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Published December 7, 2017 | public
Journal Article Open

Fractal assembly of micrometre-scale DNA origami arrays with arbitrary patterns


Self-assembled DNA nanostructures enable nanometre-precise patterning that can be used to create programmable molecular machines and arrays of functional materials. DNA origami is particularly versatile in this context because each DNA strand in the origami nanostructure occupies a unique position and can serve as a uniquely addressable pixel. However, the scale of such structures has been limited to about 0.05 square micrometres, hindering applications that demand a larger layout and integration with more conventional patterning methods. Hierarchical multistage assembly of simple sets of tiles can in principle overcome this limitation, but so far has not been sufficiently robust to enable successful implementation of larger structures using DNA origami tiles. Here we show that by using simple local assembly rules that are modified and applied recursively throughout a hierarchical, multistage assembly process, a small and constant set of unique DNA strands can be used to create DNA origami arrays of increasing size and with arbitrary patterns. We illustrate this method, which we term 'fractal assembly', by producing DNA origami arrays with sizes of up to 0.5 square micrometres and with up to 8,704 pixels, allowing us to render images such as the Mona Lisa and a rooster. We find that self-assembly of the tiles into arrays is unaffected by changes in surface patterns on the tiles, and that the yield of the fractal assembly process corresponds to about 0.95^(m − 1) for arrays containing m tiles. When used in conjunction with a software tool that we developed that converts an arbitrary pattern into DNA sequences and experimental protocols, our assembly method is readily accessible and will facilitate the construction of sophisticated materials and devices with sizes similar to that of a bacterium using DNA nanostructures.

Additional Information

© 2017 Macmillan Publishers Limited, part of Springer Nature. Received: 04 June 2017; Accepted: 16 October 2017; Published online: 06 December 2017. Data Availability: All data generated or analysed during this study are included in the paper (and its Supplementary Information). We thank R. M. Murray for sharing an acoustic liquid-handling robot and P. W. K. Rothemund for sharing a qPCR machine. We thank E. Winfree and P. W. K. Rothemund for critiques on the manuscript. G.T. was supported by a BWF grant (1010684). P.P. was supported by a NIH/NRSA training grant (5 T32 GM07616). L.Q. was supported by a Career Award at the Scientific Interface from the Burroughs Wellcome Fund (1010684) and a Faculty Early Career Development Award from NSF (1351081). Author Contributions: G.T. and P.P. initiated the project, designed and performed the experiments, and analysed the data; P.P. developed the software tool; G.T., P.P. and L.Q. wrote the manuscript; and L.Q. guided the project. Competing interests: A provisional patent application has been filed for this work, submitted to the Office of Technology Transfer at the California Institute of Technology.

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September 22, 2023
September 22, 2023