Chemically Tuned, Reversible Fluorogenic Electrophile for Live Cell Nanoscopy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 it