Segmentation and analysis of mother machine data: SAM

Code for analysis of mother machine data

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. These microfluidic growth chambers are known as
mother machines. In this kind of experiments bacterial growth can be studied for numerous generations with
high resolution and temporal precision. But like any other experiment mother machine data shows considerable
intensity fluctuations, cell intrusion, cell overlapping, filamentation etc. The large amount of data produced
in such experiments makes it hard to manual analysis and correction of such unwanted aberrations. We have
developed a modular code SAM, for detection of 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 script to process large amount of data. It has been tested for many different
experimental data and is publicly available in Github.