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A temporally resolved DNA framework state machine in living cells

Nature Machine Intelligence volume 5, pages 980–990 (2023)Cite this article

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Abstract

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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Fig. 1: Design and characterization of the DFSM.
Fig. 2: State switching of the DFSM as shown with fluorescence patterns.
Fig. 3: Kinetics of state switching sensitive to the temporal order of inputs.
Fig. 4: Temporal mapping of the DFSM responsive to NCL on cells.
Fig. 5: Internalization and operation switching of DFSMs in living cells.
Fig. 6: Operation of DFSM–CRISPR for NCL targeting in living cells.

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Data availability

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.

Code availability

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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Acknowledgements

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.).

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Author notes

  1. These authors contributed equally: Yan Zhao, Shuting Cao, Yue Wang, Fan Li.

Authors and Affiliations

  1. Institute of Materiobiology, Department of Chemistry, College of Science, Shanghai University, Shanghai, China

    Yan Zhao, Linjie Guo, Ying Zhu, Lihua Wang & Jiang Li

  2. The Interdisciplinary Research Center, Shanghai Synchrotron Radiation Facility, Zhangjiang Laboratory, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, China

    Yan Zhao, Linjie Guo, Ying Zhu, Lihua Wang & Jiang Li

  3. Institute of Biomedical Health Technology and Engineering, Shenzhen Bay Laboratory, Shenzhen, China

    Yan Zhao

  4. Division of Physical Biology, CAS Key Laboratory of Interfacial Physics and Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China

    Shuting Cao, Yue Wang & Lixuan Lin

  5. Xiangfu Laboratory, Jiashan, China

    Shuting Cao

  6. Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

    Fan Li & Xiaolei Zuo

  7. School of Chemistry and Chemical Engineering, New Corner Stone Science Laboratory, Frontiers Science Center for Transformative Molecules, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China

    Fei Wang, Xiaolei Zuo & Chunhai Fan

  8. Zhangjiang Laboratory, Shanghai, China

    Fei Wang & Lihua Wang

  9. Key Laboratory for Organic Electronics and Information Displays (KLOEID), Institute of Advanced Materials (IAM) and School of Materials Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China

    Jie Chao

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Contributions

C.F., J.L. and L.W. directed the research. C.F., J.L., L.W. and Y. Zhao conceived and designed the experiments. Y. Zhao, S.C., Y.W., F.L., L.G. and L.L. carried out the experiments and analysed data. F.W., J.C., X.Z. and Y. Zhu provided suggestions and technical support on the project. J.L. and C.F. wrote and revised the paper. All authors discussed the results and commented on the paper.

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Correspondence to Jiang Li or Chunhai Fan.

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Extended data

Extended Data Fig. 1 FACS gating strategy.

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.

Extended Data Fig. 2 FACS gating strategy.

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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Supplementary Methods, Figs. 1–18, and Tables 1 and 2.

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Source Data Fig. 4

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Source Data Fig. 5

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Source Data Fig. 6

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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

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