13. A Novel Method of Culturing Murine Induced Pluripotent Stem Cells (iPSCs)
Bibliographic record
Abstract
Similar to embryonic stem cells (ESCs), iPSCs have the ability to differentiate into all three cell lineages, while not being subjected to the ethical complications attributed to ESCs. Our project goal was to develop a novel method of culturing murine iPSCs, reprogrammed from mouse embryonic fibroblasts, in large-scale quantities while maintaining their pluripotent characteristics and genomic integrity over a long term period. Stirred Suspension Bioreactors (SSBs) propose several benefits over static culture systems and facilitate the large-scale, economical expansion required for clinical studies. In our work with the SSB system, various techniques were used to promote and analyze the continued pluripotency of the murine iPSCs. Our results showed that iPSCs maintained their pluripotent state in SSBs and retained their ability to differentiate into different cell lineages after long term culture. miPSC were induced to differentiate into heart, bone and cartilage tissues at the end of maintenance period. The cumulative cell-fold expansion of iPSCs in suspension culture was 8.8×1010 cells, with 75% mean pluripotency at each passage. The cells expressed the major pluripotency markers as detected by RT-PCR. This study confirms the utility of the SSB system as a tool to cultivate large quantities of functional mouse iPSCs. SSBs have tremendous potential in future clinical applications and work has now begun to applying this system towards culturing human iPSCs. One potential application is the derivation of disease and patient derived iPSCs, which can recapitulate the disease phenotype for use as an in vitro model.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".