M3i

@article{,

title = {Optimization of fluorogenic RNA-based biosensors using droplet-based microfluidic ultrahigh-throughput screening},

author = {A Autour and F Bouhedda and R Cubi and M Ryckelynck},

url = {https://www.ncbi.nlm.nih.gov/pubmed/30902664?dopt=Abstract},

doi = {10.1016/j.ymeth.2019.03.015},

isbn = {30902664},

year  = {2019},

date = {2019-01-01},

journal = {Methods},

volume = {161},

pages = {46-53},

abstract = {Biosensors are biological molecules able to detect and report the presence of a target molecule by the emission of a signal. Nucleic acids are particularly appealing for the design of such molecule since their great structural plasticity makes them able to specifically interact with a wide range of ligands and their structure can rearrange upon recognition to trigger a reporting event. A biosensor is typically made of three main domains: a sensing domain that is connected to a reporting domain via a communication module in charge of transmitting the sensing event through the molecule. The communication module is therefore an instrumental element of the sensor. This module is usually empirically developed through a trial-and-error strategy with the testing of only a few combinations judged relevant by the experimenter. In this work, we introduce a novel method combining the use of droplet-based microfluidics and next generation sequencing. This method allows to functionally characterize up to a million of different sequences in a single set of experiments and, by doing so, to exhaustively test every possible sequence permutations of the communication module. Here, we demonstrate the efficiency of the approach by isolating a set of optimized RNA biosensors able to sense theophylline and to convert this recognition into fluorescence emission.},

keywords = {Aptasensors Fluorogenic biosensors High-throughput screening Light-up aptamer Next generation sequencing RNA, RYCKELYNCK, Unité ARN},

pubstate = {published},

tppubtype = {article}

}