Cell cycle kinetics of expanding populations of neural stem and progenitor cells in vitro
Bibliographic record
Abstract
Neural stem cells (NSCs) are undifferentiated, primitive cells with important potential applications including the replacement of neural tissue lost due to neurodegenerative diseases, including Parkinson's disease, as well as brain and spinal cord injuries, including stroke. We have developed methods to rapidly expand populations of mammalian stem and progenitor cells in neurosphere cultures. In the present study, flow cytometry was used in order to understand cell cycle activation and proliferation of neural stem and progenitor cells in suspension bioreactors. First, a protocol was developed to analyze the cell cycle kinetics of NSCs. As expected, neurosphere cells were found to cycle slowly, with a very small proportion of the cell population undergoing mitosis at any time. Large fractions (65-70%) of the cells were detected in G1, even in rapidly proliferating cultures, and significant fractions (20%) of the cells were in G0. Second, it was observed that different culturing methods influence both the proportion of neurosphere cells in each phase of the cell cycle and the fraction of actively proliferating cells. The results show that suspension culture does not significantly alter the cell cycle progression of neurosphere cells, while long-term culture (>60 days) results in significant changes in cell cycle kinetics. This suggests that when developing a process to produce neural stem cells for clinical applications, it is imperative to track the cell cycle kinetics, and that a short-term suspension bioreactor process can be used to successfully expand neurosphere cells.
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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".