A novel optical tracer for VMAT2 applied to live cell measurements of vesicle maturation in cultured human β-cells
Bibliographic record
Abstract
The islet β-cells integrate external signals to modulate insulin secretion to better regulate blood glucose levels during periods of changing metabolic demand. The vesicular monoamine transporter type 2 (VMAT2), an important regulator of CNS neurotransmission, has an analogous role in the endocrine pancreas as a key control point of insulin secretion, with additional roles in regulating β-cell differentiation and proliferation. Here we report on the synthesis and biological characterisation of a fluorescent ligand for VMAT2 suitable for live cell imaging. Staining for VMAT2 and dopamine in live β-cell cultures show colocalisation in specific vesicles and reveal a heterogeneous population with respect to cell size, shape, vesicle number, size, and contents. Staining for VMAT2 and zinc ion, as a surrogate for insulin, reveals a wide range of vesicle sizes. Immunohistochemistry shows larger β-cell vesicles enriched for proinsulin, whereas smaller vesicles predominantly contain the processed mature insulin. In β-cell cultures obtained from nondiabetic donors, incubation at non-stimulatory glucose concentrations promotes a shift in vesicle diameter towards the more mature insulin vesicles at the expense of the larger immature insulin secretory vesicle population. We anticipate that this probe will be a useful reagent to identify living β-cells within complex mixtures for further manipulation and characterisation.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".