Understanding the Fluorescence Change in Red Genetically Encoded Calcium Ion Indicators
Bibliographic record
Abstract
For over 20 years, genetically encoded Ca 2+ indicators have illuminated dynamic Ca 2+ signaling activity in living cells and, more recently, whole organisms. We are just now beginning to understand how they work. Various fluorescence colors of these indicators have been developed, including red. Red ones are promising because longer wavelengths of light scatter less in tissue, making it possible to image deeper. They are engineered from a red fluorescent protein that is circularly permuted and fused to a Ca 2+ -sensing domain. When Ca 2+ binds, a conformational change in the sensing domain causes a change in fluorescence. Three factors can contribute to this fluorescence change: 1) a shift in the protonation equilibrium of the chromophore, 2) a change in fluorescence quantum yield, and 3) a change in the extinction coefficient or the two-photon cross section, depending on if it is excited with one or two photons. Here, we conduct a systematic study of the photophysical properties of a range of red Ca 2+ indicators to determine which factors are the most important. In total, we analyzed nine indicators, including jRGECO1a, K-GECO1, jRCaMP1a, R-GECO1, R-GECO1.2, CAR-GECO1, O-GECO1, REX-GECO1, and a new variant termed jREX-GECO1. We find that these 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 larger with two-photon excitation compared to one-photon because of a change in the two-photon cross section, by up to a factor of two.
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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 | 0.000 |
| 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".