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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.

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

Set variables

Load image and stardist model

Segment Nuclei and extract values from image

Track Nuclei and save csv

label

ERK

area

timepoint

track_id

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

Detect Collective Events

Filter Collective Events

timepoint

track_id

clTrackID

label

ERK

area

ERK.resc

ERK.bin

Plot NoodlePlot

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

Prepare Data

Remap measured Ratio to segmented labels

Open Napari and add Layers

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.

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

Set variables

Load image and stardist model

Segment Nuclei and extract values from image

Track Nuclei and save csv

label

ERK

area

timepoint

track_id

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

Detect Collective Events

Filter Collective Events

timepoint

track_id

clTrackID

label

ERK

area

ERK.resc

ERK.bin

Plot NoodlePlot

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

Prepare Data

Remap measured Ratio to segmented labels

Open Napari and add Layers

133.91586

161.19741

29.385113

309

0.780246

157.38387

171.76452

26.158065

310

0.575343

121

157.41818

112.88182

133

22.369697

330

0.312039

122

118.09012

132.79361

29.494186

344

0.862141

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

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])

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))

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')

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')

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')

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

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])

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))

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')

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')

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')

Detecting Collective signalling events in epithelial Cells

hashtagData Preparation

hashtagImport libraries and define custom functions

hashtagSet variables

hashtagLoad image and stardist model

hashtagSegment Nuclei and extract values from image

hashtagTrack Nuclei and save csv

hashtagAnalyse data with ARCOS

hashtagImports

hashtagDetect Collective Events

hashtagFilter Collective Events

hashtagPlot NoodlePlot

hashtagVisualize Events in Napari

hashtagImports

hashtagPrepare Data

hashtagRemap measured Ratio to segmented labels

hashtagOpen Napari and add Layers

Detecting Collective signalling events in epithelial Cells

hashtagData Preparation

hashtagImport libraries and define custom functions

hashtagSet variables

hashtagLoad image and stardist model

hashtagSegment Nuclei and extract values from image

hashtagTrack Nuclei and save csv

hashtagAnalyse data with ARCOS

hashtagImports

hashtagDetect Collective Events

hashtagFilter Collective Events

hashtagPlot NoodlePlot

hashtagVisualize Events in Napari

hashtagImports

hashtagPrepare Data

hashtagRemap measured Ratio to segmented labels

hashtagOpen Napari and add Layers

Data Preparation

Import libraries and define custom functions

Set variables

Load image and stardist model

Segment Nuclei and extract values from image

Track Nuclei and save csv

Analyse data with ARCOS

Imports

Detect Collective Events

Filter Collective Events

Plot NoodlePlot

Visualize Events in Napari

Imports

Prepare Data

Remap measured Ratio to segmented labels

Open Napari and add Layers

Data Preparation

Import libraries and define custom functions

Set variables

Load image and stardist model

Segment Nuclei and extract values from image

Track Nuclei and save csv

Analyse data with ARCOS

Imports

Detect Collective Events

Filter Collective Events

Plot NoodlePlot

Visualize Events in Napari

Imports

Prepare Data

Remap measured Ratio to segmented labels

Open Napari and add Layers