Optical Estimation of Membrane Potential Values Using Fluorescence Lifetime Imaging Microscopy and Hybrid Chemical-Genetic Voltage Indicators
Bibliographic record
Abstract
Introduction: ), the voltage across a cell membrane, is an important biophysical phenomenon, central to the physiology of cells, tissues, and organisms. Voltage-sensitive fluorescent indicators are a powerful method for interrogating membrane potential in living systems, but most indicators are best suited for detecting changes in membrane potential rather than measuring values of the membrane potential. One promising approach is to use fluorescence lifetime imaging microscopy (FLIM) in combination of chemically synthesized dyes to estimate a value of membrane potential. However, a drawback is that chemically synthesized dyes show poor specificity of staining. Objectives: To address this problem, we applied a chemical-genetic voltage imaging approach to FLIM to enable optical estimation of membrane potential values from genetically defined cells. Results: In this report, we detail the characterization and evaluation of two of these systems in mammalian cells. We further validate the use of a FLIM-based chemical genetic voltage indicator in mammalian neurons. Conclusions: Finally, we discuss opportunities for future improvements to chemical-genetic FLIM-based voltage indicators.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".