Raddaoui, N, Croce, S, Geiger, F et al. (8 more authors) (2020) Supersensitive Multifluorophore RNA‐FISH for Early Virus Detection and Flow‐FISH by Using Click Chemistry. ChemBioChem, 21 (15). 2214-2218-2218. ISSN 1439-4227
Abstract
The reliable detection of transcription events through the quantification of the corresponding mRNA is of paramount importance for the diagnostics of infections and diseases. The quantification and localization analysis of the transcripts of a particular gene allows disease states to be characterized more directly compared to an analysis on the transcriptome wide level. This is particularly needed for the early detection of virus infections as now required for emergent viral diseases, e. g. Covid‐19. In situ mRNA analysis, however, is a formidable challenge and currently performed with sets of single‐fluorophore‐containing oligonucleotide probes that hybridize to the mRNA in question. Often a large number of probe strands (>30) are required to get a reliable signal. The more oligonucleotide probes are used, however, the higher the potential off‐target binding effects that create background noise. Here, we used click chemistry and alkyne‐modified DNA oligonucleotides to prepare multiple‐fluorophore‐containing probes. We found that these multiple‐dye probes allow reliable detection and direct visualization of mRNA with only a very small number (5–10) of probe strands. The new method enabled the in situ detection of viral transcripts as early as 4 hours after infection.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Wiley Periodicals, Inc. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
Keywords: | asymptotic expansion method; dimensional splitting method; linear elastic shell |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Biological Sciences (Leeds) > School of Molecular and Cellular Biology (Leeds) > Bioinformatics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 09 Jun 2020 14:36 |
Last Modified: | 25 Jun 2023 22:18 |
Status: | Published |
Publisher: | Wiley |
Identification Number: | 10.1002/cbic.202000081 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:161707 |