Early stages of drug discovery often depend on relatively simple reporter assays or phenotypic readouts, providing little or no information on the drug’s mechanism of action (MOA). Gene expression profiling technologies like RNA-sequencing enable a more comprehensive characterization of compounds by measuring the activity of molecular pathways. This information can complement phenotypic readouts and can be used to prioritize candidate compounds for further testing. RNA expression profiling also serves as a generic test that can be applied to any drug development pipeline without the need for target-dependent customization.
In this webinar, Pieter Mestdagh will present a workflow that processes 384 cell lysates with RNA seq to generate expression data analyzed at the pathway level. Reliable pathway insights can be obtained at high throughput and relatively low cost while not being limited to a predefined set of genes or pathways. In cell perturbation screenings (small molecules, RNAi, antisense or CRISPR), the application can provide in depth information on the mode of action underlying the induced cellular phenotypes as well as molecular similarity scores to identify those perturbations acting similar to a reference condition or via shared molecular mechanisms.
Review a workflow showing that shallow sequencing of crude cell lysates reproducibly detects over 5000 genes with at least 10 reads.
Demonstrate how subsampling of deep sequencing datasets shows that differential pathway analysis is largely unaffected when reducing the number of genes to this level.
Discuss cell perturbation screenings and the ways this methodology can be applied to repurpose off-target siRNA hits from library screens to reveal novel candidate therapeutic targets for drug development.
Demonstrate how coupling the data generated from the workflow with a tailored visualization platform can facilitate data interpretation.