Neurodegeneration Vacuole Quantification

Scripts for vacuole quantification in 3D whole-mount 2-photon images of D. melanogaster brains

Main publication: https://www.cell.com/iscience/fulltext/S2589-0042(23)02129-6

Main publication Citation : Elguero, J. E., Liu, G., Tiemeyer, K., Bandyadka, S., Gandevia, H., Duro, L., Yan, Z., & McCall, K. (2023). Defective phagocytosis leads to neurodegeneration through systemic increased innate immune signaling. iScience, 0(0). https://doi.org/10.1016/j.isci.2023.108052

Protocol paper: https://www.sciencedirect.com/science/article/pii/S2666166724001825?ref=cra_js_challenge&fr=RR-1

Protocol paper citation: Liu G, Bandyadka S, McCall K. Protocol to analyze 3D neurodegenerative vacuoles in Drosophila melanogaster. STAR Protoc. 2024;5: 103017. doi:10.1016/j.xpro.2024.103017

Github/Zenodo citation :

Aim

To identify whether there is a statistically significant difference in the number, size, and spatial distribution of neurodegeneration vacuoles in whole-mount 2-photon microscopy images of D.melanogaster brains

Data

Whole-mount 2-photon imaging

• Red : phalloidin (muscle)
• Green : Hoechst (nucleus)

Methods

1. Convert raw confocal image files to RBG/8-bit greyscale TIFF format

Z-stacks acquired from confocal microscopes need to be converted to RGB stacks or 8bit greyscale stacks in the TIFF format for use with WEBKNOSSOS for annotation. The FIJI macro provided - code/maketiff.ijm can be used to batch process raw images. The macro takes in .nd2 files and performs the following operations for each .nd2 file -

• split channels
• merge channels
• convert to RGB tiff
• convert to 8bit greyscale tiff

The macro can be modified to work with other common microscopy raw formats such as .oif When the macro is run in FIJI, it will open a dialog box to select the input folder containing all the .nd2 files. Once the input folder is selected, another dialog box to get the output destination folder will open up. Once the input and output folder are selected, all the .nd2 files in the input folder will be converted to RGB stacks and 8-bit greyscale stacks and will be available in the selected destination folder for downstream analysis. Make sure to not include any other files other than the necessary .nd2 files in the input folder selected. Input .nd2 files must not contain any spaces or special characters other than underscores in the filename. An example of an ideal input filename is - best_input_file_123.tif

2. Webknossos workflow to identify vacuoles

2a. Upload confocal images to webknossos

• Use prairie reader plugin to import xml files into FIJI
• Convert stack to RGB
• Save merged RGB z-stacks in Tiff format
• Upload RGB TIFF z-stacks of individual brains to Webknossos

2b. Annotate vacuoles

• Create a new volume annotation
• Identify each vacuole visually. For each vacuole
  • create a new segment id
  • Paint over vacuoles in some z-stack levels using the quick select automatic segmentation tool
  • Use the interpolation tool to automatically fill in the vacuole
  • When the vacuole is fully annotated, create a 3d mesh for the segment
• Repeat the above steps to annotate the whole brain
• Download the mesh for vacuoles and whole brain as stl files

Example annotated data is deposited at Zenodo:

3. Python script to quantify vacuoles

# Create a Python3 virtual environment and activate
python3 -m venv vacuolequant
source vacuolequant/bin/activate


# Install all packages at once 
pip3 install -r requirements.txt

# Alternatively install necessary packages individually 
pip3 install ipykernel 
pip3 install numpy-stl 
pip3 install matplotlib 
pip3 install seaborn
pip3 install numpy 
pip3 install pandas 
pip3 install jupyter
pip3 install scipy



# Create Jupyter notebook kernel
python3 -m ipykernel install --name=vacuolequant --user 
jupyter notebook 

# Run notebook for vacuole quantification, statistical analysis, and visualization
quantify_vacuoles_fromMesh_LiuBandyadka2024.ipynb # To quantify vacuoles from mesh files 
quantify_vacuoles_fromWebknossosexport_LiuBandyadka2024.ipynb # To quantify vacuoles from csv files 

References

1. https://docs.webknossos.org/webknossos/volume_annotation.html
2. https://numpy-stl.readthedocs.io/