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Segmentation and quantification of subcellular structures in fluorescence microscopy images using Squassh

Nature Protocols volume 9, pages 586–596 (2014)Cite this article

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Abstract

Detection and quantification of fluorescently labeled molecules in subcellular compartments is a key step in the analysis of many cell biological processes. Pixel-wise colocalization analyses, however, are not always suitable, because they do not provide object-specific information, and they are vulnerable to noise and background fluorescence. Here we present a versatile protocol for a method named 'Squassh' (segmentation and quantification of subcellular shapes), which is used for detecting, delineating and quantifying subcellular structures in fluorescence microscopy images. The workflow is implemented in freely available, user-friendly software. It works on both 2D and 3D images, accounts for the microscope optics and for uneven image background, computes cell masks and provides subpixel accuracy. The Squassh software enables both colocalization and shape analyses. The protocol can be applied in batch, on desktop computers or computer clusters, and it usually requires <1 min and <5 min for 2D and 3D images, respectively. Basic computer-user skills and some experience with fluorescence microscopy are recommended to successfully use the protocol.

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Figure 1: Workflow of the Squassh protocol illustrated on endosome segmentation.
Figure 2: Screenshots of the graphical user interface of the Squassh software.
Figure 3: Illustration of how the parameters affect segmentation results by using endosomes in a close-up view of a Cherry-RAB5-transfected HEK293 cell as an example.
Figure 4: Segmentation and colocalization of EEA1 and RAB5.
Figure 5: Anticipated results of the Squassh protocol.
Figure 6: Benchmarks of the Squassh protocol.

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References

  1. Sbalzarini, I.F. Modeling and simulation of biological systems from image data. Bioessays 35, 482–490 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Adler, J. & Parmryd, I. Quantifying colocalization by correlation: the Pearson correlation coefficient is superior to the Mander's overlap coefficient. Cytometry A. 77, 733–742 (2010).

    Article  PubMed  Google Scholar 

  3. Bolte, S. & Cordelieres, F.P. A guided tour into subcellular colocalization analysis in light microscopy. J. Microsc. 224, 213–232 (2006).

    Article  CAS  PubMed  Google Scholar 

  4. Manders, E.M.M., Verbeek, F.J. & Aten, J.A. Measurement of co-localization of objects in dual-colour confocal images. J. Microsc. 169, 375–382 (1993).

    Article  PubMed  Google Scholar 

  5. Li, Q. et al. A syntaxin 1, Gαo, and N-type calcium channel complex at a presynaptic nerve terminal: analysis by quantitative immunocolocalization. J. Neurosci. 24, 4070–4081 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. van Steensel, B. et al. Partial colocalization of glucocorticoid and mineralocorticoid receptors in discrete compartments in nuclei of rat hippocampus neurons. J. Cell Sci. 109 (Part 4): 787–792 (1996).

    CAS  PubMed  Google Scholar 

  7. Costes, S.V. et al. Automatic and quantitative measurement of protein-protein colocalization in live cells. J. Biophys. 86, 3993–4003 (2004).

    Article  CAS  Google Scholar 

  8. Ramirez, O., Garcia, A., Rojas, R., Couve, A. & Hartel, S. Confined displacement algorithm determines true and random colocalization in fluorescence microscopy. J. Microsc. 239, 173–183 (2010).

    Article  CAS  PubMed  Google Scholar 

  9. Helmuth, J.A., Paul, G. & Sbalzarini, I.F. Beyond co-localization: inferring spatial interactions between sub-cellular structures from microscopy images. BMC Bioinformatics 11, 372 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Bolte, S. & Cordelières, F.P. Reply to letter to the editor. J. Microsc. 227, 84–85 (2007).

    Article  Google Scholar 

  11. Zinchuk, V., Wu, Y., Grossenbacher-Zinchuk, O. & Stefani, E. Quantifying spatial correlations of fluorescent markers using enhanced background reduction with protein proximity index and correlation coefficient estimations. Nat. Protoc. 6, 1554–1567 (2011).

    Article  CAS  PubMed  Google Scholar 

  12. Danuser, G. Computer vision in cell biology. Cell 147, 973–978 (2011).

    Article  CAS  PubMed  Google Scholar 

  13. Paul, G., Cardinale, J. & Sbalzarini, I.F. Coupling image restoration and segmentation: a generalized linear model/Bregman perspective. Int. J. Comput. Vis. 104, 69–93 (2013).

    Article  Google Scholar 

  14. Abramoff, M.D., Magalhães, P.J. & Ram, S.J. Image processing with ImageJ. Biophoton. Int. 11, 36–42 (2004).

    Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  17. Paul, G., Cardinale, J. & Sbalzarini, I.F. An alternating split Bregman algorithm for multi-region segmentation. in Proc. 45th IEEE Asilomar Conf. Signals, Systems, and Computers 426–430 (Asilomar, 2011).

  18. Helmuth, J.A. & Sbalzarini, I.F. Deconvolving active contours for fluorescence microscopy images. in Proc. Intl. Symp. Visual Computing (ISVC), vol. 5875 of Lecture Notes in Computer Science, 544–553 (Springer, 2009).

  19. Cardinale, J., Paul, G. & Sbalzarini, I.F. Discrete region competition for unknown numbers of connected regions. IEEE Trans. Image Process. 21, 3531–3545 (2012).

    Article  PubMed  Google Scholar 

  20. Helmuth, J.A., Burckhardt, C.J., Greber, U.F. & Sbalzarini, I.F. Shape reconstruction of subcellular structures from live cell fluorescence microscopy images. J. Struct. Biol. 167, 1–10 (2009).

    Article  CAS  PubMed  Google Scholar 

  21. R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2012).

  22. Shivanandan, A., Radenovic, A. & Sbalzarini, I.F. MosaicIA: An ImageJ/Fiji plug-in for spatial pattern and interaction analysis. BMC Bioinformatics 14, 349 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Eliceiri, K.W. et al. Biological imaging software tools. Nat. Methods 9, 697–710 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Carpenter, A.E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Sommer, C., Straehle, C., Koethe, U. & Hamprecht, F.A. Ilastik: Interactive learning and segmentation toolkit. Genome Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on 10.1109/ISBI.2011.5872394 (IEEE, 2012).

  26. Sternberg, S.R. Biomedical image processing. Computer 16, 22–34 (1983).

    Article  Google Scholar 

  27. Chambers, J., Freeny, A. & Heiberger, R. Analysis of variance; designed experiments. in Statistical Models in S 145–193 (Wadsworth and Brooks/Cole Advanced Books and Software, 1992).

  28. Miller, J.R. Simultaneous Statistical Inference (Springer-Verlag, 1981).

  29. Sbalzarini, I.F. & Koumoutsakos, P. Feature point tracking and trajectory analysis for video imaging in cell biology. J. Struct. Biol. 151, 182–195 (2005).

    Article  CAS  PubMed  Google Scholar 

  30. Linkert, M. et al. Metadata matters: access to image data in the real world. J. Cell Biol. 189, 777–782 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Zhang, B., Zerubia, J. & Olivo-Marin, J.C. Gaussian approximations of fluorescence microscope point-spread function models. Appl. Opt. 46, 1819–1829 (2007).

    Article  PubMed  Google Scholar 

  32. Sönnichsen, B., De Renzis, S., Nielsen, E., Rietdorf, J. & Zerial, M. Distinct membrane domains on endosomes in the recycling pathway visualized by multicolor imaging of Rab4, Rab5, and Rab11. J. Cell Biol. 149, 901–914 (2000).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Ballmer-Hofer, K., Andersson, A.E., Ratcliffe, L.E. & Berger, P. Neuropilin-1 promotes VEGFR-2 trafficking through Rab11 vesicles thereby specifying signal output. Blood 118, 816–826 (2011).

    Article  CAS  PubMed  Google Scholar 

  34. Kriz, A. et al. A plasmid-based multigene expression system for mammalian cells. Nat. Commun. 1, 120 (2010).

    Article  PubMed  Google Scholar 

  35. Matsuo, H. et al. Role of LBPA and Alix in multivesicular liposome formation and endosome organization. Science 303, 531–534 (2004).

    Article  CAS  PubMed  Google Scholar 

  36. Fitzgerald, D.J. et al. Protein complex expression by using multigene baculoviral vectors. Nat. Methods 3, 1021–1032 (2006).

    Article  CAS  PubMed  Google Scholar 

  37. Niemann, A., Ruegg, M., La Padula, V., Schenone, A. & Suter, U. Ganglioside-induced differentiation associated protein 1 is a regulator of the mitochondrial network: new implications for Charcot-Marie-Tooth disease. J. Cell Biol. 170, 1067–1078 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Ruusuvuori, P. et al. Evaluation of methods for detection of fluorescence labeled subcellular objects in microscope images. BMC Bioinformatics 11, 248 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank J. Cardinale (MOSAIC Group) for help with the software implementation of the ImageJ plug-in and B. Cheeseman (MOSAIC Group) for the voiceover in the video tutorial (Supplementary Video 1). This work was supported by SystemsX.ch, the Swiss initiative in systems biology under grant IPP-2011-113, evaluated by the Swiss National Science Foundation. The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 290605 (PSI-FELLOW/COFUND) and from the German Federal Ministry of Research and Education under funding code 031A099.

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Author notes

  1. Grégory Paul

    Present address: Present address: ETH Zurich, Computer Vision Laboratory, Zürich, Switzerland.,

Authors and Affiliations

  1. Paul Scherrer Institute, Biomolecular Research, Molecular Cell Biology, Villigen PSI, Switzerland

    Aurélien Rizk, Milica Bugarski, Maysam Mansouri & Philipp Berger

  2. MOSAIC Group, Center of Systems Biology Dresden, Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany

    Aurélien Rizk, Grégory Paul, Pietro Incardona & Ivo F Sbalzarini

  3. ETH Zurich, Institute of Molecular Health Sciences, Zürich, Switzerland

    Axel Niemann

  4. Center for Microscopy and Image Analysis, University of Zurich, Zürich, Switzerland

    Urs Ziegler

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Contributions

P.B., I.F.S. and U.Z. designed the project; G.P., I.F.S. and A.R. developed the image-processing algorithm; A.R., P.I. and I.F.S. were involved in software development and implementation; A.R., I.F.S., G.P. and P.B. were involved in benchmark design; experimental data were obtained by M.B., M.M., A.N. and P.B.; and A.R., I.F.S. and P.B. wrote the manuscript with input from G.P., A.N. and U.Z. Figures were prepared by A.R. and P.B., and the video tutorial was created by I.F.S.

Corresponding authors

Correspondence to Philipp Berger or Ivo F Sbalzarini.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Instruction video for how to download, install, and start using the Squassh software. (MOV 83485 kb)

Supplementary Data

The ZIP archive provided in the supplementary data contains the .lif extractor macro for ImageJ/Fiji, the Squassh software plugin at the time of writing (note that a newer version may be available on the MOSAIC web page) including example data and example R script output used in Supplementary Video 1, the raw 2D image analyzed in Fig. 1, the raw 3D image analyzed in Fig. 4, and all .csv files required to reproduce the results shown in the Supplementary Note. (ZIP 29688 kb)

Supplementary Note

The supplementary note contains additional information on the image-segmentation algorithm, technical information about the software implementation, the formulas defining the colocalization measures used, and the cell-culture and microscopy protocols. Moreover, it contains figures showing the different visualization options, examples of typical output produced by the R statistical analysis script, and examples of fluorescence images used in the analysis. (PDF 8347 kb)

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Rizk, A., Paul, G., Incardona, P. et al. Segmentation and quantification of subcellular structures in fluorescence microscopy images using Squassh. Nat Protoc 9, 586–596 (2014). https://doi.org/10.1038/nprot.2014.037

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