Michigan State University: Dataset Details, ID=GSE184506

Title: Dose-response hepatic single nuclei RNA sequencing of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) treated mice

Accession Number: GSE184506

Link to Dataset: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE184506

Repository: Gene Expression Omnibus (GEO)

Data Type(s): Gene Expression

Experiment Type(s): Expression profiling by high throughput sequencing

Organism(s): Mus musculus

Summary: Dose-response is the cornerstone of safety assessments to determine risk. Innovations in transcriptomic technologies has recently led to the ability to profile gene expression at the single-cell level, enabling comprehensive assessment of adaptive and adverse responses at unprecedented resolution. To demonstrate its application of dose dependent study designs, we performed single nuclei sequencing of livers from male C57BL/6 mice gavaged with the persistent environmental contaminant 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) at doses of 0.01, 0.03, 0.1, 0.3, 1, 3, 10, or 30 µg/kg or sesame oil vehicle. Following quality control, a total of 131,613 nuclei were sequenced representing 11 distinct cell (sub)types. Our findings reveal dose-dependent changes in relative levels of distinct cell (sub)types such as hepatocytes and macrophages, including the emergence of NASH-associated macrophages (NAMs) characterized by elevated Gpnmb expression. Zonally resolved hepatocytes could also be characterized demonstrating initial induction of aryl hydrocarbon receptor (AhR) target genes such as Cyp1a1 in the central region of the liver lobule while central and portal induction is observed at higher doses. Moreover, we show that zero-inflation inherent to single-cell technologies pose challenges for dose-dependent differential expression analysis and present a novel single-cell Bayesian test method that outperforms common two-group test methods. Collectively, our results demonstrate that single-cell technology can be used to further elucidate mechanisms associated within specific-cell (sub)types and interactions between different cell (sub)types to support the risk assessment paradigm.

Publication(s) associated with this dataset:

• Nault R, Saha S, Bhattacharya S, Dodson J, Sinha S, Maiti T, Zacharewski TR. 2022. Benchmarking of a Bayesian single cell RNAseq differential gene expression test for dose-response study designs. Nucleic Acids Res doi:10.1093/nar/gkac019 PMID:35061903