Fluorogenic Targeting of Voltage-Sensitive Dyes to Neurons
Bibliographic record
Abstract
We present a method to target voltage-sensitive fluorescent dyes to specified cells using an enzyme-catalyzed fluorogenic reaction on cell surfaces. The dye/enzyme hybrids are composed of a photoinduced electron transfer (PeT)-based fluorescent voltage indicator and a complementary enzyme expressed on the cell surface. Action of the exogenous enzyme on the dye results in fluorogenic activation of the dye, enabling fast voltage imaging in defined neurons with sensitivity surpassing those of purely genetically encoded approaches. We employ a bulky methylcyclopropylacetoxymethyl ether to diminish the fluorescence of a PeT-based voltage-sensitive dye, or VoltageFluor. The hydrolytically stable ether can be removed by the action of porcine liver esterase (PLE) to reveal the bright unmodified VoltageFluor. We established that the chemically modified VoltageFluor is a substrate for PLE in vitro and in live cells. When PLE is targeted to the external face of cell membranes, it controls the apparent staining of cells. The use of neuron-specific promoters can direct staining to mammalian neurons to provide clear detection of neuronal action potentials in single trials. All of the new VoltageFluors targeted by esterase expression (VF-EXs) report single spikes in cultured mammalian neurons. The best, VF-EX2, does so with a signal-to-noise ratio nearly double that of comparable genetically encoded voltage reporters. By targeting PLE to neurons, VF-EX2 can interrogate the neuromodulatory effects of serotonin in cultured hippocampal neurons. Taken together, our results show that a combination of synthetic chemistry and biochemistry enables bright and fast voltage imaging from genetically defined neurons in culture.
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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 | 0.000 |
| 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".