Organelle-Targeting Polymer Dots Exhibiting Thermally Activated Delayed Fluorescence for Subcellular Imaging
Bibliographic record
Abstract
Selective imaging of specific subcellular structures provides valuable information about the cellular microenvironment. Materials exhibiting thermally activated delayed fluorescence (TADF) are rapidly emerging as metal-free probes with long-lived emission for intracellular time-gated imaging applications. Polymers incorporating TADF emitters can self-assemble into luminescent nanoparticles, termed polymer dots (Pdots), and this strategy enables them to circumvent the limitations of commercial organelle trackers and small molecule TADF emitters. In this study, diblock copolymers comprised of a hydrophilic block containing organelle-targeting monomers and a hydrophobic TADF-active block were synthesized by ring-opening metathesis polymerization (ROMP). Oxanorbornene-based monomers incorporating morpholine and triphenylphosphonium groups for lysosome and mitochondria targeting, respectively, were also synthesized. ROMP by sequential addition yielded well-defined diblock copolymers with dispersities <1.28. To analyze the effect of tuning the hydrophilic corona on cellular viability and uptake, we prepared Pdots with poly(ethylene glycol) (PEG) and bis-guanidinium (BGN) coronas, resulting in limited and efficient cellular uptake, respectively. Red-emissive Pdots with BGN-based coronas and organelle-targeting functionality were obtained with quantum yields up to 12% in water under air. Colocalization analysis confirmed that lysosome and mitochondria labeling in live HeLa cells was accomplished within 2 h of incubation, affording Pearson's correlation coefficients of 0.37 and 0.70, respectively. The potential application of these Pdots for time-resolved imaging is highlighted by a proof of concept using time-gated spectroscopy, which effectively separates the delayed emission of the TADF Pdots from the background autofluorescence of biological serum.
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.
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 | 0.000 |
| 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".