Characterizing Nanoparticles in Biological Matrices: Tipping Points in Agglomeration State and Cellular Delivery <i>In Vitro</i>
Bibliographic record
Abstract
Understanding the delivered cellular dose of nanoparticles is imperative in nanomedicine and nanosafety, yet is known to be extremely complex because of multiple interactions between nanoparticles, their environment, and the cells. Here, we use 3-D reconstruction of agglomerates preserved by cryogenic snapshot sampling and imaged by electron microscopy to quantify the "bioavailable dose" that is presented at the cell surface and formed by the process of individual nanoparticle sequestration into agglomerates in the exposure media. Critically, using 20 and 40 nm carboxylated polystyrene-latex and 16 and 85 nm silicon dioxide nanoparticles, we show that abrupt, dose-dependent "tipping points" in agglomeration state can arise, subsequently affecting cellular delivery and increasing toxicity. These changes are triggered by shifts in the ratio of the total nanoparticle surface area to biomolecule abundance, with the switch to a highly agglomerated state effectively changing the test article midassay, challenging the dose-response paradigm for nanosafety experiments. By characterizing nanoparticle numbers per agglomerate, we show these tipping points can lead to the formation of extreme agglomeration states whereby 90% of an administered dose is contained and delivered to the cells by just the top 2% of the largest agglomerates. We thus demonstrate precise definition, description, and comparison of the nanoparticle dose formed in different experimental environments and show that this description is critical to understanding cellular delivery and toxicity. We further empirically "stress-test" the commonly used dynamic light scattering approach, establishing its limitations to present an analysis strategy that significantly improves the usefulness of this popular nanoparticle characterization technique.
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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 | 0.001 |
| 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 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".