Understanding the Ca <sup>2+</sup> -dependent Fluorescence Change in Red Genetically Encoded Ca <sup>2+</sup> Indicators
Bibliographic record
Abstract
Abstract Genetically encoded Ca 2+ indicators (GECIs) are widely used to illuminate dynamic Ca 2+ signaling activity in living cells and tissues. Various fluorescence colors of GECIs are available, including red. Red GECIs are promising because longer wavelengths of light scatter less in tissue, making it possible to image deeper. They are engineered from a circularly permuted red fluorescent protein fused to a Ca 2+ sensing domain, calmodulin and a calmodulin-binding peptide. A conformational change in the sensing domain upon binding Ca 2+ causes a change in the fluorescence intensity of the fluorescent protein. Three factors could contribute to this change in fluorescence: 1) a shift in the protonation state of the chromophore, 2) a change in fluorescence quantum yield, and 3) a change in the extinction coefficient for one-photon excitation or the two-photon cross section for two-photon excitation. We conducted a systematic study of the photophysical properties of a select cohort of red GECIs in their Ca 2+ -free and Ca 2+ -saturated states to determine which factors are most important for the Ca 2+ -dependent change in fluorescence. In total, we analyzed nine red GECIs, including jRGECO1a, K-GECO1, jRCaMP1a, R-GECO1, R-GECO1.2, CAR-GECO1, O-GECO1, REX-GECO1, and a new variant termed jREX-GECO1. We found that these red GECIs could be separated into three classes that each rely on a particular set of factors. Furthermore, in some cases the magnitude of the change in fluorescence was different depending on one-photon excitation or two-photon excitation by up to a factor of two.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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 teacher head, 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".