nanoFeatures: a cross-platform application to characterize nanoparticles from super-resolution microscopy images

Abstract

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