Simultaneous fluorescent detection of multiplexed miRNA of liver cancer based on DNA tetrahedron nanotags

Zhao, Hongyu; Wang, Minhui; Xiong, Xianrong; Liu, Yuanjian; Chen, Xiaoye

doi:10.1016/j.talanta.2019.120677

Cited by 29 publications

(11 citation statements)

References 22 publications

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“…In this equation, c 2 was the concentration of Let-7a, fraction 2 was the ratio of ETE dimers to total number of particles, and the LOD was as low as 0.53 fM (3σ/ k ). Compared with other methods − for microRNA sensing, the proposed detection method was more sensitive.…”

Section: Resultsmentioning

confidence: 95%

Two-Dimensional Analysis Method for Highly Sensitive Detection of Dual MicroRNAs in Breast Cancer Cells

et al. 2023

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Multiple biomarker detection is crucial for early clinical diagnosis, and it is significant to achieve the simultaneous detection of multiple biomarkers with the same nanomaterial. In this work, the hairpin DNA strands were selectively modified on the surface of gold nanorods (AuNRs) to construct two kinds of nanoprobes by rational design. When in the presence of dual microRNAs, AuNRs were assembled to form end-to-end (ETE) and side-by-side (SBS) dimers. Compared with a single AuNR, the dark-field scattering intensity and red color percentage variation of dimers were extremely distinguished, which could be developed for dual microRNA detection by combining the red color percentage and scattering intensity with the data processing method of principal component analysis to construct a two-dimensional analysis method. Especially, the fraction of AuNR dimers presented a linear relationship with the amount of microRNAs. Based on this, microRNA-21 and microRNA Let-7a in breast cancer cells were detected with the detection limits of 1.72 and 0.53 fM, respectively. This method not only achieved the sensitive detection of dual microRNAs in human serum but also realized the high-resolution intracellular imaging, which developed a new way for the oriented assembly of nanomaterials and biological detection in living cells.

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Section: Resultsmentioning

confidence: 95%

Two-Dimensional Analysis Method for Highly Sensitive Detection of Dual MicroRNAs in Breast Cancer Cells

et al. 2023

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“…Based on that, the MB-DT multicomponent nucleic acid enzyme (MNAzyme) system with AuNPs as elemental markers was proposed for the highly sensitive quantification of miRNA-155 by ICP-MS [ 66 ]. A new method for the simultaneous detection of miRNA-21, miRNA-122, and miRNA-223 was presented based on DNA tetrahedral nanolabeling and fluorescence resonance energy transfer (FRET) [ 67 ]. The FRET effect between TOTO-1 and the other three fluorescent dyes has been shown to allow multiple sensitive quantifications of the three miRNAs in 10% of human serum samples.…”

Section: Applications Of Dna Nanostructures For Biosensingmentioning

confidence: 99%

Programmable Nanostructures Based on Framework-DNA for Applications in Biosensing

Liu

Wang

Chao

2023

Sensors

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DNA has been actively utilized as bricks to construct exquisite nanostructures due to their unparalleled programmability. Particularly, nanostructures based on framework DNA (F-DNA) with controllable size, tailorable functionality, and precise addressability hold excellent promise for molecular biology studies and versatile tools for biosensor applications. In this review, we provide an overview of the current development of F-DNA-enabled biosensors. Firstly, we summarize the design and working principle of F-DNA-based nanodevices. Then, recent advances in their use in different kinds of target sensing with effectiveness have been exhibited. Finally, we envision potential perspectives on the future opportunities and challenges of biosensing platforms.

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“…To solve this problem, biomolecules have been used as templates to stabilize AgNCs. − Benefitting from the development of DNA nanotechnology, , DNA has been regarded as a promising template for AgNC synthesis. DNA-templated AgNCs have good structural stability and fluorescence tunability; therefore, they have been widely used as fluorescent probes in many fields, such as biological analysis and disease diagnosis. − …”

Section: Introductionmentioning

confidence: 99%

Predicting the Fluorescence Properties of Hairpin-DNA-Templated Silver Nanoclusters via Deep Learning

Zhai

Guan

et al. 2022

ACS Appl. Nano Mater.

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DNA-templated silver nanoclusters (AgNCs) are widely used as fluorescent probes in various fields owing to their structural stability and fluorescence tunability. The selection of suitable DNA sequences and synthesis conditions with a predictable method is therefore needed, which benefits the preparation of DNA-templated AgNCs with the desired properties and further extension of their application in other fields. Here, we develop and propose a deep learning protocol based on recurrent neural network (RNN) algorithms to predict the fluorescence properties of hairpin-DNA-templated AgNCs (H-AgNCs). Using the stem sequence of hairpin DNA and experimental parameters as input features, the RNN model can identify the number of fluorescence emission peak of H-AgNCs and the fluorescence color of single-peaked H-AgNCs (spH-AgNCs) with the training accuracies of 81.4% and 83.0%, respectively. For each color category, the DNA base distributions of the hairpin stem on the testing set are consistent with those of the training set, indicating the good generalization ability of the constructed model. In addition, a prediction accuracy of 56% is experimentally verified using 20 testing samples, which is 2.3 times higher than that of the random probability, indicating the potential uses of the model for experimental guidance.

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Simultaneous fluorescent detection of multiplexed miRNA of liver cancer based on DNA tetrahedron nanotags

Cited by 29 publications

References 22 publications

Two-Dimensional Analysis Method for Highly Sensitive Detection of Dual MicroRNAs in Breast Cancer Cells

Two-Dimensional Analysis Method for Highly Sensitive Detection of Dual MicroRNAs in Breast Cancer Cells

Programmable Nanostructures Based on Framework-DNA for Applications in Biosensing

Predicting the Fluorescence Properties of Hairpin-DNA-Templated Silver Nanoclusters via Deep Learning

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