Innovative Research for a Sustainable Future www.epa.gov/research Logan J. Everett l everett.logan@epa.gov l 919-541-2495 Overview Screen Design & Analysis Scalable Quality Control Differential Gene Expression Analysis Summary & References High-Throughput Chemical Safety Screening Using Targeted RNA-seq Logan J. Everett, Joshua A. Harrill, Derik Haggard, Joseph Bundy, Beena Vallanat, Imran Shah, Richard Judson U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology & Exposure Research Triangle Park, North Carolina, U.S.A. • U.S. EPA has proposed a tiered testing strategy using New Approach Methodologies (NAMs) to identify hazards from chemical exposure and characterize their dose-response relationships. The first tier of testing will utilize NAMs that are high-throughput and provide broad biological coverage [1]. • Targeted RNA-seq of cultured human cells provides a platform for high-throughput transcriptomics (HTTr) screening that covers >20,000 genes and a wide array of biological responses and pathways [2]. • We successfully piloted the targeted RNA-seq approach for HTTr to predict biological pathway altering concentrations (BPACs) [3] and have now scaled this approach to screen over 1,200 chemicals in three distinct cell types. • We have developed rigorous quality control procedures to remove aberrant samples that are scalable to tens of thousands of sequencing libraries per study. • Differential expression models [4] reveal dose-responsive accumulation of transcriptional changes across cell types and chemicals tested, and signature-based analysis is used to derive potency and mechanism of action information for each chemical. Figure 1. Design and analysis of large-scale high-throughput transcriptomic screens. Left: Dose plates are prepared with ~40 test chemical samples at 8 concentrations (half log10 spacing, single replicates) and a standard set of reference chemicals for each cell type. Cells are grown on the test plate, then treated with chemical samples from the dose plate. Positions of each chemical treatment are randomized on every plate by an automated liquid handling system. Each test plate is generated in triplicate using the same test chemicals, but with separate cell culture batches. QC reference standards are manually added to each test plate before transcriptomic profiling. Right: Raw data from targeted RNA-seq samples is rapidly aligned to known probe sequences producing counts of uniquely aligned reads for each probe in each sample. Probe counts are used to derive QC metrics and are stored in a database layer. Subsequent analysis is performed independently for each chemical. Count data for all concentrations, replicates, and plate-matched vehicle controls are extracted. DESeq2 [4] is used to compute moderated fold-changes, which are then input to a novel method for modeling concentration-responsive activity for a catalog of known gene signatures. Figure 3. Quality control failure rates by sample and cell type. Individual HTTr profiles were excluded from further analysis due to errors in acoustic dispensing of chemicals (red), >50% cell death indicated by cell viability assay (green), or abnormal parameters from a battery of bioinformatic checks applied to sequencing data results (blue, see Figure 2). Greater than 98% of samples pass all QC filters, demonstrating the ability to reliably scale this workflow to studies encompassing thousands of chemicals and samples. Acoustic dispenser logs identify problems with chemical handling Apoptosis/cell viability assays identify cytotoxic concentrations (>50% cell death) Bioinformatic QC checks remove: • Low read depth samples • High rate of alignment failure • Samples with low gene coverage • Samples with irregular count distributions N = 2,509 41,952 36,208 35,559 The views expressed are those of the presenter and do not necessarily reflect the views or policies of the U.S. Environmental Protection Agency. 1. Th