To date, approximately 200 chemicals have been tested in US Environmental Protection Agency (EPA) or Organization for Economic Co-operation and Development (OECD) developmental neurotoxicity (DNT) guideline studies, leaving thousands of chemicals without traditional animal information on DNT hazard potential. To address this data gap, a battery of in vitro DNT new approach methodologies (NAMs) has been proposed. Evaluation of the performance of this battery will increase the confidence in its use to determine DNT chemical hazards. One approach to evaluate DNT NAM performance is to use a set of chemicals to evaluate sensitivity and specificity. Since a list of chemicals with potential evidence of in vivo DNT has been established, this study aims to develop a curated list of "negative" chemicals for inclusion in a "DNT NAM evaluation set". A workflow, including a literature search followed by an expert-driven literature review, was used to systematically screen 39 chemicals for lack of DNT effect. Expert panel members evaluated the scientific robustness of relevant studies to inform chemical categorizations. Following review, the panel discussed each chemical and made categorical determinations of "Favorable", "Not Favorable", or "Indeterminate" reflecting acceptance, lack of suitability, or uncertainty given specific limitations and considerations, respectively. The panel determined that 10, 22, and 7 chemicals met the criteria for "Favorable", "Not Favorable", and "Indeterminate", for use as negatives in a DNT NAM evaluation set. Ultimately, this approach not only supports DNT NAM performance evaluation but also highlights challenges in identifying large numbers of negative DNT chemicals.

The United States Environmental Protection Agency has proposed a tiered testing strategy for chemical hazard evaluation based on new approach methods (NAMs). The first tier includes in vitro profiling assays applicable to many (human) cell types, such as high-throughput transcriptomics (HTTr) and high-throughput phenotypic profiling (HTPP). The goals of this study were to: (1) harmonize the seeding density of U-2 OS human osteosarcoma cells for use in both assays; (2) compare HTTr- versus HTPP-derived potency estimates for 11 mechanistically diverse chemicals; (3) identify candidate reference chemicals for monitoring assay performance in future screens; and (4) characterize the transcriptional and phenotypic changes in detail for all-trans retinoic acid (ATRA) as a model compound known for its adverse effects on osteoblast differentiation. The results of this evaluation showed that (1) HTPP conducted at low (400 cells/well) and high (3000 cells/well) seeding densities yielded comparable potency estimates and similar phenotypic profiles for the tested chemicals; (2) HTPP and HTTr resulted in comparable potency estimates for changes in cellular morphology and gene expression, respectively; (3) three test chemicals (etoposide, ATRA, dexamethasone) produced concentration-dependent effects on cellular morphology and gene expression that were consistent with known modes-of-action, demonstrating their suitability for use as reference chemicals for monitoring assay performance; and (4) ATRA produced phenotypic changes that were highly similar to other retinoic acid receptor activators (AM580, arotinoid acid) and some retinoid X receptor activators (bexarotene, methoprene acid). This phenotype was observed concurrently with autoregulation of the RARB gene. Both effects were prevented by pre-treating U-2 OS cells with pharmacological antagonists of their respective receptors. Thus, the observed phenotype could be considered characteristic of retinoic acid pathway activation in U-2 OS cells. These findings lay the groundwork for combinatorial screening of chemicals using HTTr and HTPP to generate complementary information for the first tier of a NAM-based chemical hazard evaluation strategy.

Studies in in vivo rodent models have been the accepted approach by regulatory agencies to evaluate potential developmental neurotoxicity (DNT) of chemicals for decades. These studies, however, are inefficient and cannot meet the demand for the thousands of chemicals that need to be assessed for DNT hazard. As such, several in vitro new approach methods (NAMs) have been developed to circumvent limitations of these traditional studies. The DNT NAMs, some of which utilize human-derived cell models, are intended to be employed in a testing battery approach, each focused on a specific neurodevelopmental process. The need for multiple assays, however, to evaluate each process can prolong testing and prioritization of chemicals for more in depth assessments. Therefore, a multi-endpoint higher-throughput approach to assess DNT hazard potential would be of value. Accordingly, we have adapted a high-throughput phenotypic profiling (HTPP) approach for use with human-derived neural progenitor (hNP1) cells. HTPP is a fluorescence-based assay that quantitatively measures alterations in cellular morphology. This approach, however, required optimization of several laboratory procedures prior to chemical screening. First, we had to determine an appropriate cell plating density in 384-well plates. We then had to identify the minimum laminin concentration required for optimal cell growth and attachment. And finally, we had to evaluate whether addition of antibiotics to the culture medium would alter cellular morphology. We selected 6,000 cells/well as an appropriate plating density, 20 µg/ml laminin for optimal cell growth and attachment, and antibiotic addition in the culture medium. After optimizing hNP1 cell culture conditions for HTPP, it was then necessary to select appropriate in-plate assay controls from a reference chemical set. These reference chemicals were previously demonstrated to elicit unique phenotypic profiles in various other cell types. Aphidicolin, bafilomycin A1, berberine chloride, and cucurbitacin I induced robust phenotypic profiles as compared to dimethyl sulfoxide vehicle control in the hNP1 cells, and thus can be employed as in-plate assay controls for subsequent chemical screens. We have optimized HTPP for hNP1 cells, and consequently this approach can now be assessed as a potential NAM for DNT hazard evaluation and results compared to previously developed DNT assays.

Screening new compounds for potential bioactivities against cellular targets is vital for drug discovery and chemical safety. Transcriptomics offers an efficient approach for assessing global gene expression changes, but interpreting chemical mechanisms from these data is often challenging. Connectivity mapping is a potential data-driven avenue for linking chemicals to mechanisms based on the observation that many biological processes are associated with unique gene expression signatures (gene signatures). However, mining the effects of a chemical on gene signatures for biological mechanisms is challenging because transcriptomic data contain thousands of noisy genes. New connectivity mapping approaches seeking to distinguish signal from noise continue to be developed, spurred by the promise of discovering chemical mechanisms, new drugs, and disease targets from burgeoning transcriptomic data. Here, we analyze these approaches in terms of diverse transcriptomic technologies, public databases, gene signatures, pattern-matching algorithms, and statistical evaluation criteria. To navigate the complexity of connectivity mapping, we propose a harmonized scheme to coherently organize and compare published workflows. We first standardize concepts underlying transcriptomic profiles and gene signatures based on various transcriptomic technologies such as microarrays, RNA-Seq, and L1000 and discuss the widely used data sources such as Gene Expression Omnibus, ArrayExpress, and MSigDB. Next, we generalize connectivity mapping as a pattern-matching task for finding similarity between a query (e.g., transcriptomic profile for new chemical) and a reference (e.g., gene signature of known target). Published pattern-matching approaches fall into two main categories: vector-based use metrics like correlation, Jaccard index, etc., and aggregation-based use parametric and nonparametric statistics (e.g., gene set enrichment analysis). The statistical methods for evaluating the performance of different approaches are described, along with comparisons reported in the literature on benchmark transcriptomic data sets. Lastly, we review connectivity mapping applications in toxicology and offer guidance on evaluating chemical-induced toxicity with concentration-response transcriptomic data. In addition to serving as a high-level guide and tutorial for understanding and implementing connectivity mapping workflows, we hope this review will stimulate new algorithms for evaluating chemical safety and drug discovery using transcriptomic data.

Phenotypic profiling assays are untargeted screening assays that measure a large number (hundreds to thousands) of cellular features in response to a stimulus and often yield diverse and unanticipated profiles of phenotypic effects, leading to challenges in distinguishing active from inactive treatments. Here, we compare a variety of different strategies for hit identification in imaging-based phenotypic profiling assays using a previously published Cell Painting data set. Hit identification strategies based on multiconcentration analysis involve curve fitting at several levels of data aggregation (e.g., individual feature level, aggregation of similarly derived features into categories, and global modeling of all features) and on computed metrics (e.g., Euclidean and Mahalanobis distance metrics and eigenfeatures). Hit identification strategies based on single-concentration analysis included measurement of signal strength (e.g., total effect magnitude) and correlation of profiles among biological replicates. Modeling parameters for each approach were optimized to retain the ability to detect a reference chemical with subtle phenotypic effects while limiting the false-positive rate to 10%. The percentage of test chemicals identified as hits was highest for feature-level and category-based approaches, followed by global fitting, whereas signal strength and profile correlation approaches detected the fewest number of active hits at the fixed false-positive rate. Approaches involving fitting of distance metrics had the lowest likelihood for identifying high-potency false-positive hits that may be associated with assay noise. Most of the methods achieved a 100% hit rate for the reference chemical and high concordance for 82% of test chemicals, indicating that hit calls are robust across different analysis approaches.

Poster presented to the Intelligent Systems for Molecular Biology virtual meeting July 2020

Many toxicological test methods, including assays of cell viability and function, require an evaluation of concentration-response data. This often involves curve fitting, and the resulting mathematical functions are then used to determine the concentration at which a certain deviation from the control value occurs (e.g., a decrease of cell viability by 15%). Such a threshold is called the benchmark response (BMR). For a toxicological test, it is often of interest to determine the concentration of test compound at which a pre-defined BMR of, e.g., 10, 25 or 50% is reached. The concentration at which the modelled curve crosses the BMR is called the benchmark concentration (BMC). We present a user-friendly, web-based tool (BMCeasy), designed for operators without programming skills and profound statistical background, to determine BMCs and their confidence intervals. BMCeasy allows simultaneous analysis of viability plus a functional test endpoint, and it yields absolute BMCs with confidence intervals for any BMR. Besides an explanation of the algorithm underlying BMCeasy, this article also gives multiple examples of data outputs. BMCeasy was used within the EU-ToxRisk project for preparing data packages that were submitted to regulatory authorities, demonstrating the real-life applicability of the tool.

Poster presented at the ISMB 2020 conference (virtual).

Presentation given to the Summer School on Innovative Approaches in Science Webinar June 2020

Cell Painting is a high-throughput phenotypic profiling assay that uses fluorescent cytochemistry to visualize a variety of organelles and high-content imaging to derive a large number of morphological features at the single-cell level. Most Cell Painting studies have used the U-2 OS cell line for chemical or functional genomics screening. The Cell Painting assay can be used with many other human-derived cell types, given that the assay is based on the use of fluoroprobes that label organelles that are present in most (if not all) human cells. Questions remain, however, regarding the optimization steps required and overall ease of deployment of the Cell Painting assay to novel cell types. Here, we used the Cell Painting assay to characterize the phenotypic effects of 14 phenotypic reference chemicals in concentration-response screening mode across six biologically diverse human-derived cell lines (U-2 OS, MCF7, HepG2, A549, HTB-9 and ARPE-19). All cell lines were labeled using the same cytochemistry protocol, and the same set of phenotypic features was 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 among a group of diverse, morphologically distinct human-derived cell lines without the requirement for cell type-specific optimization of cytochemistry protocols.

Presentation to the Society of Biomolecular Imaging and Informatics (SBI2) Conference in September 2019

Poster presented to the European Society for Alternatives to Animal Testing (EUSAAT) annual conference in October 2019

The present study adapted an existing high content imaging-based high-throughput phenotypic profiling (HTPP) assay known as "Cell Painting" for bioactivity screening of environmental chemicals. This assay uses a combination of fluorescent probes to label a variety of organelles and measures a large number of phenotypic features at the single cell level in order to detect chemical-induced changes in cell morphology. First, a small set of candidate phenotypic reference chemicals (n = 14) known to produce changes in the cellular morphology of U-2 OS cells were identified and screened at multiple time points in concentration-response format. Many of these chemicals produced distinct cellular phenotypes that were qualitatively similar to those previously described in the literature. A novel workflow for phenotypic feature extraction, concentration-response modeling and determination of in vitro thresholds for chemical bioactivity was developed. Subsequently, a set of 462 chemicals from the ToxCast library were screened in concentration-response mode. Bioactivity thresholds were calculated and converted to administered equivalent doses (AEDs) using reverse dosimetry. AEDs were then compared to effect values from mammalian toxicity studies. In many instances (68%), the HTPP-derived AEDs were either more conservative than or comparable to the in vivo effect values. Overall, we conclude that the HTPP assay can be used as an efficient, cost-effective and reproducible screening method for characterizing the biological activity and potency of environmental chemicals for potential use in in vitro-based safety assessments.

While there are many methods to quantify the synthesis, localization, and pool sizes of proteins and DNA during physiological responses and toxicological stress, only few approaches allow following the fate of carbohydrates. One of them is metabolic glycoengineering (MGE), which makes use of chemically modified sugars (CMS) that enter the cellular biosynthesis pathways leading to glycoproteins and glycolipids. The CMS can subsequently be coupled (via bio-orthogonal chemical reactions) to tags that are quantifiable by microscopic imaging. We asked here, whether MGE can be used in a quantitative and time-resolved way to study neuronal glycoprotein synthesis and its impairment. We focused on the detection of sialic acid (Sia), by feeding human neurons the biosynthetic precursor N-acetyl-mannosamine, modified by an azide tag. Using this system, we identified non-toxic conditions that allowed live cell labeling with high spatial and temporal resolution, as well as the quantification of cell surface Sia. Using combinations of immunostaining, chromatography, and western blotting, we quantified the percentage of cellular label incorporation and effects on glycoproteins such as polysialylated neural cell adhesion molecule. A specific imaging algorithm was used to quantify Sia incorporation into neuronal projections, as potential measure of complex cell function in toxicological studies. When various toxicants were studied, we identified a subgroup (mitochondrial respiration inhibitors) that affected neurite glycan levels several hours before any other viability parameter was affected. The MGE-based neurotoxicity assay, thus allowed the identification of subtle impairments of neurochemical function with very high sensitivity.

Many types of assays in cell biology, pharmacology and toxicology generate data in which a parameter is measured in a reference system (negative control) and then also under conditions of increasing stress or drug exposure. To make such data easily comparable, they are normalized, i.e., the initial value of the system (e.g., viability or transport function) is set to 100%, and all data are indicated relative to this value. Then, curves are fitted through the data points and summary data of the system behavior are determined. For this, a benchmark response (BMR) is given (e.g., a curve drop by 15 or 50%), and the corresponding benchmark concentration (BMC15 or BMC50) is determined. Especially for low BMRs, this procedure is not very robust and often results in incorrect summary data. It is often neglected that a second normalization (re-normalization) is necessary to make the data suitable for curve fitting. It is also frequently overlooked that this requires knowledge of the system behavior at very low stress conditions. Here, good in vitro practice guidance for the re-normalization procedure is provided so that data of higher fidelity can be generated and presented.

Poster presented at SETAC North America Focused Topic Meeting : High-Throughput Screening April 2018

Antimicrobial peptides present a broad spectrum of therapeutic applications, including their use as anticancer peptides. These peptides have as target microbial, normal, and cancerous cells. The oncological properties of these peptides may occur by membranolytic mechanisms or non-membranolytics. In this work, we demonstrate for the first time the cytotoxic effects of the cationic alpha-helical antimicrobial peptide LyeTx I-b on glioblastoma lineage U87-MG. The anticancer property of this peptide was associated with a membranolytic mechanism. Loss of membrane integrity occurred after incubation with the peptide for 15min, as shown by trypan blue uptake, reduction of calcein-AM conversion, and LDH release. Morphological studies using scanning electron microscopy demonstrated disruption of the plasma membrane from cells treated with LyeTx I-b, including the formation of holes or pores. Transmission electron microscopy analyses showed swollen nuclei with mild DNA condensation, cell volume increase with an electron-lucent cytoplasm and organelle vacuolization, but without the rupture of nuclear or plasmatic membranes. Morphometric analyses revealed a high percentage of cells in necroptosis stages, followed by necrosis and apoptosis at lower levels. Necrostatin-1, a known inhibitor of necroptosis, partially protected the cells from the toxicity of the peptide in a concentration-dependent manner. Imaging flow cytometry confirmed that 59% of the cells underwent necroptosis after 3-h incubation with the peptide. It is noteworthy that LyeTx I-b showed only mild cytotoxicity against normal fibroblasts of human and monkey cell lines and low hemolytic activity in human erythrocytes. All data together point out the anticancer potential of this peptide.

Migration of neural crest cells (NCCs) is one of the pivotal processes of human fetal development. Malformations arise if NCC migration and differentiation are impaired genetically or by toxicants. In the currently available test systems for migration inhibition of NCC (MINC), the manual generation of a cell-free space results in extreme operator dependencies, and limits throughput. Here a new test format was established. The assay avoids scratching by plating cells around a commercially available circular stopper. Removal of the stopper barrier after cell attachment initiates migration. This microwell-based circular migration zone NCC function assay (cMINC) was further optimized for toxicological testing of human pluripotent stem cell (hPSC)-derived NCCs. The challenge of obtaining data on viability and migration by automated image processing was addressed by developing a freeware. Data on cell proliferation were obtained by labelling replicating cells, and by careful assessment of cell viability for each experimental sample. The role of cell proliferation as an experimental confounder was tested experimentally by performing the cMINC in the presence of the proliferation-inhibiting drug cytosine arabinoside (AraC), and by a careful evaluation of mitotic events over time. Data from these studies led to an adaptation of the test protocol, so that toxicant exposure was limited to 24 h. Under these conditions, a prediction model was developed that allows classification of toxicants as either inactive, leading to unspecific cytotoxicity, or specifically inhibiting NC migration at non-cytotoxic concentrations.

Halogen-free organophosphorus flame retardants are considered as replacements for the phased-out class of polybrominated diphenyl ethers (PBDEs). However, toxicological information on new flame retardants is still limited. Based on their excellent flame retardation potential, we have selected three novel 9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide (DOPO) derivatives and assessed their toxicological profile using a battery of in vitro test systems in order to provide toxicological information before their large-scale production and use. PBDE-99, applied as a reference compound, exhibited distinct neuro-selective cytotoxicity at concentrations ≥10 µM. 6-(2-((6-oxido-6H-dibenzo[c,e][1,2]oxaphosphinin-6-yl)amino)ethoxy)-6H-dibenzo[c,e][1,2]oxaphosphinine 6-oxide (ETA-DOPO) and 6,6′-(ethane-1,2-diylbis(oxy))bis(6H-dibenzo[c,e][1,2]oxaphosphinine-6-oxide) (EG-DOPO) displayed adverse effects at concentrations >10 µM in test systems reflecting the properties of human central and peripheral nervous system neurons, as well as in a set of non-neuronal cell types. DOPO and its derivative 6,6′-(ethane-1,2-diylbis(azanediyl))bis(6H-dibenzo[c,e][1,2]oxaphosphinine-6-oxide) (EDA-DOPO) were neither neurotoxic, nor did they exhibit an influence on neural crest cell migration, or on the integrity of human skin equivalents. The two compounds furthermore displayed no inflammatory activation potential, nor did they affect algae growth or daphnia viability at concentrations ≤400 µM. Based on the superior flame retardation properties, biophysical features suited for use in polyurethane foams, and low cytotoxicity of EDA-DOPO, our results suggest that it is a candidate for the replacement of currently applied flame retardants.