Probing bacterial cell biology using image cytometry
Probing bacterial cell biology using image cytometry
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DOI：
10.1111/mmi.13591
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发表时间：
2017-03-01
影响因子：
3.6
通讯作者：
Wiggins, Paul A.
中科院分区：
文献类型：
--
作者：
Cass, Julie A.;Stylianidou, Stella;Wiggins, Paul A.
自动化荧光显微镜的进步使得细菌细胞的快照和延时成像变得司空见惯，但在分析方面仍然存在根本的挑战。在高通量实验中收集的海量数据需要一种快速可靠的自动化方法来分析荧光强度和定位、细胞形态和增殖以及其他描述符。受使用流式细胞术进行群体水平分析的有效而易操作的方法的启发，我们开发了一个框架和工具来促进图像细胞术中的类似分析。这些工具可以可视化和选通(生成亚群)70多个细胞描述符，包括细胞大小、年龄和荧光。该方法非常适合于多孔成像、高细胞密度(每帧数千个细胞)的细菌培养分析和完整的细胞周期成像。我们简要描述了对四个不同应用程序的分析，以强调该工具的广泛适用性。
Advances in automated fluorescence microscopy have made snapshot and time-lapse imaging of bacterial cells commonplace, yet fundamental challenges remain in analysis. The vast quantity of data collected in high-throughput experiments requires a fast and reliable automated method to analyze fluorescence intensity and localization, cell morphology and proliferation as well as other descriptors. Inspired by effective yet tractable methods of population-level analysis using flow cytometry, we have developed a framework and tools for facilitating analogous analyses in image cytometry. These tools can both visualize and gate (generate subpopulations) more than 70 cell descriptors, including cell size, age and fluorescence. The method is well suited to multi-well imaging, analysis of bacterial cultures with high cell density (thousands of cells per frame) and complete cell cycle imaging. We give a brief description of the analysis of four distinct applications to emphasize the broad applicability of the tool.