Griottes

MSC spheroid data

This data table contains single cell information (cell position and cell type) from MSC spheroids. The single-cell data have been extracted from the 3D_spheroid_multichannel_image.tiff image using `cell_property_extraction.get_cell_properties` method from `Griottes`. An example snippet is included below:

python
import tifffile
import pandas
from griottes.analyse import cell_property_extraction

spheroid_image = tifffile.imread('3D_spheroid_multichannel_image.tiff')

prop = cell_property_extraction.get_cell_properties(
spheroid_image,
mask_channel = 3,
analyze_fluo_channels = True,
fluo_channel_analysis_method = 'local_voronoi',
cell_geometry_properties = True,
radius = 35,
labeled_voronoi_tesselation = False,
percentile = 95,
min_area = 400
)

This yields the following data table:

| z | x | y | area | label | vec_0 | vec_1 | vec_2 | theta | psi | eccentricity | mean_intensity_channel_0 | percentile_intensity_channel_0 | mean_intensity_channel_1 | percentile_intensity_channel_1 | mean_intensity_channel_2 | percentile_intensity_channel_2 |
|----:|----:|----:|-------:|--------:|------------:|-----------:|----------:|----------:|------------:|---------------:|---------------------------:|---------------------------------:|---------------------------:|---------------------------------:|---------------------------:|---------------------------------:|
| 1 | 92 | 367 | 707 | 107 | -0.00339815 | -0.847943 | 0.530077 | -1.0121 | -0.00339816 | 4.36504 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 1 | 392 | 424 | 113 | -0.364766 | 0.112381 | -0.924292 | 3.0206 | -0.373382 | 3.62718 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 44 | 371 | 2128 | 119 | -0.015851 | 0.763084 | -0.646105 | 2.27337 | -0.0158516 | 9.26529 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 10 | 174 | 3213 | 122 | -0.121095 | 0.634749 | 0.763171 | 0.693789 | -0.121393 | 4.66191 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 17 | 237 | 1414 | 123 | -0.0417044 | -0.0517952 | -0.997787 | -3.08973 | -0.0417165 | 5.23785 | 0 | 0 | 0 | 0 | 0 | 0 |

We include data to reproduce examples in the Jupyter Notebooks. The 3D MSC spheroid imaging data will be included shortly.

Zebrafish telencephalon image

Multichannel labeled image including fluorescence intensities for DAPI, ZO1, GFAP and PCNA. The structure of the image is available immediately below:

python
import matplotlib.pyplot as plt
from tifffile import imread

image_path = 'zebrafish_brain_cell_labels.tiff'
image = imread(image_path)

title = ['DAPI',
'ZO1',
'GFAP',
'PCNA',
'labels']

fig, ax = plt.subplots(1,5, figsize = (10,3))

for i in range(5):

ax[i].imshow(image[...,i], cmap = 'gray')
ax[i].set_title(title[i])
ax[i].axis('off')



Data tables

The release contains two relevant CSV files with information on single-cell positions (x and y coordinates), cell types etc. from which to build the network.

Zebrafish telencephalon data

This data table contains single cell information (cell position and cell type) based on manual analysis of imaging data.

python
import pandas

zebrafish_cell_data = pandas.read_csv('zebrafish_cell_properties.csv')
print(zebrafish_cell_data.head().to_markdown(index=False))


| x | y | z | cell_type | cell_properties | label | GFAP | PCNA |
|--------:|--------:|--------:|------------:|:------------------|--------:|-------:|-------:|