Directed Evolution of a Genetically Encoded Bioluminescent Ca2+ Sensor
Bibliographic record
Abstract
The use of genetically encoded fluorescent sensors for the calcium ion (Ca2+) has revolutionized neuroscience research by allowing for the recording of dozens of neurons at the single-cell level in living animals. However, fluorescence imaging has some limitations such as the need for excitation light, which can result in a highly auto-fluorescent background and phototoxicity. In contrast, bioluminescent sensors using luciferase do not require excitation light, making them ideal for non-invasive deep tissue imaging in mammals. Our lab has previously developed a bioluminescent Ca2+ sensor CaMBI to image Ca2+ activity in the mouse liver, but its responsiveness to Ca2+ changes was suboptimal. To improve the performance of this sensor, we applied directed evolution to screen for genetic variants with increased responsiveness. Through several rounds of evolution, we identified variants with more than five times improved responsiveness in vitro. We characterized the improved sensors in culture cell lines and dissociated rat neurons and confirmed that they exhibited a higher sensitivity to changes in intracellular Ca2+ levels compared to their progenitor. These optimized Ca2+ sensors have the potential for non-invasive imaging of Ca2+ activity in vivo, particularly in the brain.
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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 | 0.000 |
| 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) | 0.000 | 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".