Issue 6, 2022

Intelligent and robust DNA robots capable of swarming into leakless nonlinear amplification in response to a trigger

Abstract

Nonlinear DNA signal amplification with an enzyme-free isothermal self-assembly process is uniquely useful in nanotechnology and nanomedicine. However, progress in this direction is hampered by the lack of effective design models of leak-resistant DNA building blocks. Here, we propose two conceptual models of intelligent and robust DNA robots to perform a leakless nonlinear signal amplification in response to a trigger. Two conceptual models are based on super-hairpin nanostructures, which are designed by innovating novel principles in methodology and codifying them into embedded programs. The dynamical and thermodynamical analyses reveal the critical elements and leak-resistant mechanisms of the designed models, and the leak-resistant behaviors of the intelligent DNA robots and morphologies of swarming into nonlinear amplification are separately verified. The applications of the designed models are also illustrated in specific signal amplification and targeted payload enrichment via integration with an aptamer, a fluorescent molecule and surface-enhanced Raman spectroscopy. This work has the potential to serve as design guidelines of intelligent and robust DNA robots and leakless nonlinear DNA amplification, and also as the design blueprint of cargo delivery robots with the performance of swarming into nonlinear amplification in response to a target automatically, facilitating their future applications in biosensing, bioimaging and biomedicine.

Graphical abstract: Intelligent and robust DNA robots capable of swarming into leakless nonlinear amplification in response to a trigger

Supplementary files

Article information

Article type
Communication
Submitted
14 Jan 2022
Accepted
22 Apr 2022
First published
22 Apr 2022

Nanoscale Horiz., 2022,7, 634-643

Intelligent and robust DNA robots capable of swarming into leakless nonlinear amplification in response to a trigger

S. Li, Y. Cheng, M. Qin, G. Zhou, P. Li and L. Yang, Nanoscale Horiz., 2022, 7, 634 DOI: 10.1039/D2NH00018K

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