Robust bioprocess design and evaluation of commercial media for the serial expansion of human induced pluripotent stem cell aggregate cultures in vertical-wheel bioreactors
Bibliographic record
Abstract
BACKGROUND: While pluripotent stem cell (PSC) therapies move toward clinical and commercial applications at a rapid rate, manufacturing reproducibility and robustness are notable bottlenecks in regulatory approval. Therapeutic applications of PSCs require large cell quantities to be generated under highly robust, well-defined, and economically viable conditions. Small-scale and short-term process optimization, however, is often performed in a linear fashion that does not account for time needed to verify the bioprocess protocols and analysis methods used. Design of a reproducible and robust bioprocess should be dynamic and include a continuous effort to understand how the process will respond over time and to different stresses before transitioning into large-scale production where stresses will be amplified. METHODS: bioreactors, to evaluate key process attributes through long-term (serial passage) suspension culture. This was done to access overall process robustness when performed with various commercially available media and cell lines. Process output variables including growth kinetics, aggregate morphology, harvest efficiency, genomic stability, and functional pluripotency were assessed through short and long-term culture. RESULTS: The robust nature of the expansion protocol was demonstrated over a six-day culture period where spherical aggregate formation and expansion were observed with high-fold expansions for all five commercial media tested. Profound differences in cell growth and quality were revealed only through long-term serial expansion and in-vessel dissociation operations. Some commercial media formulations tested demonstrated maintenance of cell growth rates, aggregate morphology, and high harvest recovery efficiencies through three bioreactor serial passages using multiple PSC lines. Exceptional bioprocess robustness was even demonstrated with sustained growth and quality maintenance over 10 serial bioreactor passages. However, some commercial media tested proved less equipped for serial passage cultures in bioreactors as cultures led to cell lysis during dissociation, reduction in growth rates, and a loss of aggregate morphology. CONCLUSIONS: This study demonstrates the importance of systematic selection and testing of bioprocess input variables, with multiple bioprocess output variables through serial passages to create a truly reproducible and robust protocol for clinical and commercial PSC production using scalable bioreactor systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 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) | 0.000 | 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 teacher head, 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".