Encoding quantized fluorescence states with fractal DNA frameworks

Jiang Li, Jiangbing Dai, Shuoxing Jiang, Mo Xie, Tingting Zhai, Linjie Guo, Shuting Cao, Shu Xing, Zhibei Qu, Yan Zhao, Fei Wang, Yang Yang, Lei Liu, Xiaolei Zuo, Lihua Wang (), Hao Yan () and Chunhai Fan ()
Additional contact information
Jiang Li: Chinese Academy of Sciences
Jiangbing Dai: Chinese Academy of Sciences
Shuoxing Jiang: Arizona State University
Mo Xie: Chinese Academy of Sciences
Tingting Zhai: Shanghai Jiao Tong University
Linjie Guo: Chinese Academy of Sciences
Shuting Cao: Chinese Academy of Sciences
Shu Xing: Chinese Academy of Sciences
Zhibei Qu: Shanghai Jiao Tong University
Yan Zhao: Chinese Academy of Sciences
Fei Wang: Shanghai Jiao Tong University
Yang Yang: Chinese Academy of Sciences
Lei Liu: Chinese Academy of Sciences
Xiaolei Zuo: Shanghai Jiao Tong University
Lihua Wang: Chinese Academy of Sciences
Hao Yan: Arizona State University
Chunhai Fan: Shanghai Jiao Tong University

Nature Communications, 2020, vol. 11, issue 1, 1-10

Abstract: Abstract Signal amplification in biological systems is achieved by cooperatively recruiting multiple copies of regulatory biomolecules. Nevertheless, the multiplexing capability of artificial fluorescent amplifiers is limited due to the size limit and lack of modularity. Here, we develop Cayley tree-like fractal DNA frameworks to topologically encode the fluorescence states for multiplexed detection of low-abundance targets. Taking advantage of the self-similar topology of Cayley tree, we use only 16 DNA strands to construct n-node (n = 53) structures of up to 5 megadalton. The high level of degeneracy allows encoding 36 colours with 7 nodes by site-specifically anchoring of distinct fluorophores onto a structure. The fractal topology minimises fluorescence crosstalk and allows quantitative decoding of quantized fluorescence states. We demonstrate a spectrum of rigid-yet-flexible super-multiplex structures for encoded fluorescence detection of single-molecule recognition events and multiplexed discrimination of living cells. Thus, the topological engineering approach enriches the toolbox for high-throughput cell imaging.

Date: 2020
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DOI: 10.1038/s41467-020-16112-z

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