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 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).

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

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

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).

Methods S1–S2 detailing laboratory and data analysis methods; tables describing materials used (Table S1), assay wells excluded from analyses (Table S2), sourcing information for test chemicals (Table S3), cell culture media (Table S4) and assay reagents (Table S5), and descriptions and metadata for data files S1–S9 (PDF) File S1 containing the 2D chemical structures for all antiozonant compounds tested in this study (PDF) File S2 containing the list of features used for profile correlation analysis (XLSX) File S3 containing graphs of each cell viability and Cell Painting concentration–response curve for all tested chemicals in the study (PDF) File S4 containing the curve-fitting results for the cell viability assay (XLSX) File S5 containing the curve-fitting results for the Cell Painting assay (XLSX) File S6 containing a list of all features collected during Cell Painting and their metadata (XLSX) File S7 containing the normalized well-level values for the Cell Painting assay (XLSX) File S8 containing global Mahalanobis distance values for the Cell Painting assay, 1 per well (XLSX) File S9 containing category Mahalanobis distance values for the Cell Painting assay, 49 per well (XLSX). This dataset is associated with the following publication: Harris, F., M. Jankowski, D. Villeneuve, and J. Harrill. Phenotypic Profiling of 6PPD, 6PPD-quinone and Structurally Diverse Antiozonants in RTgill-W1 Cells Using the Cell Painting Assay. Environmental Science & Technology Letters. American Chemical Society, Washington, DC, USA, 12(6): 695-701, (2025).

Chemical toxicity can arise from disruption of specific biomolecular functions or through more generalized cell stress and cytotoxicity-mediated processes. Here, concentration-dependent responses of 1063 chemicals including pharmaceuticals, natural products, pesticidals, consumer, and industrial chemicals across a diverse battery of 821 in vitro assay endpoints from 7 high-throughput assay technology platforms were analyzed in order to better distinguish between these types of activities. Both cell-based and cell-free assays showed a rapid increase in the frequency of responses at concentrations where cell stress / cytotoxicity responses were observed in cell-based assays. Chemicals that were positive on at least two viability/cytotoxicity assays within the concentration range tested (typically up to 100 M) activated a median of 12% of assay endpoints while those that were not cytotoxic in this concentration range activated 1.3% of the assays endpoints. The results suggest that activity can be broadly divided into: (1) specific biomolecular interactions against one or more targets (e.g., receptors or enzymes) at concentrations below which overt cytotoxicity-associated activity is observed; and (2) activity associated with cell stress or cytotoxicity, which may result from triggering of specific cell stress pathways, chemical reactivity, physico-chemical disruption of proteins or membranes, or broad low-affinity non-covalent interactions. Chemicals showing a greater number of specific biomolecular interactions are generally designed to be bioactive (pharmaceuticals or pesticidal active ingredients), while intentional food-use chemicals tended to show the fewest specific interactions. The analyses presented here provide context for use of these data in ongoing studies to predict in vivo toxicity from chemicals lacking extensive hazard assessment. This dataset is associated with the following publication: Judson , R., K. Houck , M. Martin , A. Richard , T. Knudsen , I. Shah , S. Little , J. Wambaugh , W. Setzer , P. Kothiya , J. Phuong , D. Filer , D. Smith , D. Reif, D. Rotroff, N. Kleinstreuer, N. Sipes, M. Xia, R. Huang, K. Crofton , and R. Thomas. (Toxicological Sciences) Analysis of the Effects of Cell Stress and Cytotoxicity on In Vitro Assay Activity Across a Diverse Chemical and Assay Space. TOXICOLOGICAL SCIENCES. Society of Toxicology, 1-47, (2016).

Chemical toxicity can arise from disruption of specific biomolecular functions or through more generalized cell stress and cytotoxicity-mediated processes. Here, concentration-dependent responses of 1063 chemicals including pharmaceuticals, natural products, pesticidals, consumer, and industrial chemicals across a diverse battery of 821 in vitro assay endpoints from 7 high-throughput assay technology platforms were analyzed in order to better distinguish between these types of activities. Both cell-based and cell-free assays showed a rapid increase in the frequency of responses at concentrations where cell stress / cytotoxicity responses were observed in cell-based assays. Chemicals that were positive on at least two viability/cytotoxicity assays within the concentration range tested (typically up to 100 M) activated a median of 12% of assay endpoints while those that were not cytotoxic in this concentration range activated 1.3% of the assays endpoints. The results suggest that activity can be broadly divided into: (1) specific biomolecular interactions against one or more targets (e.g., receptors or enzymes) at concentrations below which overt cytotoxicity-associated activity is observed; and (2) activity associated with cell stress or cytotoxicity, which may result from triggering of specific cell stress pathways, chemical reactivity, physico-chemical disruption of proteins or membranes, or broad low-affinity non-covalent interactions. Chemicals showing a greater number of specific biomolecular interactions are generally designed to be bioactive (pharmaceuticals or pesticidal active ingredients), while intentional food-use chemicals tended to show the fewest specific interactions. The analyses presented here provide context for use of these data in ongoing studies to predict in vivo toxicity from chemicals lacking extensive hazard assessment. This dataset is associated with the following publication: Judson , R., K. Houck , M. Martin , A. Richard , T. Knudsen , I. Shah , S. Little , J. Wambaugh , W. Setzer , P. Kothiya , J. Phuong , D. Filer , D. Smith , D. Reif, D. Rotroff, N. Kleinstreuer, N. Sipes, M. Xia, R. Huang, K. Crofton , and R. Thomas. (Toxicological Sciences) Analysis of the Effects of Cell Stress and Cytotoxicity on In Vitro Assay Activity Across a Diverse Chemical and Assay Space. TOXICOLOGICAL SCIENCES. Society of Toxicology, 1-47, (2016).