DRUG-seq Provides Unbiased Biological Activity Readouts for Drug Discovery

Jingyao Li; Daniel J. Ho; Martin Hénault; Chian Yang; Marilisa Neri; Robin Ge; Steffen Renner; Leandra Mansur; Alicia Lindeman; Brian Kelly; Tayfun Tumkaya; Xiaoling Ke; Gilberto J Soler-Llavina; Gopi Shanker; Carsten Russ; Marc Hild; Caroline Gubser Keller; Jeremy L. Jenkins; Kathleen A. Worringer; Frederic D. Sigoillot; Robert J. Ihry

doi:10.1021/acschembio.1c00920

GenomicsACS Chemical BiologyMay 4, 2022

Peer-reviewed journal article

DRUG-seq Provides Unbiased Biological Activity Readouts for Drug Discovery

Jingyao Li iD, Daniel J. Ho, Martin Hénault, Chian Yang, Marilisa Neri iD, Robin Ge, Steffen Renner iD, Leandra Mansur iD, Alicia Lindeman, Brian Kelly, Tayfun Tumkaya iD, Xiaoling Ke, Gilberto J Soler-Llavina iD, Gopi Shanker, Carsten Russ, Marc Hild iD, Caroline Gubser Keller iD, Jeremy L. Jenkins iD, Kathleen A. Worringer iD, Frederic D. Sigoillot iD, Robert J. Ihry iD

Abstract

Unbiased transcriptomic RNA-seq data has provided deep insights about biological processes. However, its impact in drug discovery has been narrow given high costs and low throughput. Proof-of-concept studies with Digital RNA with pertUrbation of Genes (DRUG)-seq demonstrated the potential to address this gap. We extended the DRUG-seq platform by subjecting it to rigorous testing and by adding an open-source analysis pipeline. The results demonstrate high reproducibility and ability to resolve the mechanism(s) of action for a diverse set of compounds. Overall, the protocol and open-source analysis pipeline are a step towards industrializing RNA-seq for high complexity transcriptomics studies performed at a saturating scale.

PDF DOI