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A phenomics approach for antiviral drug discovery - Images, analysis pipelines and feature data

dataset
posted on 22.03.2021, 16:09 authored by Jonne RietdijkJonne Rietdijk, Marianna Tampere, Aleksandra Pettke, Polina Georgieva, Maris LapinsMaris Lapins, Ulrika Warpman Berglund, Ola Spjuth, Marjo-Riitta Puumalainen, Jordi Carreras Puigvert
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.

Funding

This project received funding within the SciLifeLab National COVID-19 Research Program and Knut och Alice Wallenbergs stiftelse (KAW 2020.0182). This project also received funding from the Swedish Research Council (2017-05631 for M-R.P; 2020-03731 for OS and JCP), FORMAS (2018-00924 for OS and JR) and the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no 722729. M.T. was supported by SSF (FID15-0010). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

History

Publisher

Uppsala University