Expansion of Undifferentiated Murine Embryonic Stem Cells as Aggregates in Suspension Culture Bioreactors
Bibliographic record
Abstract
Pluripotent embryonic stem cells (ESCs) have recently been considered as a primary material for regenerating tissues lost to injuries and degenerative diseases. For clinical implementation of this technology, a quality controlled, reproducible culture system is necessary for the expansion and differentiation of the cells. Used in many bioprocess applications, suspension bioreactors have gained considerable attention for the regulated large-scale expansion of cells. The current study presents a bioreactor process for the large-scale expansion of undifferentiated murine ESCs as aggregates. In this system, the level of ESC aggregation and differentiation was effectively controlled by adjusting shear forces and inoculation density, achieving a 31-fold expansion in 5 days. Pluripotency markers Oct-4, Nanog, SSEA-1, ALP, and rex-1 were assessed using flow cytometry analysis and gene expression profiles and showed that the undifferentiated nature of the cells within the ESC aggregates was maintained. Colony-forming efficiencies and embryoid body formation tests of the expanded cultures demonstrated that characteristic functional attributes of undifferentiated cells were not lost. Overcoming a major impediment in the area of ESC expansion, this study describes a successful process for the controlled and reproducible largescale expansion of ESCs using suspension culture bioreactors.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".