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Issue 5, 2019
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A label-free, ultra-highly sensitive and multiplexed SERS nanoplasmonic biosensor for miRNA detection using a head-flocked gold nanopillar

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Abstract

Cell-free micro-ribonucleic acids (cf-miRNAs) regulate most human genes by controlling the mRNA translation and have been considered as the most promising biomarkers for cancer diagnosis and prognosis. Due to their low abundance, short length, and similar sequences, an attractive approach that is ultrasensitive and label-free is highly demanded. Herein, we develop a label-free, ultra-high sensitivity and selectivity multiplex miRNA using surface-enhanced Raman scattering to detect cancer-associated miRNAs with head-flocked gold nanopillars as a substrate. Using a complementary DNA probe platform and an ultra-high sensitivity SERS based biosensor, we achieved high selectivity in targeting miRNAs with extremely low detection limits, without any chemical or enzymatic reactions and evaluated the correlation between the miRNA expression level and the Raman signal intensity. A reproducible SERS signal is monitored via uniformly fabricated elastic head-flocked gold nanopillars through a mapping method. This system supports the detection of cell-free miRNAs in blood, which are utilized and quantified as biomarkers to diagnose and provide a prognosis for cancers and intractable diseases. In the foreseeable future, this advanced label-free miRNA detection system is highly promising for a less tumor-invasive biopsy for the early diagnosis and prognosis of cancer.

Graphical abstract: A label-free, ultra-highly sensitive and multiplexed SERS nanoplasmonic biosensor for miRNA detection using a head-flocked gold nanopillar

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Publication details

The article was received on 10 Sep 2018, accepted on 13 Dec 2018 and first published on 03 Jan 2019


Article type: Paper
DOI: 10.1039/C8AN01745J
Citation: Analyst, 2019,144, 1768-1776

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    A label-free, ultra-highly sensitive and multiplexed SERS nanoplasmonic biosensor for miRNA detection using a head-flocked gold nanopillar

    W. H. Kim, J. U. Lee, S. Song, S. Kim, Y. J. Choi and S. J. Sim, Analyst, 2019, 144, 1768
    DOI: 10.1039/C8AN01745J

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