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.

Mapping Antibody Domain Exposure on Nanoparticle Surfaces Using DNA-PAINT.

Decorating nanoparticles with antibodies (Ab) is a key strategy for targeted drug delivery and imaging. For this purpose, the orientation of the antibody on the nanoparticle is crucial to maximize fragment antibody-binding (Fab) exposure and thus antigen binding. Moreover, the exposure of the fragment crystallizable (Fc) domain may lead to the engagement of immune cells through one of the Fc receptors. Therefore, the choice of the chemistry for nanoparticle-antibody conjugation is key for the biological performance, and methods have been developed for orientation-selective functionalization. Despite the importance of this issue, there is a lack of direct methods to quantify the antibodies' orientation on the nanoparticle's surface. Here, we present a generic methodology that enables for multiplexed, simultaneous imaging of both Fab and Fc exposure on the surface of nanoparticles, based on super-resolution microscopy. Fab-specific Protein M and Fc-specific Protein G probes were conjugated to single stranded DNAs and two-color DNA-PAINT imaging was performed. Hereby, we quantitatively addressed the number of sites per particle and highlight the heterogeneity in the Ab orientation and compared the results with a geometrical computational model to validate data interpretation. Moreover, super-resolution microscopy can resolve particle size, allowing the study of how particle dimensions affect antibody coverage. We show that different conjugation strategies modulate the Fab and Fc exposure which can be tuned depending on the application of choice. Finally, we explored the biomedical importance of antibody domain exposure in antibody dependent cell mediated phagocytosis (ADCP). This method can be used universally to characterize antibody-conjugated nanoparticles, improving the understanding of relationships between structure and targeting capacities in targeted nanomedicine.

Identification of fluorescently-barcoded nanoparticles using machine learning.

Barcoding of nano- and micro-particles allows distinguishing multiple targets at the same time within a complex mixture and is emerging as a powerful tool to increase the throughput of many assays. Fluorescent barcoding is one of the most used strategies, where microparticles are labeled with dyes and classified based on fluorescence color, intensity, or other features. Microparticles are ideal targets due to their relative ease of detection, manufacturing, and higher homogeneity. Barcoding is considerably more challenging in the case of nanoparticles (NPs), where their small size results in a lower signal and greater heterogeneity. This is a significant limitation since many bioassays require the use of nano-sized carriers. In this study, we introduce a machine-learning-assisted workflow to write, read, and classify barcoded PLGA-PEG NPs at a single-particle level. This procedure is based on the encapsulation of fluorescent markers without modifying their physicochemical properties (writing), the optimization of their confocal imaging (reading), and the implementation of a machine learning-based barcode reader (classification). We found nanoparticle heterogeneity as one of the main factors that challenges barcode separation, and that information extracted from the dyes' nanoscale confinement effects (such as Förster Resonance Energy Transfer, FRET) can aid barcode identification. Moreover, we provide a guide to reaching the optimal trade-off between the number of simultaneous barcodes and classification accuracy supporting the use of this workflow for a variety of bioassays.