Tracking Insulin- and Glucagon-Expressing Cells In Vitro and In Vivo Using a Double-Reporter Human Embryonic Stem Cell Line
Bibliographic record
Abstract
Human embryonic stem cell (hESC)-derived pancreatic α- and β-cells can be used to develop cell replacement therapies to treat diabetes. However, recent published differentiation protocols yield varying amounts of α- and β-cells amid heterogeneous cell populations. To visualize and isolate hESC-derived α- and β-cells, we generated a GLUCAGON-2A-mScarlet and INSULIN-2A-EGFP dual fluorescent reporter (INSEGFPGCGmScarlet) hESC line using CRISPR/Cas9. We established robust expression of EGFP and mScarlet fluorescent proteins in insulin- and glucagon-expressing cells, respectively, without compromising the differentiation or function of these cells. We also showed that the insulin- and glucagon-expressing bihormonal population at the maturing endocrine cell stage (stage 6) of our pancreatic islet differentiation lose insulin expression over time, while maintaining an α-like expression profile, suggesting these bihormonal cells are cell-autonomously fated to become α-like cells. We also demonstrated this cell line can be used to monitor hESC-derived insulin- and glucagon-expressing cells, and hESC-derived islet morphology in vivo, by transplanting them into the anterior chamber of the eye in mice. Together, the INSEGFPGCGmScarlet hESC line provides an efficient strategy for tracking populations of hESC-derived β- and α-like cells. Article Highlights Differentiation protocols used to generate stem cell–derived islet cells yield heterogenous cell populations. We generated a human embryonic stem cell line that reports insulin- and glucagon-expressing cells in vitro and in vivo without altering their differentiation or function. We showed some insulin- and glucagon-expressing bihormonal cells are cell-autonomously fated to become α-like cells. This reporter cell line can be used to further study and improve stem cell–derived islet differentiation and transplantation.
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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".