A sensitive orange fluorescent calcium ion indicator for imaging neural activity
Bibliographic record
Abstract
Genetically encoded calcium indicators (GECIs) are vital tools for fluorescence-based visualization of neuronal activity with high spatial and temporal resolution. However, current highest-performance GECIs are predominantly green or red fluorescent, limiting multiplexing options and efficient excitation with fixed-wavelength femtosecond lasers operating at 1030 nm. In an effort to overcome these limitations, we developed OCaMP, an orange fluorescent GECI engineered from O-GECO1 through targeted substitutions to improve calcium affinity while retaining the favorable photophysical properties of mOrange2. OCaMP exhibits improved two-photon cross-section, responsiveness, photostability, and calcium affinity relative to O-GECO1. In cultured neurons, zebrafish, and mouse cortex, OCaMP outperforms the red GECIs jRCaMP1a and jRGECO1a in sensitivity, photostability, and signal-to-noise ratio. Here we show that OCaMP, an orange fluorescent GECI, is a robust tool for high-fidelity neural imaging optimized for wavelengths above 1000 nm and a practical option within the spectral gap between existing green and red GECIs.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".