Imaging Platform.
Chemical Biology and Therapeutics Science Program.
Gigascience. 2017 Dec 1;6(12):1-5. doi: 10.1093/gigascience/giw014.
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.
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.
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.
通过对受扰样本进行自动化显微镜检查获得的大规模图像集，可以对每种扰动（例如来自多样化文库的小分子）诱导的细胞状态进行详细比较。可以从每张图像中提取出细胞形态的高度多重测量值，并随后针对许多应用进行挖掘。
该显微镜数据集包括 919,265 个五通道视野，代表 30,616 种已测试化合物，可在“细胞图像库”（CIL）存储库中获得。它还包含包含从每个图像中的每个细胞提取的形态特征的数据文件，包括单细胞水平和群体平均（即每个孔）水平；还提供了生成形态特征的图像分析工作流程。质量控制指标作为元数据提供，指示视野失焦或包含高荧光物质或碎片。最后，还提供了化合物处理的化学注释。
由于用于处理单细胞形态测量的计算算法和方法尚不成熟，因此该数据集可为更广泛的科学界应用形态（基于图像的）分析提供有用的资源。该数据集可用于多种目的，包括小分子文库富集和化学作用机制研究，例如靶标鉴定。与遗传扰动数据集的集成可以识别特定疾病或与基因相关表型的小分子类似物，这些小分子类似物可用作探针或未来治疗药物开发的潜在起点。
