Optimized serial expansion of human induced pluripotent stem cells using low-density inoculation to generate clinically relevant quantities in vertical-wheel bioreactors
Bibliographic record
Abstract
Abstract Human induced pluripotent stem cells (hiPSCs) have generated a great deal of attention owing to their capacity for self-renewal and differentiation into the three germ layers of the body. Their discovery has facilitated a new era in biomedicine for understanding human development, drug screening, disease modeling, and cell therapy while reducing ethical issues and risks of immune rejection associated with traditional embryonic stem cells. Bioreactor-based processes have been the method of choice for the efficient expansion and differentiation of stem cells in controlled environments. Current protocols for the expansion of hiPSCs use horizontal impeller, paddle, or rocking wave mixing method bioreactors which require large static cell culture starting populations and achieve only moderate cell fold increases. This study focused on optimizing inoculation, agitation, oxygen, and nutrient availability for the culture of hiPSCs as aggregates in single-use, low-shear, vertical-wheel bioreactors. Under optimized conditions, we achieved an expansion of more than 30-fold in 6 days using a small starting population of cells and minimal media resources throughout. Importantly, we showed that that this optimized bioreactor expansion protocol could be replicated over four serial passages resulting in a cumulative cell expansion of 1.06E6-fold in 28 days. Cells from the final day of the serial passage were of high quality, maintaining a normal karyotype, pluripotent marker staining, and the ability to form teratomas in vivo. These findings demonstrate that a vertical-wheel bioreactor-based bioprocess can provide optimal conditions for efficient, rapid generation of high-quality hiPSCs to meet the demands for clinical manufacturing of therapeutic cell products. Significance statement This study has developed a new method to grow human induced pluripotent stem cells in large quantities through serial passaging in vertical-wheel bioreactors. Cells were cultured from small starting numbers, in optimized conditions, resulting in economical, reproducible culture techniques for high-quality populations. These advances will have significant economic and practical applications in stem cell therapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
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".