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A nanopore interface for higher bandwidth DNA computing

Author

Listed:
  • Karen Zhang

    (University of Washington)

  • Yuan-Jyue Chen

    (Microsoft Research)

  • Delaney Wilde

    (University of Washington)

  • Kathryn Doroschak

    (University of Washington)

  • Karin Strauss

    (Microsoft Research)

  • Luis Ceze

    (University of Washington)

  • Georg Seelig

    (University of Washington
    University of Washington
    University of Washington)

  • Jeff Nivala

    (University of Washington
    University of Washington)

Abstract

DNA has emerged as a powerful substrate for programming information processing machines at the nanoscale. Among the DNA computing primitives used today, DNA strand displacement (DSD) is arguably the most popular, with DSD-based circuit applications ranging from disease diagnostics to molecular artificial neural networks. The outputs of DSD circuits are generally read using fluorescence spectroscopy. However, due to the spectral overlap of typical small-molecule fluorescent reporters, the number of unique outputs that can be detected in parallel is limited, requiring complex optical setups or spatial isolation of reactions to make output bandwidths scalable. Here, we present a multiplexable sequencing-free readout method that enables real-time, kinetic measurement of DSD circuit activity through highly parallel, direct detection of barcoded output strands using nanopore sensor array technology (Oxford Nanopore Technologies’ MinION device). These results increase DSD output bandwidth by an order of magnitude over what is currently feasible with fluorescence spectroscopy.

Suggested Citation

  • Karen Zhang & Yuan-Jyue Chen & Delaney Wilde & Kathryn Doroschak & Karin Strauss & Luis Ceze & Georg Seelig & Jeff Nivala, 2022. "A nanopore interface for higher bandwidth DNA computing," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-32526-3
    DOI: 10.1038/s41467-022-32526-3
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    References listed on IDEAS

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    1. Li-Qun Gu & Orit Braha & Sean Conlan & Stephen Cheley & Hagan Bayley, 1999. "Stochastic sensing of organic analytes by a pore-forming protein containing a molecular adapter," Nature, Nature, vol. 398(6729), pages 686-690, April.
    2. Lulu Qian & Erik Winfree & Jehoshua Bruck, 2011. "Neural network computation with DNA strand displacement cascades," Nature, Nature, vol. 475(7356), pages 368-372, July.
    3. Kevin M. Cherry & Lulu Qian, 2018. "Scaling up molecular pattern recognition with DNA-based winner-take-all neural networks," Nature, Nature, vol. 559(7714), pages 370-376, July.
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    Cited by:

    1. Xuyang Zhao & Junyao Li & Qingyuan Fan & Jing Dai & Yanping Long & Ronghui Liu & Jixian Zhai & Qing Pan & Yi Li, 2024. "Composite Hedges Nanopores codec system for rapid and portable DNA data readout with high INDEL-Correction," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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