Bioloc3D
Authors/Creators
- 1. Co-first author
Contributors
Project leader:
Description
Bioloc3D: an automatized and user-friendly toolset to quantify fluorescent profiles and their colocalization(s) in 3D.
Description
The goal of Bioloc3D is to quantify fluorescent labeling across entire stacks of confocal images and to automatize the statistical analysis of the data. It is an all-in-one bundle for the three-dimensional quantification of individualized fluorescent profiles.
Bioloc3D is a free alternative to commercial solutions with segmentation and analysis programs. It is a user-friendly tool that has been developed to offer the user a straight-to-the-point tool to avoid parasitic information or accessibility issues.
The bundle is made of two components. The first one, Bioloc3D-Imaging, is an ImageJ toolset that is used to individualize fluorescent elements from 2 different channels and identify possible colocalization(s) in 3D. To do so, the core of Bioloc3D has been structured around the “3D Objects Counter” plugin, used to enumerate the number of elements in 3D and provide valuable characteristics about it (e.g. volume, integrated density, etc). The second component, Bioloc3D-Plotting is a Python script that will automatically analyze Excel files obtained through the ImageJ tool to plot data and run the appropriate statistical test.
Installation
To install Bioloc3D in ImageJ, just drag/drop the .ijm file in the "toolset" folder of the application (restart ImageJ if needed).
Note: The macro will be completely open-source after publication.
Software Requirements
Bioloc3D needs plugins to run correctly on ImageJ :
- MorphoLibJ
- Read and Write Excel
- 3D Simple Segmentation
- 3D Objects Counter
Keep ImageJ updated, the last version of the software is advised.
To report any issues, please click here
Notes
Files
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The method paper is still in preparation.
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Additional details
References
- F.Cordelières, & S.Bolte. (2006). A guided tour into subcellular colocalization analysis in light microscopy. Journal of Microscopy, 224(April), 213–232.
- Legland, D., Arganda-Carreras, I., & Andrey, P. (2016). MorphoLibJ: integrated library and plugins for mathematical morphology with ImageJ. Bioinformatics, 32(22), 3532–3534. https://doi.org/10.1093/bioinformatics/btw413
- Ollion, J., Cochennec, J., Loll, F., Escudé, C., & Boudier, T. (2013). TANGO: a generic tool for high-throughput 3D image analysis for studying nuclear organization. Bioinformatics, 29(14), 1840–1841. https://doi.org/10.1093/bioinformatics/btt276