Spying on Neuronal Membrane Potential with Genetically Targetable Voltage Indicators
Bibliographic record
Abstract
imaging. Chemically synthesized voltage-sensitive dyes that use photoinduced electron transfer as a voltage-sensing trigger offer high voltage sensitivity and fast-response kinetics, but targeting chemical indicators to specific cells remains an outstanding challenge. Here, we present a new family of readily functionalizable, fluorescein-based voltage-sensitive fluorescent dyes (sarcosine-VoltageFluors) that can be covalently attached to a genetically encoded cell surface receptor to achieve voltage imaging from genetically defined neurons. We synthesized four new VoltageFluor derivatives that possess carboxylic acid functionality for simple conjugation to flexible tethers. The best of this new group of dyes was conjugated via a polyethylene glycol (PEG) linker to a small peptide (SpyTag, 13 amino acids) that directs binding and formation of a covalent bond with its binding partner, SpyCatcher (15 kDa). The new VoltageSpy dyes effectively label cells expressing cell-surface SpyCatcher, display good voltage sensitivity, and maintain fast-response kinetics. In cultured neurons, VoltageSpy dyes enable robust, single-trial optical detection of action potentials at neuronal soma with sensitivity exceeding genetically encoded voltage indicators. Importantly, genetic targeting of chemically synthesized dyes enables VoltageSpy to report on action potentials in axons and dendrites in single trials, tens to hundreds of micrometers away from the cell body. Genetic targeting of synthetic voltage indicators with VoltageSpy enables voltage imaging with low nanomolar dye concentration and offers a promising method for allying the speed and sensitivity of synthetic indicators with the enhanced cellular resolution of genetically encoded probes.
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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 | 0.001 |
| 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".