10-8: PLURIPOTENT STEM CELL-DERIVED ISLET ENCAPSULATION IN ALGINATE BEADS VIA A SCALABLE EMULSION-BASED PROCESS
Bibliographic record
Abstract
Introduction: Pancreatic islet transplantation can restore insulin production in type 1 diabetes but is limited by donor scarcity and the need for lifelong immunosuppression. Differentiation of human pluripotent stem cells into islet-like cell clusters (SC-islets) represents a promising alternative. Scaling up production of SC-islets in conventional stirred tank bioreactors can lead to product heterogeneity and loss due to the damage which can be imparted near the impeller and agglomeration of SC-islets in lower-shear regions. We hypothesized that encapsulating SC-islets in alginate microbeads using a highly scalable emulsion-based system could provide a more uniform mechanical environment during upscaled production in suspension. Methods: SC-islets were produced from a 7-stage directed differentiation protocol and encapsulated at the end of Stage 6 using stirred emulsification and internal gelation. The effect of alginate concentration on bead mechanical properties, SC-islet survival, SC-islet diameter, gene expression (PDX1, NKX6.1) and glucose-responsive insulin secretion was assessed after up to 25 days of immobilized culture. Cultures were maintained either in small-scale agitated 6-well plates, or in vertical wheel bioreactors. At the end of Stage 7, non-encapsulated SC-islets were implanted into the renal capsule while encapsulated SC-islets were implanted into the peritoneal space. Results: Encapsulation did not significantly affect the viability of SC-islets nor glucose responsiveness based on the stimulation index, indicating that the physical barrier did not hinder oxygen or insulin diffusion. High-concentration alginate beads increased the fraction of glucagon-expressing cells, indicating that hydrogel stiffness may impact SC-islet cell fate decisions during maturation. Viable encapsulated SC-islets were recovered after 2 days in vivo, and human C-peptide was detected in blood samples after seven days, demonstrating insulin secretion, with experiments ongoing. Conclusions: Emulsification and internal gelation is a cost-effective, scalable process for SC-islet encapsulation, promising for long-term bioreactor culture and transplantation. These results support its potential for efficient SC-islet bioprocessing and transplantation applications.
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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 | 0.000 | 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".