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

Nature Protocols volume 14, pages 3144–3161 (2019)Cite this article

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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.

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Fig. 1: Processing pipeline.
Fig. 2: GUI.
Fig. 3: Segmentation and tracking of bacteria and spots in BACMMAN.
Fig. 4: Segmentation performances.

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Data availability

The example datasets used in this protocol are available at the zenodo.org repository (https://doi.org/10.5281/zenodo.3243467).

Code availability

Source code for BACMMAN is available at github.com/jeanollion/bacmman under GNU General Public License v3.0.

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Acknowledgements

We acknowledge J. Elf (Uppsala University), S. Uphoff (Oxford University), H. Salman (Pittsburgh University) and anonymous reviewer 4 for kindly providing datasets from their labs that allowed us to test the applicability of our software. This work was funded by the Agence Nationale de Recherche (grant ANR-14-CE09-0015-01 to M.E.) and by the city of Paris (program Emergences 2018 to M.E.).

Author information

Authors and Affiliations

  1. Laboratoire Jean Perrin, UMR 8237 Sorbonne Universités, UPMC Université Paris, Paris, France

    Jean Ollion, Marina Elez & Lydia Robert

  2. Institute of Systems and Synthetic Biology, UMR 8030, CNRS, Commissariat à l’Energie Atomique et aux Energies Alternatives, Genopole, Université d’Evry Val-d’Essonne, Université Paris Saclay, Evry, France

    Marina Elez

  3. Micalis Institute, Institut National de la Recherche Agronomique, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France

    Lydia Robert

Authors
  1. Jean Ollion
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Contributions

All authors wrote the article and contributed to tests and documentation. J.O. developed the software; L.R. and J.O. analyzed the software performance; L.R. generated dataset 1; and M.E. generated datasets 2 and 3.

Corresponding author

Correspondence to Jean Ollion.

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The authors declare no competing interests.

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Peer review information Nature Protocols thanks Thomas Julou, Christian C. Sach and other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Key references using this protocol

Robert, L. et al. Science 359, 1283–1286 (2018): http://science.sciencemag.org/content/359/6381/1283

Robert, L., Ollion, J. & Elez, M. Nat. Protoc (2019): https://doi.org/10.1038/s41596-019-0215-x.

Key data used in this protocol

Robert, L. et al. Science 359, 1283–1286 (2018): http://science.sciencemag.org/content/359/6381/1283

Integrated supplementary information

Supplementary Figure 1 Segmentation of fluorescent bacteria.

a: input image, illustrating fluorescence heterogeneity within and between cells. b: Image of edges computed with a sigma transform of the gaussian smoothed image. Along the cell contours, fluorescence intensities are maximal at the interface between cells and background, and minimal at the boundary between cells. c: regions obtained by a watershed algorithm computed on image b. For each region, the median value of the fluorescence intensity within this region in image A is displayed through a color code (see color bar below). d: Interfaces between the different regions shown in C. For each interface, the color indicates the value of the first decile of the distribution of pixel intensity in image B at this interface. e: Maximum Eigenvalue of the Hessian transform (MEH). MEH is maximal at the interfaces between cells. f: Regions obtained by watershed algorithm computed on E within the foreground region defined using image c. For each region, the color indicates the median intensity of image A within this region. g: mean intensity of image E divided by mean intensity of image A at the interfaces between foreground regions displayed in F. H Final result of the segmentation process.

Supplementary Figure 2 Spine coordinate system for fluorescent spot tracking.

Red fluorescence images of bacteria in a microchannel at 3 successive frames: F, F+1 and F+2. Magnified images show the spine coordinate system, with the segmented contour of the cell in yellow, the spine in pink and the spine radial directions in blue. The spine coordinates of a spot (red arrow) at frame F appear in red. The same spot is observed at frame F+1 and F+2 (green arrows). At frames F+1 and F+2, the projection of the spot from frame F is indicated with a red arrow and its projected coordinates appear in red. The distance between the location of the spot at frame F and its location at frame F+1 (or between frames F+1 and F+2) is the distance between the green and red arrow.

Supplementary Figure 3 Bacteria segmentation in phase-contrast images.

a: Input image. b: Pre-filtered image. c: Image of edges computed with a sigma transform on the gaussian smoothed transform of B. d: partitioning obtained by a watershed algorithm computed on C. For each region, the color indicates the median intensity within this region in image B (see color bar below). This shows that a simple thresholding can remove most of the background regions, except small border artefacts that can be removed by filtering out thin objects. E: Maximum eigenvalue of the hessian transform. f: Partitioning obtained by a watershed algorithm computed on E within the foreground region defined from image D. For each region, the color indicates the median intensity within this region in image B. g: mean value of E divided by mean value of B at the interface between foreground regions displayed in F. H: Result of segmentation.

Supplementary information

Supplementary Information

Supplementary Figs. 1–3, Supplementary Notes and Supplementary Tables 1 and 2

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Ollion, J., Elez, M. & Robert, L. High-throughput detection and tracking of cells and intracellular spots in mother machine experiments. Nat Protoc 14, 3144–3161 (2019). https://doi.org/10.1038/s41596-019-0216-9

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