Chromatin Patterns

Extracting and quantifying chromatin patterns from routine Hematoxylin and Eosin (H&E) stained images offers important insights into nuclear architecture, an important indicator of cellular state that undergoes profound alterations in various disease pathologies, particularly cancer. This analysis workflow provides an example of how Histolytics can be used to extract and quantify chromatin patterns from H&E stained images, focusing on the following key steps:

• Segmentation of individual chromatin clumps within cell nuclei.
• Computation of key quantitative features, such as chromatin clump area and the proportion of chromatin clump area to the total nuclear area.

These interpretable metrics of chromatin organization can reveal significant biological insights; for example, a low chromatin clump-to-nucleus area ratio might indicate dispersed chromatin, while a higher proportion can reflect increased chromatin condensation or hyperchromatism—hallmarks often associated with malignancy.

Let's start the workflow demonstration by loading the example data:

In [1]:

from histolytics.data import hgsc_cancer_he, hgsc_cancer_nuclei

he = hgsc_cancer_he()
nuc = hgsc_cancer_nuclei()

nuc.plot(figsize=(10, 10), column="class_name")

from histolytics.data import hgsc_cancer_he, hgsc_cancer_nuclei he = hgsc_cancer_he() nuc = hgsc_cancer_nuclei() nuc.plot(figsize=(10, 10), column="class_name")

Out[1]:

<Axes: >

Convert vector data to raster format¶

First, we will need to convert the vector data of nuclei to raster format. This is because we are working with dense rgb-image data, meaning that the nuclei masks need to be in also in dense raster-format. The conversion allows us to be able to point to the nuclear regions within the image and focus the segmentation analysis within those regions. The conversion can be done using the gdf2inst and gdf2sem functions from the histolytics package.

In [2]:

from histolytics.utils.raster import gdf2inst, gdf2sem
import matplotlib.pyplot as plt
from skimage.color import label2rgb


cls_dict = {
    "neoplastic": 1,
    "connective": 2,
    "inflammatory": 3,
}

# convert the nuclei gdf into raster mask
inst_mask = gdf2inst(nuc, width=he.shape[1], height=he.shape[0])
type_mask = gdf2sem(nuc, class_dict=cls_dict, width=he.shape[1], height=he.shape[0])

fig, ax = plt.subplots(1, 3, figsize=(18, 6))
ax[0].imshow(he)
ax[1].imshow(label2rgb(inst_mask, bg_label=0, alpha=0.5))
ax[2].imshow(label2rgb(type_mask, bg_label=0, alpha=0.5))
plt.show()

from histolytics.utils.raster import gdf2inst, gdf2sem import matplotlib.pyplot as plt from skimage.color import label2rgb cls_dict = { "neoplastic": 1, "connective": 2, "inflammatory": 3, } # convert the nuclei gdf into raster mask inst_mask = gdf2inst(nuc, width=he.shape[1], height=he.shape[0]) type_mask = gdf2sem(nuc, class_dict=cls_dict, width=he.shape[1], height=he.shape[0]) fig, ax = plt.subplots(1, 3, figsize=(18, 6)) ax[0].imshow(he) ax[1].imshow(label2rgb(inst_mask, bg_label=0, alpha=0.5)) ax[2].imshow(label2rgb(type_mask, bg_label=0, alpha=0.5)) plt.show()

Chromatin clump segmentation¶

Next, we will segment the chromatin clumps from the nuclei. This can be done using the chromatin_clumps function. It takes the rasterized nuclei mask and the rgb image as input, and returns a list of segmented chromatin clumps, chromatin clump area per nuclei, and the chromatin-to-nuclei (area) proportion per nuclei. We will segment the chromatins for only the neoplastic nuclei in this example.

In [3]:

import numpy as np
from histolytics.nuc_feats.chromatin import chromatin_clumps

neo_mask = inst_mask.copy()
neo_mask[type_mask != cls_dict["neoplastic"]] = 0

chrom_mask, chrom_areas, chrom_nuc_props = chromatin_clumps(he, neo_mask)

import numpy as np from histolytics.nuc_feats.chromatin import chromatin_clumps neo_mask = inst_mask.copy() neo_mask[type_mask != cls_dict["neoplastic"]] = 0 chrom_mask, chrom_areas, chrom_nuc_props = chromatin_clumps(he, neo_mask)

In [4]:

from histolytics.utils.plot import draw_thing_contours
from skimage.measure import label


fig, ax = plt.subplots(1, 2, figsize=(16, 8))

contours = draw_thing_contours(label(chrom_mask), he, chrom_mask)
ax[0].imshow(he)
ax[1].imshow(contours)

from histolytics.utils.plot import draw_thing_contours from skimage.measure import label fig, ax = plt.subplots(1, 2, figsize=(16, 8)) contours = draw_thing_contours(label(chrom_mask), he, chrom_mask) ax[0].imshow(he) ax[1].imshow(contours)

Out[4]:

<matplotlib.image.AxesImage at 0x737e363bd880>

Let's take a bit of a closer look at the segmented chromatin clumps.

In [5]:

from histolytics.utils.plot import draw_thing_contours
from skimage.measure import label


fig, ax = plt.subplots(1, 2, figsize=(16, 8))

# Take a center crop of size 512x512 from chrom_mask and he for zoomed-in illustration
h, w = chrom_mask.shape
crop_size = 512
start_y = (h - crop_size) // 2
start_x = (w - crop_size) // 2

chrom_mask_crop = chrom_mask[
    start_y : start_y + crop_size, start_x : start_x + crop_size
]
he_crop = he[start_y : start_y + crop_size, start_x : start_x + crop_size]

contours = draw_thing_contours(label(chrom_mask_crop), he_crop, chrom_mask_crop)
ax[0].imshow(he_crop)
ax[1].imshow(contours)

from histolytics.utils.plot import draw_thing_contours from skimage.measure import label fig, ax = plt.subplots(1, 2, figsize=(16, 8)) # Take a center crop of size 512x512 from chrom_mask and he for zoomed-in illustration h, w = chrom_mask.shape crop_size = 512 start_y = (h - crop_size) // 2 start_x = (w - crop_size) // 2 chrom_mask_crop = chrom_mask[ start_y : start_y + crop_size, start_x : start_x + crop_size ] he_crop = he[start_y : start_y + crop_size, start_x : start_x + crop_size] contours = draw_thing_contours(label(chrom_mask_crop), he_crop, chrom_mask_crop) ax[0].imshow(he_crop) ax[1].imshow(contours)

Out[5]:

<matplotlib.image.AxesImage at 0x737e35fe3e00>

The results look reasonable by eyeballing. If you feel like you want to convert the chromatin masks into a geodataframe you can do it in the following way:

In [6]:

from histolytics.utils.raster import inst2gdf

inst2gdf(
    label(chrom_mask).astype(np.int32),
    chrom_mask.astype(np.int32),
    class_dict={1: "chromatin"},
    min_size=3,
)

from histolytics.utils.raster import inst2gdf inst2gdf( label(chrom_mask).astype(np.int32), chrom_mask.astype(np.int32), class_dict={1: "chromatin"}, min_size=3, )

Out[6]:

	id	class_name	geometry
0	2	chromatin	POLYGON ((655.99 154.225, 655.785 154.795, 655...
1	4	chromatin	POLYGON ((695.99 167.225, 695.785 167.795, 695...
2	1	chromatin	POLYGON ((683.99 136.225, 683.785 136.795, 683...
3	5	chromatin	POLYGON ((719.961 206.225, 719.139 206.795, 71...
4	7	chromatin	POLYGON ((624.039 226.225, 624.86 226.775, 627...
...	...	...	...
365	522	chromatin	POLYGON ((1115.99 1304.45, 1115.785 1305.589, ...
366	524	chromatin	POLYGON ((1101.99 1317.225, 1101.785 1317.795,...
367	523	chromatin	POLYGON ((1116.971 1316.225, 1116.355 1316.795...
368	528	chromatin	POLYGON ((1055.981 1372.225, 1055.57 1372.795,...
369	529	chromatin	POLYGON ((1055.01 1389.899, 1055.215 1392.13, ...

370 rows × 3 columns

Or alternatively, you can just add the computed chromatin features to the existing geodataframe:

In [7]:

nuc.loc[nuc["class_name"] == "neoplastic", "chromatin_clump_area"] = chrom_areas
nuc.loc[nuc["class_name"] == "neoplastic", "chromatin_nucleus_proportion"] = (
    chrom_nuc_props
)
nuc

nuc.loc[nuc["class_name"] == "neoplastic", "chromatin_clump_area"] = chrom_areas nuc.loc[nuc["class_name"] == "neoplastic", "chromatin_nucleus_proportion"] = ( chrom_nuc_props ) nuc

Out[7]:

	geometry	class_name	chromatin_clump_area	chromatin_nucleus_proportion
0	POLYGON ((1394.01 0, 1395.01 1.99, 1398 3.99, ...	connective	NaN	NaN
1	POLYGON ((1391 2.01, 1387 2.01, 1384.01 3.01, ...	connective	NaN	NaN
2	POLYGON ((1382.99 156.01, 1380 156.01, 1376.01...	connective	NaN	NaN
3	POLYGON ((1321 170.01, 1317.01 174.01, 1312.01...	connective	NaN	NaN
4	POLYGON ((1297.01 0, 1299.01 2.99, 1302 5.99, ...	connective	NaN	NaN
...	...	...	...	...
1290	POLYGON ((258 495, 258 496, 255 496, 255 497, ...	inflammatory	NaN	NaN
1291	POLYGON ((855.25 359, 851 360.01, 849.01 361.0...	connective	NaN	NaN
1292	POLYGON ((841 405, 841 406, 840 406, 840 407, ...	neoplastic	8.0	0.008869
1293	POLYGON ((954 506, 954 507, 952 507, 952 508, ...	neoplastic	3.0	0.003783
1294	POLYGON ((772 473, 772 474, 771 474, 771 475, ...	connective	NaN	NaN

1295 rows × 4 columns

Let's visualize which neoplastic nuclei have the highest chromatin clump area to nucleus area proportion i.e. the most chromatin-rich nuclei.

In [8]:

ax = nuc.plot(
    figsize=(10, 10),
    column="chromatin_nucleus_proportion",
    legend=True,
)
ax.set_axis_off()

ax = nuc.plot( figsize=(10, 10), column="chromatin_nucleus_proportion", legend=True, ) ax.set_axis_off()

Note: the gdf.plot appears upside down, this is because the origin in the raster data is in the top left corner, while in the geodataframe it is in the bottom left corner.

Conclusion¶

In this workflow tutorial we have demonstrated how to extract chromatin clumps from nuclei in histological images and how to compute chromatin clump area and the proportion of chromatin clump area to the total nuclear area, and visualize the results. This type of analysis can be used to quantify chromatin organization and can provide insights into the cellular state and disease pathologies.

In [ ]: