Using quantitative models to predict the biological interactions of nanoparticles will accelerate the translation of nanotechnology. Here, we characterized the serum protein corona 'fingerprint' formed around a library of 105 surface-modified gold nanoparticles. Applying a bioinformatics-inspired approach, we developed a multivariate model that uses the protein corona fingerprint to predict cell association 50% more accurately than a model that uses parameters describing nanoparticle size, aggregation state, and surface charge. Our model implicates a set of hyaluronan-binding proteins as mediators of nanoparticle-cell interactions. This study establishes a framework for developing a comprehensive database of protein corona fingerprints and biological responses for multiple nanoparticle types. Such a database can be used to develop quantitative relationships that predict the biological responses to nanoparticles and will aid in uncovering the fundamental mechanisms of nano-bio interactions.
We have shown that nanoparticles (NPs) can be used as ligand-multimerization platforms to activate specific cellular receptors in vivo. Nanoparticles coated with autoimmune disease-relevant peptide-major histocompatibility complexes (pMHC) blunted autoimmune responses by triggering the differentiation and expansion of antigen-specific regulatory T cells in vivo. Here, we define the engineering principles impacting biological activity, detail a synthesis process yielding safe and stable compounds, and visualize how these nanomedicines interact with cognate T cells. We find that the triggering properties of pMHC-NPs are a function of pMHC intermolecular distance and involve the sustained assembly of large antigen receptor microclusters on murine and human cognate T cells. These compounds show no off-target toxicity in zebrafish embryos, do not cause haematological, biochemical or histological abnormalities, and are rapidly captured by phagocytes or processed by the hepatobiliary system. This work lays the groundwork for the design of ligand-based NP formulations to re-program in vivo cellular responses using nanotechnology.
Rapid, simple, and cost-effective diagnostics are needed to improve healthcare at the point of care (POC). However, the most widely used POC diagnostic, the lateral flow immunoassay (LFA), is ~1000-times less sensitive and has a smaller analytical range than laboratory tests, requiring a confirmatory test to establish truly negative results. Here, a rational and systematic strategy is used to design the LFA contrast label (i.e., gold nanoparticles) to improve the analytical sensitivity, analytical detection range, and antigen quantification of LFAs. Specifically, we discovered that the size (30, 60, or 100 nm) of the gold nanoparticles is a main contributor to the LFA analytical performance through both the degree of receptor interaction and the ultimate visual or thermal contrast signals. Using the optimal LFA design, we demonstrated the ability to improve the analytical sensitivity by 256-fold and expand the analytical detection range from 3 log10 to 6 log10 for diagnosing patients with inflammatory conditions by measuring C-reactive protein. This work demonstrates that, with appropriate design of the contrast label, a simple and commonly used diagnostic technology can compete with more expensive state-of-the-art laboratory tests.
The surface of nanoparticles changes immediately after intravenous injection because blood proteins adsorb on the surface. How this interface changes during circulation and its impact on nanoparticle distribution within the body is not understood. Here, we developed a workflow to show that the evolution of proteins on nanoparticle surfaces predicts the biological fate of nanoparticles in vivo. This workflow involves extracting nanoparticles at multiple time points from circulation, isolating the proteins off the surface and performing proteomic mass spectrometry. The mass spectrometry protein library served as inputs, while blood clearance and organ accumulation were used as outputs to train a supervised deep neural network that predicts nanoparticle biological fate. In a double-blinded study, we tested the network by predicting nanoparticle spleen and liver accumulation with upward of 94% accuracy. Our neural network discovered that the mechanism of liver and spleen uptake is due to patterns of a multitude of nanoparticle surface adsorbed proteins. There are too many combinations to change these proteins manually using chemical or biological inhibitors to alter clearance. Therefore, we developed a technique that uses the host to act as a bioreactor to prepare nanoparticles with predictable clearance patterns that reduce liver and spleen uptake by 50% and 70%, respectively. These techniques provide opportunities to both predict nanoparticle behavior and also to engineer surface chemistries that are specifically designed by the body.
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