Second and third generation voltage-sensitive fluorescent proteins for monitoring membrane potential
Bibliographic record
Abstract
Over the last decade, optical neuroimaging methods have been enriched by engineered biosensors derived from fluorescent protein (FP) reporters fused to protein detectors that convert physiological signals into changes of intrinsic FP fluorescence. These FP-based indicators are genetically encoded, and hence targetable to specific cell populations within networks of heterologous cell types. Among this class of biosensors, the development of optical probes for membrane potential is both highly desirable and challenging. A suitable FP voltage sensor would indeed be a valuable tool for monitoring the activity of thousands of individual neurons simultaneously in a non-invasive manner. Previous prototypic genetically-encoded FP voltage indicators achieved a proof of principle but also highlighted several difficulties such as poor cell surface targeting and slow kinetics. Recently, we developed a new series of FRET-based Voltage-Sensitive Fluorescent Proteins (VSFPs), referred to as VSFP2s, with efficient targeting to the plasma membrane and high responsiveness to membrane potential signaling in excitable cells. In addition to these FRET-based voltage sensors, we also generated a third series of probes consisting of single FPs with response kinetics suitable for the optical imaging of fast neuronal signals. These newly available genetically-encoded reporters for membrane potential will be instrumental for future experimental approaches directed toward the understanding of neuronal network dynamics and information processing in the brain. Here, we review the development and current status of these novel fluorescent 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".