µIVC-Useq: a microfluidic-assisted high-throughput functionnal screening in tandem with next generation sequencing and artificial neural network to rapidly characterize RNA molecules.

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 (µIVC), an efficient screening strategy in which reactions are performed in picoliter droplets at rates of several thousand per second. We later improved µIVC 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 "µIVC-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.