Scalable Bioreactor-based Suspension Approach to Generate Stem Cell–derived Islets From Healthy Donor-derived iPSCs
Bibliographic record
Abstract
BACKGROUND: Induced pluripotent stem cells (iPSCs) offer the potential to generate autologous iPSC-derived islets (iPSC islets), however, remain limited by scalability and product safety. METHODS: Herein, we report stagewise characterization of cells generated following a bioreactor-based differentiation protocol. Cell characteristics were assessed using flow cytometry, quantitative reverse transcription polymerase chain reaction, patch clamping, functional assessment, and in vivo functional and immunohistochemistry evaluation. Protocol yield and costs are assessed to determine scalability. RESULTS: Differentiation was capable of generating 90.4% PDX1 + /NKX6.1 + pancreatic progenitors and 100% C-peptide + /NKX6.1 + iPSC islet cells. However, 82.1%, 49.6%, and 0.9% of the cells expressed SOX9 (duct), SLC18A1 (enterochromaffin cells), and CDX2 (gut cells), respectively. Explanted grafts contained mature monohormonal islet-like cells, however, CK19 + ductal tissues persist. Using this protocol, semi-planar differentiation using 150 mm plates achieved 5.72 × 10 4 cells/cm 2 (total 8.3 × 10 6 cells), whereas complete suspension differentiation within 100 mL Vertical-Wheel bioreactors significantly increased cell yield to 1.1 × 10 6 cells/mL (total 105.0 × 10 6 cells), reducing costs by 88.8%. CONCLUSIONS: This study offers a scalable suspension-based approach for iPSC islet differentiation within Vertical-Wheel bioreactors with thorough characterization of the ensuing product to enable future protocol comparison and evaluation of approaches for off-target cell elimination. Results suggest that bioreactor-based suspension differentiation protocols may facilitate scalability and clinical implementation of iPSC islet 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".