DNA nanostructures characterized via dual nanopore devices

DNA nanotechnology uses predictable interactions of nucleic acids to precisely construct complex nanostructures. Characterizing these assembled structures at the single-molecule level is crucial for validating their design and functionality. Nanopore sensing is a promising single molecule technique for this purpose as it is label-free, solution-based and high-throughput. Here, we present a dual nanopore device that incorporates dynamic feedback to manage the translocation of DNA origami structures. Compared with conventional single nanopore devices, we obtain translocation events of the same molecule through the two distinct nanopores as well as the time-of-flight between the pores. Machine learning classification methods are used in tandem with classical analysis of dwell-time/blockade distributions to analyze the complex multi-translocation events generated by different nanostructures. Using this approach, we demonstrate the ability to distinguish DNA nanostructures with different length and/or small structural differences, which are difficult to detect using conventional single nanopore sensing. This work establishes the dual nanopore devices as a powerful tool for DNA nanostructure characterization, enhancing nanopore sensing as technique for characterizing DNA nanostructures.