A phenomics approach for antiviral drug discovery - Images, analysis pipelines and feature data doi-10-17044-scilifelab-14188403-0 https://doi.org/10.17044/SCILIFELAB.14188403 Swedish National Data Service Svensk nationell datatjänst Landing page A phenomics approach for antiviral drug discovery - Images, analysis pipelines and feature data doi-10-17044-scilifelab-14188403-0 https://doi.org/10.17044/SCILIFELAB.14188403 Rietdijk, Jonne Spjuth, Ola Swedish National Data Service Svensk nationell datatjänst Landing page Abstract: The current COVID-19 pandemic has highlighted the need for new and fast methods to identify novel or repurposed therapeutic drugs. Here we present a method for untargeted phenotypic drug screening of virus-infected cells, combining 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 the 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. The method can be used in drug discovery for morphological profiling of novel antiviral compounds on both infected and non-infected cells. Screen description: The images are of MRC-5 human lung fibroblasts infected with Human coronavirus 229E (CoV-229E) and treated with a panel of nine host- and virus-targeting antivirals. Cells are labelled with five labels that characterise seven cellular components (from the "Cell Painting" assay) as well as with a Coronavirus pan monoclonal antibody combined with a secondary antibody. This experiment consists of 5 plates. Each plate has 60 wells, and 9 fields of view per well. Each field was imaged in five channels (detection wavelengths), and each channel is stored as a separate, grayscale image file in TIFF format.The channel names (w1-w5) correspond to the following stains: w1 = Hoechst 33342 (HOECHST); w2= Coronavirus pan Monoclonal Antibody (FIPV3-70) + Goat Anti-Mouse IgG H&L secondary antibody (MITO); w3= Wheat Germ Agglutinin/Alexa Fluor 555 + Phalloidin/Alexa Fluor 568 (PHAandWGA); w4= SYTO 14 green (SYTO); w5= Concanavalin A/Alexa Fluor 488 (CONC) Organization of files: 1) Raw image data: - MRC5_HCoV229_Plate1.tar.gz - MRC5_HCoV229_Plate2.tar.gz - MRC5_Plate3.tar.gz - MRC5_Plate4.tar.gz - MRC5_HCoV229_Plate5.tar.gz 2) Image analysis pipelines (CellProfiler 4.0.7): Cell Profiler project with a subset of images to try out the analysis pipeline:- Example_PipelineAndData.tar.gz Quality control, illumination correction and feature extraction pipelines:- AnalysisPipelines.tar.gz 3) Extracted feature data: - features_MRC5_HCoV229_Plate1.tar.gz - features_MRC5_HCoV229_Plate2.tar.gz- features_MRC5_Plate3.tar.gz- features_MRC5_Plate4.tar.gz- features_MRC5_HCoV229_Plate5.tar.gz Metadata: The file “Metadata_MRC5_HCoV229E_plate1-5.csv“ contains the metadata in CSV format, with the following fields: - Plate_id: corresponds to the experimental plate - Well: well allocation in the 96-well plate - virus: "virus +" when cells are exposed to virus, and "virus -' for non-infected controls- Compound: name of compound - Dose [μM]: dose of compound For full information, see the manuscript to which this data is linked. Access to data through an external actor. Åtkomst till data via extern aktör.