The function of an RNA is intimately linked to its structure. Many approaches encompassing X-ray crystallography, NMR, structural probing, or in silico predictions have been developed to establish structural models, sometimes with a precision down to atomic resolution. Yet these models still require experimental validation through the preparation and functional assay of mutants, which can rapidly become time consuming and laborious. Such limitations can be overcome using high-throughput functional screenings that may not only help in validating the model, but also inform on the mutational robustness of a structural element and the extent to which a sequence can be modified without altering RNA function, an important set of information to assist RNA engineering. We introduced the microfluidic-assisted in vitro compartmentalization (µIVC), an efficient and cost-effective screening strategy in which reactions are performed in picoliter droplets at rates of several thousand per second. We later improved µIVC efficiency by using it in tandem with high-throughput sequencing, though a laborious bioinformatic step was still required at the end of the process. In the present work, we further 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.

中文翻译：

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µIVC-Useq：一种微流体辅助的高通量功能筛选，与下一代测序和人工神经网络相结合，可快速表征 RNA 分子

RNA的功能与其结构密切相关。已经开发了包括 X 射线晶体学、NMR、结构探测或计算机预测在内的许多方法来建立结构模型，有时精确到原子分辨率。然而，这些模型仍需要通过突变体的制备和功能测定进行实验验证，这很快就会变得耗时且费力。可以使用高通量功能筛选来克服这些限制，这不仅有助于验证模型，还可以告知结构元素的突变稳健性以及在不改变 RNA 功能的情况下可以修改序列的程度，这是一个重要的集合信息以协助 RNA 工程。我们介绍了微流体辅助体外分隔 (µIVC)，一种有效且具有成本效益的筛选策略，其中反应在皮升液滴中以每秒数千的速率进行。我们后来通过将其与高通量测序结合使用来提高 µIVC 效率，尽管在该过程结束时仍需要一个费力的生物信息学步骤。在目前的工作中，我们通过实施能够实现无监督生物信息学分析的人工神经网络，进一步提高了管道的自动化水平。我们通过快速识别一组易于被点亮 RNA 适体 SRB-2 的关键域接受的序列来证明这种“μIVC-Useq”技术的效率。这项工作不仅为这种适配体的设计方式提供了一些新的思路，而且还使我们能够轻松识别性能提高 10 倍的新变体。