Dataset DOI: 10.5061/dryad.2280gb66p

Description of the data and file structure

This Code is for the paper titled 'Selective vulnerability of cerebral vasculature to NOTCH3 variants in small vessel disease and rescue by phosphodiesterase-5 inhibitor'. It is permanently open and free to read/download for the entire research community (no access code is needed).

1.'C224Y.zip':The data contains the stress fiber samples used in this article, including one folder labeled 'C224Y'. The results of stress fiber identification for each image, including image edges and cell nuclei, are stored in the 'image' folder, while the rest of the data contains various parameters such as detected coordinates and spatial dispersion.

C224Y:

The images in this folder were captured to examine the distribution of the actin cytoskeleton in induced pluripotent stem cell (iPSC)-derived vascular smooth muscle cells (VSMCs) from a CADASIL patient. This patient carries the Cys224Tyr mutation.

C224Y_iso

The images in this folder were captured to examine the distribution of the actin cytoskeleton in iPSC derived-VSMCs from a CADASIL patient’s isogenic control. In this isogenic control line, the NOTCH3 Cys224Tyr mutation has been corrected, resulting in the same genomic background as the CADASIL patient carrying the C224Y mutation.

C224Y_iso_SIL

The images in this folder were captured to examine the distribution of the actin cytoskeleton in iPSC derived-VSMCs from a CADASIL patient’s isogenic control treated with 10 nM phosphodiesterase 5 (PED5) inhibitor sildenafil.  

C224Y_iso_SNAP

The images in this folder were captured to examine the distribution of the actin cytoskeleton in iPSC derived-VSMCs from a CADASIL patient’s isogenic control treated with 100 nM S-Nitroso-N-acetyl-DL-penicillamine (SNAP).

C224Y_SIL

The images in this folder were captured to examine the distribution of the actin cytoskeleton in iPSC derived-VSMCs from a CADASIL patient treated with 10 nM PED5 inhibitor sildenafil.  

C224Y_SNAP

The images in this folder were captured to examine the distribution of the actin cytoskeleton in iPSC derived-VSMCs from a CADASIL patient treated with 100 nM SNAP.

'C224Y.xlsx' stores the merged results of spatial dispersion.

Each sample folder contains 'angle', 'distance', 'dis_angle', 'image', and 'Spatial_dispersion.csv',

'Distance' (pixel) : The distance from the center point of the blue nucleus to each stress fiber (green). It is the input of the file 'dis_mean.py', and the output of the file ' BlueAreaCenter2dis_2.py'.

'angle' (°): Record the angle between each stress fiber (green) and the horizontal 180° line .  It is the input of the file 'dis_mean.py', and the output of the file ' BlueAreaCenter2dis_2.py'.

'dis_angle'(pixel/°): The first column represents the mean distance, while the second column represents the mean angle. It is the output of the file 'dis_mean.py'.

'image': Effect diagram of stressfiber and cell nucleus recognition. It is the output of the file ''BlueAreaCenter2dis_2.py'.

'image/outline':Stress fiber recognition effect diagram. It is the output of the file ' BlueAreaCenter2dis_2.py'.

'spatial dispersion': For the "Spatial dispersion under average area" with a value range of [0, 1]. No Unit. It is the output of the file 'Spatial_dispersion.py'.

The above files can assist in presenting the recognition results on images, with the recognition results stored in the "image" folder. For instructions on how to use these files, please refer to the usage instructions in the following 'py' file.

2.' BlueAreaCenter2dis_2.py': Identify the blue cell nucleus region, perform edge detection on the stress fibers, and perform segmentation. Calculate the distance from the center point to each edge of the stress fibers, as well as the angle between each edge of the stress fibers and the horizontal line, and save them to the 'Angle' folder. For images containing multiple blue regions, the distance from the center of each blue region to the edge of the stress fibers needs to be calculated, and then the average of these distances is taken as the distance from a stress fiber to the center point. In this process, blue regions with smaller areas and stress fiber edges with shorter lengths are eliminated.

Runing Steps:

Unzip 'C224Y.zip' to see the corresponding folder,  and Modify the directory in the BlueAreaCenter2dis_2.py file.

image = cv2.imread("./C224Y/C224Y_SNAP(Treatment 2)/0" + str(p) + "_C224Y_SNAP.png")

//output the file path and filename of stress fiber edge and blue area

output_path = "./C224Y/result/C224Y_SNAP(Treatment 2)/image/0" + str(p) + "_C224Y_SNAP.png"

lunkuo_path = "./C224Y/result/C224Y_SNAP(Treatment 2)/image/outline/0" + str(p) + "_C224Y_SNAP.png"

//Output the file path and filename of the binary image for the edge contour:

// File path and filename of the calculated distances saved to the CSV file

dis_csv = "./C224Y/result/C224Y_SNAP(Treatment 2)/distance/0" + str(p) + "_C224Y_SNAP.csv"

// File path and filename of the calculated angles saved to the CSV file

angle_csv = "./C224Y/result/C224Y_SNAP(Treatment 2)/angle/0" + str(p) + "_C224Y_SNAP.csv"

After making the modifications, you can run the script directly.

3. 'dis_mean.py': Save the average distance and angle values of stress fiber, corresponding folder:('dis_angle', 'distance', 'angle')

# set the path

// Path to the folder where the distance CSV files from BlueAreaCenter2dis_2.py are saved (only the path, not the filename)

dis_folder_path = './C224Y/result/C224Y_SNAP(Treatment 2)/distance'

// Path to the folder where the angle CSV files from BlueAreaCenter2dis_2.py are saved (only the path, not the filename)

angle_folder_path = './C224Y/result/C224Y_SNAP(Treatment 2)/angle'

// Path to save the average distance and angle values (only the path, not the filename)

output_path = './C224Y/result/C224Y_SNAP(Treatment 2)/dis_angle'

4. 'Spatial_dispersion.py': calculating spatial dispersion

// Output path for the spatial dispersion results

dsp_csv = "./C224Y/result/C224Y_SNAP(Treatment 2)/Spatial_dispersion.csv"

data = pd.read_csv("./C224Y/result/C224Y_SNAP(Treatment 2)/dis_angle/0" + str(p) + "_C224Y_SNAP_averages.csv", header=None)

to the output_path from dis_mean.py, including the filename.

Execution Order:

First, run BlueAreaCenter2dis_2.py, then execute dis_mean.py, and finally run Spatial_dispersion.py.

Access information

Other publicly accessible locations of the data:

• Github: https://github.com/zhaoaite/ActinDetection