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A Cell Segmentation/Tracking Tool Based on Machine Learning

  • Heather S. Deter
  • Marta Dies
  • Courtney C. Cameron
  • Nicholas C. ButzinEmail author
  • Javier BucetaEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 2040)

Abstract

The ability to gain quantifiable, single-cell data from time-lapse microscopy images is dependent upon cell segmentation and tracking. Here, we present a detailed protocol for obtaining quality time-lapse movies and introduce a method to identify (segment) and track cells based on machine learning techniques (Fiji’s Trainable Weka Segmentation) and custom, open-source Python scripts. To provide a hands-on experience, we provide datasets obtained using the aforementioned protocol.

Key words

Computational image analysis Single-cell quantification Cell lineage analysis Cell segmentation Cell tracking Machine learning Fluorescence microscopy Bacterial growth 

Supplementary material

454918_1_En_19_MOESM1_ESM.docx (26 kb)
Supplementary Table S1 Abbreviations (DOCX 26 kb)

References

  1. 1.
    Rosenfeld N, Young JW, Alon U, Swain PS, Elowitz MB (2005) Gene regulation at the single-cell level. Science 307:1962–1965.  https://doi.org/10.1126/science.1106914CrossRefPubMedGoogle Scholar
  2. 2.
    Campos M, Surovtsev IV, Kato S, Paintdakhi A, Beltran B, Ebmeier SE, Jacobs-Wagner C (2014) A constant size extension drives bacterial cell size homeostasis. Cell 159:1433–1446.  https://doi.org/10.1016/j.cell.2014.11.022CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Brehm-Stecher BF, Johnson EA (2004) Single-cell microbiology: tools, technologies, and applications. Microbiol Mol Biol Rev 68:538–559, table of contents.  https://doi.org/10.1128/MMBR.68.3.538-559.2004CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Butzin NC, Mather WH (2015) Synthetic genetic oscillators. Rev Cell Biol Mol Med 2:100–125Google Scholar
  5. 5.
    Ferry MS, Razinkov IA, Hasty J (2011) Microfluidics for synthetic biology: from design to execution. Methods Enzymol 497:295–372.  https://doi.org/10.1016/B978-0-12-385075-1.00014-7CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Nketia TA, Sailem H, Rohde G, Machiraju R, Rittscher J (2017) Analysis of live cell images: Methods, tools and opportunities. Methods 115:65–79.  https://doi.org/10.1016/j.ymeth.2017.02.007CrossRefPubMedGoogle Scholar
  7. 7.
    Vallotton P, Turnbull L, Whitchurch CB, Mililli L (2010) Segmentation of dense 2D bacilli populations. 2010 International Conference on Digital Image Computing: Techniques and Applications, IEEE. p 82–86Google Scholar
  8. 8.
    Chowdhury S, Kandhavelu M, Yli-Harja O, Ribeiro AS (2013) Cell segmentation by multi-resolution analysis and maximum likelihood estimation (MAMLE). BMC Bioinformatics 14(Suppl 10):S8.  https://doi.org/10.1186/1471-2105-14-S10-S8CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Sadanandan SK et al (2016) Segmentation and track-analysis in time-lapse imaging of bacteria. IEEE J Sel Top Signal Process 10:174–184.  https://doi.org/10.1109/Jstsp.2015.2491304CrossRefGoogle Scholar
  10. 10.
    Hu Y, Wang S, Ma N, Hingley-Wilson SM, Rocco A, McFadden J, Tang HL (2017) Trajectory energy minimization for cell growth tracking and genealogy analysis. R Soc Open Sci 4:170207.  https://doi.org/10.1098/rsos.170207CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Ducret A, Quardokus EM, Brun YV (2016) MicrobeJ, a tool for high throughput bacterial cell detection and quantitative analysis. Nat Microbiol 1:16077.  https://doi.org/10.1038/nmicrobiol.2016.77CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Boyle EA, Li YI, Pritchard JK (2017) An expanded view of complex traits: from polygenic to omnigenic. Cell 169:1177–1186.  https://doi.org/10.1016/j.cell.2017.05.038CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Schindelin J et al (2012) Fiji: an open-source platform for biological-image analysis. Nat Methods 9:676–682.  https://doi.org/10.1038/nmeth.2019CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Paintdakhi A, Parry B, Campos M, Irnov I, Elf J, Surovtsev I, Jacobs-Wagner C (2016) Oufti: an integrated software package for high-accuracy, high-throughput quantitative microscopy analysis. Mol Microbiol 99:767–777.  https://doi.org/10.1111/mmi.13264CrossRefPubMedGoogle Scholar
  15. 15.
    Dimopoulos S, Mayer CE, Rudolf F, Stelling J (2014) Accurate cell segmentation in microscopy images using membrane patterns. Bioinformatics 30:2644–2651.  https://doi.org/10.1093/bioinformatics/btu302CrossRefPubMedGoogle Scholar
  16. 16.
    Kamentsky L et al (2011) Improved structure, function and compatibility for CellProfiler: modular high-throughput image analysis software. Bioinformatics 27:1179–1180.  https://doi.org/10.1093/bioinformatics/btr095CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Stylianidou S, Brennan C, Nissen SB, Kuwada NJ, Wiggins PA (2016) SuperSegger: robust image segmentation, analysis and lineage tracking of bacterial cells. Mol Microbiol 102:690–700.  https://doi.org/10.1111/mmi.13486CrossRefPubMedGoogle Scholar
  18. 18.
    Arganda-Carreras I, Kaynig V, Rueden C, Eliceiri KW, Schindelin J, Cardona A, Sebastian Seung H (2017) Trainable Weka segmentation: a machine learning tool for microscopy pixel classification. Bioinformatics 33:2424–2426.  https://doi.org/10.1093/bioinformatics/btx180CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Deter HS, Dies M, Cameron CC, Butzin NC, Buceta J (2018) A bacteria segmentation/tracking tool based on machine learning. http://osf.io/gdxen/. doi:  https://doi.org/10.17605/osf.io/gdxen
  20. 20.
    Deter HS (2018) Cell segmentation and tracking tools. YouTube. https://goo.gl/uJ3j8A
  21. 21.
    Green MR, Sambrook J, Sambrook J (2012) Molecular cloning: a laboratory manual, 4th edn. Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NYGoogle Scholar
  22. 22.
  23. 23.
    Rueden CT, Schindelin J, Hiner MC, DeZonia BE, Walter AE, Arena ET, Eliceiri KW (2017) ImageJ2: ImageJ for the next generation of scientific image data. BMC Bioinformatics 18:529.  https://doi.org/10.1186/s12859-017-1934-zCrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Lutz R, Bujard H (1997) Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Res 25:1203–1210CrossRefGoogle Scholar
  25. 25.
    Young JW et al (2011) Measuring single-cell gene expression dynamics in bacteria using fluorescence time-lapse microscopy. Nat Protoc 7:80–88.  https://doi.org/10.1038/nprot.2011.432CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Parry-Hill M, Sutter RT, Davidson MW (2018) Microscope alignment for Köhler illumination. Nikon. https://wwwmicroscopyucom/tutorials/kohler. Accessed 7 July 2018
  27. 27.
    Arganda-Carreras I, Kaynig V, Rueden C, Eliceiri KW, Schindelin J, Cardona A, Seung HS (2017) Trainable Weka segmentation: a machine learning tool for microscopy pixel classification. Bioinformatics 33:2424–2426.  https://doi.org/10.1093/bioinformatics/btx180CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
  29. 29.
  30. 30.
    (2018) Python Software Foundation. https://pypi.org
  31. 31.
    Libav (2018). https://libav.org
  32. 32.
    Deter HS (2018) CellTracking. https://github.com/hdeter/CellTracking
  33. 33.
  34. 34.
    Beanshell (2018). http://www.beanshell.org
  35. 35.

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Biology and Microbiology DepartmentSouth Dakota State UniversityBrookingsUSA
  2. 2.Chemical and Biomolecular Engineering DepartmentLehigh UniversityBethlehemUSA
  3. 3.Bioengineering DepartmentLehigh UniversityBethlehemUSA

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