Gene expression data from the Fluidigm qRT-PCR arrays was analyzed in R (v3.6.1; R Foundation for Statistical Computing, 2019). Prior to processing through the tcpl package, each qRT-PCR primer set was annotated as an individual assay endpoint (aeid) for analyses. For each plate, well types were designated for test compound wells (t), positive controls (c), (that is phenobarbital) and neutral controls (n, DMSO). Fold-change in the number of amplification cycles needed to pass the background threshold (Ct) for 96 transcripts to (ftp://newftp.epa.gov/COMPTOX/CCTE_Publication_Data/CCED_Publication_Data/Wambaugh/ToxCast_LTEA, file LTEA_Level2_20191119.zip) were normalized to the geometric mean of three housekeeping genes (ACTB, GAPDH, POLR2A) to generate ΔCt values (cval). Prior to calculating the response values (rval), or ΔΔCt, for each transcript (n = 96) per well, the baseline value (bval), the plate-wise median of the neutral control wells, was generated for each plate (the normalization process is described in detail in supplemental file SupFile4-DeltaCTCalculation.docx). The bval was subtracted from the cval to yield the rval or log2 Fold Change per transcript. Gene expression data from the Fluidigm qRT-PCR arrays was analyzed in R (v3.6.1; R Foundation for Statistical Computing, 2019). Prior to processing through the tcpl package, each qRT-PCR primer set was annotated as an individual assay endpoint (aeid) for analyses. For each plate, well types were designated for test compound wells (t), positive controls (c), (that is phenobarbital) and neutral controls (n, DMSO). Fold-change in the number of amplification cycles needed to pass the background threshold (Ct) for 96 transcripts to (ftp://newftp.epa.gov/COMPTOX/CCTE_Publication_Data/CCED_Publication_Data/Wambaugh/ToxCast_LTEA, file LTEA_Level5_20191119.zip) were normalized to the geometric mean of three housekeeping genes (ACTB, GAPDH, POLR2A) to generate ΔCt values (cval). Prior to calculating the response values (rval), or ΔΔCt, for each transcript (n = 96) per well, the baseline value (bval), the plate-wise median of the neutral control wells, was generated for each plate (the normalization process is described in detail in supplemental file SupFile4-DeltaCTCalculation.docx). The bval was subtracted from the cval to yield the rval or log2 Fold Change per transcript. Supplemental File LTEA_Inucyte_Images.zip is comprised of 20,493 images totaling more than 15 gigabytes. Cell morphology images were acquired for each well/plate with an Essen IncuCyte™ FLR automated phase-contrast microscope located inside a tissue culture incubator. Six 96-well culture plates were loaded into the instrument and imaged for an elapsed time (~24 minutes). The IncuCyte™ software was used for image capturing and export of images in JPEG format. This dataset is associated with the following publication: Franzosa, J., J. Bonzo, J. Jack, N.C. Baker, P. Kothiya, R. Witek, P. Hurban, S. Siferd, S. Hester, I. Shah, S. Ferguson, K. Houck, and J. Wambaugh. High-throughput toxicogenomic screening of chemicals in the environment using metabolically competent hepatic cell cultures. npj Systems Biology and Applications. Springer Nature Group, New York, NY, 7: Article 7, (2021).

We introduce a new high-throughput transcriptomics (HTTr) platform comprised of a collagen sandwich primary rat hepatocyte culture and the TempO-Seq assay for screening and prioritizing potential hepatotoxicants. We selected 14 chemicals based on their risk of drug-induced liver injury (DILI) and tested them in hepatocytes at two treatment concentrations. HTTr data was generated using the TempO-Seq whole transcriptome and S1500+ assays. The HTTr platform exhibited high reproducibility between technical replicates (r>0.9) but biological replication was greater for TempO-Seq S1500+ (r>0.85) than for the whole transcriptome (r>0.7). Reproducibility between biological replicates was dependent on the strength of transcriptional effects induced by a chemical treatment. Despite targeting a smaller number of genes, the S1500+ assay clustered chemical treatments and produced gene set enrichment analysis (GSEA) scores comparable to those of the whole transcriptome. Connectivity mapping showed a high-level of reproducibility between TempO-Seq data and Affymetrix GeneChip data from the Open TG-GATES project with high concordance between the S1500+ gene set and whole transcriptome. Taken together, our results provide guidance on selecting the number of technical and biological replicates and support the use of TempO-Seq S1500+ assay for a high-throughput platform for screening hepatotoxicants. FASTQ files and read counts data have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) (GSE152128). This dataset is associated with the following publication: Lee, F., I. Shah, Y.T. Soong, J. Xing, I.C. Ng, F. Tasnim, and H. Yu. Reproducibility and Robustness of High-Throughput S1500+ Transcriptomics on Primary Rat Hepatocytes for Chemical-Induced Hepatotoxicity Assessment. Current Research in Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 2: 282-295, (2021).

We introduce a new high-throughput transcriptomics (HTTr) platform comprised of a collagen sandwich primary rat hepatocyte culture and the TempO-Seq assay for screening and prioritizing potential hepatotoxicants. We selected 14 chemicals based on their risk of drug-induced liver injury (DILI) and tested them in hepatocytes at two treatment concentrations. HTTr data was generated using the TempO-Seq whole transcriptome and S1500+ assays. The HTTr platform exhibited high reproducibility between technical replicates (r>0.9) but biological replication was greater for TempO-Seq S1500+ (r>0.85) than for the whole transcriptome (r>0.7). Reproducibility between biological replicates was dependent on the strength of transcriptional effects induced by a chemical treatment. Despite targeting a smaller number of genes, the S1500+ assay clustered chemical treatments and produced gene set enrichment analysis (GSEA) scores comparable to those of the whole transcriptome. Connectivity mapping showed a high-level of reproducibility between TempO-Seq data and Affymetrix GeneChip data from the Open TG-GATES project with high concordance between the S1500+ gene set and whole transcriptome. Taken together, our results provide guidance on selecting the number of technical and biological replicates and support the use of TempO-Seq S1500+ assay for a high-throughput platform for screening hepatotoxicants. FASTQ files and read counts data have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) (GSE152128). This dataset is associated with the following publication: Lee, F., I. Shah, Y.T. Soong, J. Xing, I.C. Ng, F. Tasnim, and H. Yu. Reproducibility and Robustness of High-Throughput S1500+ Transcriptomics on Primary Rat Hepatocytes for Chemical-Induced Hepatotoxicity Assessment. Current Research in Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 2: 282-295, (2021).

Datasets used in RD-023418: Adverse Outcome Pathway-Driven Identification of Rat Hepatocarcinogens in Short-Term Assays. This dataset is associated with the following publication: Rooney, J., T. Hill, C. Qin, F. Sistare, and C. Corton. Adverse outcome pathway-driven identification of rat liver tumorigens in short-term assays. TOXICOLOGY AND APPLIED PHARMACOLOGY. Academic Press Incorporated, Orlando, FL, USA, 356: 99-113, (2018).

Datasets used in RD-023418: Adverse Outcome Pathway-Driven Identification of Rat Hepatocarcinogens in Short-Term Assays. This dataset is associated with the following publication: Rooney, J., T. Hill, C. Qin, F. Sistare, and C. Corton. Adverse outcome pathway-driven identification of rat liver tumorigens in short-term assays. TOXICOLOGY AND APPLIED PHARMACOLOGY. Academic Press Incorporated, Orlando, FL, USA, 356: 99-113, (2018).

Supplementary data for "Tia Tate, Grace Patlewicz, Imran Shah, A comparison of machine learning approaches for predicting hepatotoxicity potential using chemical structure and targeted transcriptomic data, Computational Toxicology, Volume 29, 2024, 100301, ISSN 2468-1113, https://doi.org/10.1016/j.comtox.2024.100301.". This dataset is associated with the following publication: Tate, T., G. Patlewicz, and I. Shah. A comparison of machine learning approaches for predicting hepatotoxicity potential using chemical structure and targeted transcriptomic data. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 29: 100301, (2024).

This repository contains the necessary data, python source code and jupyter notebooks to reproduce the results from our manuscript, "Estimating Hepatotoxic Doses Using High-content Imaging in Primary Hepatocytes." Using in vitro data to estimate point of departure (POD) values is an important component of new approach method (NAM)-based chemical risk assessments. In this case study we evaluated a NAM for hepatotoxicity based on rat primary hepatocytes, high-content imaging (HCI) and in vitro to in vivo extrapolation (IVIVE). This dataset is associated with the following publication: Shah, I., T. Antonijevic, B. Chambers, J. Harrill, and R. Thomas. Estimating Hepatotoxic Doses Using High-content Imaging in Primary Hepatocytes. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 183(2): 285-301, (2021).

This repository contains the necessary data, python source code and jupyter notebooks to reproduce the results from our manuscript, "Estimating Hepatotoxic Doses Using High-content Imaging in Primary Hepatocytes." Using in vitro data to estimate point of departure (POD) values is an important component of new approach method (NAM)-based chemical risk assessments. In this case study we evaluated a NAM for hepatotoxicity based on rat primary hepatocytes, high-content imaging (HCI) and in vitro to in vivo extrapolation (IVIVE). This dataset is associated with the following publication: Shah, I., T. Antonijevic, B. Chambers, J. Harrill, and R. Thomas. Estimating Hepatotoxic Doses Using High-content Imaging in Primary Hepatocytes. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 183(2): 285-301, (2021).

A number of brominated flame retardants (BFRs) have been reported to interfere with the thyroid signaling pathway and cause oxidative stress in birds, yet the underlying shifts in gene expression associated with these effects remain poorly understood. In this study, we measured hepatic transcriptional responses of 31 genes in American kestrel hatchlings following in ovo exposure to one of three high-volume alternative BFRs: 1,2-bis(2,4,6-tribromophenoxy) ethane (BTPBE), bis(2-ethylhexyl)-2,3,4,5-tetrabromophthalate (TBPH), or 2-ethylhexyl-2,3,4,5-tetrabromobenzoate (EHTBB). Hatchling kestrels exhibited shifts in the expression of genes related to oxidative stress (CYP, GSTA, SOD, GPx), thyroid hormone metabolism and transport (DIO, TTR), lipid and protein metabolism (PPAR, HMGCR, FAB1, LPL), and cytokine-mediated inflammation (TLR, IL-18, IRF7, STAT3, RACK1, CEBPB). Male and female hatchlings differed in which genes were differentially expressed as well as the direction of the effect (up- vs. down-regulation). These results build upon our previous findings of increased oxidative stress and disrupted thyroid signaling pathway in the same hatchlings. Furthermore, our results indicate that inflammatory responses appear to occur in female hatchlings exposed to BTBPE and EHTBB in ovo. Gene expression analysis revealed multiple affected pathways, adding to the growing evidence that sublethal physio-logical effects are complex and are a concern for birds exposed to BTBPE, EHTBB, or TBPH in ovo.

Dataset for Robarts et al., 'Hepatic Transcriptome Comparative In Silico Analysis Reveals Similar Pathways and Targets Altered by Legacy and Alternative Per- and Polyfluoroalkyl Substances in Mice' published in Toxics, DOI https://doi.org/10.3390/toxics11120963, PMCID 10748317. This dataset is associated with the following publication: Robarts, D., J. Dai, C. Lau, U. Apte, and J. Corton. Hepatic Transcriptome Comparative In Silico Analysis Reveals Similar Pathways and Targets Altered by Legacy and Alternative Per- and Polyfluoroalkyl Substances in Mice. Toxics. MDPI, Basel, SWITZERLAND, 11(12): 963, (2023).