High-throughput detection and tracking of cells and intracellular spots in mother machine experiments.

Jean Ollion,Marina Elez,Lydia Robert

doi:10.1038/s41596-019-0216-9

Abstract

The analysis of bacteria at the single-cell level is essential to characterization of processes in which cellular heterogeneity plays an important role. BACMMAN (bacteria mother machine analysis) is a software allowing fast and reliable automated image analysis of high-throughput 2D or 3D time-series images from experiments using the 'mother machine', a very popular microfluidic device allowing biological processes in bacteria to be investigated at the single-cell level. Here, we describe how to use some of the BACMMAN features, including (i) segmentation and tracking of bacteria and intracellular fluorescent spots, (ii) visualization and editing of the results, (iii) configuration of the image-processing pipeline for different datasets and (iv) BACMMAN coupling to data analysis software for visualization and analysis of data subsets with specific properties. Among software specifically dedicated to the analysis of mother machine data, only BACMMAN allows segmentation and tracking of both bacteria and intracellular spots. For a single position, single channel with 1,000 frames (2-GB dataset), image processing takes ~6 min on a regular computer. Numerous implemented algorithms, easy configuration and high modularity ensure wide applicability of the BACMMAN software.

Highlights

A large part of our current understanding of cellular biology has been gained through the study of phenotypes at the population level
We describe how to use some of the BACMMAN features including i) segmentation and tracking of bacteria and intracellular fluorescent spots, ii) visualization and editing of the results iii) configuration of the image processing pipeline for different datasets, and iv) BACMMAN coupling to data analysis software for visualization and analysis of data subsets with specific properties
BACMMAN offers a solution for fast and reliable analysis of high-throughput data from videomicroscopy experiments where bacteria grow in the mother machine microfluidic chip

Summary

Introduction

A large part of our current understanding of cellular biology has been gained through the study of phenotypes at the population level. It was designed to allow segmentation and tracking of E. coli cells growing inside the microchannels of the mother machine, imaged either in phase-contrast or in fluorescence, as well as segmentation and tracking of intracellular fluorescent spots corresponding to non-repaired DNA replication errors, i.e. emerging mutations. The relative error in cell length is independent of cell length and is around 1-2% for both phase-contrast and fluorescence images This analysis allows comparison of BACMMAN with the recently developed software MoMA, for which 2-3% relative error was estimated using this method. When varying the relevant parameters in a reasonable range, we found that the relative difference between the results of phase contrast and fluorescence segmentation was always smaller than 7% This analysis does not give a precise estimation of accuracy, it suggests that any systematic bias in cell length estimation should be small. Run the tasks in headless mode: after installing as a stand-alone application (see above), run the following command from bacmman-headless/target directory: (replace by the actual version and by the path to a .json task file generated in 3): java -cp dependency/*:bacmman-headless-.jar bacmman.ui.ProcessTasks

Procedures

Anticipated Results

References:

Fluorescence Experiments

Tracking of bacteria

Phase Contrast Experiments