The environments in living cells are highly heterogeneous and compartmentalized, posing a grand challenge for the deployment of theranostic agents with spatiotemporal precision. Despite rapid advancements in creating nanodevices responsive to various cues in cellular environments, it remains difficult to control their operations based on the temporal sequence of these cues. Here, inspired by the temporally resolved process of viral invasion in nature, we design a DNA framework state machine (DFSM) that can target specific chromatin loci in living cells in a temporally controllable manner. The DFSM is composed of a six-helix DNA framework with multiple locks that can be opened via DNA strand displacement. The opening of locks at different locations results in distinct structural configurations of the DFSM. We show that the DFSM can switch among up to six structural states with reversibility, in response to the temporally ordered molecular inputs, including DNA keys, adenosine triphosphate or nucleolin. By implementing state switching of the DFSM in living cells, we demonstrate temporally controlled CRISPR–Cas9 targeting towards specific chromatin loci, which sheds light on biocomputing and smart theranostics in complex biological environments.
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The data that support the findings of this study are available within the paper and its Supplementary Information files. Source data are provided with this paper.
The code for the algorithm used for the DNA framework state machine in this work is available in the GitHub repository at https://github.com/HalseyWang/DNA-framework-state-machine ref. 66.
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This work was supported by the National Key R&D Program of China (2020YFA0908900 to J.L.), the National Natural Science Foundation of China (T2188102, 21991134 and 21834007 to C.F.; 22022410 and 82050005 to Y. Zhu), and the New Cornerstone Investigator Program (to C.F.).
The authors declare no competing interests.
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Gating strategy to quantify cellular fluorescence in Fig. 5b. a) Gating strategy to sort single cells (P1) according to FSC vs SSC. Then the FITC intensity of cells in P1 was analyzed to determine the cellular fluorescence (taking blank group as an example). b) The same strategy was used to quantify the cellular fluorescence from the DNA nanostructures in Fig. 5b.
Gating strategy used for Fig. 6f. Gating strategy to sort single cells (R1) according to aspect ratio vs area. Then the FITC intensity of cells in R1 was analyzed to determine the cellular EGFP fluorescence (taking DFSM-sgRNA+key group as an example). The cells in R2 (FITC intensity less than 15000) were defined as EGFP negative cells. The same strategy was used to sort cells in untreated and DFSM-sgRNA groups.
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Zhao, Y., Cao, S., Wang, Y. et al. A temporally resolved DNA framework state machine in living cells. Nat Mach Intell 5, 980–990 (2023). https://doi.org/10.1038/s42256-023-00707-4