Passaging Protocols for Mammalian Neural Stem Cells in Suspension Bioreactors
Bibliographic record
Abstract
Mammalian neural stem cells (NSC) offer great promise as therapeutic agents for the treatment of central nervous system disorders. As a consequence of the large numbers of cells that will be needed for drug testing and transplantation studies, it is necessary to develop protocols for the large-scale expansion of mammalian NSC. Neural stem cells and early progenitor cells can be expanded in vitro as aggregates in controlled bioreactors using carefully designed media. The first objective of this study was to determine if it is possible to maintain a population of murine neural stem and progenitor cells as aggregates in suspension culture bioreactors over extended periods of time. We discovered that serial passaging of a mixture of aggregates sizes resulted in high viabilities, high viable cell densities, and good control of aggregate diameter. When the NSC aggregates were serially subcultured three times without mechanical dissociation, a total multiplication ratio of 2.9 x 10(3) was achieved over a period of 12 days, whereas the aggregate size was controlled (mean diameter less than 150 microm) below levels at which necrosis would occur. Moreover, cell densities of 1.0 x 10(6) cells/mL were repeatedly achieved in batch culture with viabilities exceeding 80%. The second objective was to examine the proliferative potential of single cells shed from the surface of these aggregates. We found that the single cells, when subcultured, retained the capacity to generate new aggregates, gave rise to cultures with high viable cell densities and were able to differentiate into all of the primary cell phenotypes in the central nervous system.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".