DRUG-seq for miniaturized high-throughput transcriptome profiling in drug discovery

Chaoyang Ye, Daniel J. Ho, Marilisa Neri, Chian Yang, Tripti Kulkarni, Ranjit Randhawa, Martin Henault, Nadezda Mostacci, Pierre Farmer, Steffen Renner, Robert Ihry, Leandra Mansur, Caroline Gubser Keller, Gregory McAllister, Marc Hild, Jeremy Jenkins and Ajamete Kaykas ()
Additional contact information
Chaoyang Ye: Novartis Institutes for Biomedical Research
Daniel J. Ho: Novartis Institutes for Biomedical Research
Marilisa Neri: Novartis Institutes for Biomedical Research
Chian Yang: Novartis Institutes for Biomedical Research
Tripti Kulkarni: Novartis Institutes for Biomedical Research
Ranjit Randhawa: Novartis Institutes for Biomedical Research
Martin Henault: Novartis Institutes for Biomedical Research
Nadezda Mostacci: Novartis Institutes for Biomedical Research
Pierre Farmer: Novartis Institutes for Biomedical Research
Steffen Renner: Novartis Institutes for Biomedical Research
Robert Ihry: Novartis Institutes for Biomedical Research
Leandra Mansur: Novartis Institutes for Biomedical Research
Caroline Gubser Keller: Novartis Institutes for Biomedical Research
Gregory McAllister: Novartis Institutes for Biomedical Research
Marc Hild: Novartis Institutes for Biomedical Research
Jeremy Jenkins: Novartis Institutes for Biomedical Research
Ajamete Kaykas: Novartis Institutes for Biomedical Research

Nature Communications, 2018, vol. 9, issue 1, 1-9

Abstract: 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.

Date: 2018
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DOI: 10.1038/s41467-018-06500-x

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