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Issue 6, 2021

DNA origami: an outstanding platform for functions in nanophotonics and cancer therapy

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Abstract

Due to the proposal and evolution of the DNA origami technique over the past decade, DNA molecules have been utilized as building blocks for the precise construction of nanoscale architectures. Benefiting from the superior programmability of DNA molecules, the sequence-dependent recognition mechanism and robust complementation among DNA strands make it possible to customize almost arbitrary structures. Such an assembly strategy bypasses some of the limits of conventional fabrication methods; the fabrication accuracy and complexity of the target product are unprecedentedly promoted as well. Furthermore, due to the spatial addressability of the final products, nanostructures assembled through the DNA origami technique can also serve as a versatile platform for the spatial positioning of functional elements, represented by colloidal nanoparticles (NPs). The subsequent fabrication of heterogeneous functional nanoarchitectures is realized via modifying colloidal NPs with DNA strands and manipulating them to anchor into DNA origami templates. This has given rise to investigations of their novel properties in nanophotonics and therapeutic effects towards some diseases. In this review, we survey the crucial progress in the development of DNA origami design, assembly and structural analysis and summarize available applications in nanophotonics and cancer therapy based on the object-dressed DNA origami complex. Moreover, we elucidate the development of this field and discuss the potential directions of this kind of application-oriented nanomanufacturing.

Graphical abstract: DNA origami: an outstanding platform for functions in nanophotonics and cancer therapy

Article information


Submitted
31 Oct 2020
Accepted
01 Feb 2021
First published
03 Feb 2021

Analyst, 2021,146, 1807-1819
Article type
Minireview

DNA origami: an outstanding platform for functions in nanophotonics and cancer therapy

L. Dai, P. Liu, X. Hu, X. Zhao, G. Shao and Y. Tian, Analyst, 2021, 146, 1807 DOI: 10.1039/D0AN02160A

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