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You will find all scripts in scripts folder, with subfolders fro Python and ImageJ Scripts.
Data availabe from:
sort_wells.ijm is script that sorts all images of defined format (default .tif) in folder based on well name in their filename. The well names must be defined.
Process_WFolder_macro_v1.ijm is script for BioImage analysis of wells based on DAPI channel and Cy3 channels. (Saved as separate images).
This protocol documents an image data flow utilized and inspired by CLIJx-Assistant.
- We start our image data flow with image_1, a DAPI channel with nuclei.
- Following, we applied "Copy" on image_1 and got a new image out, image_2.
- As the next step, we applied "Otsu" auto threshold on image_2 and got a new image mask out, image_3. The threshold values are saved and used on all DAPI images from the same well.
- Afterward, we applied "Analyze Particles" on image_3, and got out a region fo nuclei as a Region of Interest (ROI) set. All ROIs touching edges are skipped.
- In the next step, we open image_4, which is the Cy3 channel. We applied "Copy" on image_4, and got a new image out, image_5.
- We applied background substruction with rolling ball of size 50 on image_5 to subtract local background value from intensity measurements, and got image_6 out.
- Afterward, image_6 is selected for measuring features under ROIs from the previous step.
- The process Log and measured features from Cy3 channel for the whole well and summary are saved in "Results" subfolder in the CSV table. A flattened image_4 with ROIs outlines is saved as JPEG for later inspection.
The macro logs version of ImageJ and BioImage plugin version on each run was tested in ImageJ version 1.53t99. The logs are also containing information about image size, count of objects, and threshold values.
The results are saved in new subfolder in each Well folder. It contains numerical results per detected object in .CSV, LOG, flatten .JPEG of Cy3 channels with ROIs outlines and .ZIP folder with saved ROIs for each image.
vis_CA_allOpened.ijm script was used for brightness enhancement. It applies run("Enhance Contrast", "saturated=0.35"); on active image, and then repeats the same transform from active image on all opened images.
enhance_ROI_outlines_flatten.ijm makes edges of ROIs size 5 and flattens image for export as visualization for paper or on web.
Ideal management of packages and enviroments is through anaconda or miniconda.
conda install mamba -c conda-forge
mamba create --name julab python=3.9 jupyterlab -c conda-forge
Change your working directory with cd workDir, where workDir is path to where you saved the .ipynb files. Activate conda enviroment and start jupyter-lab with following commands:
conda activate julab
jupyter-lab
optional: Code Formatting Jupyter Notebooks with Black
mamba install -c conda-forge jupyterlab_code_formatter black isort
The following scripts were used for CSV outputs processing:
SF_dataVis_and_statistics_mean_4h.ipynbSF_dataVis_and_statistics_mean_24h.ipynb
They expect folder with CSVs from each well, which is main result of Process_WFolder_macro_v1.ijm. The result are charts, statistic comparison and relative area change with folds.
sort_wells.ijm is script that sorts all images of defined format (default .tif) in folder based on well name in their filename. The well names must be defined.
- The firs step is to do a quality check and remove any low quality images
- Next step is to get all well names based on file names. Example for naming convention
ChannelName_YYYYMMD_Well_PossitionInWell_AcqRun.tiff, the Well name would be after second_. - Run
sort_wells.ijmscript. - Select path to cleaned up images.
- Specify well names to sort
Process_WFolder_macro_v1.ijm is script for BioImage analysis of wells based on DAPI channel and Cy3 channels. (Saved as separate images).
- The firs step is to specify path to folder with images sorted into wells.
- Second step is specify well names to process.
