Cellular imaging by targeted assembly of hot-spot SERS and photoacoustic nanoprobes using split-fluorescent protein scaffolds
Bibliographic record
Abstract
The in cellulo assembly of plasmonic nanomaterials into photo-responsive probes is of great interest for many bioimaging and nanophotonic applications but remains challenging with traditional nucleic acid scaffolds-based bottom-up methods. Here, we address this quandary using split-fluorescent protein (FP) fragments as molecular glue and switchable Raman reporters to assemble gold or silver plasmonic nanoparticles (NPs) into photonic clusters directly in live cells. When targeted to diffusing surface biomarkers in cancer cells, the NPs self-assemble into surface-enhanced Raman-scattering (SERS) nanoclusters having hot spots homogenously seeded by the reconstruction of full-length FPs. Within plasmonic hot spots, autocatalytic activation of the FP chromophore and near-field amplification of its Raman fingerprints enable selective and sensitive SERS imaging of targeted cells. This FP-driven assembly of metal colloids also yields enhanced photoacoustic signals, allowing the hybrid FP/NP nanoclusters to serve as contrast agents for multimodal SERS and photoacoustic microscopy with single-cell sensitivity.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".