Gold nanoparticles have the potential to be used in biomedical applications from diagnostics to drug delivery. However, interactions of gold nanoparticles with different biomolecules in the cellular environment result in the formation of a “protein corona”—a layer of protein formed around a nanoparticle, which induces changes in the properties of nanoparticles. In this work we developed methods to reproducibly synthesize spheroidal and star-shaped gold nanoparticles, and carried out a physico-chemical characterization of synthesized anionic gold nanospheroids and gold nanostars through transmission electron microscopy (TEM), dynamic light scattering (DLS), zeta potential (ZP), nanoparticles tracking analysis (NTA), ultraviolet-visible (UV–Vis) spectroscopy and estimates of surface-enhanced Raman spectroscopy (SERS) signal enhancement ability. We analyzed how they interact with proteins after pre-incubation with bovine serum albumin (BSA) via UV–Vis, DLS, ZP, NTA, SERS, cryogenic TEM (cryo-TEM) and circular dichroism (CD) spectroscopy. The tests demonstrated that the protein adsorption on the particles’ surfaces was different for spheroidal and star shaped particles. In our experiments, star shaped particles limited the protein corona formation at SERS “hot spots”. This benefits the small-molecule sensing of nanostars in biological media. This work adds more understanding about protein corona formation on gold nanoparticles of different shapes in biological media, and therefore guides design of particles for studies in vitro and in vivo.

Colonization of distant organs by tumor cells is a critical step of cancer progression. The initial avascular stage of this process (micrometastasis) remains almost inaccessible to study due to the lack of relevant experimental approaches. Herein, we introduce an in vitro/in vivo model of organ-specific micrometastases of triple-negative breast cancer (TNBC) that is fully implemented in a cost-efficient chick embryo (CE) experimental platform. The model was built as three-dimensional (3D) tissue engineering constructs (TECs) combining human MDA-MB-231 cells and decellularized CE organ-specific scaffolds. TNBC cells colonized CE organ-specific scaffolds in 2–3 weeks, forming tissue-like structures. The feasibility of this methodology for basic cancer research, drug development, and nanomedicine was demonstrated on a model of hepatic micrometastasis of TNBC. We revealed that MDA-MB-231 differentially colonize parenchymal and stromal compartments of the liver-specific extracellular matrix (LS-ECM) and become more resistant to the treatment with molecular doxorubicin (Dox) and Dox-loaded mesoporous silica nanoparticles than in monolayer cultures. When grafted on CE chorioallantoic membrane, LS-ECM-based TECs induced angiogenic switch. These findings may have important implications for the diagnosis and treatment of TNBC. The methodology established here is scalable and adaptable for pharmacological testing and cancer biology research of various metastatic and primary tumors.

Morphologies of nanoparticles and aggregates play an important role in their properties for a range of applications. In particular, significant synthesis efforts have been directed toward controlling nanoparticle morphology and aggregation behavior in biomedical applications, as their size and shape have a significant impact on cellular uptake. Among several techniques for morphological characterization, transmission electron microscopy (TEM) can provide direct and accurate characterization of nanoparticle/aggregate morphology details. Nevertheless, manually analyzing a large number of TEM images is still a laborious process. Hence, there has been a surge of interest in employing machine learning methods to analyze nanoparticle size and shape. In order to achieve accurate nanoparticle analysis using machine learning methods, reliable and automated nanoparticle segmentation from TEM images is critical, especially when the nanoparticle image contrast is weak and the background is complex. These challenges are particularly pertinent in biomedical applications. In this work, we demonstrate an efficient, robust, and automated nanoparticle image segmentation method suitable for subsequent machine learning analysis. Our method is robust for noisy, low-electron-dose cryo-TEM images and for TEM cell images with complex, strong-contrast background features. Moreover, our method does not require any a priori training datasets, making it efficient and general. The ability to automatically, reliably, and efficiently segment nanoparticle/aggregate images is critical for advancing precise particle/aggregate control in biomedical applications.

The hard and soft protein corona of mesoporous silica particles and its integrity is significantly affected by their morphology, with spherical particles offering a homogenous protein coating which results in enhanced cellular uptake.

Single-particle cryogenic electron microscopy (cryoEM) is a widely used technique for structure determination of biomacromolecules to near-atomic resolution. Random distributions of these molecules in vitrified ice are necessary to accumulate enough two-dimensional views to generate a complete three-dimensional (3-D) reconstruction. However, interactions between the sample and the air-water interface (AWI) that occur during vitrification often bias the views of the sample, a phenomenon termed preferred orientation, limiting our ability to obtain 3-D reconstructions. Surfactants are often used as sample additives to prevent AWI-induced deterioration, but no general strategy exists for surfactant choice, requiring laborious screening for each sample. To circumvent these issues, we developed SurfACT, a cocktail of diverse surfactants with distinct physicochemical properties that limits AWI-dependent sample denaturation and orientation bias, while mitigating individual surfactant-specific drawbacks. Here we demonstrate SurfACT’s effectiveness with four proteins plagued by AWI-induced issues, including two species of hemagglutinin (HA), molybdenum-iron protein (MoFeP) from the nitrogenase enzyme, and aldolase. All four samples show drastically improved viewing distribution and map completeness when SurfACT is applied. Cryogenic electron tomography demonstrates that SurfACT redistributes particles from the AWI into the bulk ice, driving signal recovery and inhibiting denaturation. This versatile sample additive minimizes sample-specific screening and expands the capabilities and range of suitable samples for cryoEM.