aicscytoparam

3D Cell Parameterization

Spherical harmonics coefficients-based parameterization of the cytoplasm and nucleoplasm for 3D cells

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Installation

Stable Release: pip install aicscytoparam
Development Head: pip install git+https://github.com/AllenCell/aics-cytoparam.git

How to use

Here we outline an example of how to use aicscytoparam to create a parameterization of a 3D cell. In this case, the 3D cells will be represented by a cell segementation, nuclear segmentation and a fluorescent protein (FP) image representing the fluorescent signal of a tagged protein.

# Import required packages
import numpy as np
import matplotlib.pyplot as plt
from aicscytoparam import cytoparam
from skimage import morphology as skmorpho

# First create a cuboid cell with an off-center cuboid nucleus
# and get the spherical harmonics coefficients of this cell and nucleus:
w = 100
mem = np.zeros((w, w, w), dtype = np.uint8)
mem[20:80, 20:80, 20:80] = 1
nuc = np.zeros((w, w, w), dtype = np.uint8)
nuc[40:60, 40:60, 30:50] = 1

# Create an FP signal located in the top half of the cell and outside the
# nucleus:
gfp = np.random.rand(w**3).reshape(w,w,w)
gfp[mem==0] = 0
gfp[:, w//2:] = 0
gfp[nuc>0] = 0

# Vizualize a center xy cross-section of our cell:
plt.imshow((mem + nuc)[w//2], cmap='gray')
plt.imshow(gfp[w // 2], cmap='gray', alpha=0.25)
plt.axis('off')

# Use aicsshparam to expand both cell and nuclear shapes in terms of spherical
# harmonics:
coords, coeffs_centroid = cytoparam.parameterize_image_coordinates(
    seg_mem=mem,
    seg_nuc=nuc,
    lmax=16, # Degree of the spherical harmonics expansion
    nisos=[32, 32] # Number of interpolation layers
)
coeffs_mem, centroid_mem, coeffs_nuc, centroid_nuc = coeffs_centroid

# Run the cellular mapping to create a parameterized intensity representation
# for the FP image:
gfp_representation = cytoparam.cellular_mapping(
    coeffs_mem=coeffs_mem,
    centroid_mem=centroid_mem,
    coeffs_nuc=coeffs_nuc,
    centroid_nuc=centroid_nuc,
    nisos=[32, 32],
    images_to_probe=[('gfp', gfp)]
).data.squeeze()

# The FP image is now encoded into a representation of its shape:
print(gfp_representation.shape)

(65, 8194)

# Now we want to morph the FP image into a round cell.
# First we create the round cell:

from skimage import morphology as skmorpho
mem_round = skmorpho.ball(w // 3) # radius of our round cell
nuc_round = skmorpho.ball( w// 3) # radius of our round nucleus
# Erode the nucleus so it becomes smaller than the cell
nuc_round = skmorpho.binary_erosion(
    nuc_round, selem=np.ones((20, 20, 20))
    ).astype(np.uint8)

# Vizualize a center xy cross-section of our round cell:
plt.imshow((mem_round + nuc_round)[w // 3], cmap='gray')
plt.axis('off')

# Next we need to parameterize the coordinates of our round
# cell:
coords_round, _ = cytoparam.parameterize_image_coordinates(
    seg_mem=mem_round,
    seg_nuc=nuc_round,
    lmax=16,
    nisos=[32, 32]
)

# Now we are ready to morph the FP image into our round cell:
gfp_morphed = cytoparam.morph_representation_on_shape(
    img=mem_round + nuc_round,
    param_img_coords=coords_round,
    representation=gfp_representation
)
# Visualize the morphed FP image:
plt.imshow((mem_round + nuc_round)[w // 3], cmap='gray')
plt.imshow(gfp_morphed[w // 3], cmap='gray', alpha=0.25)
plt.axis('off')

Reference

For an example of how this package was used to analyse a dataset of over 200k single-cell images at the Allen Institute for Cell Science, please check out our paper in bioaRxiv.

Development

See CONTRIBUTING.md for information related to developing the code.

Questions?

If you have any questions, feel free to leave a comment in our Allen Cell forum: https://forum.allencell.org/.

Free software: Allen Institute Software License