain to perform N-CFCA. The cultures are replenished with proliferation medium every seven days for three weeks to allow the plated cells to exhibit their full proliferative potential and then the frequency of neural progenitor and bona fide neural stem cells is calculated respectively by counting the number of colonies that are < 2mm and the ones that are ≥2mm in reference to the number of cells that were initially plated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".