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

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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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.

Author information

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.

Correspondence to Philipp Berger or Ivo F Sbalzarini.

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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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