Reduced Differentiation Efficiency of Murine Embryonic Stem Cells in Stirred Suspension Bioreactors
Bibliographic record
Abstract
The use of embryonic stem cells (ESCs) for regenerative medicine has generated increased attention due to the favorable attributes of these cells; namely, they are pluripotent and possess long-term self-renewal capacity. The initial aims of the present study were: (i) to use stirred suspension bioreactors to expand and differentiate ESCs into osteogenic and chondrogenic cell types and (ii) to explore if these ESC-derived cells influenced skeletal healing in an in vivo fracture model. We show that differentiation protocols used in static culture are insufficient when applied directly to suspension culture bioreactors. Moreover, when bioreactor-differentiated cells are transplanted into a burr-hole defect in bone, severe disruption of the bone architecture was noted at the fracture site, as determined by microcomputed tomography (microCT) imaging and histopathology. Further characterization of the bioreactor-differentiated cultures revealed that a subpopulation of cells in the resulting aggregates expressed the pluripotency marker Oct-4 in the nucleus. Nuclear Oct-4 expression persisted even after 30 days of culture in the absence of leukemia inhibitory factor (LIF). Remarkably, and unlike ESCs differentiated into skeletal cell types in static cultures, bioreactor-differentiated aggregates implanted subcutaneously into SCID mice formed teratomas. The development of effective ESC differentiation protocols for suspension bioreactors will require a more complete understanding of the environmental conditions within these culture systems and the influence that these conditions have on the regulation of pluripotency and differentiation in ESCs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".