Generation of Insulin-Producing Islet-Like Clusters from Human Embryonic Stem Cells
Bibliographic record
Abstract
Recent success in pancreatic islet transplantation has energized the field to discover an alternative source of stem cells with differentiation potential to beta cells. Generation of glucose-responsive, insulin-producing beta cells from self-renewing, pluripotent human ESCs (hESCs) has immense potential for diabetes treatment. We report here the development of a novel serum-free protocol to generate insulin-producing islet-like clusters (ILCs) from hESCs grown under feeder-free conditions. In this 36-day protocol, hESCs were treated with sodium butyrate and activin A to generate definitive endoderm coexpressing CXCR4 and Sox17, and CXCR4 and Foxa2. The endoderm population was then converted into cellular aggregates and further differentiated to Pdx1-expressing pancreatic endoderm in the presence of epidermal growth factor, basic fibroblast growth factor, and noggin. Soon thereafter, expression of Ptf1a and Ngn3 was detected, indicative of further pancreatic differentiation. The aggregates were finally matured in the presence of insulin-like growth factor II and nicotinamide. The temporal pattern of pancreas-specific gene expression in the hESC-derived ILCs showed considerable similarity to in vivo pancreas development, and the final population contained representatives of the ductal, exocrine, and endocrine pancreas. The hESC-derived ILCs contained 2%-8% human C-peptide-positive cells, as well as glucagon- and somatostatin-positive cells. Insulin content as high as 70 ng of insulin/mug of DNA was measured in the ILCs, representing levels higher than that of human fetal islets. In addition, the hESC-derived ILCs contained numerous secretory granules, as determined by electron microscopy, and secreted human C-peptide in a glucose-dependent manner. Disclosure of potential conflicts of interest is found at the end of this article.
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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".