In the present study, we adapted an existing phenotypic profiling assay (“Cell Painting”, (Bray et al., 2016)) to be compatible with in-house microfluidics capabilities for 384-well culture format, chemical exposures and fluorescent cytochemistry in order to facilitate concentration-response screening of several hundred environmental chemicals. In this assay, human-derived cells were labeled with multiple fluorescent probes to visualize various subcellular organelles and structural features. High content image analysis workflows were used to measure hundreds of morphological features at the level of the individual cell (i.e. shape of the cells, intensity, texture and distribution of fluorescent labels, etc.). The resultant data were then used to calculate well-level summary values, perform high-throughput concentration-response modeling and generate phenotypic response profiles. First, we identified and screened a set of candidate phenotypic reference chemicals for use as plate-based controls for evaluating HTPP assay performance during large-scale screening studies and identified an optimal exposure duration for HTPP screening. Second, we screened a set of 462 environmental chemicals in the U-2 OS cell model and derived in vitro potency estimates for bioactivity of all active chemicals. In addition, we demonstrated the technical reproducibility of the HTPP assay in concentration-response screening mode using the previously identified phenotypic reference chemicals. Next, we used reverse dosimetry to calculate administered equivalent doses (AEDs) corresponding to the thresholds for chemical bioactivity and compared those values to in vivo effect values from mammalian toxicity studies. This dataset is associated with the following publication: Nyffeler, J., C. Willis, R. Lougee, A. Richard, K. Friedman, and J. Harrill. Bioactivity screening of environmental chemicals using imaging-based high-throughput phenotypic profiling. TOXICOLOGY AND APPLIED PHARMACOLOGY. Academic Press Incorporated, Orlando, FL, USA, 389: 114876, (2020).

Cell Painting is a high-throughput, phenotypic profiling assay that uses fluorescent cytochemistry o visualize a variety of organelles and high-content imaging to derive a large number of morphological features at the single cell level. Here, we used the Cell Painting assay to characterize the phenotypic effects of sixteen phenotypic reference chemicals in concentration- response screening mode across six biologically diverse human-derived cell lines (U-2 OS, MCF7, HepG2, A549, HTB-9, ARPE-19). All cell lines were labeled using the same cytochemistry protocol and the same set of phenotypic features were calculated. We found it necessary to optimize image acquisition settings and cell segmentation parameters for each cell type but did not adjust the cytochemistry protocol. For some reference chemicals, similar subsets of phenotypic features corresponding to a particular organelle were associated with the highest effect magnitudes in each affected cell type. Overall, for certain chemicals the Cell Painting assay yielded qualitatively similar biological activity profiles across a group of diverse, morphologically distinct human-derived cell lines without the requirement for cell-type specific optimization of cytochemistry protocols. This dataset is associated with the following publication: Willis, C., J. Nyffeler, and J. Harrill. Phenotypic Profiling of Reference Chemicals Across Biologically Diverse Cell Types Using the Cell Painting Assay. SLAS Discovery. SAGE Publications, THOUSAND OAKS, CA, USA, 25(7): 755-769, (2020).

Dataset for Nyffeler et al., 'Application of Cell Painting for chemical hazard evaluation in support of screening-level chemical assessments', Toxicology & Applied Pharmacology, Vol 468, 116513, June 1, 2023, DOI https://doi.org/10.1016/j.taap.2023.116513. This dataset is associated with the following publication: Nyffeler, J., C. Willis, F. Harris, M. Foster, B. Chambers, M. Culbreth, R. Brockway, S. Davidson-Fritz, D. Dawson, I. Shah, K. Paul-Friedman, D. Chang, L. Everett, J. Wambaugh, G. Patlewicz, and J. Harrill. Application of Cell Painting for chemical hazard evaluation in support of screening-level chemical assessments. TOXICOLOGY AND APPLIED PHARMACOLOGY. Academic Press Incorporated, Orlando, FL, USA, 468: 116513, (2023).

The ToxCast program has generated in vitro screening data on over a thousand chemicals to assess potential disruption of important biological processes and assist in hazard identification and chemical testing prioritization. Few results have been reported for complex mixtures. To extend these ToxCast efforts to mixtures, we tested extracts from 30 organically grown fruits and vegetables in concentration-response in the BioMAP® assays. BioMAP systems use human primary cells primed with endogenous pathway activators to identify phenotypic perturbations related to proliferation, inflammation, immunomodulation, and tissue remodeling. This dataset is associated with the following publication: Wetmore, B., R. Clewell, B. Cholewa, B. Parks, S. Pendse, M. Black, K. Mansouri, S. Haider, E. Berg, R. Judson, K. Houck, M. Martin, H. Clewell III, M. Andersen, R. Thomas, and P. McMullen. Assessing Bioactivity-Exposure Profiles of Fruits and Vegetables in the BioMAP Profiling System. TOXICOLOGY IN VITRO. Elsevier Science Ltd, New York, NY, USA, 54: 41-57, (2019).

Background: Chemical toxicity testing is being transformed by advances in biology and computer modeling, concerns over animal use and the thousands of environmental chemicals lacking toxicity data. EPA's ToxCast program aims to address these concerns by screening and prioritizing chemicals for potential human toxicity using in vitro assays and in silico approaches. Objectives: This project aims to evaluate the use of in vitro assays for understanding the types of molecular and pathway perturbations caused by environmental chemicals and to build initial prioritization models of in vivo toxicity. Methods: We tested 309 mostly pesticide active chemicals in 467 assays across 9 technologies, including high-throughput cell-free assays and cell-based assays in multiple human primary cells and cell lines, plus rat primary hepatocytes. Both individual and composite scores for effects on genes and pathways were analyzed. Results: Chemicals display a broad spectrum of activity at the molecular and pathway levels. Many expected interactions are seen, including endocrine and xenobiotic metabolism enzyme activity. Chemicals range in promiscuity across pathways, from no activity to affecting dozens of pathways. We find a statistically significant inverse association between the number of pathways perturbed by a chemical at low in vitro concentrations and the lowest in vivo dose at which a chemical causes toxicity. We also find associations between a small set in vitro assays and rodent liver lesion formation. Conclusions: This approach promises to provide meaningful data on the thousands of untested environmental chemicals, and to guide targeted testing of environmental contaminants.

*********** Note to Josh Harrill- I don't have a copy of the final manuscript so could you please add the description of this dataset (just delete this comment and enter or cut and paste and then it should be ready to route by clicking on 'Submit for Review' button above) **********. This dataset is associated with the following publication: Nyffeler, J., D. Haggard, C. Willis, W. Setzer, R. Judson, K. Paul-Friedman, L. Everett, and J. Harrill. Comparison of Approaches for Determining Bioactivity Hits from High-Dimensional Profiling Data. SLAS Discovery. SAGE Publications, THOUSAND OAKS, CA, USA, 26(2): 292-308, (2021).

We use a supervised machine learning strategy to systematically investigate the relative importance of study type, machine learning algorithm, and type of descriptor on predicting in vivo repeat-dose toxicity at the organ-level. A total of 985 compounds were represented using chemical structural descriptors, ToxPrint chemotype descriptors, and bioactivity descriptors from ToxCast in vitro high-throughput screening assays. Using ToxRefDB, a total of 35 target organ outcomes were identified that contained at least 100 chemicals (50 positive and 50 negative). Supervised machine learning was performed using Naïve Bayes, k-nearest neighbor, random forest, classification and regression trees, and support vector classification approaches. Model performnce was assessed based on F1 scores using five-fold cross-validation with balanced bootstrap replicates. Fixed effects modeling showed the variance in F1 scores was explained mostly by target organ outcome, followed by descriptor type, machine learning algorithm, and interactions between these three factors. A combination of bioactivity and chemical structure or chemotype descriptors were the most predictive. Model performance improved with more chemicals (up to a maximum of 24%) and these gains were correlated (ρ= 0.92) with the number of chemicals. This dataset is associated with the following publication: Liu, J., G. Patlewicz, A. Williams, R. Thomas, and I. Shah. (Chemical Research in Toxicology) Predicting organ toxicity using in vitro bioactivity data and chemical structure. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, USA, 30: 2046−2059, (2017).

Dataset for Jesse Rogers et al., 'Integrating Transcriptomic and Targeted New Approach Methodologies into a Tiered Framework for Chemical Bioactivity Screening' published in Environmental Health Perspectives, Vol 133, Issue 6, 067013, June 2025. DOI: https://doi.org/10.1289/EHP16024, PMC12165737 R scripts for reproducing all analyses are available on Github (https://github.com/USEPA/CompTox-HTTr-RCAS). All sequencing data are available via the Gene Expression Omnibus repository (accessionnumbers GSE274318 for U-2 OS and GSE284321 for HepaRG). High-throughput screening assay data are available from InvitroDB via download 29 or the USEPA CompTox Chemicals Dashboard(https://comptox.epa.gov/dashboard/). This dataset is associated with the following publication: Rogers, J., J. Bundy, J. Harrill, R. Judson, K. Friedman, and L. Everett. Integrating Transcriptomic and Targeted New Approach Methodologies into a Tiered Framework for Chemical Bioactivity Screening. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 133(6): 067013, (2025).

Thousands of chemicals are directly added to or come in contact with food, many of which have undergone little to no toxicological evaluation. The landscape of the food-relevant chemical universe was evaluated using cheminformatics, and subsequently the bioactivity of food-relevant chemicals across the publicly available ToxCast highthroughput screening program was assessed. In total, 8659 food-relevant chemicals were compiled including direct food additives, food contact substances, and pesticides. Of these food-relevant chemicals, 4719 had curated structure definition files amenable to defining chemical fingerprints, which were used to cluster chemicals using a selforganizing map approach. Pesticides, and direct food additives clustered apart from one another with food contact substances generally in between, supporting that these categories not only reflect different uses but also distinct chemistries. Subsequently, 1530 food-relevant chemicals were identified in ToxCast comprising 616 direct food additives, 371 food contact substances, and 543 pesticides. Bioactivity across ToxCast was filtered for cytotoxicity to identify selective chemical effects. Initiating analyses from strictly chemical-based methodology or bioactivity/cytotoxicity-driven evaluation presents unbiased approaches for prioritizing chemicals. Although bioactivity in vitro is not necessarily predictive of adverse effects in vivo, these data provide insight into chemical properties and cellular targets through which foodrelevant chemicals elicit bioactivity. This dataset is associated with the following publication: Karmaus , A., D. Filer , M. Martin , and K. Houck. (FOOD AND CHEMICAL TOXICOLOGY) Evaluation of food-relevant chemicals in the ToxCast high-throughput screening program. FOOD AND CHEMICAL TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 92: 188-196, (2016).

Supplemental data for "Schaupp CM, Maloney EM, Mattingly K, Olker JH, Villeneuve DL. Comparison of in silico, in vitro, and in vivo toxicity benchmarks suggests a role for ToxCast data in ecological hazard assessment. Toxicol Sci. 2023 Jul 25:kfad072. doi: 10.1093/toxsci/kfad072. Epub ahead of print. PMID: 37490521.". This dataset is associated with the following publication: Schaupp, C., E. Maloney, K. Mattingly, J. Olker, and D. Villeneuve. Comparison of in silico, in vitro, and in vivo toxicity benchmarks suggests a role for ToxCast data in ecological hazard assessment.. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 195(2): 145-154, (2023).