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            <a:FirstGiven>Jonne</a:FirstGiven>
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            <a:FirstGiven>Marianna</a:FirstGiven>
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              <r:String>Marianna Tampere</r:String>
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            <a:FirstGiven>Polina</a:FirstGiven>
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            <a:FirstGiven>Maris</a:FirstGiven>
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              <r:String>Maris Lapins</r:String>
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        <r:URN>urn:ddi:se.researchdata:doi-10-17044-scilifelab-14188403.Individual-0:2.0</r:URN>
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            <a:FirstGiven>Ulrika Warpman</a:FirstGiven>
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              <r:String>Ulrika Warpman Berglund</r:String>
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              <r:String>Ola Spjuth</r:String>
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            <a:FirstGiven>Marjo-Riitta</a:FirstGiven>
            <a:LastFamily>Puumalainen</a:LastFamily>
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              <r:String>Marjo-Riitta Puumalainen</r:String>
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            <a:FirstGiven>Jordi Carreras</a:FirstGiven>
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              <r:String>Jordi Carreras Puigvert</r:String>
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      <r:Title>
        <r:String xml:lang="en">A phenomics approach for antiviral drug discovery - Images, analysis pipelines and feature data</r:String>
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          <r:URN>urn:ddi:se.researchdata:doi-10-17044-scilifelab-14188403.Individual-0:2.0</r:URN>
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          <r:String xml:lang="sv">Uppsala universitet</r:String>
          <r:String xml:lang="en">Uppsala University</r:String>
        </r:PublisherName>
      </r:Publisher>
      <r:Publisher>
        <r:PublisherName>
          <r:String xml:lang="sv">Uppsala universitet</r:String>
          <r:String xml:lang="en">Uppsala University</r:String>
        </r:PublisherName>
      </r:Publisher>
      <r:PublicationDate>
        <r:SimpleDate>2021-03-22</r:SimpleDate>
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        <r:IdentifierContent>10.17044/SCILIFELAB.14188403</r:IdentifierContent>
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    <r:Abstract>
      <r:Content xml:lang="en">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&amp;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.</r:Content>
    </r:Abstract>
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        <r:URN>urn:ddi:se.researchdata:doi-10-17044-scilifelab-14188403.TopicalCoverage:2.0</r:URN>
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        <r:Subject xml:lang="sv" controlledVocabularyID="3" controlledVocabularyName="Standard för svensk indelning av forskningsämnen 2025">Medicin och hälsovetenskap</r:Subject>
        <r:Subject xml:lang="en" controlledVocabularyID="10408" controlledVocabularyName="Standard för svensk indelning av forskningsämnen 2025">Biochemistry</r:Subject>
        <r:Subject xml:lang="sv" controlledVocabularyID="10408" controlledVocabularyName="Standard för svensk indelning av forskningsämnen 2025">Biokemi</r:Subject>
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