ELRIG Drug Discovery, Liverpool, UK
October 19, 2021
The characterization of gene expression changes has broad applications, including (1) screening of RNA-based or small molecule drugs which function by direct modulation of gene expression, e.g., by altering pre-mRNA splicing, (2) verifying effects of protein-targeting drugs on given cellular pathways, and (3) toxicity profiling or assessing target selectivity. Today, assays such as multiplexed reverse transcription qPCR (RT-qPCR) or bead-based technologies like the QuantiGeneTM assay can be performed on automated platforms to enable screening of gene expression at scale. These technologies allow a large increase in throughput, but to date, there exists no shared analysis workflow which can consistently and efficiently process high-volume data for all gene expression assays. In this poster, we present a new, highly automated workflow in Genedata Screener which enables major efficiency gains and standardized, high-quality results. This workflow provides built-in functionality for core processing and quality control (QC) steps common to all gene expression assays, such as: normalization to house-keeping genes, which can be flexibly assigned by the user; a dose-response curve fit method purpose-built for Fold Change measurements, with configurable parameter restriction and result validity rules; automatic QC, including masking of unreliable measurements and flagging of cytotoxic compounds, and dedicated QC plots. Additionally, the workflow contains technology-specific features, such as the ability to view raw amplification curves for RT-qPCR experiments and robust, automated determination of Ct values. Recently, this workflow was deployed at Evotec to streamline RT-qPCR-based screens from compound logistics to one-click reporting of results, significantly shortening cycle times.