DNA-PAINT super-resolution imaging data of surface exposed active sites on particles

Point-Spread Function Deformations Unlock 3D Localization Microscopy on Spherical Nanoparticles

Nanoparticles (NPs) have proven their applicability in biosensing, drug delivery, and photothermal therapy, but their performance depends critically on the distribution and number of functional groups on their surface. When studying surface functionalization using super-resolution microscopy, the NP modifies the fluorophore's point-spread function (PSF). This leads to systematic mislocalizations in conventional analyses employing Gaussian PSFs. Here, we address this shortcoming by deriving the analytical PSF model for a fluorophore near a spherical NP. Its calculation is four orders of magnitude faster than numerical approaches and thus feasible for direct use in localization algorithms. We fit this model to individual 2D images from DNA-PAINT experiments on DNA-coated gold NPs and demonstrate extraction of the 3D positions of functional groups with <5 nm precision, revealing inhomogeneous surface coverage. Our method is exact, fast, accessible, and poised to become the standard in super-resolution imaging of NPs for biosensing and drug delivery applications.

Advanced optical imaging for the rational design of nanomedicines

Despite the enormous potential of nanomedicines to shape the future of medicine, their clinical translation remains suboptimal. Translational challenges are present in every step of the development pipeline, from a lack of understanding of patient heterogeneity to insufficient insights on nanoparticle properties and their impact on material-cell interactions. Here, we discuss how the adoption of advanced optical microscopy techniques, such as super-resolution optical microscopies, correlative techniques, and high-content modalities, could aid the rational design of nanocarriers, by characterizing the cell, the nanomaterial, and their interaction with unprecedented spatial and/or temporal detail. In this nanomedicine arena, we will discuss how the implementation of these techniques, with their versatility and specificity, can yield high volumes of multi-parametric data; and how machine learning can aid the rapid advances in microscopy: from image acquisition to data interpretation.

Enzyme Purification Improves the Enzyme Loading, Self-Propulsion, and Endurance Performance of Micromotors

Enzyme-powered micro- and nanomotors make use of biocatalysis to self-propel in aqueous media and hold immense promise for active and targeted drug delivery. Most (if not all) of these micro- and nanomotors described to date are fabricated using a commercially available enzyme, despite claims that some commercial preparations may not have a sufficiently high degree of purity for downstream applications. In this study, the purity of a commercial urease, an enzyme frequently used to power the motion of micro- and nanomotors, was evaluated and found to be impure. After separating the hexameric urease from the protein impurities by size-exclusion chromatography, the hexameric urease was subsequently characterized and used to functionalize hollow silica microcapsules. Micromotors loaded with purified urease were found to be 2.5 times more motile than the same micromotors loaded with unpurified urease, reaching average speeds of 5.5 μm/s. After comparing a number of parameters, such as enzyme distribution, protein loading, and motor reusability, between micromotors functionalized with purified vs unpurified urease, it was concluded that protein purification was essential for optimal performance of the enzyme-powered micromotor.

Nanoscale Mapping of Recombinant Viral Proteins: From Cells to Virus-Like Particles

Influenza recombinant proteins and virus-like particles (VLPs) play an important role in vaccine development (e.g., CadiFlu-S). However, their production from mammalian cells suffers from low yields and lack of control of the final VLPs. To improve these issues, characterization techniques able to visualize and quantify the different steps of the process are needed. Fluorescence microscopy represents a powerful tool able to image multiple protein targets; however, its limited resolution hinders the study of viral constructs. Here, we propose the use of super-resolution microscopy and in particular of DNA-point accumulation for imaging in nanoscale topography (DNA-PAINT) microscopy as a characterization method for recombinant viral proteins on both cells and VLPs. We were able to quantify the amount of the three main influenza proteins (hemagglutinin (HA), neuraminidase (NA), and ion channel matrix protein 2 (M2)) per cell and per VLP with nanometer resolution and single-molecule sensitivity, proving that DNA-PAINT is a powerful technique to characterize recombinant viral constructs.

Quantifying the effect of PEG architecture on nanoparticle ligand availability using DNA-PAINT

The importance of PEG architecture on nanoparticle (NP) functionality is known but still difficult to investigate, especially at a single particle level. Here, we apply DNA Point Accumulation for Imaging in Nanoscale Topography (DNA-PAINT), a super-resolution microscopy (SRM) technique, to study the surface functionality in poly(lactide-co-glycolide)-poly(ethylene glycol) (PLGA-PEG) NPs with different PEG structures. We demonstrated how the length of the PEG spacer can influence the accessibility of surface chemical functionality, highlighting the importance of SRM techniques to support the rational design of functionalized NPs.

Correlating Super-Resolution Microscopy and Transmission Electron Microscopy Reveals Multiparametric Heterogeneity in Nanoparticles

The functionalization of nanoparticles with functional moieties is a key strategy to achieve cell targeting in nanomedicine. The interplay between size and ligand number is crucial for the formulation performance and needs to be properly characterized to understand nanoparticle structure-activity relations. However, there is a lack of methods able to measure both size and ligand number at the same time and at the single particle level. Here, we address this issue by introducing a correlative light and electron microscopy (CLEM) method combining super-resolution microscopy (SRM) and transmission electron microscopy (TEM) imaging. We apply our super-resCLEM method to characterize the relationship between size and ligand number and density in PLGA-PEG nanoparticles. We highlight how heterogeneity found in size can impact ligand distribution and how a significant part of the nanoparticle population goes completely undetected in the single-technique analysis. Super-resCLEM holds great promise for the multiparametric analysis of other parameters and nanomaterials.