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Issue 21, 2018
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Highly sensitive and multiplexed miRNA analysis based on digitally encoded silica microparticles coupled with RCA-based cascade amplification

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Abstract

Currently, miRNA analysis is significant for understanding miRNA regulation networks and clinical diagnostics and therapy. Analytical strategies feasible for multiplex miRNA-sensitive analysis are still in high demand. Herein, we propose a novel strategy for miRNA analysis by coupling cascade amplification with digitally encoded silica microparticles. The microparticles are precisely fabricated in a digital form through a one-step deposition strategy and are highly efficient for multiplex analysis. The cascade amplification composed of RCA and nicking-assisted strand-displacement amplification (SDA) exhibits high amplification efficiency and requires no complicated sequence design, thus improving the compatibility with base-stacking hybridization on our microparticles. Parallel and sensitive analyses for let-7a and miR-21 in one pot without mutual interference have been achieved with both high sensitivity (LOD, ∼0.5 fM) and wide dynamic range (10 pM–1 fM). Moreover, our strategy exhibits high specificity for miRNAs of homologous sequence and good anti-interference ability in a complex sample matrix. Considering that there are up to 128 (27) kinds of microparticles available, our strategy can be applied for dozens of miRNA-sensitive analyses in one pot, and it has great potential for miRNA signature analysis as well as widespread clinical applications.

Graphical abstract: Highly sensitive and multiplexed miRNA analysis based on digitally encoded silica microparticles coupled with RCA-based cascade amplification

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Publication details

The article was received on 24 Jul 2018, accepted on 06 Sep 2018 and first published on 11 Sep 2018


Article type: Paper
DOI: 10.1039/C8AN01393D
Citation: Analyst, 2018,143, 5137-5144
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    Highly sensitive and multiplexed miRNA analysis based on digitally encoded silica microparticles coupled with RCA-based cascade amplification

    S. Liu, H. Fang, C. Sun, N. Wang and J. Li, Analyst, 2018, 143, 5137
    DOI: 10.1039/C8AN01393D

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