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

Citations Over TimeTop 10% of 2022 papers

Abstract

[Image: see text] 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.

Related Papers

• → Transcriptome analysis of human ageing in male skin shows mid-life period of variability and central role of NF-κB(2016)61 cited
• → Unlocking immune-mediated disease mechanisms with transcriptomics(2021)30 cited
• → Characterization of Early Developments in the Splenic Leukocyte Transcriptome of NOD Mice(2003)5 cited
• → Triplet Frequencies Implementation in Total Transcriptome Analysis(2019)
• → Studying gene expression profiles in specialized brain regions by microSAGE(2000)