Cristina Izquierdo-Lozano’s research while affiliated with Eindhoven University of Technology and other places

Bioinformatics and cheminformatics are established disciplines, but nanoinformatics, the development of computational tools for understanding and designing nanomaterials, is still in its infancy. In light of the new data-driven approaches for nanomaterials discovery, there is a growing need for in silico tools tailored to analyze nanomaterials datasets. This is particularly crucial for soft materials, where a crystalline structure cannot be obtained and therefore the characterization datasets are less structured, and there are no standard methods for data mining. Here we present a computational package capable of visualizing, describing, and comparing nanoparticle datasets obtained with super-resolution microscopy at the single-particle and single-molecule level. Our method allows us to: i) visualize multiparametric nanoparticle datasets to grasp material properties and heterogeneity; ii) have a quantitative evaluation of a material through a series of molecular descriptors, and iii) compare different materials quantitatively and globally, going beyond comparison of a single property. We applied this method to a library of targeted nanoparticles revealing particle heterogeneity, similarities, and correlations between the synthesis and the physicochemical properties of the different nanomaterials. Finally, we show the potential of this approach to reveal batch-to-batch variations in time and between users hidden in standard analysis.

Glycosylation profoundly influences cellular function, yet deciphering its intricate patterns remains a formidable challenge. Current techniques often compromise sensitivity, multiplexing, or the ability to capture in-situ cell-to-cell variations. To address these limitations, we introduce ‘Lectin-PAINT,’ a super-resolution imaging method enabling multiplexed live-cell visualization of the cellular glycocalyx at the single-cell and single-molecule levels. Lectin-PAINT leverages the reversible binding of lectins to specific carbohydrate families to perform point accumulation in nanoscale topography (PAINT), enabling the identification, mapping, and tracking of carbohydrates with a resolution beyond the diffraction limit. Our technique harnesses a tailored lectin library, spanning key carbohydrate recognition, offering insights into their abundance, affinity, and mobility. Through 8-color super-resolution imaging, we extract more than 350 glycosylation parameters with single-cell resolution, creating a cell’s ‘glycotype’ or glycan fingerprint. We showcase the power of this approach by glycotyping and categorizing a diverse set of cancer cell types, shedding light on the heterogeneity and variability of the glycocalyx in cancer. In the future, this research will contribute to the more fundamental understanding of changes in the glycocalyx due to disease.

Super-resolution microscopy and Single-Molecule Localization Microscopy (SMLM) are a powerful tool to characterize synthetic nanomaterials used for many applications such as drug delivery. In the last decade, imaging techniques like STORM, PALM, and PAINT have been used to study nanoparticle size, structure, and composition. While imaging has progressed significantly, often image analysis did not follow accordingly and many studies are limited to qualitative and semi-quantitative analysis. Therefore, it is imperative to have a robust and accurate method to analyze SMLM images of nanoparticles and extract quantitative features from them. Here we introduce nanoFeatures , a cross-platform Matlab-based app for the automatic and quantitative analysis of super-resolution images. NanoFeatures makes use of clustering algorithms to identify nanoparticles from the raw data (localization list) and extract quantitative information about size, shape, and molecular abundance at the single-particle and single-molecule levels. Moreover, it applies a series of quality controls, increasing data quality and avoiding artifacts. NanoFeatures , thanks to its intuitive interface is also accessible to non-experts and will facilitate analysis of super-resolution microscopy for materials scientists and nanotechnologies. This easy accessibility to expansive feature characterization at the single particle level will bring us one step closer to understanding the relationship between nanostructure features and their efficiency. https://github.com/n4nlab/nanoFeatures

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.