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Mol Microbiol. 2016 Nov;102(4):690-700. doi: 10.1111/mmi.13486. Epub 2016 Sep 23.

SuperSegger: robust image segmentation, analysis and lineage tracking of bacterial cells.

Author information

1
Department of Physics, University of Washington, Seattle, WA, 98195, USA.
2
Department of StemPhys, Niels Bohr Institute, University of Copenhagen, Copenhagen, 2100, Denmark.
3
Department of Physics, Central Washington University, Ellensburg, WA, 98926, USA.
4
Department of Bioengineering, University of Washington, Seattle, WA, 98195, USA.
5
Department of Microbiology, University of Washington, Seattle, WA, 98195, USA.

Abstract

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.

PMID:
27569113
DOI:
10.1111/mmi.13486
[Indexed for MEDLINE]
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