Gas Vesicle-Blood Interactions Enhance Ultrasound Imaging Contrast.

Gas vesicles (GVs) are genetically encoded, air-filled protein nanostructures of broad interest for biomedical research and clinical applications, acting as imaging and therapeutic agents for ultrasound, magnetic resonance, and optical techniques. However, the biomedical applications of GVs as systemically injectable nanomaterials have been hindered by a lack of understanding of GVs' interactions with blood components, which can significantly impact in vivo behavior. Here, we investigate the dynamics of GVs in the bloodstream using a combination of ultrasound and optical imaging, surface functionalization, flow cytometry, and mass spectrometry. We find that erythrocytes and serum proteins bind to GVs and shape their acoustic response, circulation time, and immunogenicity. We show that by modifying the GV surface we can alter these interactions and thereby modify GVs' in vivo performance. These results provide critical insights for the development of GVs as agents for nanomedicine.

The exit of nanoparticles from solid tumours.

Nanoparticles enter tumours through endothelial cells, gaps or other mechanisms, but how they exit is unclear. The current paradigm states that collapsed tumour lymphatic vessels impair the exit of nanoparticles and lead to enhanced retention. Here we show that nanoparticles exit the tumour through the lymphatic vessels within or surrounding the tumour. The dominant lymphatic exit mechanism depends on the nanoparticle size. Nanoparticles that exit the tumour through the lymphatics are returned to the blood system, allowing them to recirculate and interact with the tumour in another pass. Our results enable us to define a mechanism of nanoparticle delivery to solid tumours alternative to the enhanced permeability and retention effect. We call this mechanism the active transport and retention principle. This delivery principle provides a new framework to engineer nanomedicines for cancer treatment and detection.

Gas vesicle-blood interactions enhance ultrasound imaging contrast.

Gas vesicles (GVs) are genetically encoded, air-filled protein nanostructures of broad interest for biomedical research and clinical applications, acting as imaging and therapeutic agents for ultrasound, magnetic resonance, and optical techniques. However, the biomedical applications of GVs as a systemically injectable nanomaterial have been hindered by a lack of understanding of GVs' interactions with blood components, which can significantly impact in vivo performance. Here, we investigate the dynamics of GVs in the bloodstream using a combination of ultrasound and optical imaging, surface functionalization, flow cytometry, and mass spectrometry. We find that erythrocytes and serum proteins bind to GVs and shape their acoustic response, circulation time, and immunogenicity. We show that by modifying the GV surface, we can alter these interactions and thereby modify GVs' in vivo performance. These results provide critical insights for the development of GVs as agents for nanomedicine.

A proposed mathematical description of in vivo nanoparticle delivery.

Nanoparticles are promising vehicles for the precise delivery of molecular therapies to diseased sites. Nanoparticles interact with a series of tissues and cells before they reach their target, which causes less than 1% of administered nanoparticles to be delivered to these target sites. Researchers have been studying the nano-bio interactions that mediate nanoparticle delivery to develop guidelines for designing nanoparticles with enhanced delivery properties. In this review article, we describe these nano-bio interactions with a series of mathematical equations that quantitatively define the nanoparticle delivery process. We employ a compartment model framework to describe delivery where nanoparticles are either (1) at the site of administration, (2) in the vicinity of target cells, (3) internalized by the target cells, or (4) sequestered away in off-target sites or eliminated from the body. This framework explains how different biological processes govern nanoparticle transport between these compartments, and the role of intercompartmental transport rates in determining the final nanoparticle delivery efficiency. Our framework provides guiding principles to engineer nanoparticles for improved targeted delivery.

DNA-Controlled Encapsulation of Small Molecules in Protein Nanoparticles.

A nanoparticle can hold multiple types of therapeutic and imaging agents for disease treatment and diagnosis. However, controlling the storage of molecules in nanoparticles is challenging, because nonspecific intermolecular interactions are used for encapsulation. Here, we used specific DNA interactions to store molecules in nanoparticles. We made nanoparticles containing DNA anchors to capture DNA-conjugated small molecules. By changing the sequences and stoichiometry of DNA anchors, we can control the amount and ratio of molecules with different chemical properties in the nanoparticles. We modified the cytotoxicity of our nanoparticles to cancer cells by changing the ratio of encapsulated drugs (mertansine and doxorubicin). Specifically controlling the storage of multiple types of molecules allows us to optimize the properties of combination drug and imaging nanoparticles.

Supervised Learning and Mass Spectrometry Predicts the in Vivo Fate of Nanomaterials.

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.