GliaMorph Toolkit to process and analyse Müller glia morphology (implemented as Fiji Macros to allow modular application).
Publication: Elisabeth Kugler, Isabel Bravo, Xhuljana Durmishi, Stefania Marcotti, Sara Beqiri, Alicia Carrington, Brian M. Stramer, Pierre Mattar, Ryan B. MacDonald; GliaMorph: A modular image analysis toolkit to quantify Müller glial cell morphology. Development 2023; dev.201008. doi: https://doi.org/10.1242/dev.201008;
Step-by-step protocol: Kugler, E., Breitenbach, E.-M., & MacDonald, R. (2023). Glia cell morphology analysis using the fiji gliaMorph toolkit. Current Protocols, 3, e654. doi: 10.1002/cpz1.654; https://currentprotocols.onlinelibrary.wiley.com/doi/10.1002/cpz1.654
Example Data: Data Link: https://zenodo.org/record/5747597 DATA DOI: 10.5281/zenodo.5747597 Minimum Example Data 29112021: Data Link: https://zenodo.org/record/5735442 DATA DOI: 10.5281/zenodo.5735442
YouTube Screencasts: https://www.youtube.com/hashtag/gliamorph (recorded by Sara Beqiri and Karim Nizam, 2022)
Code Author: Elisabeth Kugler
Project Leads: Elisabeth Kugler (code, formal analysis, supervision) and Ryan MacDonald (data, resources, supervision)
Project Contributors: Eva-Maria Breitenbach (tester), Alicia Carrington (data and tester), Isabel Bravo (data and tester), Stefania Marcotti (code: https://github.com/OakesLab/AFT-Alignment_by_Fourier_Transform), Brian M. Stramer (resources), and Pierre Mattar (data and resources).
Contact: kugler.elisabeth[at]gmail.com
BSD 3-Clause License
Copyright (c) [2021], [Elisabeth C. Kugler, University College London, United Kingdom]
All rights reserved.
This GitHub repository will be maintained until at least 2023 by Elisabeth Kugler. However, as #GliaMorph tools are meant to be used and useful, the code and tools are meant to change and be adaptable. Please help us make the most out of #GliaMorph:
- Raise issues for improvements and create pull requests for code adaptions in Github (as described by Robert Haase: https://focalplane.biologists.com/2021/09/04/collaborative-bio-image-analysis-script-editing-with-git/).
- Contribute to the discussion (https://github.com/ElisabethKugler/GliaMorph/discussions).
- Use the image.sc forum for discussions / questions / how-to's (https://forum.image.sc/)
- Please use the hashtag #GliaMorph (especially on social media), so we can communicate effectively around the tool.
- For specific questions, please contact kugler.elisabeth[at]gmail.com.
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step1_cziToTiffTool.ijm: Macro for czi to tiff conversion
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step2_DeconvolutionTool.ijm: Macro for deconvolution of confocal images using theoretical or experimental PSF
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step3_90DegreeRotationTool.ijm: Macro for 90 degree rotation - optional before rotationTool
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step4_subregionTool.ijm: Semi-automatic ROI selection tool with ROI selection from MIP
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step4_subregionToolWithinStack.ijm: Semi-automatic ROI selection tool within stack with ROI within stack
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step5_splitChannelsTool.ijm: Macro to split channels and save them in separate folders
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step6_zonationTool.ijm: Analysis of zebrafish retina zonation based on intensity profiles
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step7_SegmentationTool.ijm: Segmentation of MG cells
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step8_QuantificationTool.ijm: Quantification of MG features in segmented images
- “Fiji > Help > Update > Manage update sites”
- Select “3D ImageJ Suite”, “Neuroanatomy”, and “IJBP-Plugins”
- Click “Close”
- Click “Apply Changes”.
Both are required for the point spread function (PSF) deconvolution.
a) Extension 1: Diffraction PSF 3D to generate a theoretical PSF: details at https://www.optinav.info/Iterative-Deconvolve-3D.htm Download “Diffraction_PSF_3D.class” from https://github.com/ElisabethKugler/GliaMorph (found under “other”) - copy and paste this it into Fiji > Plugin folder > restart Fiji. Check if "Plugins > Diffraction PSF 3D" is there. (Author: Bob Dougherty; Permission: 13.12.2021 - via email between Bob Dougherty and Elisabeth Kugler; Link: https://www.optinav.info/Diffraction-PSF-3D.htm; Licence: Copyright (c) 2005, OptiNav, Inc.All rights reserved).
b) Extension 2: DeconvolutionLab2 for PSF deconvolution (Sage et al., 2017): follow the installation guide http://bigwww.epfl.ch/deconvolution/deconvolutionlab2/.
Acquired by Dr Ryan MacDonald at the Institute of Ophthalmology, University College London (http://zebrafishucl.org/macdonald-lab). Processed by Dr Elisabeth Kugler at the Institute of Ophthalmology, University College London (https://www.elisabethkugler.com/).
Data Link: https://zenodo.org/record/5735442#.YaUPG9DP02w DATA DOI: 10.5281/zenodo.5735442
Good practice - close all unnecessary windows in Fiji and do not click things while Macros are running.
#1 Three images of zebrafish retina at 3dpf in the double-transgenic Tg(TP1bglob:VenusPest)s940 (Ninov et al., 2012) and Tg(CSL:mCherry)jh11 (also known as Tg(Tp1bglob:hmgb1-mCherry)jh11 (Parsons et al., 2009). These are in the folder "ExampleData_GitHub_GliaMorph_KuglerEtAl".
#2 application of step4_subregionTool.ijm: This will need "RoiSetLine.zip" to be drawn on the MIPs and be applied to the 3D stacks. It will deliver images that are comparable against each other (rotated, cropped in x-and-y, reduced in z) - based on user-selected parameters. For this example we leave the default parameters unchanged, which will deliver an image of width 60um, height as per ROI, depth of 15um, and a sigma of 10um (sigma is attached at the bottom of the ROI - this not only accounts for the MG curvature, but also allows inclusion of underlying blood vessels). This step only takes a few minutes.
#3 application of step5_splitChannelsTool.ijm: Apply this step to the images in the folder "zDir" (these are the comparable images: rotated, cropped in x-and-y, reduced in z) to split channels - in this case, choose 2 as input parameter as the data are from double-transgenics with 2 channels. For the remaining steps we will focus on one of the reporter lines, namely Tg(Tp1bglob:hmgb1-mCherry)jh11. This step only takes a few minutes.
#4 application of step6_zonationTool.ijm: Analyse texture / zonation - for our example, we do this for "2CDir" Tg(Tp1bglob:hmgb1-mCherry)jh11. Parameters as follows: yes, no, 1920, 10 - we only change the second parameter to "no" - leave the others >> see image. This step only takes a few minutes. ![image] (https://user-images.githubusercontent.com/67630046/143879883-ff37752a-ff5a-4a60-9ed9-a8922f688eb6.png) see output data in the folder "ZonationTool"
#5 application of step7_SegmentationTool.ijm: Next, we want to segment the data - for this we use again the stacks (i.e. 3D tiffs) from the folder "2CDir". The data will be segmented / binarized / thresholded and outputs will be saved in folder "TH" - these are again 3D stacks (MIP folder contained in the folder). This step only takes a little bit longer, but normally less than 5min per image.
#6 application of step8_QuantificationTool.ijm: Quantification of MG parameters - this uses the folder "TH" as input - so this means 3D stacks that are segmented. This is the most time-consuming step, that can take up to about 40min per image depending on computer spec and image size.