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Area of Science:

  • Transcriptomics
  • Drug Discovery
  • Neuroscience

Background:

  • RNA sequencing (RNA-seq) provides biological insights but is limited in drug discovery by cost and throughput.
  • Digital RNA with perturbation of Genes (DRUG)-seq shows promise for high-throughput transcriptomics.

Purpose of the Study:

  • To enhance the DRUG-seq platform for rigorous testing and analysis.
  • To demonstrate its utility in drug discovery, specifically for schizophrenia therapeutics.
  • To validate its ability to identify on-target and off-target compound effects.

Main Methods:

  • Extended and rigorously tested the DRUG-seq platform.
  • Developed an open-source analysis pipeline for DRUG-seq data.
  • Applied the platform to study positive allosteric modulators of the NMDA receptor in human stem cell-derived neurons.

Main Results:

  • Demonstrated high reproducibility of the DRUG-seq platform.
  • Successfully resolved mechanisms of action for diverse compounds.
  • Identified on-target and off-target effects for NMDA receptor modulators.
  • Showcased data integration into a schizophrenia drug discovery project.

Conclusions:

  • The enhanced DRUG-seq protocol and analysis pipeline industrialize high-complexity transcriptomics at scale.
  • This approach accelerates drug discovery by enabling detailed mechanistic insights.
  • The platform is valuable for developing therapeutics targeting complex neurological disorders.