Characterizing Nanoparticles in Biological Matrices: Tipping Points in Agglomeration State and Cellular Delivery In Vitro.
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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. |
Year of Publication | 2017
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Journal | ACS Nano
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Volume | 11
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Issue | 12
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Pages | 11986-12000
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Date Published | 2017 12 26
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ISSN | 1936-086X
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DOI | 10.1021/acsnano.7b03708
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PubMed ID | 29072897
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