Improving the Photostability of Red- and Green-Emissive Single-Molecule Fluorophores via Ni<sup>2+</sup> Mediated Excited Triplet-State Quenching
Bibliographic record
Abstract
Methods to improve the photostability/photon output of fluorophores without compromising their signal stability are of paramount importance in single-molecule fluorescence (SMF) imaging applications. We show herein that Ni 2+ provides a suitable photostabilizing agent for three green-emissive (Cy3, ATTO532, Alexa532) and three red-emissive (Cy5, Alexa647, ATTO647N) fluorophores, four of which are regularly utilized in SMF studies. Ni 2+ works via photophysical quenching of the triplet excited state eliminating the potential for reactive intermediates being formed. Measurements of survival time, average intensity, and mean number of photons collected for the six fluorophores show that Ni 2+ increased their photostability 10- to 45-fold, comparable to photochemically based systems, without compromising the signal intensity or stability. Comparative studies with existing photostabilizing strategies enabled us to score different photochemical and photophysical stabilizing systems, based on their intended application. The realization that Ni 2+ allowed achieving a significant increase in photon output both for green- and red-emissive fluorophores positions Ni 2+ as a widely applicable tool to mitigate photobleaching, most suitable for multicolor single-molecule fluorescence studies.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".