Large‐scale expansion of feeder‐free mouse embryonic stem cells serially passaged in stirred suspension bioreactors at low inoculation densities directly from cryopreservation
Bibliographic record
Abstract
Abstract Embryonic stem cells (ESCs) have almost unlimited proliferation capacity in vitro and can retain the ability to contribute to all cell lineages, making them an ideal platform material for cell‐based therapies. ESCs are traditionally cultured in static flasks on a feeder layer of murine embryonic fibroblast cells. Although sufficient to generate cells for research purposes, this approach is impractical to achieve large quantities for clinical applications. In this study, we have developed protocols that address a variety of challenges that currently bottleneck clinical translation of ESCs expanded in stirred suspension bioreactors. We demonstrated that mouse ESCs (mESCs) cryopreserved in the absence of feeder cells could be thawed directly into stirred suspension bioreactors at extremely low inoculation densities (100 cells/ml). These cells sustained proliferative capacity through multiple passages and various reactor sizes and geometries, producing clinically relevant numbers (109 cells) and maintaining pluripotency phenotypic and functional properties. Passages were completed in stirred suspension bioreactors of increasing scale, under defined batch conditions which greatly improved resource efficiency. Output mESCs were analyzed for pluripotency marker expression (SSEA‐1, SOX‐2, and Nanog) through flow cytometry, and spontaneous differentiation and teratoma analysis was used to demonstrate functional maintenance of pluripotency.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".