Optimizing Genetically Encoded Calcium Indicators to Measure Presynaptic Calcium Transients
Bibliographic record
Abstract
Neurotransmitter release is modulated by multiple regulatory mechanisms that control several stages of synaptic vesicle (SV) exocytosis. At the final stage, SV fusion with the presynaptic membrane requires calcium influx through voltage-gated calcium channels, and regulatory mechanisms that alter the surface expression or conductance of calcium channels have large effects on neurotransmitter release. To determine how these mechanisms contribute to synapse-specific modulations of neurotransmitter release and synaptic strength, we require a means to monitor presynaptic calcium transients at individual synapses. Genetically encoded calcium indicators (GECIs), engineered proteins that change their fluorescence emission properties upon calcium binding, generally lack the sensitivity to measure such transients in response to isolated stimuli. Therefore, we modified the GECI, GCaMP3, by altering its sensitivity for calcium. Our results suggest the modified GCaMP-based presynaptically targeted GECIs are excellent tools to quantify presynaptic calcium transients at individual synapses in response to isolated action potentials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".