Differentiation of stem cell-derived pancreatic progenitors into insulin-secreting islet clusters in a multiwell-based static 3D culture system
Bibliographic record
Abstract
Orbital shaker-based suspension culture systems have been in widespread use for differentiating human pluripotent stem cell (hPSC)-derived pancreatic progenitors toward islet-like clusters during endocrine induction stages. However, reproducibility between experiments is hampered by variable degrees of cell loss in shaking cultures, which contributes to variable differentiation efficiencies. Here, we describe a 96-well-based static suspension culture method for differentiation of pancreatic progenitors into hPSC-islets. Compared with shaking culture, this static 3D culture system induces similar islet gene expression profiles during differentiation processes but significantly reduces cell loss and improves cell viability of endocrine clusters. This static culture method results in more reproducible and efficient generation of glucose-responsive, insulin-secreting hPSC-islets. The successful differentiation and well-to-well consistency in 96-well plates also provides a proof of principle that the static 3D culture system can serve as a platform for small-scale compound screening experiments as well as facilitating further protocol development.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".