4.8 Article

Programmable intracellular DNA biocomputing circuits for reliable cell recognitions

期刊

CHEMICAL SCIENCE
卷 10, 期 10, 页码 2989-2997

出版社

ROYAL SOC CHEMISTRY
DOI: 10.1039/c8sc05217d

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资金

  1. National Basic Research Program of China (973 Program) [2015CB932601]
  2. National Natural Science Foundation of China [21874103, 81602610, 81472735, 21503151]
  3. Jiangsu Provincial Natural Science Foundation of China [BK20161248, BK20160381]
  4. Fundamental Research Funds for the Central Universities [2042018kf0210]
  5. Open Fund of Key Laboratory of Analytical Chemistry for Biology and Medicine (Wuhan University), Ministry of Education [ACBM2017005]

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Dynamic nucleic acid-based biocircuits have spurred substantial research efforts for diagnosis or biomedical applications at the molecular level; nevertheless, it still remains a challenge to design programmable molecular circuit devices for autonomous and accurate diagnosis of low abundance biomolecules in a complex intracellular environment. Herein, a reconfigurable hybridization-based chain reaction is introduced to assemble modular biocomputing circuits that include a general sensing module and a versatile processing module. By modular sensing module design, we realized multiple endogenous miRNA-initiated biocomputing operations, including binary logic gates (OR, AND, INHIBIT and XOR), and more advanced concatenated logic circuits (XOR-AND, XOR-INHIBIT, and XOR-OR) in different living cells. The sensing module transduces the primary miRNA sensing event into an intermediate trigger for activating the processing module that further transduces the specific analyte recognition pattern into an amplified fluorescence readout. Based on an appropriate selection of multiple miRNA analytes, various miRNA expression patterns could be utilized for sensitive and selective cell discriminations. The inherent synergistically accelerated recognition and hybridization features of our biocomputing systems contribute to the amplified detection of multiplex endogenous miRNAs in living cells, thus providing an efficient toolbox for more accurate diagnosis and programmable therapeutics.

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