A genetically encoded far‐red fluorescent calcium ion biosensor derived from a biliverdin‐binding protein
Bibliographic record
Abstract
Abstract Far‐red and near‐infrared (NIR) genetically encoded calcium ion (Ca2+) indicators (GECIs) are powerful tools for in vivo and multiplexed imaging of neural activity and cell signaling. Inspired by a previous report to engineer a far‐red fluorescent protein (FP) from a biliverdin (BV)‐binding NIR FP, we have developed a far‐red fluorescent GECI, designated iBB‐GECO1, from a previously reported NIR GECI. iBB‐GECO1 exhibits a relatively high molecular brightness, an inverse response to Ca2+ with ΔF/Fmin = −13, and a near‐optimal dissociation constant (Kd) for Ca2+ of 105 nM. We demonstrate the utility of iBB‐GECO1 for four‐color multiplexed imaging in MIN6 cells and five‐color imaging in HEK293T cells. Like other BV‐binding GECIs, iBB‐GECO1 did not give robust signals during in vivo imaging of neural activity in mice, but did provide promising results that will guide future engineering efforts. Significance Genetically encoded calcium ion (Ca2+) indicators (GECIs) compatible with common far‐red laser lines (~630–640 nm) on commercial microscopes are of critical importance for their widespread application to deep‐tissue multiplexed imaging of neural activity. In this study, we engineered a far‐red excitable fluorescent GECI, designated iBB‐GECO1, that exhibits a range of preferable specifications such as high brightness, large fluorescence response to Ca2+, and compatibility with multiplexed imaging in mammalian cells.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".