Plasmonic labeling of subcellular compartments in cancer cells: multiplexing with fine-tuned gold and silver nanoshells
Bibliographic record
Abstract
an entirely reformulated synthesis. The new method yielded ultramonodisperse samples, with polydispersity indexes (PI) as low as 0.02 and narrow extinction bands suited for multiplex analysis. A library of nanoshell samples with localized surface plasmon resonances (LSPR) spanning across the visible range was synthesized. Hyperspectral analysis revealed that the average scattering spectrum of 100 nanoshells matched closely to the spectrum of a single nanoshell, indicating an unprecedented low level of nanoparticle-to-nanoparticle variation for this type of system. A cell labeling experiment, targeting different subcellular compartments in MCF-7 human breast cancer cells, demonstrated that these monodisperse nanoparticles can be used as a multiplex platform for single cell analysis at the intracellular and extracellular level. Antibody-coated gold nanoshells targeted the plasma membrane, while silver nanoshells coated with a nuclear localization signal (NLS) targeted the nuclear membrane. A fluorescence counterstaining experiment, as well as single cell hyperspectral microscopy showed the excellent selectivity and specificity of each type of nanoparticle for its designed subcellular compartment. A time-lapse photodegradation experiment confirmed the enhanced stability of the nanoshells over fluorescent labeling and their capabilities for long-term live cell imaging.
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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 | 0.000 |
| 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 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".