New bioengineering insights into human neural precursor cell expansion in culture
Bibliographic record
Abstract
Understanding initial cell growth, interactions associated with the process of expansion of human neural precursor cells (hNPCs), and cellular events pre- and postdifferentiation are important for developing bioprocessing protocols to reproducibly generate multipotent cells that can be used in basic research or the treatment of neurodegenerative disorders. Herein, we report the in vitro responses of telencephalon hNPCs grown in a serum-free growth medium using time-lapse live imaging as well as cell-surface marker, aggregate size, and immunocytochemical analyses. Time-lapse analysis of hNPC initial expansion indicated that cell-surface attachment in stationary culture and the frequency of cell-cell interaction in suspension conditions are important for subsequent aggregate formation and hNPC growth. In the absence of cell-surface attachment in low-attachment stationary culture, large aggregates of cells were formed and expansion was adversely affected. The majority of the telencephalon hNPCs expressed CD29, CD90, and CD44 (cell surface markers involved in cell-ECM and cell-cell interactions to regulate biological functions such as proliferation), suggesting that cell-surface attachment and cell-cell interactions play a significant role in the subsequent formation of cell aggregates and the expansion of hNPCs. Before differentiation, about 90% of the cells stained positive for nestin and expressed two neural precursor cells surface markers (CD133 and CD24). Upon withdrawal of growth cytokines, hNPCs first underwent cell division and then differentiated preferentially towards a neuronal rather than a glial phenotype. This study provides key information regarding human NPC behavior under different culture conditions and favorable culture conditions that are important in establishing reproducible hNPC expansion protocols.
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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 | 0.001 |
| 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".