Stylianidou Stella, Brennan Connor, Nissen Silas B, Kuwada Nathan J, Wiggins Paul A
Department of Physics, University of Washington, Seattle, WA, 98195, USA.
Department of StemPhys, Niels Bohr Institute, University of Copenhagen, Copenhagen, 2100, Denmark.
Mol Microbiol. 2016 Nov;102(4):690-700. doi: 10.1111/mmi.13486. Epub 2016 Sep 23.
Many quantitative cell biology questions require fast yet reliable automated image segmentation to identify and link cells from frame-to-frame, and characterize the cell morphology and fluorescence. We present SuperSegger, an automated MATLAB-based image processing package well-suited to quantitative analysis of high-throughput live-cell fluorescence microscopy of bacterial cells. SuperSegger incorporates machine-learning algorithms to optimize cellular boundaries and automated error resolution to reliably link cells from frame-to-frame. Unlike existing packages, it can reliably segment microcolonies with many cells, facilitating the analysis of cell-cycle dynamics in bacteria as well as cell-contact mediated phenomena. This package has a range of built-in capabilities for characterizing bacterial cells, including the identification of cell division events, mother, daughter and neighbouring cells, and computing statistics on cellular fluorescence, the location and intensity of fluorescent foci. SuperSegger provides a variety of postprocessing data visualization tools for single cell and population level analysis, such as histograms, kymographs, frame mosaics, movies and consensus images. Finally, we demonstrate the power of the package by analyzing lag phase growth with single cell resolution.
许多定量细胞生物学问题需要快速且可靠的自动图像分割，以便逐帧识别和连接细胞，并表征细胞形态和荧光。我们展示了SuperSegger，这是一个基于MATLAB的自动化图像处理软件包，非常适合对细菌细胞的高通量活细胞荧光显微镜进行定量分析。SuperSegger整合了机器学习算法以优化细胞边界，并采用自动错误解析来可靠地逐帧连接细胞。与现有软件包不同，它能够可靠地分割包含许多细胞的微菌落，有助于分析细菌中的细胞周期动态以及细胞接触介导的现象。该软件包具有一系列用于表征细菌细胞的内置功能，包括识别细胞分裂事件、母细胞、子细胞和相邻细胞，以及计算细胞荧光、荧光灶的位置和强度的统计数据。SuperSegger提供了各种用于单细胞和群体水平分析的后处理数据可视化工具，如直方图、波形图、帧拼接图、电影和一致性图像。最后，我们通过以单细胞分辨率分析延迟期生长来展示该软件包的强大功能。
