Background Large-scale image sets acquired by automated microscopy of perturbed samples enable a detailed comparison of cell states induced by each perturbation, such as a small molecule from a diverse library. Highly multiplexed measurements of cellular morphology can be extracted from each image and subsequently mined for a number of applications. Findings This microscopy dataset includes 919 265 five-channel fields of view, representing 30 616 tested compounds, available at "The Cell Image Library" (CIL) repository. It also includes data files containing morphological features derived from each cell in each image, both at the single-cell level and population-averaged (i.e., per-well) level; the image analysis workflows that generated the morphological features are also provided. Quality-control metrics are provided as metadata, indicating fields of view that are out-of-focus or containing highly fluorescent material or debris. Lastly, chemical annotations are supplied for the compound treatments applied. Conclusions Because computational algorithms and methods for handling single-cell morphological measurements are not yet routine, the dataset serves as a useful resource for the wider scientific community applying morphological (image-based) profiling. The dataset can be mined for many purposes, including small-molecule library enrichment and chemical mechanism-of-action studies, such as target identification. Integration with genetically perturbed datasets could enable identification of small-molecule mimetics of particular disease- or gene-related phenotypes that could be useful as probes or potential starting points for development of future therapeutics.

中文翻译：

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使用细胞绘画分析的3万种小分子处理的图像和形态学数据集。

背景技术通过自动显微镜对受干扰样品进行采集获得的大型图像集，可以对每种受干扰所诱导的细胞状态（例如来自不同文库的小分子）进行详细比较。可以从每个图像中提取细胞形态的高度复用的测量值，然后将其挖掘出来用于多种应用。调查结果该显微镜数据集包括919265个五通道视野，代表30616种经过测试的化合物，可从“细胞图像库”（CIL）资料库中获得。它还包括数据文件，这些数据文件包含从每个图像中的每个细胞衍生的形态特征，包括单细胞水平和群体平均水平（即每孔）；还提供了生成形态特征的图像分析工作流程。质量控制指标以元数据的形式提供，表示散焦或包含高荧光物质或碎屑的视场。最后，为所应用的复合处理提供化学注释。结论由于处理单细胞形态学测量的计算算法和方法还不是常规方法，因此该数据集可作为更广泛的应用形态学（基于图像）分析的科学界的有用资源。可以出于多种目的而挖掘数据集，包括小分子库富集和化学作用机理研究，例如目标识别。与遗传扰动的数据集整合可以识别具有特定疾病或基因相关表型的小分子模拟物，这些模拟物可用作探针或开发未来治疗方法的潜在起点。