Gold-Labeled Block Copolymer Micelles Reveal Gold Aggregates at Multiple Subcellular Sites
Bibliographic record
Abstract
There is increasing interest in the usefulness of block copolymer micelles as drug delivery vehicles. However, their subcellular distribution has not been explored extensively, mostly because of the lack of adequately labeled block copolymers. In a previous study, we showed that fluorescently labeled block copolymer micelles entered living cells and co-localized with cytoplasmic organelles selectively labeled with fluorescent dyes. The details of the observed co-localizations were, however, limited by the resolution of the fluorescence approach, which is ca. 500 nm. Using transmission electron microscopy (TEM), we established time- and concentration-dependent subcellular distributions of gold-labeled micelles within human embryonic kidney (HEK 293) cells and human lung carcinoma (A549) cells. Gold particles were incorporated into poly(4-vinylpyridine)-block-poly(ethylene oxide) (P4VP21-b-PEO45) micelles. Data from dynamic light scattering (DLS) and TEM analyses revealed that the sizes of the gold particles ranged from 4 to 8 nm. The cells survived up to 24 h in the presence of low gold-labeled micelle concentrations (0.73 microg/mL), but cell death occurred at higher concentrations (i.e., kidney cells are more susceptible than lung cells). Over 24 h periods of equivalent exposure, lung cells internalized significantly more gold-incorporated micelles than kidney cells. Although micelles were added to the cell culture media as dispersed colloidal particles, the presence of serum in these media caused aggregation. These aggregates occurred mainly close to the cell plasma membrane at early times (5-10 min); however, at later times (24 h) aggregated particles were seen inside endosomes and lysozomes. Thus, gold-incorporated (labeled) micelles can serve as a valuable extension of the fluorescence approach to visualizing the localization of micelles in subcellular compartments, improving the resolution by at least 20-fold.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.004 |
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 teacher head, 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".