OmniSegger:细菌细胞延时图像分析管道。

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Teresa W Lo, Kevin J Cutler, H James Choi, Paul A Wiggins
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引用次数: 0

摘要

延时拍摄[-50mm][-4mm]请展开作者“H. James Choi”的名字。显微镜是研究细菌细胞生物学的有力工具。开发便于对这些数据集进行自动化分析的管道是该领域的长期目标。在本文中,我们描述了OmniSegger管道开发为一个开源,模块化和整体的算法套件,其输入是原始显微镜图像,其输出是广泛的定量细胞分析,包括动态细胞细胞术数据和细胞可视化。本文中描述的更新版本介绍了两个主要改进:(i)对细胞形态的鲁棒性和(ii)对一系列常见成像模式的支持。为了证明对细胞形态的稳健性,我们对大肠杆菌的增殖动力学进行了分析,分析了一种诱导成丝的药物。为了证明对新图像模式的扩展支持,我们分析了通过五种不同模式成像的细胞:相衬,两种明场模式,细胞质和膜荧光。总之,这条管道将大大增加细菌显微镜可处理分析的范围。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
OmniSegger: A time-lapse image analysis pipeline for bacterial cells.

Time-lapse [-50mm][-4mm]Please expand the first name for author "H. James Choi".microscopy is a powerful tool to study the biology of bacterial cells. The development of pipelines that facilitate the automated analysis of these datasets is a long-standing goal of the field. In this paper, we describe the OmniSegger pipeline developed as an open-source, modular, and holistic suite of algorithms whose input is raw microscopy images and whose output is a wide range of quantitative cellular analyses, including dynamical cell cytometry data and cellular visualizations. The updated version described in this paper introduces two principal refinements: (i) robustness to cell morphologies and (ii) support for a range of common imaging modalities. To demonstrate robustness to cell morphology, we present an analysis of the proliferation dynamics of Escherchia coli treated with a drug that induces filamentation. To demonstrate extended support for new image modalities, we analyze cells imaged by five distinct modalities: phase-contrast, two brightfield modalities, and cytoplasmic and membrane fluorescence. Together, this pipeline should greatly increase the scope of tractable analyses for bacterial microscopists.

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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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