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Detecting Collective signalling events in epithelial Cells

This section outlines an example pipeline written in python to get from images of epithelial cells to quantification with ARCOS and visualization with napari.
The following describes an example image processing pipeline written in python to analyse collective events in an MDCK epithelium. What we are aiming for can be seen in the gif above. First, the erk measurement is extracted from the images and the individual nuclei are tracked. The second step shows how to analyse the data with ARCOS. Similar results can be achieved with standalone tools such as CellProfiler and Ilastik. And both the R and Python packages can be used after preparing the data.
In the following zip file, you can find both the input data and the generated output from the image segmentation pipeline and ARCOS.
example_data.zip
16MB
Binary
Example Dataset, credit Paolo Gagliardi

Data Preparation

The data in this example is from an MDCK cell line that stably expresses a fluorescent FRET biosensor reporting the activity of the ERK kinase.
In the first step, the nuclei are segmented using the stardist python package.
Subsequently, the mean intensity of individual objects in the ratio image is measured and the individual nuclei are tracked over time.

Import libraries and define custom functions

import os
import skimage
import numpy
import errno
from stardist.models import StarDist2D
from csbdeep.utils import normalize
from skimage.measure import regionprops, regionprops_table
from skimage.util import map_array
import pandas as pd
import trackpy
def create_folders(path: str, folder: list):
for i in folder:
folder_to_make = os.path.join(path, i)
try:
os.makedirs(folder_to_make)
print(f'folder "{i}" created')
except OSError as e:
print(f'folder "{i}" alrady exists')
if e.errno != errno.EEXIST:
raise
def remap_segmentation(df: pd.DataFrame, segmentation: list, timepoint_column: str = 'timepoint', label_column: str = 'label', measure_column: str = 'ERK') -> list:
tracked_numpy = df[[timepoint_column, label_column, measure_column]].sort_values(timepoint_column).to_numpy()
grouped_numpy = numpy.split(tracked_numpy,numpy.unique(tracked_numpy[:,0], return_index = True)[1][1:])
ratio_remapped = []
for img, grp in zip(segmentation, grouped_numpy):
img_copy = map_array(img, grp[:,1], grp[:, 2])
ratio_remapped.append(img_copy)
return ratio_remapped

Set variables

PATH = 'example_data' # where is you data located
FOLDER = 'mdck_ekar' # subfolder of PATH where images are stored
OUT_DATA = 'data' # subfolder of PATH where csv is stored
OUT_LABELS = 'stardist' # subfolder of PATH where stardist segmentation is stored
FILENAME = 'C3-041_Ori.tif'
full_path = os.path.join(PATH, FOLDER)
orig_images_path = os.path.join(PATH, FOLDER)
out_path_csv = os.path.join(PATH, OUT_DATA)
create_folders(PATH, [OUT_DATA, OUT_LABELS])

Load image and stardist model

model = StarDist2D.from_pretrained('2D_versatile_fluo') # standard stardist model for 2d segmentation
image_data = skimage.io.imread(os.path.join(orig_images_path, FILENAME))

Segment Nuclei and extract values from image

out_path_stardist = os.path.join(PATH, OUT_LABELS, 'stardist.tif')
segmentation = []
df = []
for t, tp_data in enumerate(image_data):
print(f'analysing timepoint {t}')
labels, _ = model.predict_instances(normalize(tp_data))
labels = skimage.segmentation.clear_border(labels)
dic = regionprops_table(labels, tp_data, properties=['label', 'centroid', 'intensity_mean', 'area'])
dic['timepoint'] = numpy.repeat(t, len(dic['label']))
df.append(pd.DataFrame(dic))
skimage.segmentation.clear_border(labels)
segmentation.append(labels)
# optionally save segmentation
skimage.io.imsave(out_path_stardist, numpy.stack(segmentation))
Example frame stardist segmentation

Track Nuclei and save csv

df_full = pd.concat(df)
df_full = df_full.rename(columns={"centroid-1": "x", "centroid-0": "y", 'intensity_mean': 'ERK'})
df_full = df_full.sort_values(['timepoint'])
df_tracked = trackpy.link_df(df_full, search_range = 10, memory = 2, t_column = 'timepoint')
df_tracked = df_tracked.reset_index(drop=True).rename(columns={'particle': "track_id"})
df_tracked.to_csv(out_path_csv+'\\tracked_data_fret.csv')
label
y
x
ERK
area
timepoint
track_id
1
234.0361
234.21661
24.241877
277
0
0
15
247.75668
17.486647
20.970326
337
0
1
14
201.73333
275.54409
22.105376
465
0
2
13
282.29854
274.37136
23.169903
412
0
3
12
79.808989
26.254682
23.794007
267
0
4

Analyse data with ARCOS

In this example, the python package arcos4py is used. But the same analysis could be carried out easily with the R package or the napari plugin.

Imports

from arcos4py import ARCOS
from arcos4py.tools import filterCollev
from arcos4py.plotting import NoodlePlot

Detect Collective Events

ts = ARCOS(df_tracked, ["x", "y"], 'timepoint','track_id', 'ERK')
ts.interpolate_measurements()
ts.bin_measurements(biasMet='none', binThr=0.28)
df_arcos = ts.trackCollev(eps=40, minClsz=5)

Filter Collective Events

filterer = filterCollev(df_arcos, 'timepoint', 'clTrackID', 'track_id')
ts_filtered = filterer.filter(25, 10)
timepoint
track_id
x
y
clTrackID
label
ERK
area
ERK.resc
ERK.bin
41
45
178.5023
159.9447
2
84
24.069124
434
0.309875
1
41
69
133.91586
161.19741
2
66
29.385113
309
0.780246
1
41
80
157.38387
171.76452
2
79
26.158065
310
0.575343
1
41
121
157.41818
112.88182
2
133
22.369697
330
0.312039
1
41
122
118.09012
132.79361
2
92
29.494186
344
0.862141
1

Plot NoodlePlot

NoodlePlot(ts_filtered, 'clTrackID', 'track_id', 'timepoint', 'x', 'y').plot('x')

Visualize Events in Napari

This step is optional and only one way to visualize events but can be usefull to validate correct event detection.

Imports

from napari import Viewer
TAB20 = [
"#1f77b4",
"#aec7e8",
"#ff7f0e",
"#ffbb78",
"#2ca02c",
"#98df8a",
"#d62728",
"#ff9896",
"#9467bd",
"#c5b0d5",
"#8c564b",
"#c49c94",
"#e377c2",
"#f7b6d2",
"#7f7f7f",
"#c7c7c7",
"#bcbd22",
"#dbdb8d",
"#17becf",
"#9edae5",
]

Prepare Data

np_data = df_tracked[['track_id', 'timepoint', 'y', 'x']].to_numpy()
colors = numpy.take(numpy.array(TAB20), ts_filtered['clTrackID'].unique(), mode="wrap")
df_w_colors = pd.merge(ts_filtered, pd.DataFrame(data={'colors': colors, 'clTrackID': ts_filtered['clTrackID'].unique()}))
points_data = df_w_colors[['timepoint', 'y', 'x']].to_numpy()
colors_data = df_w_colors['colors'].to_numpy('str')

Remap measured Ratio to segmented labels

ratio_remapped = remap_segmentation(df_tracked, segmentation)
ratio_remapped = numpy.stack(ratio_remapped)

Open Napari and add Layers

viewer = Viewer()
viewer.add_image(image_data, name='ERK Ratio image', colormap='inferno')
viewer.add_image(ratio_remapped, colormap='viridis')
viewer.add_labels(numpy.stack(segmentation), name='segmentation', visible=False)
viewer.add_tracks(np_data, name='cell tracks')
viewer.add_points(points_data, face_color=colors_data, name='collective events')
Screenshot of the visualization in napari
arcos_example.ipynb
96KB
Binary
Download the jupyter notebook