An INSULIN and IAPP dual reporter enables tracking of functional maturation of stem cell-derived insulin producing cells
Bibliographic record
Abstract
OBJECTIVE: Human embryonic stem cell (hESC; SC)-derived pancreatic β cells can be used to study diabetes pathologies and develop cell replacement therapies. Although current differentiation protocols yield SCβ cells with varying degrees of maturation, these cells still differ from deceased donor human β cells in several respects. We sought to develop a reporter cell line that could be used to dynamically track SCβ cell functional maturation. METHODS: ) hESC line using CRISPR/Cas9. Pluripotent SC were then differentiated using a 7-stage protocol to islet-like cells. Immunohistochemistry, flow cytometry, qPCR, GSIS and electrophysiology were used to characterise resulting cell populations. RESULTS: We observed robust expression of EGFP and mScarlet fluorescent proteins in insulin- and IAPP-expressing cells without any compromise to their differentiation. We show that the proportion of insulin-producing cells expressing IAPP increases over a 4-week maturation period, and that a subset of insulin-expressing cells remain IAPP-free. Compared to this IAPP-free population, we show these insulin- and IAPP-expressing cells are less polyhormonal, more glucose-sensitive, and exhibit decreased action potential firing in low (2.8 mM) glucose. CONCLUSIONS: hESC line provides a useful tool for tracking populations of maturing hESC-derived β cells in vitro. This tool has already been shared with 3 groups and is freely available to all.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".