Efficient Differentiation of Pluripotent Stem Cells to NKX6-1<sup>+</sup> Pancreatic Progenitors
Bibliographic record
Abstract
Pluripotent stem cells have the ability to self renew and differentiate to multiple lineages, making them an attractive source for the generation of pancreatic progenitor cells that can be used for the study of and future treatment of diabetes. This article outlines a four-stage differentiation protocol designed to generate pancreatic progenitor cells from human embryonic stem cells (hESCs). This protocol can be applied to a number of human pluripotent stem cell (hPSC) lines. The approach taken to generate pancreatic progenitor cells is to differentiate hESCs to accurately model key stages of pancreatic development. This begins with the induction of the definitive endoderm, which is achieved by culturing the cells in the presence of Activin A, basic Fibroblast Growth Factor (bFGF) and CHIR990210. Further differentiation and patterning with Fibroblast Growth Factor 10 (FGF10) and Dorsomorphin generates cells resembling the posterior foregut. The addition of Retinoic Acid, NOGGIN, SANT-1 and FGF10 differentiates posterior foregut cells into cells characteristic of pancreatic endoderm. Finally, the combination of Epidermal Growth Factor (EGF), Nicotinamide and NOGGIN leads to the efficient generation of PDX1+/NKX6-1+ cells. Flow cytometry is performed to confirm the expression of specific markers at key stages of pancreatic development. The PDX1+/NKX6-1+ pancreatic progenitors at the end of stage 4 are capable of generating mature β cells upon transplantation into immunodeficient mice and can be further differentiated to generate insulin-producing cells in vitro. Thus, the efficient generation of PDX1+/NKX6-1+ pancreatic progenitors, as demonstrated in this protocol, is of great importance as it provides a platform to study human pancreatic development in vitro and provides a source of cells with the potential of differentiating to β cells that could eventually be used for the treatment of diabetes.
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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 | 0.001 |
| 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".