Barros Maya, Kasirajan Gayathri, Jones Amara, Schlichting Aidan, Ruiz-Ciancio Dario, Lin Li-Hsien, Narayan Chandan, Veeramani Suresh, Thiel Kristina W, Kennedy George Clare, Darcy Isabel, Thiel William
Department of Internal Medicine, University of Iowa, Iowa City, IA.
Division of Cardiovascular Medicine, University of Iowa, Iowa City, IA.
bioRxiv. 2025 Aug 30:2025.08.27.672406. doi: 10.1101/2025.08.27.672406.
The SELEX process to identify RNA and DNA aptamers relies on sequencing selection rounds to detect highly specific aptamers through patterns of aptamer accumulation or enrichment. However, this approach infers rather than quantify aptamer specificity. Here we present a novel strategy for directly quantifying aptamer specificity within enriched libraries termed Aptamer Specificity Sequencing for Efficient Targeting (ASSET). The ASSET framework takes experimental samples and replicates testing the specificity of an aptamer library and prepares them for next-generation sequencing (NGS) with a . This enables robust data normalization, calculation of aptamer with statistical significance, and the creation of of individual aptamers across multiple targets and non-targets. By integrating ASSET specificity scores with conventional selection round sequencing data, aptamers can be easily classified as true or false positives and negatives, allowing for easy separation of true positive aptamers. Compared to conventional methods for identifying aptamer candidates, such as measuring abundance or enrichment, ASSET specificity scores show a strong correlation with experimentally measured specificity. This supports ASSET as a more effective metric for selecting lead candidates following SELEX. ASSET is an easily implemented framework that accelerates the identification of highly specific aptamers, thereby expediting aptamer discovery for therapeutic and diagnostic applications.
用于鉴定RNA和DNA适配体的SELEX过程依赖于测序选择轮次,通过适配体积累或富集模式来检测高度特异性的适配体。然而,这种方法是推断而非量化适配体特异性。在此,我们提出了一种在富集文库中直接量化适配体特异性的新策略,称为高效靶向适配体特异性测序(ASSET)。ASSET框架采用实验样本并重复测试适配体文库的特异性,并用一种(此处原文缺失具体内容)为下一代测序(NGS)做准备。这实现了强大的数据归一化、具有统计学意义的适配体计算,以及跨多个靶标和非靶标的单个适配体(此处原文缺失具体内容)的创建。通过将ASSET特异性评分与传统选择轮次测序数据相结合,适配体可以轻松分类为真阳性或假阳性及阴性,从而便于分离真阳性适配体。与用于鉴定适配体候选物的传统方法(如测量丰度或富集度)相比,ASSET特异性评分与实验测量的特异性显示出很强的相关性。这支持ASSET作为SELEX后选择先导候选物的更有效指标。ASSET是一个易于实施的框架,可加速高度特异性适配体的鉴定,从而加快用于治疗和诊断应用的适配体发现。