Kerkhoff Yannic, Wedepohl Stefanie, Nie Chuanxiong, Ahmadi Vahid, Haag Rainer, Block Stephan
Institute of Chemistry and Biochemistry, Freie Universität Berlin, Berlin, Germany.
MethodsX. 2022 Sep 2;9:101834. doi: 10.1016/j.mex.2022.101834. eCollection 2022.
The ability to automatically analyze large quantities of image data is a valuable tool for many biochemical assays, as it rapidly provides reliable data. Here, we describe a fast and robust Fiji macro for the analysis of cellular fluorescence microscopy images with single-cell resolution. The macro presented here was validated by successful reconstruction of fluorescent and non-fluorescent cell mixing ratios (for fluorescence fractions ranging between 0 and 100%) and applied to quantify the efficiency of transfection and virus infection inhibition. It performed well compared with manually obtained image quantification data. Its use is not limited to the cases shown here but is applicable for most monolayered cellular assays with nuclei staining. We provide a detailed description of how the macro works and how it is applied to image data. It can be downloaded free of charge and may be used by and modified according to the needs of the user. • Rapid, simple, and reproducible segmentation of eukaryotic cells in confluent cellular assays • Open-source software for use without technical or computational expertise • Single-cell analysis allows identification and quantification of virus infected cell populations and infection inhibition.
自动分析大量图像数据的能力对于许多生化分析而言是一种有价值的工具，因为它能快速提供可靠的数据。在此，我们描述了一种用于以单细胞分辨率分析细胞荧光显微镜图像的快速且强大的斐济宏程序。此处展示的宏程序通过成功重建荧光和非荧光细胞混合比例（荧光分数范围为0至100%）得到验证，并应用于量化转染效率和病毒感染抑制情况。与手动获取的图像量化数据相比，它表现良好。其应用不限于此处所示的案例，而是适用于大多数进行细胞核染色的单层细胞分析。我们详细描述了该宏程序的工作原理以及如何将其应用于图像数据。它可免费下载，用户可根据自身需求使用和修改。• 在汇合细胞分析中对真核细胞进行快速、简单且可重复的分割 • 无需技术或计算专业知识即可使用的开源软件 • 单细胞分析可识别和量化病毒感染细胞群体以及感染抑制情况。
