*********** 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).

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).

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).

All of the code used to analyze and report the data as well as build confidence in the approach is available as a supplementary RMarkdown report, and a tool to derive PODBioactivity and PODRead-Across is available as an RShiny web-application. The data used in the workflow are either available on public databases or are included in the supplementary material to allow for reproducibility of results. The results and output of the workflow (i.e., chemical info, PODs, etc.) are provided in the supplementary material (available as a download from the journal article). This dataset is associated with the following publication: Beal, M., M. Gagne, S. Kulkarni, G. Patlewicz, R. Thomas, and T. Barton-Maclaren. Implementing in vitro Bioactivity Data to Modernize Priority Setting of Chemical Inventories. ALTEX. Society ALTEX Edition, Kuesnacht, SWITZERLAND, 39(1): 123-139, (2022).

All of the code used to analyze and report the data as well as build confidence in the approach is available as a supplementary RMarkdown report, and a tool to derive PODBioactivity and PODRead-Across is available as an RShiny web-application. The data used in the workflow are either available on public databases or are included in the supplementary material to allow for reproducibility of results. The results and output of the workflow (i.e., chemical info, PODs, etc.) are provided in the supplementary material (available as a download from the journal article). This dataset is associated with the following publication: Beal, M., M. Gagne, S. Kulkarni, G. Patlewicz, R. Thomas, and T. Barton-Maclaren. Implementing in vitro Bioactivity Data to Modernize Priority Setting of Chemical Inventories. ALTEX. Society ALTEX Edition, Kuesnacht, SWITZERLAND, 39(1): 123-139, (2022).

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).

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).

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).

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).

Dataset for "Nicolas Chantel I., Linakis Matthew W., Minto Melyssa S., Mansouri Kamel, Clewell Rebecca A., Yoon Miyoung, Wambaugh John F., Patlewicz Grace, McMullen Patrick D., Andersen Melvin E., Clewell III Harvey J, Estimating provisional margins of exposure for data-poor chemicals using high-throughput computational methods, Frontiers in Pharmacology, 13, 2022, https://www.frontiersin.org/articles/10.3389/fphar.2022.980747,10.3389/fphar.2022.980747"