4.8 Article

Programming cell-free biosensors with DNA strand displacement circuits

Journal

NATURE CHEMICAL BIOLOGY
Volume 18, Issue 4, Pages 385-+

Publisher

NATURE PORTFOLIO
DOI: 10.1038/s41589-021-00962-9

Keywords

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Funding

  1. Northwestern University's Graduate School Cluster in Biotechnology, System, and Synthetic Biology
  2. Ryan Fellowship
  3. McCormick School of Engineering Terminal Year Fellowship
  4. Department of Defense through the National Defense Science & Engineering Graduate (NDSEG) Fellowship program
  5. NSF CAREER [1452441]
  6. NSF MCB RAPID [1929912]
  7. Crown Family Center for Jewish and Israel Studies at Northwestern University
  8. Searle Funds at The Chicago Community Trust
  9. Biotechnology Training Program

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Cell-free biosensors have been enhanced by interfacing them with toehold-mediated strand displacement circuits, enabling the construction of various logical functions and an analog-to-digital converter circuit for encoding the concentration range of detected molecules. This work lays the foundation for creating 'smart' diagnostics using molecular computations to improve the speed and utility of biosensors.
Cell-free biosensors are powerful platforms for monitoring human and environmental health. Here, we expand their capabilities by interfacing them with toehold-mediated strand displacement circuits, a dynamic DNA nanotechnology that enables molecular computation through programmable interactions between nucleic acid strands. We develop design rules for interfacing a small molecule sensing platform called ROSALIND with toehold-mediated strand displacement to construct hybrid RNA-DNA circuits that allow fine-tuning of reaction kinetics. We use these design rules to build 12 different circuits that implement a range of logic functions (NOT, OR, AND, IMPLY, NOR, NIMPLY, NAND). Finally, we demonstrate a circuit that acts like an analog-to-digital converter to create a series of binary outputs that encode the concentration range of the molecule being detected. We believe this work establishes a pathway to create 'smart' diagnostics that use molecular computations to enhance the speed and utility of biosensors.

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