scholarly journals Single-Molecule Detection of SARS-CoV-2 by Plasmonic Sensing of Isothermally Amplified Nucleic Acids

Author(s):  
Haihang Ye ◽  
Chance Nowak ◽  
Yaning Liu ◽  
Yi Li ◽  
Tingting Zhang ◽  
...  

AbstractSingle-molecule detection of pathogens such as SARS-CoV-2 is key to combat infectious diseases outbreak and pandemic. Currently colorimetric sensing with loop-mediated isothermal amplification (LAMP) provides simple readouts but suffers from intrinsic non-template amplification. Herein, we report that plasmonic sensing of LAMP amplicons via DNA hybridization allows highly specific and single-molecule detection of SARS-CoV-2 RNA. Our work has two important advances. First, we develop gold and silver alloy (Au-Ag) nanoshells as plasmonic sensors that have 4-times stronger extinction in the visible wavelengths and give 20-times lower detection limit for oligonucleotides than Au nanoparticles. Second, we demonstrate that the diagnostic method allows cutting the complex LAMP amplicons into short repeats that are amendable for hybridization with oligonucleotide-functionalized nanoshells. This additional sequence identification eliminates the contamination from non-template amplification. The detection method is a simple and single-molecule diagnostic platform for virus testing at its early representation.Table of Content

2003 ◽  
Vol 100 (13) ◽  
pp. 7605-7610 ◽  
Author(s):  
M. Singh-Zocchi ◽  
S. Dixit ◽  
V. Ivanov ◽  
G. Zocchi

2011 ◽  
Vol 6 (2) ◽  
pp. 126-132 ◽  
Author(s):  
Sebastian Sorgenfrei ◽  
Chien-yang Chiu ◽  
Ruben L. Gonzalez ◽  
Young-Jun Yu ◽  
Philip Kim ◽  
...  

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yiren Wang ◽  
Mashari Alangari ◽  
Joshua Hihath ◽  
Arindam K. Das ◽  
M. P. Anantram

Abstract Background The all-electronic Single Molecule Break Junction (SMBJ) method is an emerging alternative to traditional polymerase chain reaction (PCR) techniques for genetic sequencing and identification. Existing work indicates that the current spectra recorded from SMBJ experimentations contain unique signatures to identify known sequences from a dataset. However, the spectra are typically extremely noisy due to the stochastic and complex interactions between the substrate, sample, environment, and the measuring system, necessitating hundreds or thousands of experimentations to obtain reliable and accurate results. Results This article presents a DNA sequence identification system based on the current spectra of ten short strand sequences, including a pair that differs by a single mismatch. By employing a gradient boosted tree classifier model trained on conductance histograms, we demonstrate that extremely high accuracy, ranging from approximately 96 % for molecules differing by a single mismatch to 99.5 % otherwise, is possible. Further, such accuracy metrics are achievable in near real-time with just twenty or thirty SMBJ measurements instead of hundreds or thousands. We also demonstrate that a tandem classifier architecture, where the first stage is a multiclass classifier and the second stage is a binary classifier, can be employed to boost the single mismatched pair’s identification accuracy to 99.5 %. Conclusions A monolithic classifier, or more generally, a multistage classifier with model specific parameters that depend on experimental current spectra can be used to successfully identify DNA strands.


2021 ◽  
Author(s):  
Li-juan Wang ◽  
Le Liang ◽  
Bing-jie Liu ◽  
BingHua Jiang ◽  
Chun-yang Zhang

A controlled T7 transcription-driven symmetric amplification cascade machinery is developed for single-molecule detection of multiple repair glycosylases.


Author(s):  
Xiaojia Jiang ◽  
Mingsong Zang ◽  
Fei Li ◽  
Chunxi Hou ◽  
Quan Luo ◽  
...  

Biological nanopore-based techniques have attracted more and more attention recently in the field of single-molecule detection, because they allow the real-time, sensitive, high-throughput analysis. Herein, we report an engineered biological...


2021 ◽  
Vol 33 (7) ◽  
pp. 2005133
Author(s):  
Le Liang ◽  
Peng Zheng ◽  
Chi Zhang ◽  
Ishan Barman

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