Abstract
Cell-based assays are an attractive option to measure gene expression response to exposure, but the cost of whole-transcriptome RNA sequencing has been a barrier to the use of gene expression profiling for in vitro toxicity screening. In addition, standard RNA sequencing adds variability due to variable transcript length and amplification. Targeted probe-sequencing technologies such as TempO-Seq, with transcriptomic representation that can vary from hundreds of genes to the entire transcriptome, may reduce some components of variation. Analyses of high-throughput toxicogenomics data require renewed attention to read-calling algorithms and simplified dose–response modeling for datasets with relatively few samples. Using data from induced pluripotent stem cell-derived cardiomyocytes treated with chemicals at varying concentrations, we describe here and make available a pipeline for handling expression data generated by TempO-Seq to align reads, clean and normalize raw count data, identify differentially expressed genes, and calculate transcriptomic concentration–response points of departure. The methods are extensible to other forms of concentration–response gene-expression data, and we discuss the utility of the methods for assessing variation in susceptibility and the diseased cellular state.
Highlights
Among the key challenges in contemporary toxicity testing is addressing increasing numbers of commodity chemicals with insufficient toxicity characterization, a trend that is at least partially attributable to the limitations associated with in vivo testing strategies
These challenges were described in the National Toxicology Program’s (NTP) 2004 Vision and Roadmap for the 21st Century, and the National Research Council’s (NRC) report on Toxicity Testing in the 21st Century (National Research Council, 2007), which envisioned a strategic shift from exclusive reliance on animal-derived data in chemical regulation to the implementation of novel data streams, including high-throughput in vitro testing, omics data, and computational modeling
The transcriptomic analysis methods discussed here focus on assessment of differentially expressed genes (DEGs) and concentration–response assessment using counts from TempOSeq experiments
Summary
Among the key challenges in contemporary toxicity testing is addressing increasing numbers of commodity chemicals with insufficient toxicity characterization, a trend that is at least partially attributable to the limitations associated with in vivo testing strategies. Additional challenges are associated with animal to human extrapolation, as well as concerns over the ethics and expense of animal testing These challenges were described in the National Toxicology Program’s (NTP) 2004 Vision and Roadmap for the 21st Century, and the National Research Council’s (NRC) report on Toxicity Testing in the 21st Century (National Research Council, 2007), which envisioned a strategic shift from exclusive reliance on animal-derived data in chemical regulation to the implementation of novel data streams, including high-throughput in vitro testing, omics data, and computational modeling. The Toxicogenomics Project-Genomics Assisted Toxicity Evaluation Systems (TGGATEs) database has compiled toxicological endpoints and gene expression data from rats (in vivo and in vitro primary hepatocytes) and humans (in vitro primary hepatocytes) on 170 hepato- and renal-toxicants at multiple doses and time points (Uehara et al, 2010; Igarashi et al, 2015)
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