Efficient suspension bioreactor expansion of murine embryonic stem cells on microcarriers in serum‐free medium
Bibliographic record
Abstract
Large numbers of cells will be required for successful embryonic stem cell (ESC)-based cellular therapies or drug discovery, thus raising the need to develop scaled-up bioprocesses for production of ESCs and their derived progeny. Traditionally, ESCs have been propagated in adherent cultures in static flasks on fibroblasts layers in serum-containing medium. Direct translation of two-dimensional flatbed cultures to large-scale production of the quantities of cells required for therapy simply by increasing the number of dishes or flasks is not practical or economical. Here, we describe successful scaled-up production of ESCs on microcarriers in a stirred culture system in a serum-free medium. Cells expanded on CultiSpher S, Cytodex 3, and Collagen microcarriers showed superior cell-fold expansions of 439, 193, and 68, respectively, without excessive agglomeration, compared with 27 in static culture. In addition, the ESCs maintained their pluripotency after long-term culture (28 days) in serum-free medium. This is the first time mESCs have been cultured on microcarriers without prior exposure to serum and/or fibroblasts, while also eliminating the excessive agglomeration plaguing earlier studies. These protocols provide an economical, practical, serum-free means for expanding ESCs in a stirred suspension bioprocess.
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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 | 0.001 | 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 | 0.001 |
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".