High density continuous production of murine pluripotent cells in an acoustic perfused bioreactor at different oxygen concentrations
Bibliographic record
Abstract
Strategies for the production of pluripotent stem cells (PSCs) rely on serially dissociated adherent or aggregate-based culture, consequently limiting robust scale-up of cell production, on-line control and optimization of culture conditions. We recently developed a method that enables continuous (non-serially dissociated) suspension culture-mediated reprogramming to pluripotency. Herein, we use this method to demonstrate the scalable production of PSCs and early derivatives using acoustic filter technology to enable continuous oxygen-controlled perfusion culture. Cell densities of greater than 1 × 10⁷ cells/mL were achieved after 7 days of expansion at a specific growth rate (µ) of 0.61 ± 0.1 day⁻¹ with a perfusion rate (D) of 5.0 day⁻¹. A twofold increase in maximum cell density (to greater than 2.5 × 10⁷ cells/mL) was achieved when the medium dissolved oxygen was reduced (5% DO). Cell densities and viabilities >80% were maintained for extended production periods during which maintenance of pluripotency was confirmed by stable expression of pluripotency factors (SSEA-1 and Nanog), as well as the capacity to differentiate into all three germ layers. This work establishes a versatile biotechnological platform for the production of pluripotent cells and derivatives in an integrated, scalable and intensified stirred suspension culture.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".