ASSET: A Framework for Decoding Aptamer Specificity of an Enriched Library by Next-Generation Sequencing of Experimental Samples

Abstract

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 known internal reference sequence. This enables robust data normalization, calculation of aptamer specificity scores with statistical significance, and the creation of specificity profiles 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.