Quantitative, in situ visualization of intracellular insulin vesicles in pancreatic beta cells
Bibliographic record
Abstract
Characterizing relationships between Zn 2+ , insulin, and insulin vesicles is of vital importance to the study of pancreatic beta cells. However, the precise content of Zn 2+ and the specific location of insulin inside insulin vesicles are not clear, which hinders a thorough understanding of the insulin secretion process and diseases caused by blood sugar dysregulation. Here, we demonstrated the colocalization of Zn 2+ and insulin in both single extracellular insulin vesicles and pancreatic beta cells by using an X-ray scanning coherent diffraction imaging (ptychography) technique. We also analyzed the elemental Zn 2+ and Ca 2+ contents of insulin vesicles using electron microscopy and energy dispersive spectroscopy (EDS) mapping. We found that the presence of Zn 2+ is an important characteristic that can be used to distinguish insulin vesicles from other types of vesicles in pancreatic beta cells and that the content of Zn 2+ is proportional to the size of insulin vesicles. By using dual-energy contrast X-ray microscopy and scanning transmission X-ray microscopy (STXM) image stacks, we observed that insulin accumulates in the off-center position of extracellular insulin vesicles. Furthermore, the spatial distribution of insulin vesicles and their colocalization with other organelles inside pancreatic beta cells were demonstrated using three-dimensional (3D) imaging by combining X-ray ptychography and an equally sloped tomography (EST) algorithm. This study describes a powerful method to univocally describe the location and quantitative analysis of intracellular insulin, which will be of great significance to the study of diabetes and other blood sugar diseases.
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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 | 0.000 |
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".