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.

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))

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')
labelyxERKareatimepointtrack_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)
timepointtrack_idxyclTrackIDlabelERKareaERK.rescERK.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')
PreviousNapari Plugin "arcos-gui"NextAnalysing Collective Phenomena in Honeybees

Last updated