Résumé
Aptamers, nucleic acid ligands against specific targets, have emerged as drug candidates, sensors, imaging tools, and nanotechnology building blocks. The most successful method for their development has been SELEX (Systematic Evolution of Ligands by EXponential Enrichment), an iterative procedure that is labor- and time-intensive and often enriches candidates for criteria other than those desired. Here we present UltraSelex, a non-iterative method that combines biochemical partitioning, high-throughput sequencing, and computational background minimization through statistical rank modeling. This approach avoids the common bias for abundant sequences and selects high-affinity ligands, even if they are extremely scarce. In six independent UltraSelex experiments (three towards each target), we discovered high-affinity aptamers for a fluorogenic silicon rhodamine dye, and a protein target, the SARS-CoV-2 RNA-dependent RNA polymerase. These aptamers enabled live-cell RNA imaging and efficient enzyme inhibition, respectively. The wet-lab partitioning part of UltraSelex can be completed in a few hours, and including sequencing and rank modeling via a public web server, the identification of lead candidates can be accomplished in about one day. UltraSelex provides a rapid route to novel drug candidates and diagnostic tools with greatly improved performance.