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 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 | 0.001 | 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".