Peak emission wavelength and fluorescence lifetime are coupled in far-red, GFP-like fluorescent proteins
Bibliographic record
Abstract
The discovery and use of fluorescent proteins revolutionized cell biology by allowing the visualization of proteins in living cells. Advances in fluorescent proteins, primarily through genetic engineering, have enabled more advanced analyses, including Förster resonance energy transfer (FRET) and fluorescence lifetime imaging microscopy (FLIM) and the development of genetically encoded fluorescent biosensors. These fluorescence protein-based sensors are highly effective in cells grown in monolayer cultures. However, it is often desirable to use more complex models including tissue explants, organoids, xenografts, and whole animals. These types of samples have poor light penetration owing to high scattering and absorption of light by tissue. Far-red light with a wavelength between 650-900nm is less prone to scatter, and absorption by tissues and can thus penetrate more deeply. Unfortunately, there are few fluorescent proteins in this region of the spectrum, and they have sub-optimal fluorescent properties including low brightness and short fluorescence lifetimes. Understanding the relationships between the amino-acid sequences of far-red fluorescence proteins and their photophysical properties including peak emission wavelengths and fluorescence lifetimes would be useful in the design of new fluorescence proteins for this region of the spectrum. We used both site-directed mutagenesis and gene-shuffling between mScarlet and mCardinal fluorescence proteins to create new variants and assess their properties systematically. We discovered that for far-red, GFP-like proteins the emission maxima and fluorescence lifetime have a strong inverse correlation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".