Glucose principally regulates insulin secretion in mouse islets by controlling the numbers of granule fusion events per cell
Bibliographic record
Abstract
AIMS/HYPOTHESIS: In dispersed single beta cells the response of each cell to glucose is heterogeneous. In contrast, within an islet, cell-to-cell communication leads to glucose inducing a more homogeneous response. For example, increases in NAD(P)H and calcium are relatively uniform across the cells of the islet. These data suggest that secretion of insulin from single beta cells within an islet should also be relatively homogeneous. The aim of this study was to test this hypothesis by determining the glucose dependence of single-cell insulin responses within an islet. METHODS: Two-photon microscopy was used to detect the glucose-induced fusion of single insulin granules within beta cells in intact mouse islets. RESULTS: First, we validated our assay and showed that the measures of insulin secretion from whole islets could be explained by the time course and numbers of granule fusion events observed. Subsequent analysis of the patterns of granule fusion showed that cell recruitment is a significant factor, accounting for a fourfold increase from 3 to 20 mmol/l glucose. However, the major factor is the regulation of the numbers of granule fusion events within each cell, which increase ninefold over the range of 3 to 20 mmol/l glucose. Further analysis showed that two types of granule fusion event occur: 'full fusion' and 'kiss and run'. We show that the relative frequency of each type of fusion is independent of glucose concentration and is therefore not a factor in the control of insulin secretion. CONCLUSIONS/INTERPRETATION: Within an islet, glucose exerts its main effect through increasing the numbers of insulin granule fusion events within a cell.
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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) | 0.001 | 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 | 0.000 |
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".