Dynamic <i>Ins2</i> gene activity defines β-cell maturity states
Bibliographic record
Abstract
Abstract Transcriptional and functional cellular specialization has been described for insulin-secreting β-cells of the endocrine pancreas. However, it is not clear whether β-cell heterogeneity is stable or reflects dynamic cellular states. We investigated the temporal kinetics of endogenous insulin gene activity using live cell imaging, with complementary experiments employing FACS and single cell RNA sequencing, in β-cells from Ins2 GFP knock-in mice. In vivo staining and FACS analysis of islets from Ins2 GFP mice confirmed that at a given moment, ~25% of β-cells exhibited significantly higher activity at the conserved insulin gene Ins2. Live cell imaging captured Ins2 gene activity dynamics in single β-cells over days. Autocorrelation analysis revealed a subset of cells with oscillating behavior, with mean oscillation periods of 17 hours. Increased glucose concentrations stimulated more cells to oscillate and resulted in higher average Ins2 gene activity per cell. Single cell RNA sequencing showed that Ins2 (GFP) HIGH β-cells were enriched for markers of β-cell maturity. Ins2 (GFP) HIGH β-cells were also significantly less viable at all glucose concentrations and in the context of ER stress. Collectively, our results demonstrate that the heterogeneity of insulin production, observed in mouse and human β-cells, can be accounted for by dynamic states of insulin gene activity. Blurb Previously reported pancreatic β-cell heterogeneity reflects β-cell state transitions.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".