Confocal Imaging of Neuropeptide Y-pHluorin: A Technique to Visualize Insulin Granule Exocytosis in Intact Murine and Human Islets
Bibliographic record
Abstract
Insulin secretion plays a central role in glucose homeostasis under normal physiological conditions as well as in disease. Current approaches to study insulin granule exocytosis either use electrophysiology or microscopy coupled to the expression of fluorescent reporters. However most of these techniques have been optimized for clonal cell lines or require dissociating pancreatic islets. In contrast, the method presented here allows for real time visualization of insulin granule exocytosis in intact pancreatic islets. In this protocol, we first describe the viral infection of isolated pancreatic islets with adenovirus that encodes a pH-sensitive green fluorescent protein (GFP), pHluorin, coupled to neuropeptide Y (NPY). Second, we describe the confocal imaging of islets five days after viral infection and how to monitor the insulin granule secretion. Briefly, the infected islets are placed on a coverslip on an imaging chamber and imaged under an upright laser-scanning confocal microscope while being continuously perfused with extracellular solution containing various stimuli. Confocal images spanning 50 µm of the islet are acquired as time-lapse recordings using a fast-resonant scanner. The fusion of insulin granules with the plasma membrane can be followed over time. This procedure also allows for testing a battery of stimuli in a single experiment, is compatible with both mouse and human islets, and can be combined with various dyes for functional imaging (e.g., membrane potential or cytosolic calcium dyes).
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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 | 0.001 | 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".