Novel standards in the measurement of rat insulin granules combining electron microscopy, high-content image analysis and in silico modelling
Bibliographic record
Abstract
AIMS/HYPOTHESIS: Knowledge of number, size and content of insulin secretory granules is pivotal for understanding the physiology of pancreatic beta cells. Here we re-evaluated key structural features of rat beta cells, including insulin granule size, number and distribution as well as cell size. METHODS: Electron micrographs of rat beta cells fixed either chemically or by high-pressure freezing were compared using a high-content analysis approach. These data were used to develop three-dimensional in silico beta cell models, the slicing of which would reproduce the experimental datasets. RESULTS: As previously reported, chemically fixed insulin secretory granules appeared as hollow spheres with a mean diameter of ∼350 nm. Remarkably, most granules fixed by high-pressure freezing lacked the characteristic halo between the dense core and the limiting membrane and were smaller than their chemically fixed counterparts. Based on our analyses, we conclude that the mean diameter of rat insulin secretory granules is 243 nm, corresponding to a surface area of 0.19 μm(2). Rat beta cells have a mean volume of 763 μm(3) and contain 5,000-6,000 granules. CONCLUSIONS/INTERPRETATION: A major reason for the lower mean granule number/rat beta cell relative to previous accounts is a reduced estimation of the mean beta cell volume. These findings imply that each granule contains about twofold more insulin, while its exocytosis increases membrane capacitance about twofold less than assumed previously. Our integrated approach defines new standards for quantitative image analysis of beta cells and could be applied to other cellular systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".