Functionalisation and application of gold nanoparticles for enhanced cellular targeting and imaging using four-wave-mixing microscopy
Bibliographic record
Abstract
Previous research has demonstrated that cross-linking of receptors at the cell surface can drive therapeutic payloads to intracellular locations against the flow of endogenous receptors and ligands, thus improving cellular delivery. Gold nanoparticles (GNPs) are particularly promising for this application due to their customisable surface chemistry, which allows for precise control over ligand density essential for receptor cross-linking. GNPs exhibit unique optical properties that allow their use as contrast agents in both linear and non-linear biological imaging, offering advantages over traditional fluorophores that are limited by autofluorescence, photobleaching and therefore time-limited imaging. Despite these benefits, imaging GNPs in cellular environments, especially with linear imaging modalities, is complicated by background signals and the need for live-cell compatible sample preparations. To address these issues, this project utilised Four Wave Mixing (FWM) microscopy, a non-linear imaging method that allows for the background-free visualisation of GNPs. This research has developed and validated a method to quantify ligand density directly on GNPs on a single particle basis using extinction microscopy and single fluorophore bleaching, moving beyond traditional averaged estimates and showing that particle-by-particle quantifications strongly deviate from averaged methodologies. The FWM setup was developed for live cell imaging and demonstrated its utility in tracking GNPs at the single particle level in living cells, marking a substantial step forward in understanding GNP internalisation and receptor cross-linking dynamics. Lastly, previously reported non-colocalisation between GNPs and fluorophores was addressed. We have examined multiple functionalisation procedures using our ligand quantification method and live FWMi, identifying the best functionalisation procedure to be based on utilising pre-functionalised stabilising polymers for functionalisation, resulting in high quality resources for future studies on GNP internalisation. These advancements pave the way for optimising GNP-based cellular targeting, offering new insights into targeted therapeutic interventions at the cellular level.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".