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Segmentation and analysis of mother machine data: SAM

Deb Sankar Banerjee, Godwin Stephenson, Suman G. Das
doi: https://doi.org/10.1101/2020.10.01.322685
Deb Sankar Banerjee
1Department of Physics, Carnegie Mellon University, Pittsburgh, PA 15213, USA
2Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, Bangalore 560065, India
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  • For correspondence: debsankar1988@gmail.com
Godwin Stephenson
2Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, Bangalore 560065, India
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Suman G. Das
3Institute for Biological Physics, University of Cologne, Cologne, Germany
2Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, Bangalore 560065, India
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Abstract

Time-lapse imaging of bacteria growing in micro-channels in a controlled environment has been instrumental in studying the single cell dynamics of bacterial growth. This kind of a microfluidic setup with growth chambers is popularly known as mother machine [1]. In a typical experiment with such a set-up, bacterial growth can be studied for numerous generations with high resolution and temporal precision using image processing. However, as in any other experiment involving imaging, the image data from a typical mother machine experiment has considerable intensity fluctuations, cell intrusion, cell overlapping, filamentation etc. The large amount of data produced in such experiments makes it hard for manual analysis and correction of such unwanted aberrations. We have developed a modular code for segmentation and analysis of mother machine data (SAM) for rod shaped bacteria where we can detect such aberrations and correctly treat them without manual supervision. We track cumulative cell size and use an adaptive segmentation method to avoid faulty detection of cell division. SAM is currently written and compiled using MATLAB. It is fast (∼ 15 min/GB of image) and can be efficiently coupled with shell scripting to process large amount of data with systematic creation of output file structures and graphical results. It has been tested for many different experimental data and is publicly available in Github.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵* debsankb{at}andrew.cmu.edu

  • ↵† gstephenson{at}ncbs.res.in

  • ↵‡ sdas3{at}uni-koeln.de

  • https://github.com/DebsankarBanerjee/Segmentation-and-analysis-of-mother-machine-data

  • https://github.com/DebsankarBanerjee/SAM-Trainer

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 02, 2020.
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Segmentation and analysis of mother machine data: SAM
Deb Sankar Banerjee, Godwin Stephenson, Suman G. Das
bioRxiv 2020.10.01.322685; doi: https://doi.org/10.1101/2020.10.01.322685
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Segmentation and analysis of mother machine data: SAM
Deb Sankar Banerjee, Godwin Stephenson, Suman G. Das
bioRxiv 2020.10.01.322685; doi: https://doi.org/10.1101/2020.10.01.322685

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