Time-lapse 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.
OmniSegger: A time-lapse image analysis pipeline for bacterial cells.
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作者:Lo Teresa W, Cutler Kevin J, James Choi H, Wiggins Paul A
| 期刊: | PLoS Computational Biology | 影响因子: | 3.600 |
| 时间: | 2025 | 起止号: | 2025 May 28; 21(5):e1013088 |
| doi: | 10.1371/journal.pcbi.1013088 | ||
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