Chemically Tuned, Reversible Fluorogenic Electrophile for Live Cell Nanoscopy
Bibliographic record
Abstract
We report a chemically tuned fluorogenic electrophile designed to conduct live-cell super-resolution imaging by exploiting its stochastic reversible alkylation reaction with cellular nucleophiles. Consisting of a lipophilic BODIPY fluorophore tethered to an electrophilic cyanoacrylate warhead, the new probe cyanoAcroB remains nonemissive due to internal conversion along the cyanoacrylate moiety. Intermittent fluorescence occurs following thiolate Michael addition to the probe, followed by retro-Michael reaction, tuned by the cyano moiety in the acrylate warhead and BODIPY decoration. This design enables long-term super-resolved imaging of live cells by preventing fluorescent product accumulation and background increase, while preserving the pool of the probe. We demonstrate the imaging capabilities of cyanoAcroB via two methods: (i) single-molecule localization microscopy imaging with nanometer accuracy by stochastic chemical activation and (ii) super-resolution radial fluctuation. The latter tolerates higher probe concentrations and low imaging powers, as it exploits the stochastic adduct dissociation. Super-resolved imaging with cyanoAcroB reveals that electrophile alkylation is prevalent in mitochondria and endoplasmic reticulum. The 2D dynamics of these organelles within a single cell are unraveled with tens of nanometers spatial and sub-second temporal resolution through continuous imaging of cyanoAcroB extending for tens of minutes. Our work underscores the opportunities that reversible fluorogenic probes with bioinspired warheads bring toward illuminating chemical reactions with super-resolved features in live cells.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".