Abstract
Here we report Digital RNA with pertUrbation of Genes (DRUG-seq), a high-throughput platform for drug discovery. Pharmaceutical discovery relies on high-throughput screening, yet current platforms have limited readouts. RNA-seq is a powerful tool to investigate drug effects using transcriptome changes as a proxy, yet standard library construction is costly. DRUG-seq captures transcriptional changes detected in standard RNA-seq at 1/100th the cost. In proof-of-concept experiments profiling 433 compounds across 8 doses, transcription profiles generated from DRUG-seq successfully grouped compounds into functional clusters by mechanism of actions (MoAs) based on their intended targets. Perturbation differences reflected in transcriptome changes were detected for compounds engaging the same target, demonstrating the value of using DRUG-seq for understanding on and off-target activities. We demonstrate DRUG-seq captures common mechanisms, as well as differences between compound treatment and CRISPR on the same target. DRUG-seq provides a powerful tool for comprehensive transcriptome readout in a high-throughput screening environment.
Highlights
We report Digital RNA with pertUrbation of Genes (DRUG-seq), a high-throughput platform for drug discovery
High-throughput screening has been a staple in drug discovery over the past four decades[1]
The Luminex L1000 platform, used for the Connectivity Map (CMAP), measures a fixed panel of about 1000 landmark genes and about half of the additional genes in the transcriptome are imputed in silico[4,5]
Summary
We report Digital RNA with pertUrbation of Genes (DRUG-seq), a high-throughput platform for drug discovery. 3 Chemical Biology & Therapeutics, Novartis Institutes for Biomedical Research, 250 Massachusetts, Cambridge, MA 02139, USA. 6 Chemical Biology & Therapeutics Informatics, Novartis Institutes for Biomedical Research, 250 Massachusetts, Cambridge, MA 02139, USA. L1000 has provided a very useful and cost-effective platform for transcriptional profiling It currently only measures around 1000 genes and relies on imputation of the remaining genes instead of direct measurement[5]. Whole transcriptome RNA-seq has become an attractive option to allow deeper interrogation of complex changes, yet most of the standard protocols are labor intensive and cost prohibitive for high-throughput use. It would be ideal to have a cost effective, massively parallelized transcriptome profiling method in 384- and 1536-well format to measure all genes in an unbiased manner to fully capture the transcriptional diversity induced by compound or genetic perturbation for drug discovery
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