DRUG-seq Provides Unbiased Biological Activity Readouts for Neuroscience Drug Discovery

Unbiased transcriptomic
RNA-seq data has provided deep insights
into 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. Furthermore, we demonstrate how this data can be incorporated
into a drug discovery project aiming to develop therapeutics for schizophrenia
using human stem cell-derived neurons. We identified both an on-target
activation signature, induced by a set of chemically distinct positive
allosteric modulators of the N-methyl-d-aspartate
(NMDA) receptor, and independent off-target effects. Overall, the
protocol and open-source analysis pipeline are a step toward industrializing
RNA-seq for high-complexity transcriptomics studies performed at a
saturating scale.