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.
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