Enabling Fluorescence Lifetime Imaging Multiplexing Using UnaG through Its Modification with Canonical and Noncanonical Amino Acids
Bibliographic record
Abstract
Fluorogen-activating proteins are powerful molecular tools for microscopy, including functional imaging. These proteins serve as an alternative to GFP-like proteins, as they do not require oxygen for chromophore maturation. However, the restricted selectivity of proteins to chromophores, combined with the limited number of spectral channels of conventional fluorescent microscopes, hinders the development of multicolor synthetic dyes. Additionally, the poor cell and tissue permeability of synthetic chromophores further limits their utility. In this work, we address these challenges by combining time-resolved methods with the rational design of the UnaG protein, which utilizes bilirubin as a natural chromophore. To turn UnaG into a palette of probes for fluorescence lifetime imaging microscopy (FLIM), we solved two practical problems: first, we determined the limits of bilirubin lifetime variations in response to changes in the protein structure and, second, we determined what minimal structural changes can be reliably distinguished by lifetime analysis in cellula . Combining classical point mutagenesis and the translational introduction of noncanonical amino acids, we generated UnaG with fluorescence lifetimes ranging from hundreds of picoseconds to nanoseconds. We explored the potential for further modification of the UnaG protein matrix to optimize spectral and temporal characteristics of bilirubin fluorescence and its quantitative detection through time-resolved approaches.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".