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@article{,

title = {microIVC-Useq: a microfluidic-assisted high-throughput functionnal screening in tandem with next generation sequencing and artificial neural network to rapidly characterize RNA molecules},

author = {R Cubi and F Bouhedda and M Collot and A S Klymchenko and M Ryckelynck},

url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=33952671},

doi = {10.1261/rna.077586.120},

isbn = {33952671},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Rna},

volume = {27},

number = {7},

pages = {841-853},

abstract = {The function of an RNA is intimately linked to its three-dimensional structure. X-ray crystallography or NMR allow the fine structural characterization of small RNA (e.g., aptamers) with a precision down to atomic resolution. Yet, these technics are time consuming, laborious and do not inform on mutational robustness and the extent to which a sequence can be modified without altering RNA function, an important set of information to assist RNA engineering. On another hand, thought powerful, in silico predictions still lack the required accuracy. These limitations can be overcome by using high-throughput microfluidic-assisted functional screening technologies, as they allow exploring large mutant libraries in a rapid and cost-effective manner. Among them, we recently introduced the microfluidic-assisted In Vitro Compartmentalization (microIVC), an efficient screening strategy in which reactions are performed in picoliter droplets at rates of several thousand per second. We later improved microIVC efficiency by using in tandem with high throughput sequencing, thought a laborious bioinformatic step was still required at the end of the process. In the present work, we strongly increased the automation level of the pipeline by implementing an artificial neural network enabling unsupervised bioinformatic analysis. We demonstrate the efficiency of this "microIVC-Useq" technology by rapidly identifying a set of sequences readily accepted by a key domain of the light-up RNA aptamer SRB-2. This work not only shed some new light on the way this aptamer can be engineered, but it also allowed us to easily identify new variants with an up-to 10-fold improved performance.},

note = {1469-9001 (Electronic) 

1355-8382 (Linking) 

Journal Article},

keywords = {Bioinformatics, droplet-based microfluidics, high-throughput screening, light-up RNA aptamer, RNA engineering, RYCKELYNCK, Unité ARN},

pubstate = {published},

tppubtype = {article}

}

@article{Autour2017,

title = {Ultrahigh-Throughput Improvement and Discovery of Enzymes Using Droplet-Based Microfluidic Screening},

author = {A Autour and M Ryckelynck},

url = {https://doi.org/10.3390/mi8040128},

doi = {10.3390/mi8040128},

year  = {2017},

date = {2017-04-18},

journal = {Micromachines},

volume = {8},

number = {4},

pages = {128},

abstract = {Enzymes are extremely valuable tools for industrial, environmental, and biotechnological applications and there is a constant need for improving existing biological catalysts and for discovering new ones. Screening microbe or gene libraries is an efficient way of identifying new enzymes. In this view, droplet-based microfluidics appears to be one of the most powerful approaches as it allows inexpensive screenings in well-controlled conditions and an ultrahigh-throughput regime. This review aims to introduce the main microfluidic devices and concepts to be considered for such screening before presenting and discussing the latest successful applications of the technology for enzyme discovery. },

keywords = {directed evolution, droplet-based microfluidics, enzyme improvement, high-throughput screening, RYCKELYNCK, single-cell, Unité ARN},

pubstate = {published},

tppubtype = {article}

}