A Monochromatically Excitable Green–Red Dual-Fluorophore Fusion Incorporating a New Large Stokes Shift Fluorescent Protein
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Genetically encoded sensors enable quantitative imaging of analytes in live cells. Sensors are commonly constructed by combining ligand-binding domains with one or more sensitized fluorescent protein (FP) domains. Sensors based on a single FP can be susceptible to artifacts caused by changes in sensor levels or distribution in vivo. To develop intensiometric sensors with the capacity for ratiometric quantification, dual-FP Matryoshka sensors were generated by using a single cassette with a large Stokes shift (LSS) reference FP nested within the reporter FP (cpEGFP). Here, we present a genetically encoded calcium sensor that employs green apple (GA) Matryoshka technology by incorporating a newly designed red LSSmApple fluorophore. LSSmApple matures faster and provides an optimized excitation spectrum overlap with cpEGFP, allowing for monochromatic coexcitation with blue light. The LSS of LSSmApple results in improved emission spectrum separation from cpEGFP, thereby minimizing fluorophore bleed-through and facilitating imaging using standard dichroic and red FP (RFP) emission filters. We developed an image analysis pipeline for yeast ( Saccharomyces cerevisiae ) timelapse imaging that utilizes LSSmApple to segment and track cells for high-throughput quantitative analysis. In summary, we engineered a new FP, constructed a genetically encoded calcium indicator (GA-MatryoshCaMP6s), and performed calcium imaging in yeast as a demonstration.
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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) | 0.001 | 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".