Tracking insulin- and glucagon-expressing bihormonal cells during differentiation using an <i>INSULIN</i> and <i>GLUCAGON</i> double reporter human embryonic stem cell line
Bibliographic record
Abstract
Summary Human embryonic stem cell (hESC)-derived pancreatic alpha and beta cells can be used to develop cell replacement therapies to treat diabetes. However, recent published differentiation protocols yield varying amounts of alpha and beta cells amidst heterogeneous cell populations. To visualize and isolate hESC-derived alpha and beta cells, we generated a GLUCAGON-2A- mScarlet and INSULIN-2A-EGFP dual fluorescent reporter (INS EGFP GCG mScarlet ) 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 the insulin- and glucagon-expressing bihormonal population at the maturing endocrine cell stage (Stage 6) lose insulin expression over time, while maintaining an alpha-like expression profile, suggesting these bihormonal cells are preferentially fated to become alpha-like cells in vitro . Together, the INS EGFP GCG mScarlet hESC line provides an efficient strategy for tracking populations of hESC-derived beta- and alpha-like cells.
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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".