A Workflow for Transcriptome-Wide Assessment of Antisense Oligonucleotide Selectivity

Abstract

Antisense oligonucleotides (ASOs) designed to recruit RNase H1 (gapmer ASOs) have been used successfully to downregulate the expression of therapeutic targets. Gapmer ASOs can be identified that selectively reduce the expression of transcripts containing the perfectly complementary intended ASO target site without affecting the expression of unintended transcripts (selective ASOs). However, ASOs can also be identified that reduce the expression of unintended transcripts with target sites that are not perfectly complementary to the ASO (nonselective ASOs). Currently, the understanding of in silico rules for predicting off-targets is suboptimal. In order to determine the selectivity of gapmer ASOs, we therefore developed an experimental workflow called concentration-response digital gene expression (CR-DGE). In CR-DGE, ASO treatment is performed at increasing concentrations, and the effect on the transcriptome is measured using 3′Tag-Seq. Expression data are then analyzed to identify genes with concentration-responsive knockdown. We demonstrate that CR-DGE identifies gapmer ASO concentration-responsive genes with high reproducibility and greater sensitivity than conventional single-concentration assays. Applying CR-DGE to a panel of gapmer ASOs identifies ASOs with a range of selectivity. These results demonstrate that CR-DGE can be used effectively to assess the selectivity of gapmer ASOs, offering a valuable tool for research and therapeutic development.