Development of accurate disease models and discovery of immune-modulating drugs is challenged by the immune system's highly interconnected and context-dependent nature. Here we apply deep-learning-driven analysis of cellular morphology to develop a scalable 'phenomics' platform and demonstrate its ability to identify dose-dependent, high-dimensional relationships among and between immunomodulators, toxins, pathogens, genetic perturbations, and small and large molecules at scale. High-throughput screening on this platform demonstrates rapid identification and triage of hits for TGF-β- and TNF-α-driven phenotypes. We deploy the platform to develop phenotypic models of active SARS-CoV-2 infection and of COVID-19-associated cytokine storm, surfacing compounds with demonstrated clinical benefit and identifying several new candidates for drug repurposing. The presented library of images, deep learning features, and compound screening data from immune profiling and COVID-19 screens serves as a deep resource for immune biology and cellular-model drug discovery with immediate impact on the COVID-19 pandemic.

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具有深度学习功能的基因组学的功能性免疫作图应用于免疫调节和COVID-19药物发现

精确的疾病模型的开发和免疫调节药物的发现受到免疫系统高度相互关联和背景依赖的性质的挑战。在这里，我们应用深度学习驱动的细胞形态分析，以开发可扩展的“基因组学”平台，并展示其识别免疫调节剂，毒素，病原体，遗传扰动以及大大小小的剂量之间依赖剂量的高维关系的能力。分子。在此平台上的高通量筛选显示了TGF-β-和TNF-α驱动的表型的命中的快速鉴定和分类。我们部署该平台来开发主动SARS-CoV-2感染和COVID-19相关的细胞因子风暴的表型模型，将化合物表面显示出已证明的临床益处，并确定几种新的药物用途。所提供的图像库，深度学习功能以及来自免疫谱和COVID-19筛选的化合物筛选数据库可作为免疫生物学和细胞模型药物发现的深层资源，对COVID-19大流行具有直接影响。