A monochromatically excitable green-red dual-fluorophore fusion incorporating a new large Stokes shift fluorescent protein
Bibliographic record
Abstract
ABSTRACT Genetically encoded sensors enable quantitative imaging of analytes in live cells. State-of-the-art sensors are commonly constructed by combining ligand-binding domains with one or more sensitized fluorescent protein (FP) domains. Sensors based on a single FP are susceptible to artifacts caused by differing expression levels or sensor distribution in vivo . Hence, our lab developed dual-FP Matryoshka technology introduced by a single cassette that contains a stable large Stokes shift (LSS) reference FP nested within a reporter FP (cpEGFP), allowing simple construction of intensiometric sensors with the capacity for ratiometric quantification. The first-generation Green-Orange (GO) Matryoshka cassette established proof of concept but required custom optical setups to maximize achievable dynamic range. Here, we present a genetically encoded calcium sensor that employs optimized second-generation Green-Apple (GA) Matryoshka technology that incorporates a newly designed red LSSmApple fluorophore. LSSmApple provides improved excitation spectrum overlap with cpEGFP, allowing for monochromatic co-excitation with blue light. The exceptionally large Stokes shift of LSSmApple results in improved emission spectrum separation from cpEGFP, which minimizes fluorophore bleed-through and facilitates imaging using standard dichroics and red fluorescent protein (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 fluorescent protein, 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 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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| 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 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".