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

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.gz3) 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.gzMetadata: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 compoundFor full information, see the manuscript to which this data is linked.

摘要：当前的新冠肺炎疫情凸显了识别新型或重新用途的药物所需要的新颖且快速的方法。本研究提出了一种病毒感染细胞的无靶向表型药物筛选方法，该方法结合了细胞染色技术与抗体检测病毒感染的单次实验。我们设计了一个图像分析流程，用于病毒感染细胞与非感染细胞的分割与分类，并随后提取其形态学特性。我们展示了该方法的成功之处，能够捕捉由人冠状病毒229E（CoV-229E）感染的人肺成纤维细胞（MRC-5）所引起的病毒诱导的表型特征。此外，我们证明该方法可用于包含九种宿主和病毒靶向抗病毒药物的表型药物筛选。有效抗病毒化合物的治疗逆转了宿主细胞的形态学特征，使其回归非感染状态。该方法可在药物发现过程中，对感染细胞与非感染细胞上的新型抗病毒化合物的形态学特征进行剖析。筛选描述：图像展示了人肺成纤维细胞MRC-5感染人冠状病毒229E（CoV-229E）并接受九种宿主和病毒靶向抗病毒药物处理的图像。细胞通过五种标签进行标记，这些标签表征了七个细胞成分（来自“细胞染色”实验），以及一种冠状病毒多克隆抗体与二抗结合。本实验包含5个平板，每个平板有60个孔，每个孔有9个视野。每个视野在五个通道（检测波长）中成像，每个通道存储为单独的灰度TIFF格式图像文件。通道名称（w1-w5）对应以下染色：w1 = Hoechst 33342（HOECHST）；w2=冠状病毒多克隆抗体（FIPV3-70）+山羊抗小鼠IgG H&L二抗（MITO）；w3=麦芽凝集素/Alexa Fluor 555 + 肉毒碱/Alexa Fluor 568（PHAandWGA）；w4=SYTO 14绿色（SYTO）；w5=刀豆蛋白A/Alexa Fluor 488（CONC）。文件组织：1) 原始图像数据：- MRC5_HCoV229_Plate1.tar.gz - MRC5_HCoV229_Plate2.tar.gz - MRC5_Plate3.tar.gz - MRC5_Plate4.tar.gz - MRC5_HCoV229_Plate5.tar.gz 2) 图像分析流程（CellProfiler 4.0.7）：Cell Profiler项目包含图像子集，以测试分析流程：- Example_PipelineAndData.tar.gz 质量控制、照明校正和特征提取流程：- AnalysisPipelines.tar.gz 3) 提取的特征数据：- 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 元数据：文件“Metadata_MRC5_HCoV229E_plate1-5.csv”包含CSV格式的元数据，以下字段：- Plate_id：对应实验平板- Well：96孔板中的孔位分配- virus：“virus +”表示细胞暴露于病毒，“virus -”表示非感染对照组- Compound：化合物名称- Dose [μM]：化合物剂量。欲获取完整信息，请参阅与本数据集相关的手稿。