OmniSegger: A time-lapse image analysis pipeline for bacterial cells

Teresa W Lo, Kevin J Cutler, H James Choi and Paul A Wiggins

PLOS Computational Biology, 2025, vol. 21, issue 5, 1-18

Abstract: 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.Author summary: A new generation of machine learning algorithms is pushing the boundaries of what is possible with automated analysis of microscopy images. In this paper, we describe a new tool, OmniSegger, which we recently developed to automate time-lapse image analysis. The tool was designed to solve two specific challenges: the robust analysis of bacterial cells with unusual morphologies and the use of a range of imaging modalities. Both of these challenges emerged organically in our own work and provides context for the pipeline development. Importantly, we find that the new package facilitates a previously untractable analysis of essential gene knockouts. These mutations lead to dramatic morphological changes before growth arrest and to very poor analysis performance from existing packages. In contrast, the OmniSegger pipeline can analyze these datasets and does not require the fine-tuning of a custom segmentation model. The pipeline is powered by a new segmentation algorithm, Omnipose, which we recently described elsewhere as a general-purpose cell segmentation algorithm for the analysis of single images. The current paper describes a complete time-lapse image analysis pipeline suitable for bacterial cell biology.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1013088

DOI: 10.1371/journal.pcbi.1013088

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