Generate single-cell images

Here, we are going to process the previously ingested microscopy images with the scPortrait pipeline to generate single-cell images that we can use to assess autophagosome formation at a single-cell level.

import lamindb as ln
from collections.abc import Iterable

from pathlib import Path
from scportrait.pipeline.extraction import HDF5CellExtraction
from scportrait.pipeline.project import Project
from scportrait.pipeline.segmentation.workflows import CytosolSegmentationCellpose

ln.track()

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→ connected lamindb: testuser1/test-sc-imaging

/opt/hostedtoolcache/Python/3.12.11/x64/lib/python3.12/site-packages/xarray_schema/__init__.py:1: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
  from pkg_resources import DistributionNotFound, get_distribution

→ created Transform('60U4hMpZj5fQ0000'), started new Run('vp9td0Zk...') at 2025-07-14 06:40:27 UTC

→ notebook imports: lamindb==1.8.0 scportrait==1.3.5

• recommendation: to identify the notebook across renames, pass the uid: ln.track("60U4hMpZj5fQ")

Query microscopy images

First, we query for the raw and annotated microscopy images.

input_images = ln.Artifact.filter(
    ulabels__name="autophagy imaging", description__icontains="raw image", suffix=".tif"
)

The experiment includes two genotypes (WT and EI24KO) under two treatment conditions (unstimulated vs. 14h Torin-1). Multiple clonal cell lines were imaged for each condition across several fields of view (FOVs) and imaging channels.

We’ll extract single-cell images from each FOV and annotate them with metadata including genotype, treatment condition, clonal cell line, and imaging experiment.

input_images_df = input_images.df(features=True)

display(input_images_df.head())

conditions = input_images_df["stimulation"].unique().tolist()
cell_line_clones = input_images_df["cell_line_clone"].unique().tolist()
FOVs = input_images_df["FOV"].unique().tolist()

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→ queried for all categorical features with dtype ULabel or Record and non-categorical features: (11) ['genotype', 'stimulation', 'cell_line_clone', 'channel', 'FOV', 'magnification', 'microscope', 'imaged structure', 'image_path', 'resolution', 'study']

	uid	key	genotype	stimulation	cell_line_clone	channel	FOV	magnification	microscope	imaged structure	resolution	study
id
2	Md4OouMExlWS2YfZ0000	input_data_imaging_usecase/images/Timepoint001...	WT	14h Torin-1	U2OS lcklip-mNeon mCherryLC3B clone 1	Alexa488	FOV1	20X	Opera Phenix	LckLip-mNeon	0.597976	autophagy imaging
3	KKbVRkOjQ1jdA2fx0000	input_data_imaging_usecase/images/Timepoint001...	WT	14h Torin-1	U2OS lcklip-mNeon mCherryLC3B clone 1	Alexa488	FOV2	20X	Opera Phenix	LckLip-mNeon	0.597976	autophagy imaging
4	CiQYTBNZrj0CPejK0000	input_data_imaging_usecase/images/Timepoint001...	WT	14h Torin-1	U2OS lcklip-mNeon mCherryLC3B clone 1	DAPI	FOV1	20X	Opera Phenix	DNA	0.597976	autophagy imaging
5	W6tzE7JNiM80Ruho0000	input_data_imaging_usecase/images/Timepoint001...	WT	14h Torin-1	U2OS lcklip-mNeon mCherryLC3B clone 1	DAPI	FOV2	20X	Opera Phenix	DNA	0.597976	autophagy imaging
6	YGiNq6DPfIEjtt9j0000	input_data_imaging_usecase/images/Timepoint001...	WT	14h Torin-1	U2OS lcklip-mNeon mCherryLC3B clone 1	mCherry	FOV1	20X	Opera Phenix	mCherry-LC3B	0.597976	autophagy imaging

Alternatively, we can query for the ULabel directly:

conditions = ln.ULabel.filter(
    links_artifact__feature__name="stimulation", artifacts__in=input_images
).distinct()

cell_line_clones = ln.ULabel.filter(
    links_artifact__feature__name="cell_line_clone", artifacts__in=input_images
).distinct()

FOVs = ln.ULabel.filter(
    links_artifact__feature__name="FOV", artifacts__in=input_images
).distinct()

By iterating through conditions, cell lines and FOVs, we should only have 3 images showing a single FOV to enable processing using scPortrait.

# Create artifact type feature and associated label
ln.Feature(name="artifact type", dtype=ln.ULabel).save()
ln.ULabel(name="scportrait config").save()

# Load config file for processing all datasets
config_file_af = ln.Artifact.using("scportrait/examples").get(
    key="input_data_imaging_usecase/config.yml"
)

config_file_af.description = (
    "config for scportrait for processing of cells stained for autophagy markers"
)
config_file_af.save()

# Annotate the config file with the metadata relevant to the study
config_file_af.features.add_values(
    {"study": "autophagy imaging", "artifact type": "scportrait config"}
)

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→ transferred: Artifact(uid='voi8szTkmKPiahUA0000')

Process images with scPortrait

Let’s take a look at the processing of one example FOV.

# Get input images for one example FOV
condition, cellline, FOV = conditions[0], cell_line_clones[0], FOVs[0]

images = (
    input_images.filter(ulabels=condition)
    .filter(ulabels=cellline)
    .filter(ulabels=FOV)
    .distinct()
)

# Quick sanity check - all images should share metadata except channel/structure
values_to_ignore = ["channel", "imaged structure"]
features = images.first().features.get_values()
shared_features = {k: v for k, v in features.items() if k not in values_to_ignore}

for image in images:
    image_features = image.features.get_values()
    filtered_features = {
        k: v for k, v in image_features.items() if k not in values_to_ignore
    }
    assert shared_features == filtered_features

# Get image paths in correct channel order
input_image_paths = [
    images.filter(ulabels__name=channel).one().cache()
    for channel in ["DAPI", "Alexa488", "mCherry"]
]

# Create output directory and unique project ID
output_directory = "processed_data"
unique_project_id = f"{shared_features['cell_line_clone']}/{shared_features['stimulation']}/{shared_features['FOV']}".replace(
    " ", "_"
)
project_location = f"{output_directory}/{unique_project_id}/scportrait_project"

# Create directories
Path(project_location).mkdir(parents=True, exist_ok=True)

# Initialize the scPortrait project
project = Project(
    project_location=project_location,
    config_path=config_file_af.cache(),
    segmentation_f=CytosolSegmentationCellpose,
    extraction_f=HDF5CellExtraction,
    overwrite=True,
)

# Load images and process
project.load_input_from_tif_files(
    input_image_paths, overwrite=True, channel_names=["DAPI", "Alexa488", "mCherry"]
)
project.segment()
project.extract()

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Updating project config file.
INFO     The Zarr backing store has been changed from None the new file path:                                      
         processed_data/U2OS_lcklip-mNeon_mCherryLC3B_clone_1/14h_Torin-1/FOV1/scportrait_project/scportrait.sdata 

WARNING:ome_zarr.io:version mismatch: detected: RasterFormatV02, requested: FormatV04

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Let’s look at the input images we processed.

project.plot_input_image()

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Now we can look at the results generated by scPortrait. First, the segmentation masks.

project.plot_segmentation_masks()

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And then extraction results consisting of individual single-cell images over all of the channels.

project.plot_single_cell_images()

Save and annotate results

Now we also want to save these results to the instance.

ln.Artifact.from_spatialdata(
    sdata=project.filehandler.get_sdata(),
    description="scportrait spatialdata object containing results of cells stained for autophagy markers",
    key=f"processed_data_imaging_use_case/{unique_project_id}/spatialdata.zarr",
).save()

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WARNING:ome_zarr.io:version mismatch: detected: RasterFormatV02, requested: FormatV04

INFO     The SpatialData object is not self-contained (i.e. it contains some elements that are Dask-backed from    
         locations outside /home/runner/.cache/lamindb/wv7MNjvqmYIA7JMd0000.zarr). Please see the documentation of 
         `is_self_contained()` to understand the implications of working with SpatialData objects that are not     
         self-contained.                                                                                           

INFO     The Zarr backing store has been changed from                                                              
         processed_data/U2OS_lcklip-mNeon_mCherryLC3B_clone_1/14h_Torin-1/FOV1/scportrait_project/scportrait.sdata 
         the new file path: /home/runner/.cache/lamindb/wv7MNjvqmYIA7JMd0000.zarr                                  

Artifact(uid='wv7MNjvqmYIA7JMd0000', is_latest=True, key='processed_data_imaging_use_case/U2OS_lcklip-mNeon_mCherryLC3B_clone_1/14h_Torin-1/FOV1/spatialdata.zarr', description='scportrait spatialdata object containing results of cells stained for autophagy markers', suffix='.zarr', kind='dataset', otype='SpatialData', size=6859499, hash='uakxyAGlgTnXelOTYMLN1w', n_files=75, branch_id=1, space_id=1, storage_id=1, run_id=3, created_by_id=1, created_at=2025-07-14 06:41:47 UTC)

# Define schemas for single-cell image dataset
schemas = {
    "var.T": ln.Schema(
        name="single-cell image dataset schema var",
        description="column schema for data measured in obsm[single_cell_images]",
        itype=ln.Feature,
    ).save(),
    "obs": ln.Schema(
        name="single-cell image dataset schema obs",
        features=[
            ln.Feature(name="scportrait_cell_id", dtype="int", coerce_dtype=True).save()
        ],
    ).save(),
    "uns": ln.Schema(
        name="single-cell image dataset schema uns",
        itype=ln.Feature,
        dtype=dict,
    ).save(),
}

# Create composite schema
h5sc_schema = ln.Schema(
    name="single-cell image dataset",
    otype="AnnData",
    slots=schemas,
).save()

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! you are trying to create a record with name='single-cell image dataset schema obs' but a record with similar name exists: 'single-cell image dataset schema var'. Did you mean to load it?

! you are trying to create a record with name='single-cell image dataset schema uns' but records with similar names exist: 'single-cell image dataset schema var', 'single-cell image dataset schema obs'. Did you mean to load one of them?

! you are trying to create a record with name='single-cell image dataset' but records with similar names exist: 'single-cell image dataset schema var', 'single-cell image dataset schema obs', 'single-cell image dataset schema uns'. Did you mean to load one of them?

# Curate the AnnData object
curator = ln.curators.AnnDataCurator(project.h5sc, h5sc_schema)
curator.validate()

# Save artifact with annotations
artifact = curator.save_artifact(
    key=f"processed_data_imaging_use_case/{unique_project_id}/single_cell_data.h5ad"
)

# Add metadata and labels
annotation = shared_features.copy()
annotation["imaged structure"] = [
    ln.ULabel.using("scportrait/examples").get(name=name)
    for name in ["LckLip-mNeon", "DNA", "mCherry-LC3B"]
]

artifact.features.add_values(annotation)
artifact.labels.add(ln.ULabel(name="scportrait single-cell images").save())

! 5 terms not validated in feature 'columns' in slot 'var.T': '0', '1', '2', '3', '4'
    → fix typos, remove non-existent values, or save terms via: curator.slots['var.T'].cat.add_new_from('columns')

! no values were validated for columns!

! 1 term not validated in feature 'keys' in slot 'uns': 'single_cell_images'
    → fix typos, remove non-existent values, or save terms via: curator.slots['uns'].cat.add_new_from('keys')

! no values were validated for keys!

→ returning existing schema with same hash: Schema(uid='0000000000000000', name='single-cell image dataset schema var', description='column schema for data measured in obsm[single_cell_images]', n=-1, is_type=False, itype='Feature', hash='kMi7B_N88uu-YnbTLDU-DA', minimal_set=True, ordered_set=False, maximal_set=False, branch_id=1, space_id=1, created_by_id=1, run_id=3, created_at=2025-07-14 06:41:47 UTC)

→ returning existing schema with same hash: Schema(uid='fxdP2u6cVNyhRg6Y', name='single-cell image dataset schema obs', n=1, is_type=False, itype='Feature', hash='98jtXAU6e8cL9NqcucBT8w', minimal_set=True, ordered_set=False, maximal_set=False, branch_id=1, space_id=1, created_by_id=1, run_id=3, created_at=2025-07-14 06:41:48 UTC)

→ returning existing schema with same hash: Schema(uid='0000000000000000', name='single-cell image dataset schema var', description='column schema for data measured in obsm[single_cell_images]', n=-1, is_type=False, itype='Feature', hash='kMi7B_N88uu-YnbTLDU-DA', minimal_set=True, ordered_set=False, maximal_set=False, branch_id=1, space_id=1, created_by_id=1, run_id=3, created_at=2025-07-14 06:41:47 UTC)

To process all files in our dataset efficiently, we’ll create a custom image processing function. We decorate this function with :func:~lamindb.tracked to track data lineage of the input and output artifacts.

The function will skip files that have already been processed and uploaded, improving processing time by avoiding redundant computations.

@ln.tracked()
def process_images(
    config_file_af: ln.Artifact,
    input_artifacts: Iterable[ln.Artifact],
    h5sc_schema: ln.Schema,
    output_directory: str,
) -> None:
    # Quick sanity check - all images should share metadata except channel/structure
    values_to_ignore = ["channel", "imaged structure"]
    first_features = input_artifacts.first().features.get_values()
    shared_features = {
        k: v for k, v in first_features.items() if k not in values_to_ignore
    }

    for artifact in input_artifacts:
        artifact_features = artifact.features.get_values()
        filtered_features = {
            k: v for k, v in artifact_features.items() if k not in values_to_ignore
        }
        assert shared_features == filtered_features

    # Create a unique project ID
    unique_project_id = f"{shared_features['cell_line_clone']}/{shared_features['stimulation']}/{shared_features['FOV']}".replace(
        " ", "_"
    )

    # Check if already processed
    base_key = f"processed_data_imaging_use_case/{unique_project_id}"
    try:
        ln.Artifact.using("scportrait/examples").get(
            key=f"{base_key}/single_cell_data.h5ad"
        )
        ln.Artifact.using("scportrait/examples").get(key=f"{base_key}/spatialdata.zarr")
        print("Dataset already processed. Skipping.")
        return
    except ln.Artifact.DoesNotExist:
        pass

    # Get image paths in channel order
    input_image_paths = [
        input_artifacts.filter(ulabels__name=channel).one().cache()
        for channel in ["DAPI", "Alexa488", "mCherry"]
    ]

    # Setup and process project
    project_location = f"{output_directory}/{unique_project_id}/scportrait_project"
    Path(project_location).mkdir(parents=True, exist_ok=True)

    project = Project(
        project_location=project_location,
        config_path=config_file_af.cache(),
        segmentation_f=CytosolSegmentationCellpose,
        extraction_f=HDF5CellExtraction,
        overwrite=True,
    )

    project.load_input_from_tif_files(
        input_image_paths, overwrite=True, channel_names=["DAPI", "Alexa488", "mCherry"]
    )
    project.segment()
    project.extract()

    # Save single-cell images
    curator = ln.curators.AnnDataCurator(project.h5sc, h5sc_schema)
    artifact = curator.save_artifact(key=f"{base_key}/single_cell_data.h5ad")

    annotation = shared_features.copy()
    annotation["imaged structure"] = [
        ln.ULabel.using("scportrait/examples").get(name=name)
        for name in ["LckLip-mNeon", "DNA", "mCherry-LC3B"]
    ]

    artifact.features.add_values(annotation)
    artifact.labels.add(ln.ULabel.get(name="scportrait single-cell images"))

    # Save SpatialData object
    ln.Artifact.from_spatialdata(
        sdata=project.filehandler.get_sdata(),
        description="scportrait spatialdata object containing results of cells stained for autophagy markers",
        key=f"{base_key}/spatialdata.zarr",
    ).save()

ln.Param(name="output_directory", dtype="str").save()

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Feature(uid='TPuhDFqdFu9M', name='output_directory', dtype='str', array_rank=0, array_size=0, branch_id=1, space_id=1, created_by_id=1, run_id=3, created_at=2025-07-14 06:41:48 UTC)

Now we are ready to process all of our input images and upload the generated single-cell image datasets back to our instance.

for condition in conditions:
    for cellline in cell_line_clones:
        for FOV in FOVs:
            images = (
                input_images.filter(ulabels=condition)
                .filter(ulabels=cellline)
                .filter(ulabels=FOV)
                .distinct()
            )

            if images:
                process_images(
                    config_file_af,
                    input_artifacts=images,
                    h5sc_schema=h5sc_schema,
                    output_directory=output_directory,
                )

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! cannot infer feature type of: <ArtifactQuerySet [Artifact(uid='Md4OouMExlWS2YfZ0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row01_Well01_Alexa488_zstack001_r003_c005.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='0aoXxT857VvKAGo9UQo-8g', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC), Artifact(uid='CiQYTBNZrj0CPejK0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row01_Well01_DAPI_zstack001_r003_c005.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='YRjhTt2cBLq3BukWLUKC_w', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC), Artifact(uid='YGiNq6DPfIEjtt9j0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row01_Well01_mCherry_zstack001_r003_c005.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='zptjd_tR7P2Nw6mjUWbEjw', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC)]>, returning '?

Dataset already processed. Skipping.
! cannot infer feature type of: <ArtifactQuerySet [Artifact(uid='KKbVRkOjQ1jdA2fx0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row01_Well01_Alexa488_zstack001_r004_c007.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='Mm-DsaVdkMbTreUW_ipQBQ', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:24 UTC), Artifact(uid='W6tzE7JNiM80Ruho0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row01_Well01_DAPI_zstack001_r004_c007.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='9_LVWK08Z_D4c1mvIJlTOA', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC), Artifact(uid='uuh41FAHEz0ASL2N0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row01_Well01_mCherry_zstack001_r004_c007.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='PSnZ2s1_uJa9G4_cc2HpNw', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC)]>, returning '?

Dataset already processed. Skipping.
! cannot infer feature type of: <ArtifactQuerySet [Artifact(uid='yiIMSAddDWgLgki70000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row02_Well01_Alexa488_zstack001_r005_c008.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='KUhrzT6sDoICDg9cGCUkmg', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC), Artifact(uid='DEzw4QQAsjVZ010b0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row02_Well01_DAPI_zstack001_r005_c008.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='N2xVJ8pQ9jWoywub8om_iQ', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC), Artifact(uid='hbVyCGFARHU91Kax0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row02_Well01_mCherry_zstack001_r005_c008.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='U2Hrdj8bUugok31k_uqEuw', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC)]>, returning '?

Dataset already processed. Skipping.
! cannot infer feature type of: <ArtifactQuerySet [Artifact(uid='2ie2Kjzn1O7UYhuq0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row02_Well01_Alexa488_zstack001_r008_c008.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='xkGVufN-7DDAZOeh4h7LpA', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC), Artifact(uid='fOQSb7JCK67aeN6a0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row02_Well01_DAPI_zstack001_r008_c008.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='aNYMykHkxezUxs-07xgikw', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC), Artifact(uid='qHQpdWcFu7FzF6l50000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row02_Well01_mCherry_zstack001_r008_c008.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='_eUmLccyLWPhCAZBQ8a3zA', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC)]>, returning '?

Dataset already processed. Skipping.
! cannot infer feature type of: <ArtifactQuerySet [Artifact(uid='jVytS8AyAHmHkYR30000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row03_Well01_Alexa488_zstack001_r001_c007.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='QFmofkisy-sibXAuKrpfRw', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC), Artifact(uid='cw4F6bUB9zuMthCY0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row03_Well01_DAPI_zstack001_r001_c007.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='f4Vij96NOq7M96SXfyEYXQ', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC), Artifact(uid='7TZGXvbA0JLL68hR0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row03_Well01_mCherry_zstack001_r001_c007.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='qUKQaesz-vfPUwljqPlIjA', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC)]>, returning '?

Dataset already processed. Skipping.
! cannot infer feature type of: <ArtifactQuerySet [Artifact(uid='vCVbKkzz4CnJPPKF0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row03_Well01_Alexa488_zstack001_r001_c008.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='WyabAqK4jeZtTtrQ9rvUeQ', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:24 UTC), Artifact(uid='5kMhlcDNek4RMeQF0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row03_Well01_DAPI_zstack001_r001_c008.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='eXaBPwLJJ--9SyGD7JR0ew', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC), Artifact(uid='9BmbViqMmlVhpfS00000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row03_Well01_mCherry_zstack001_r001_c008.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='EeQ_zoM4j8waIP8wP-u0Hg', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC)]>, returning '?

Dataset already processed. Skipping.
! cannot infer feature type of: <ArtifactQuerySet [Artifact(uid='OS0wBE7bviIlW7qj0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row04_Well01_Alexa488_zstack001_r001_c002.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='RwasAYhkUEeTVBFrwCbKuw', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC), Artifact(uid='ixOpuSTsyrPXdYuA0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row04_Well01_DAPI_zstack001_r001_c002.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='diUYJPHgU_SvK0zTrrzUlw', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC), Artifact(uid='6uMjKAk1aYlAV7Cf0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row04_Well01_mCherry_zstack001_r001_c002.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='9K0N7dMe59K6zYUtjE74cA', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:24 UTC)]>, returning '?

Dataset already processed. Skipping.
! cannot infer feature type of: <ArtifactQuerySet [Artifact(uid='9ZVngbl0JUS0XdZ70000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row04_Well01_Alexa488_zstack001_r001_c009.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='W1Q1RQCUj7j4e9R5Sta4wQ', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC), Artifact(uid='IzP3IAwIhmM7OORD0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row04_Well01_DAPI_zstack001_r001_c009.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='dIUw6okO0jOq6JcgsxDktA', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC), Artifact(uid='RRVS8qVx3VSw02Xu0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row04_Well01_mCherry_zstack001_r001_c009.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='9ZmlWOdgh9GFPd4BCYO7GQ', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC)]>, returning '?

Dataset already processed. Skipping.
! cannot infer feature type of: <ArtifactQuerySet [Artifact(uid='Gtwi9Pcyx8maQEWB0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row01_Well08_Alexa488_zstack001_r002_c009.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='yJMlFFYyup0JmBu_FjTa3g', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:24 UTC), Artifact(uid='sHHpiiFYWsIXMZNV0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row01_Well08_DAPI_zstack001_r002_c009.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='6pvuFREF3CQ8xr8Urak52Q', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC), Artifact(uid='hNMkrIHce1XrLZHY0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row01_Well08_mCherry_zstack001_r002_c009.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='K1jGiIE0dljR_qYP1btlyw', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC)]>, returning '?

Dataset already processed. Skipping.
! cannot infer feature type of: <ArtifactQuerySet [Artifact(uid='nSZhAypqiNZ2Ylbe0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row01_Well08_Alexa488_zstack001_r002_c010.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='uh537K7rMhMNKW-mti1vjw', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC), Artifact(uid='jzqxtoduIJ3hCbB40000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row01_Well08_DAPI_zstack001_r002_c010.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='SOxM7EH80KPXK3hzBxXYLg', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:24 UTC), Artifact(uid='M06liaIzh2OVEuJ40000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row01_Well08_mCherry_zstack001_r002_c010.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='0tV4uQYxnP6G8JOrAwMyXw', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC)]>, returning '?

Dataset already processed. Skipping.
! cannot infer feature type of: <ArtifactQuerySet [Artifact(uid='mXWwV1x42Jz9RoSO0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row02_Well08_Alexa488_zstack001_r002_c005.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='4xc4bpAMo0UaM_tc8SciIA', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC), Artifact(uid='gj0HHnoVpEqbaUJb0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row02_Well08_DAPI_zstack001_r002_c005.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='fZ4W2ePHagfGUGCcve98gQ', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC), Artifact(uid='1XnEyqVt6UGXCTmV0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row02_Well08_mCherry_zstack001_r002_c005.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='if3WCKH91_CY0ptIVYuLmA', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC)]>, returning '?

Dataset already processed. Skipping.
! cannot infer feature type of: <ArtifactQuerySet [Artifact(uid='Ov8FnKzHMNY0XVJa0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row02_Well08_Alexa488_zstack001_r002_c009.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='5cfc02GejBaoJ0h3OLWSdw', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC), Artifact(uid='Lwk8shsYe0V5bMgd0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row02_Well08_DAPI_zstack001_r002_c009.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='CmAa139DH_lQ2Plcy4BxXw', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC), Artifact(uid='YuyVn060M4FxATPz0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row02_Well08_mCherry_zstack001_r002_c009.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='gZHwk76xPz7qXiST4RuVMQ', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC)]>, returning '?

Dataset already processed. Skipping.
! cannot infer feature type of: <ArtifactQuerySet [Artifact(uid='ThuJnRAhqkp54kyU0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row03_Well08_Alexa488_zstack001_r010_c009.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='vs3t8d40w259zgJRltHlKA', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC), Artifact(uid='PquYNyshQTDd24Vw0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row03_Well08_DAPI_zstack001_r010_c009.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='1mSZ_MMg393bfTvMOl3YMQ', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC), Artifact(uid='8SFUmW0RhBNySxBO0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row03_Well08_mCherry_zstack001_r010_c009.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='fjO8ly0euUPHXoQd86weww', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC)]>, returning '?

Dataset already processed. Skipping.
! cannot infer feature type of: <ArtifactQuerySet [Artifact(uid='h4EKWveW36LIzXez0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row03_Well08_Alexa488_zstack001_r010_c010.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='ZPUWq_TI3KWNcVeVz3vOPg', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC), Artifact(uid='fdem35nw5ztUnEIM0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row03_Well08_DAPI_zstack001_r010_c010.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='XuugTTV9S5TbJKVMLOaR0w', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC), Artifact(uid='VkmKLUCaMsYFCuGE0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row03_Well08_mCherry_zstack001_r010_c010.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='A-MzQvq-kHeu1WHdVmm5bw', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC)]>, returning '?

Dataset already processed. Skipping.
! cannot infer feature type of: <ArtifactQuerySet [Artifact(uid='QDPX1ljp0eCMz80o0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row04_Well08_Alexa488_zstack001_r001_c003.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='Y1bgVRJGzIEhKFGP54fplA', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:24 UTC), Artifact(uid='Cvamog4G3a2XYGM80000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row04_Well08_DAPI_zstack001_r001_c003.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='UFjAP7uBiaM9ivE4hJzHTA', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC), Artifact(uid='6uUjyphUD4D1Hixc0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row04_Well08_mCherry_zstack001_r001_c003.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='lvFoCpbAkjg4mEtXx-D99w', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC)]>, returning '?

Dataset already processed. Skipping.
! cannot infer feature type of: <ArtifactQuerySet [Artifact(uid='Oww4y0yYuR8pxV9q0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row04_Well08_Alexa488_zstack001_r001_c005.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='YX1aIPYndKFiPl5W3OPqfQ', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC), Artifact(uid='AhBvnNKg5yJcG6LU0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row04_Well08_DAPI_zstack001_r001_c005.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='9I6amjQc298AZvER1Z2jjQ', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC), Artifact(uid='AVRTVX9gEu4LrTAP0000', is_latest=True, key='input_data_imaging_usecase/images/Timepoint001_Row04_Well08_mCherry_zstack001_r001_c005.tif', description='raw image of U2OS cells stained for autophagy markers', suffix='.tif', size=2333056, hash='rYoK8xKXBtx5aNW4AfUbUg', branch_id=1, space_id=1, storage_id=2, run_id=2, created_by_id=1, created_at=2025-03-07 13:51:25 UTC)]>, returning '?

Dataset already processed. Skipping.

example_artifact = ln.Artifact.filter(
    ulabels=ln.ULabel.get(name="scportrait single-cell images")
).first()

example_artifact.view_lineage()

ln.finish()

Show code cell output Hide code cell output

→ finished Run('vp9td0Zk') after 1m at 2025-07-14 06:41:54 UTC