Analysis of blood vessels and cells from 3D fluorescent microscopy images from "Endothelialized collagen based pseudo-islets enables tuneable subcutaneous diabetes therapy" Vlahos et al. (2019).
The following image analysis functions are used to analyze the mean insulin intensity and cell distance to the nearest blood vessel from 3D flourescent lightsheet microscopy images. The workflow is broken down into the following steps:
Step 1: Conversion of .czi files into multipage .tiff images of the individual microscopy channels.
The function 'convert_czi_to_ometiff_revised_func' is used to convert any files with the .czi extension into a downsampled multipage tiff image. The image is downsampled in the x and y resolution to achieze isotropic resolution with the z dimension. This facilitates downstream analysis by reducing the image size. The input for this function is 'data_folder' - the directory containing the .czi files.
Step 2: Pre-processing: Local and global intensity corrections of the nuclei, blood vessel and micrometastasis channels.
The function 'pre_process_islet_func' is used to normalize the flourescent intensities across the image volume for the nuclei and blood vessel channels. The inputs required are: 'nuclei' - a multipage tiff, or similar of the original nuclei channel, 'vessels' - a multipage tiff or similar of the original blood vessel image, 'save_dir' - the directory where the ouput files are saved, and 'sample_name' - the name of the sample to be processed. The ouputs are 'pre_nuclei' and 'pre_vessels' - written as multipage tiff files of the intensity normalized nuclei, blood vessel and micrometastasis channels respectively.
Step 3: ilastik image segmentation: Ilastik uses a random forest classifer based on 37 filters to segment nuclei and blood vessels from the pre-processed nuclei and blood vessel images.
More information on ilastik can be found at: https://www.ilastik.org/ or https://github.com/ilastik. The pixel classication tool was trained for at least 30 mins using all 37 default image filters with each of the pre-processed nuclei and blood vessel images. Once satisfactory performance was seen for the binary segmentation of each of the nuclei and blood vessel channels this segmentation was batch applied to other images aquired from the same tissue and animal. The output is a labelled, segmented images of the nuclei and blood vessel channels as multipage tiff files.
Step 4: Post-processing: Elimination of artifacts generated by the ilastik segmentation for the nuclei and blood vessel channels.
The 'post_process_islet_func' function removes artifacts that appeared after the ilastik segmentation. The inputs required are: 'pre_nuclei' - the pre-processed nuclei channel which is used to define the tissue boundary in the image, 'seg_nuclei' - a multipage tiff, or similar of the ilastik segmented nuclei channel, 'seg_vessels' - a multipage tiff or similar of the ilastik segmented blood vessel image, 'save_dir' - the directory where the ouput files are saved, and 'sample_name' - the name of the sample to be processed. The ouputs are 'post_nuclei', 'post_nuclei_dilate', and 'post_vessels' - the post processed binary segmentation of the nuclei, dilated nuclei, and blood vessels written as multipage tiff files.
Islet analysis: Analysis from the segmented dilated nuclei and blood vessel channels combined with the insulin intensity channel. The 'islet_analysis_main_func' function analyzes the cells, blood vessels and insulin intensity of the islet implants.
The inputs required are: 'pre_nuclei' - the pre-processed nuclei channel which is used to define the tissue boundary in the image, 'post_nuclei' - a multipage tiff, or similar of the binary post-processed segmented and dilated nuclei channel, 'post_vessels' - a multipage tiff or similar of the the binary post-processed and segmented blood vessel channel, 'insulin_ch' - a multipage tiff or similar intensity image of the insulin channel, 'save_dir' - the directory where the ouput files are saved, and 'sample_name' - the name of the sample to be processed. The outputs from this analysis are: 'cell_ins_int_img' - a multipage tiff of the mean insulin intensity per cell, 'cell_dist_vessel_img' - a multipage tiff of the cell distance to the nearest blood vessel, and 'vessel_radius_img' - a multipage tiff of the radius of the blood vessels. For the image a .csv file is output with the mean insulin intensity and distance to the nearest blood vessel of each cell in the image. For the whole tissue a .csv file is exported with the total tissue volume (in pixels), total vessel volume (in pixels) and average vessel diameter (in pixels).
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