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. 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. 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.

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抗病毒药物发现的表型组学方法

当前 COVID-19 大流行的出现和持续的全球蔓延凸显了需要以快速有效的方式识别新的或重新利用的治疗药物的方法。尽管有发现抗病毒药物的方法，但大多数人倾向于关注此类药物对给定病毒、其组成蛋白或酶活性的影响，往往忽略对宿主细胞的影响。这可能导致对测试的抗病毒化合物的功效进行部分评估，因为影响宿主细胞整体生理的潜在毒性可能会掩盖病毒感染和候选药物的影响。在这里，我们提出了一种能够基于形态学分析评估宿主细胞总体健康状况的方法，用于针对病毒感染的非靶向表型药物筛选。我们将细胞染色与基于抗体的病毒感染检测相结合，在一次检测中进行。我们设计了一个图像分析管道，用于对病毒感染和未感染细胞进行分割和分类，然后提取形态学特性。我们表明，该方法可以成功捕获感染人冠状病毒 229E (CoV-229E) 的 MRC-5 人肺成纤维细胞的病毒诱导表型特征。此外，我们证明了我们的方法可用于使用一组九种宿主和病毒靶向抗病毒药物进行表型药物筛选。用有效的抗病毒化合物治疗将宿主细胞的形态特征逆转为非感染状态。这里介绍的表型组学方法，通过加入抗病毒抗体染色，利用修改后的细胞绘画协议，可用于对宿主细胞上的病毒感染进行无偏的形态学分析。该方法可以识别抗病毒参考化合物以及新型抗病毒药物，证明其适用于作为抗病毒药物再利用和药物发现的策略。