MicrobeJ, a tool for high throughput bacterial cell detection and quantitative analysis

Abstract

Single-cell analysis of bacteria and subcellular protein localization dynamics has shown that bacteria have elaborate life cycles, cytoskeletal protein networks and complex signal transduction pathways driven by localized proteins. The volume of multidimensional images generated in such experiments and the computation time required to detect, associate and track cells and subcellular features pose considerable challenges, especially for high-throughput experiments. There is therefore a need for a versatile, computationally efficient image analysis tool capable of extracting the desired relationships from images in a meaningful and unbiased way. Here, we present MicrobeJ, a plug-in for the open-source platform ImageJ1. MicrobeJ provides a comprehensive framework to process images derived from a wide variety of microscopy experiments with special emphasis on large image sets. It performs the most common intensity and morphology measurements as well as customized detection of poles, septa, fluorescent foci and organelles, determines their subcellular localization with subpixel resolution, and tracks them over time. Because a dynamic link is maintained between the images, measurements and all data representations derived from them, the editor and suite of advanced data presentation tools facilitates the image analysis process and provides a robust way to verify the accuracy and veracity of the data.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: The MicrobeJ GUI and workflow.
Figure 2: MicrobeJ can detect various cell morphologies.
Figure 3: Detection and quantification of the localization of FtsZ-YFP and the constriction site during the cell cycle of C. crescentus.
Figure 4: Automated and manual segmentation processes.

References

  1. 1

    Schneider, C.A., Rasband, W.S. & Eliceiri, K. W. NIH image to ImageJ: 25 years of image analysis. Nature Methods 9, 671–675 (2012).

    Article  Google Scholar 

  2. 2

    Vischer, N. O. E. et al. Cell age dependent concentration of Escherichia coli divisome proteins analyzed with ImageJ and ObjectJ. Front. Microbiol. 6, 586 (2015).

    Article  Google Scholar 

  3. 3

    Liu, J., Dazzo, F. B., Glagoleva, O., Yu, B. & Jain, A. K. CMEIAS: a computer-aided system for the image analysis of bacterial morphotypes in microbial communities. Microb. Ecol. 41, 173–194 (2001).

    Article  Google Scholar 

  4. 4

    Mekterović, I., Mekterović, D. & Maglica, Z. BactImAS: a platform for processing and analysis of bacterial time-lapse microscopy movies. BMC Bioinformatics 15, 251 (2014).

    Article  Google Scholar 

  5. 5

    Christen, B. et al. High-throughput identification of protein localization dependency networks. Proc. Natl Acad. Sci. USA 107, 4681–4686 (2010).

    Article  Google Scholar 

  6. 6

    Sliusarenko, O., Heinritz, J., Emonet, T. & Jacobs-Wagner, C. High-throughput, subpixel precision analysis of bacterial morphogenesis and intracellular spatio-temporal dynamics. Mol. Microbiol. 80, 612–627 (2011).

    Article  Google Scholar 

  7. 7

    Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nature Methods 9, 676–682 (2012).

    Article  Google Scholar 

  8. 8

    Paintdakhi, A. et al. Oufti: an integrated software package for high-accuracy, high-throughput quantitative microscopy analysis. Mol. Microbiol. 99, 767–777 (2016).

    Article  Google Scholar 

  9. 9

    Guberman, J. M., Fay, A., Dworkin, J., Wingreen, N. S. & Gitai, Z. PSICIC: noise and asymmetry in bacterial division revealed by computational image analysis at sub-pixel resolution. PLoS Comput. Biol. 4, e1000233 (2008).

    Article  Google Scholar 

  10. 10

    de Chaumont, F. et al. Icy: an open bioimage informatics platform for extended reproducible research. Nature Methods 9, 690–696 (2012).

    Article  Google Scholar 

  11. 11

    Jiang, C., Brown, P. J. B., Ducret, A. & Brun, Y. V. Sequential evolution of bacterial morphology by co-option of a developmental regulator. Nature 506, 489–493 (2014).

    Article  Google Scholar 

  12. 12

    Wartel, M. et al. A versatile class of cell surface directional motors gives rise to gliding motility and sporulation in Myxococcus xanthus. PLoS Biol. 11, e1001728 (2013).

    Article  Google Scholar 

  13. 13

    Alberge, F. et al. Dynamic subcellular localization of a respiratory complex controls bacterial respiration. eLife 4, e05357 (2015).

    Article  Google Scholar 

  14. 14

    Ely, B. Genetics of Caulobacter crescentus. Methods Enzymol. 204, 372–384 (1991).

    Article  Google Scholar 

  15. 15

    Miller, J. H., Ippen, K., Scaife, J. G. & Beckwith, J. R. The promoter–operator region of the lac operon of Escherichia coli. J. Mol. Biol. 38, 413–420 (1968).

    Article  Google Scholar 

  16. 16

    Bustamante, V. H., Martínez-Flores, I., Vlamakis, H. C. & Zusman, D. R. Analysis of the Frz signal transduction system of Myxococcus xanthus shows the importance of the conserved C-terminal region of the cytoplasmic chemoreceptor FrzCD in sensing signals. Mol. Microbiol. 53, 1501–1513 (2004).

    Article  Google Scholar 

  17. 17

    Todd, E. W. & Hewitt, L. F. A new culture medium for the production of antigenic streptococcal hæmolysin. J. Pathol. Bacteriol. 35, 973–974 (1932).

    Article  Google Scholar 

  18. 18

    Kim, M.-K. & Harwood, C. S. Regulation of benzoate-CoA ligase in Rhodopseudomonas palustris. FEMS Microbiol. Lett. 83, 199–203 (1991).

    Google Scholar 

Download references

Acknowledgements

The authors thank all members of the Brun laboratory for many discussions. In particular, the authors thank B. LaSarre and C. Ellison for useful feedback about MicrobeJ and D. Kysela and L. Espinosa for discussions and reading the manuscript. The authors thank S. Schlimpert, N. Feirer, C. Grangeasse, T. Doan, P. Brown and B. LaSarre for providing images from S. venezuelae cells, A. tumefaciens, S. pneumoniae, B. subtilis, P. hirshii and R. palustris, respectively. The authors thank M. Thanbichler for the pvan-ftsZ-yfp carrying the Caulobacter strain. This research was supported by National Institutes of Health grants GM51986 and GM113172, and by seed funding from the Indiana University Office of the Vice-President for Research. This project was supported, in part, by the Indiana Clinical and Translational Sciences Institute funded, in part, by grant UL1TR001108 from the National Institutes of Health, National Center for Advancing Translational Sciences, Clinical and Translational Sciences Award.

Author information

Affiliations

Authors

Contributions

A.D. wrote the ImageJ plugin. A.D. and Y.V.B. planned the project. A.D. and E.M.Q. performed the experiments. A.D., E.M.Q. and Y.V.B. analysed the data. A.D., E.M.Q. and Y.V.B. wrote the paper.

Corresponding authors

Correspondence to Adrien Ducret or Yves V. Brun.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

Supplementary Results, Supplementary Figures 1-5, Supplementary Video legends, Supplementary Table 1 and Supplementary References. (PDF 1438 kb)

Supplementary Table 2

Fields of the MicrobeJ data structure containing particle properties or complementary information based on user-selected options. (PDF 236 kb)

Supplementary Video 1

Presentation of the MicrobeJ workflow. (MOV 15956 kb)

Supplementary Video 2

FtsZ-YFP localization in C. crescentus. (AVI 1200 kb)

Supplementary Video 3

Example of an advanced image analysis with MicrobeJ in real-time. (MOV 21966 kb)

Supplementary Video 4

FtsZ-YFP localization in a filamentous C. crescentus cell. (AVI 1970 kb)

Supplementary Video 5

Example of automated and manual segmentation with MicrobeJ. (MOV 17143 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ducret, A., Quardokus, E. & Brun, Y. MicrobeJ, a tool for high throughput bacterial cell detection and quantitative analysis. Nat Microbiol 1, 16077 (2016). https://doi.org/10.1038/nmicrobiol.2016.77

Download citation

Further reading

Search

Sign up for the Nature Briefing newsletter for a daily update on COVID-19 science.
Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing