Detection of activity-dependent vasopressin release from neuronal dendrites and axon terminals using sniffer cells
Bibliographic record
Abstract
Our understanding of neuropeptide function within neural networks would be improved by methods allowing dynamic detection of peptide release in living tissue. We examined the usefulness of sniffer cells as biosensors to detect endogenous vasopressin (VP) release in rat hypothalamic slices and from isolated neurohypophyses. Human embryonic kidney cells were transfected to express the human V1a VP receptor (V1aR) and the genetically encoded calcium indicator GCaMP6m. The V1aR couples to Gq11, thus VP binding to this receptor causes an increase in intracellular [Ca2+] that can be detected by a rise in GCaMP6 fluorescence. Dose-response analysis showed that VP sniffer cells report ambient VP levels >10 pM (EC50 = 2.6 nM), and this effect could be inhibited by the V1aR antagonist SR 49059. When placed over a coverslip coated with sniffer cells, electrical stimulation of the neurohypophysis provoked a reversible, reproducible, and dose-dependent increase in VP release using as few as 60 pulses delivered at 3 Hz. Suspended sniffer cells gently plated over a slice adhered to the preparation and allowed visualization of VP release in discrete regions. Electrical stimulation of VP neurons in the suprachiasmatic nucleus caused significant local release as well as VP secretion in distant target sites. Finally, action potentials evoked in a single magnocellular neurosecretory cell in the supraoptic nucleus provoked significant VP release from the somatodendritic compartment of the neuron. These results indicate that sniffer cells can be used for the study of VP secretion from various compartments of neurons in living tissue. NEW & NOTEWORTHY The specific functional roles of neuropeptides in neuronal networks are poorly understood due to the absence of methods allowing their real-time detection in living tissue. Here, we show that cultured “sniffer cells” can be engineered to detect endogenous release of vasopressin as an increase in fluorescence.
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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 | 0.001 | 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".