Evaluating the utility of brightfield image data for mechanism of action prediction.

Fluorescence staining techniques, such as Cell Painting, together with fluorescence microscopy have proven invaluable for visualizing and quantifying the effects that drugs and other perturbations have on cultured cells. However, fluorescence microscopy is expensive, time-consuming, labor-intensive, and the stains applied can be cytotoxic, interfering with the activity under study. The simplest form of microscopy, brightfield microscopy, lacks these downsides, but the images produced have low contrast and the cellular compartments are difficult to discern. Nevertheless, by harnessing deep learning, these brightfield images may still be sufficient for various predictive purposes. In this study, we compared the predictive performance of models trained on fluorescence images to those trained on brightfield images for predicting the mechanism of action (MoA) of different drugs. We also extracted CellProfiler features from the fluorescence images and used them to benchmark the performance. Overall, we found comparable and largely correlated predictive performance for the two imaging modalities. This is promising for future studies of MoAs in time-lapse experiments for which using fluorescence images is problematic. Explorations based on explainable AI techniques also provided valuable insights regarding compounds that were better predicted by one modality over the other.

Modeling SHH-driven medulloblastoma with patient iPS cell-derived neural stem cells.

Medulloblastoma is the most common malignant brain tumor in children. Here we describe a medulloblastoma model using Induced pluripotent stem (iPS) cell-derived human neuroepithelial stem (NES) cells generated from a Gorlin syndrome patient carrying a germline mutation in the sonic hedgehog (SHH) receptor PTCH1. We found that Gorlin NES cells formed tumors in mouse cerebellum mimicking human medulloblastoma. Retransplantation of tumor-isolated NES (tNES) cells resulted in accelerated tumor formation, cells with reduced growth factor dependency, enhanced neurosphere formation in vitro, and increased sensitivity to Vismodegib. Using our model, we identified LGALS1 to be a GLI target gene that is up-regulated in both Gorlin tNES cells and SHH-subgroup of medulloblastoma patients. Taken together, we demonstrate that NES cells derived from Gorlin patients can be used as a resource to model medulloblastoma initiation and progression and to identify putative targets.

A phenomics approach for antiviral drug discovery.

BACKGROUND: The emergence and continued global spread of the current COVID-19 pandemic has highlighted the need for methods to identify novel or repurposed therapeutic drugs in a fast and effective way. Despite the availability of methods for the discovery of antiviral drugs, the majority tend to focus on the effects of such drugs on a given virus, its constituent proteins, or enzymatic activity, often neglecting the consequences on host cells. This may lead to partial assessment of the efficacy of the tested anti-viral compounds, as potential toxicity impacting the overall physiology of host cells may mask the effects of both viral infection and drug candidates. Here we present a method able to assess the general health of host cells based on morphological profiling, for untargeted phenotypic drug screening against viral infections. RESULTS: We combine Cell Painting with antibody-based detection of viral infection in a single assay. We designed an image analysis pipeline for segmentation and classification of virus-infected and non-infected cells, followed by extraction of morphological properties. We show that this methodology can successfully capture virus-induced phenotypic signatures of MRC-5 human lung fibroblasts infected with human coronavirus 229E (CoV-229E). Moreover, we demonstrate that our method can be used in phenotypic drug screening using a panel of nine host- and virus-targeting antivirals. Treatment with effective antiviral compounds reversed the morphological profile of the host cells towards a non-infected state. CONCLUSIONS: The phenomics approach presented here, which makes use of a modified Cell Painting protocol by incorporating an anti-virus antibody stain, can be used for the unbiased morphological profiling of virus infection on host cells. The method can identify antiviral reference compounds, as well as novel antivirals, demonstrating its suitability to be implemented as a strategy for antiviral drug repurposing and drug discovery.

Corrigendum: Development of new approach methods for the identification and characterization of endocrine metabolic disruptors-a PARC project.

[This corrects the article DOI: 10.3389/ftox.2023.1212509.].

Development of new approach methods for the identification and characterization of endocrine metabolic disruptors-a PARC project.

In past times, the analysis of endocrine disrupting properties of chemicals has mainly been focused on (anti-)estrogenic or (anti-)androgenic properties, as well as on aspects of steroidogenesis and the modulation of thyroid signaling. More recently, disruption of energy metabolism and related signaling pathways by exogenous substances, so-called metabolism-disrupting chemicals (MDCs) have come into focus. While general effects such as body and organ weight changes are routinely monitored in animal studies, there is a clear lack of mechanistic test systems to determine and characterize the metabolism-disrupting potential of chemicals. In order to contribute to filling this gap, one of the project within EU-funded Partnership for the Assessment of Risks of Chemicals (PARC) aims at developing novel in vitro methods for the detection of endocrine metabolic disruptors. Efforts will comprise projects related to specific signaling pathways, for example, involving mTOR or xenobiotic-sensing nuclear receptors, studies on hepatocytes, adipocytes and pancreatic beta cells covering metabolic and morphological endpoints, as well as metabolism-related zebrafish-based tests as an alternative to classic rodent bioassays. This paper provides an overview of the approaches and methods of these PARC projects and how this will contribute to the improvement of the toxicological toolbox to identify substances with endocrine disrupting properties and to decipher their mechanisms of action.

Morphological profiling of environmental chemicals enables efficient and untargeted exploration of combination effects.

Environmental chemicals are commonly studied one at a time, and there is a need to advance our understanding of the effect of exposure to their combinations. Here we apply high-content microscopy imaging of cells stained with multiplexed dyes (Cell Painting) to profile the effects of Cetyltrimethylammonium bromide (CTAB), Bisphenol A (BPA), and Dibutyltin dilaurate (DBTDL) exposure on four human cell lines; both individually and in all combinations. We show that morphological features can be used with multivariate data analysis to discern between exposures from individual compounds, concentrations, and combinations. CTAB and DBTDL induced concentration-dependent morphological changes across the four cell lines, and BPA exacerbated morphological effects when combined with CTAB and DBTDL. Combined exposure to CTAB and BPA induced changes in the ER, Golgi apparatus, nucleoli and cytoplasmic RNA in one of the cell lines. Different responses between cell lines indicate that multiple cell types are needed when assessing combination effects. The rapid and relatively low-cost experiments combined with high information content make Cell Painting an attractive methodology for future studies of combination effects. All data in the study is made publicly available on Figshare.

Searching for neural and behavioral parameters that predict anti-aggressive effects of chronic SSRI treatment in rats.

RATIONALE: Only a subset of impulsive aggressive patients benefits from selective serotonin reuptake inhibitor (SSRI) treatment, confirming contradictory results about the association between serotonin (5-hydroxytryptamine, 5-HT) and aggression. This shows the need to define behavioral characteristics within this subgroup to move towards individualized pharmacological treatment of impulsive aggression. METHODS: Here we submitted an outbred strain of Long Evans rats to a crossover design treatment regimen with the SSRI citalopram, to test its anti-aggressive effect. Behavioral characteristics were baseline aggression, anxiety parameters as measured in the elevated plus maze and open field and cue responsivity as indicated by sign vs. goal tracking behavior. 5-HT1A receptor densities as measured by ex vivo [18F]MPPF binding were determined in the dorsal raphe nucleus, dentate gyrus, orbitofrontal cortex, infralimbic cortex and prelimbic cortex, because of the receptors' involvement in the therapeutic delay of SSRIs and aggression. RESULTS: We found statistically significant increased variance in aggressive behavior after citalopram treatment. However, none of the selected parameters predicted the citalopram treatment effect. CONCLUSION: Since aggression after citalopram treatment decreased in a subgroup of animals and increased in the other, future research should focus on other possible predictors to support treatment strategies in aggressive patients.