Automated and Simulation-Guided Multiplexed DNA-PAINT for Nanoparticle Characterization

Abstract Recent advances in lab automation have dramatically increased the throughput of material synthesis, enabling rapid screening of nanomaterials for specific applications in nanotechnology and nanomedicine. However, this progress highlights a key bottleneck: most high-resolution characterization techniques, such as electron microscopy, atomic force microscopy (AFM), and super-resolution microscopy, remain low-throughput and labor-intensive. To keep pace, characterization must evolve toward greater automation and scalability. Here, we present an integrated and automatable workflow for multicolor DNA point accumulation for imaging in nanoscale topography (DNA-PAINT) super-resolution microscopy tailored for nanoparticles. Our approach combines kinetic simulations, automated multiplexed imaging, and streamlined image analysis to enable end-to-end automation, from experimental design to quantitative data output. Simulations predict optimal experimental conditions, thus reducing the need for manual optimization. A fluidics system paired with a TIRF microscope is used to automate multiplexed imaging by rounds of imaging and probe exchange (exchange-PAINT) on multiple memorized positions without human oversight during the acquisition process. Finally, an image analysis pipeline tailored for NPs allows for the quantification of nanoparticle size and multiplexed ligand functionalization. This methodology improves the throughput and reproducibility of single-molecule localization microscopy (SMLM) using DNA-PAINT and lowers the entry barrier for non-expert users, thus paving the way for broader adoption in nanomedicine and materials discovery workflows. Competing Interest Statement The authors have declared no competing interest.