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idr0092-study.txt
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idr0092-study.txt
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# FILL IN AS MUCH INFORMATION AS YOU CAN. HINTS HAVE BEEN PUT IN SOME FIELDS AFTER THE HASH # SYMBOL. REPLACE THE HINT WITH TEXT WHERE APPROPRIATE.
# STUDY DESCRIPTION SECTION
# Section with generic information about the study including title, description, publication details (if applicable) and contact details
Comment[IDR Study Accession] idr0092
Study Title A semi-automated organoid screening method demonstrates epigenetic control of intestinal epithelial differentiation
Study Type compound library screen
Study Type Term Source REF EFO
Study Type Term Accession EFO_0007553
Study Description Intestinal organoids are an excellent model to study epithelial biology. Yet, the selection of analytical tools to accurately quantify heterogeneous organoid cultures remains limited. Here, we developed a semi-automated organoid screening method, which we applied to a library of highly specific chemical probes to identify epigenetic regulators of intestinal epithelial biology. The role of epigenetic modifiers in adult stem cell systems, such as the intestinal epithelium, is still undefined. Based on this resource dataset, we identified several targets that affected epithelial cell differentiation, including HDACs, EP300/CREBBP, LSD1, and type I PRMTs, which were verified by complementary methods. For example, we show that inhibiting type I PRMTs, which leads enhanced epithelial differentiation, blocks the growth of adenoma but not normal organoid cultures. Thus, epigenetic probes are powerful tools to study intestinal epithelial biology and may have therapeutic potential.
Study Key Words Organoids Intestinal epithelium Adult stem cells Epigenetic modifiers Library screen Bioimage quantification EP300 PRMT1
Study Organism Mus musculus
Study Organism Term Source REF NCBITaxon
Study Organism Term Accession 10090
Study Screens Number 1
Study External URL
Study BioImage Archive Accession
Study Public Release Date 2020-09-03
# Study Publication
Study PubMed ID
Study Publication Title A semi-automated organoid screening method demonstrates epigenetic control of intestinal epithelial differentiation
Study Author List Ostrop J, Zwiggelaar R, Terndrup Pedersen M, Gerbe F, Bosl K, Lindholm HT, Dıez-Sanchez A, Parmar N, Radetzki S, von Kries JP, Jay P, Jensen KB, Arrowsmith C, Oudhoff MJ
Study PMC ID
Study DOI https://doi.org/10.3389/fcell.2020.618552
# Study Contacts
Study Person Last Name Ostrop Bösl Arrowsmith Oudhoff
Study Person First Name Jenny Korbinian Cheryl Menno J.
Study Person Address CEMIR – Centre of Molecular Inflammation Research, Department of Clinical and Molecular Medicine, NTNU – Norwegian University of Science and Technology, NO-7491, Norway Computational Biological Unit, Department of Informatics, University of Bergen, Thormohlensgt 55, N-5008, Bergen, Norway Structural Genomics Consortium, University of Toronto, M5G 1L7, Toronto, Canada CEMIR – Centre of Molecular Inflammation Research, Department of Clinical and Molecular Medicine, NTNU – Norwegian University of Science and Technology, NO-7491, Norway
Study Person ORCID 0000-0003-2752-8377 0000-0003-0498-4273 0000-0002-4971-3250 0000-0002-1180-8975
Study Person Roles investigator, data analyst, experiment performer, software development data analyst, data curator, submitter material supplier role, institution investigator, institution
# Study License and Data DOI
Study License CC BY 4.0
Study License URL https://creativecommons.org/licenses/by/4.0/
Study Copyright Ostrop et al
Study Data Publisher University of Dundee
Study Data DOI https://doi.org/10.17867/10000145
Term Source Name NCBITaxon EFO CMPO Fbbi UBERON
Term Source File http://purl.obolibrary.org/obo/ http://www.ebi.ac.uk/efo/ http://www.ebi.ac.uk/cmpo/ http://purl.obolibrary.org/obo/ https://www.ebi.ac.uk/ols/ontologies/uberon
# SCREEN SECTION
# Screen Section containing all information relative to each screen in the study including materials used, protocols names and description, phenotype names and description.
# For multiple screens this section should be repeated. Copy and paste the whole section below and fill out for the next screen.
Screen Number 1
Comment[IDR Screen Name] idr0092-ostrop-organoid/screenA
Screen Sample Type tissue
Screen Description To study the role of epigenetic regulatory enzymes in the intestinal epithelium, small intestinal organoids were treated with a library of highly specific inhibitors of epigenetic modifiers provided by the Structural Genomics Consortium (SGC). Small intestinal organoids derived from four individual C57BL/6J mice were passaged by rigourous pipetting to nearly single cells at 1:4 ratio and seeded in 40µl Matrigel droplets/well in pre-warmed 24-well plates. 250µl/well basal organoid culture medium containing EGF, Noggin, and R-Spondin-1 ("ENR") was added immediately after Matrigel solidification. 250µl/well ENR + probes from the SGC Epigenetics Probes library at 2x working concentration were added within 30min. For each biological replicate DMSO vehicle controls were carried out in quadruplicates and valproic acid (VPA) was included as positive control. Media was replaced after 48h. Organoid bright-field image stacks were acquired 0h, 24h, 48h, 72h, 96h after seeding on an EVOS2 microscope (Thermo Fisher Scientific).
Screen Size Plates: 8 3D Images: 920 Planes: 23000 Average Image Dimension (XYZCT): 2080 x 1552 x 25 x 1 x 1 Total Tb: 0.199
Screen Example Images Plate 7, Well A01, Timepoint 96h
Screen Imaging Method bright-field microscopy
Screen Imaging Method Term Source REF Fbbi
Screen Imaging Method Term Accession Fbbi_00000243
Screen Technology Type compound screen
Screen Technology Type Term Source REF EFO
Screen Technology Type Term Accession EFO_0007553
Screen Type primary screen
Screen Type Term Source REF EFO
Screen Type Term Accession EFO_0007556
Screen Comments
# Library section. The library file should be supplied separately and it should contain the reagents description including, at the absolute minimum: reagent ID, sequences and position in the layout (= plate + position in the plate)
Library File Name JO_SGC_organoid_screen_idr_library.txt
Library File Format tab-delimited text
Library Type compound library
Library Type Term Source REF EFO
Library Type Term Accession EFO_0007569
Library Manufacturer Structural Genomics Consortium
Library Version 2016 SGC Epigenetic Chemical Probes
Library Experimental Conditions individual tissue technical replicate concentration of compound based treatment
Library Experimental Conditions Term Source REF EFO UBERON EFO EFO EFO
Library Experimental Conditions Term Accession
Quality Control Description visual inspection, positive control, negative control
# Protocols
Protocol Name dissection protocol growth protocol treatment protocol HCS library protocol HCS image acquisition and feature extraction protocol HCS data analysis protocol
Protocol Type dissection protocol growth protocol treatment protocol HCS library protocol HCS image acquisition and feature extraction protocol HCS data analysis protocol
Protocol Type Term Source REF EFO EFO EFO EFO EFO EFO
Protocol Type Term Accession EFO_0005519 EFO_0003789 EFO_0003969 EFO_0007571 EFO_0007572 EFO_0007573
Protocol Description Dissection protocol. Small intestinal crypts were isolated as described in https://doi.org/10.1007/978-1-62703-125-7_19. The proximal half of the intestine was rinsed, opened longitudinally, cut to small pieces after villi and mucus were scraped off, washed with PBS until the solution was clear, and incubated in 2mM EDTA/PBS for 30 min at 4°C with gentle rocking. Fragments were subsequently washed with PBS and the crypt fraction was typically collected from wash 2-5. All centrifugation steps were carried out at 300 x g. Growth protocol. Organoids were generated by seeding ca. 250-500 small intestinal crypts in a 50µl droplet of cold Matrigel (Corning #734-1101) into the middle of a pre-warmed 24-well plate. Matrigel was solidified by incubation at 37°C for 5-15min and 500µl culture medium added. Basal culture medium (""ENR"") consisted of advanced DMEM F12 (Thermo Fisher Scientific) supplemented with 1x Penicillin-Streptomycin (Sigma), 10mM HEPES (Thermo Fisher Scientific), 2mM Glutamax (Thermo Fisher Scientific), 1x B-27 (Thermo Fisher Scientific), 500mM N-Acetylcysteine (Sigma), 50ng/ml recombinant EGF (Thermo Fisher Scientific), 10% conditioned medium from a cell line producing Noggin (kind gift from Hans Clevers, Hubrecht Institute, Utrecht, The Netherlands), and 20% conditioned medium from a cell line producing R-Spondin (kind gift from Calvin Kuo, Stanford University School of Medicine, Stanford, CA, USA). ENR culture medium was replaced every 2-3 days. Organoids were passaged at 1:3-1:4 ratio by disruption with rigorous pipetting almost to single cells. Organoid fragments were centrifuged at 300 x g, resuspended in 40-50µl cold Matrigel per well, and plated on pre-warmed 24-well plates. Media was replaced after 48h. Compound treatment. The epigenetic modifier inhibitors were part of the Structural Genomics Consortium Epigenetic Chemical Probes Collection as of March 2016. Probes were reconstituted in DMSO and used at the recommended concentration. DMSO vehicle control was matched to the highest concentration used per experiment, maximal 10µM. 1mM valproic acid (VPA) was included as positive control. Probes were added within 30min after seeding. Image acquistion. Organoid brightfield 8-bit images were acquired on an Evos2 microscope (Thermo Fisher Scientific) with 2x magnification. At the starting point of the experiment, for each plate an automation setup was generated to acquire z-stacks with 50µm spacing either of a single position and most area of the Matrigel dome for each well. This automation setup was reused at consecutive timepoints. Image processing and quantification. A custom ImageJ/Fiji macro (https://doi.org/10.5281/zenodo.3951126) was used to collect single positions and layers for each well, to save a stack (ImageJ brightfield stack) and projections, and to perform a simple organoid segmentation (""ImageJ workflow""). For the segmentation, a Sobel edge detector was applied to each z-stack layer (ImageJ edge stack), a standard deviation Zprojection of the edge stack was generated, and particle analysis with optional manual correction was performed after several binary operations and thresholding. For an improved segmentation, to distinguish different organoid phenotypes, the ImageJ workflow was combined with the interactive machine learning software Ilastik. Training data was taken from the same experiment and excluded from further analysis. In a first step, pixel classification on an intensity summary projection of the ImageJ edge stack was used to separate between background and object borders. The generated pixel prediction maps were used as input in a second object classification step together with minimum projections of the ImageJ brightfield stack. The following label classes were used: Organoid, big sphere, small sphere, cluster, debris, background mislabelled as organoid, air bubble, edge of well plate.
# Phenotypes
Phenotype Name Debris Sphere_small Sphere_big Organoid Mislabelled AirBubble Edge Cluster
Phenotype Description manually trained classification in Ilastik; ProbabilityofDebris is highest probability score manually trained classification in Ilastik; ProbabilityofSphere_small is highest probability score manually trained classification in Ilastik; ProbabilityofSphere_big is highest probability score manually trained classification in Ilastik; ProbabilityofOrganoid is highest probability score manually trained classification in Ilastik; ProbabilityofMislabelled is highest probability score manually trained classification in Ilastik; ProbabilityofAirBubble is highest probability score manually trained classification for well edge in Ilastik; ProbabilityofEdge is highest probability score manually trained classification for cluster of organoids in Ilastik; ProbabilityofCluster is highest probability score
Phenotype Score Type automatic automatic automatic automatic automatic automatic automatic automatic
Phenotype Term Source REF CMPO
Phenotype Term Name
Phenotype Term Accession
# Raw Data Files
Raw Image Data Format TIFF
Raw Image Organization number of plates: 4 biological replicates, 5 timepoints, 4 technical replicates for negative controls, 2x24 well plates per biological replicate, total of 8 x 24 well plates , 1 fields per well, 1 channel per field
# Feature Level Data Files
Feature Level Data File Name JO_SGC_organoid_screen_idr_features_Ilastik.txt JO_SGC_organoid_screen_idr_features_Fiji.txt
Feature Level Data File Description This file gives information about the animals, the organoids, the compounds used to treat the organoids, the experimental conditions and phenotypic features of the organoids based on classification of Fiji segemeted objects with a manually trained Ilastik classifier
Feature Level Data File Format tab-delimited text
Feature Level Data Column Name Plate Well Characteristics [Organism] Term Source 1 REF Term Source 1 Accession Characteristics [Strain] Term Source 2 REF Term Source 2 Accession Characteristics [Tissue] Term Source 3 REF Term Source 3 Accession Characteristics [Individual] Term Source 4 REF Term Source 4 Accession Characteristics [technical replicate] Term Source 5 REF Term Source 5 Accession Characteristics [concentration of Compound µM] Term Source 6 REF Term Source 6 Accession Characteristics [time h] Term Source 7 REF Term Source 7 Accession Compound based treatment Compound PubChem CID Compound ChEBI Compound ChEMBL Compound SMILES Compound InChI Compound InChIKey Compound Name Compound Secondary Name Compound Class Compound Selectivity Compound Selectivity Synonyme Control Type Channels object_id labelimage_oid PredictedClass ProbabilityofMislabelled ProbabilityofOrganoid ProbabilityofSphere_big ProbabilityofSphere_small ProbabilityofCluster ProbabilityofDebris ProbabilityofAirBubble ProbabilityofEdges ObjectArea ObjectCenter_0 ObjectCenter_1 KurtosisofDefectArea VarianceofDefectArea DefectCenter_0 DefectCenter_1 Convexity ConvexHullCenter_0 ConvexHullCenter_1 SkewnessofDefectArea MeanDefectArea ConvexHullArea NumberofDefects MeanDefectDisplacement PrincipalAxes_0 PrincipalAxes_1 PrincipalAxes_2 PrincipalAxes_3 PrincipalAxes_4 PrincipalAxes_5 PrincipalAxes_6 PrincipalAxes_7 PrincipalAxes_8 TotalIntensity_0 TotalIntensity_1 TotalIntensity_2 KurtosisofIntensity_0 KurtosisofIntensity_1 KurtosisofIntensity_2 BoundingBoxMinimum_0 BoundingBoxMinimum_1 Principalcomponentsoftheobject_0 Principalcomponentsoftheobject_1 Principalcomponentsoftheobject_2 Principalcomponentsoftheobject_3 MeanIntensity_0 MeanIntensity_1 MeanIntensity_2 Minimumintensity_0 Minimumintensity_1 Minimumintensity_2 Maximumintensity_0 Maximumintensity_1 Maximumintensity_2 CovarianceofChannelIntensity_0 CovarianceofChannelIntensity_1 CovarianceofChannelIntensity_2 CovarianceofChannelIntensity_3 CovarianceofChannelIntensity_4 CovarianceofChannelIntensity_5 CovarianceofChannelIntensity_6 CovarianceofChannelIntensity_7 CovarianceofChannelIntensity_8 BoundingBoxMaximum_0 BoundingBoxMaximum_1 VarianceofIntensity_0 VarianceofIntensity_1 VarianceofIntensity_2 SkewnessofIntensity_0 SkewnessofIntensity_1 SkewnessofIntensity_2 Sizeinpixels Radiioftheobject_0 Radiioftheobject_1 Centeroftheobject_0 Centeroftheobject_1 TotalIntensityinneighborhood_0 TotalIntensityinneighborhood_1 TotalIntensityinneighborhood_2 KurtosisofIntensityinneighborhood_0 KurtosisofIntensityinneighborhood_1 KurtosisofIntensityinneighborhood_2 MeanIntensityinneighborhood_0 MeanIntensityinneighborhood_1 MeanIntensityinneighborhood_2 Maximumintensityinneighborhood_0 Maximumintensityinneighborhood_1 Maximumintensityinneighborhood_2 Minimumintensityinneighborhood_0 Minimumintensityinneighborhood_1 Minimumintensityinneighborhood_2 VarianceofIntensityinneighborhood_0 VarianceofIntensityinneighborhood_1 VarianceofIntensityinneighborhood_2 CovarianceofChannelIntensityinneighborhood_0 CovarianceofChannelIntensityinneighborhood_1 CovarianceofChannelIntensityinneighborhood_2 CovarianceofChannelIntensityinneighborhood_3 CovarianceofChannelIntensityinneighborhood_4 CovarianceofChannelIntensityinneighborhood_5 CovarianceofChannelIntensityinneighborhood_6 CovarianceofChannelIntensityinneighborhood_7 CovarianceofChannelIntensityinneighborhood_8 SkewnessofIntensityinneighborhood_0 SkewnessofIntensityinneighborhood_1 SkewnessofIntensityinneighborhood_2 NumberofHoles CenteroftheSkeleton_0 CenteroftheSkeleton_1 AverageBranchLength Diameter EuclideanDiameter NumberofBranches LengthoftheSkeleton FeretRatio Area
Feature Level Data Column Description Plate; running number Well on Plate Organism Ontology used to describe Characteristics [Organism] Ontology term used to describe Characteristics [Organism] mouse strain Ontology used to describe Characteristics [Strain] Ontology term used to describe Characteristics [Strain] tissue Ontology used to describe Characteristics [Tissue] Ontology term used to describe Characteristics [Tissue] individual mouse Ontology used to describe Characteristics [Indvidual] Ontology term used to describe Characteristics [Indvidual] technical replicate Ontology used to describe Characteristics [technical replicate] Ontology term used to describe Characteristics [technical replicate] concentration of compound Ontology used to describe Characteristics [concentration of Compound µM] Ontology term used to describe Characteristics [concentration of Compound µM] time after seeding in h Ontology used to describe Characteristics [time h] Ontology term used to describe Characteristics [time h] Compound PubChem CID Compound PubChem CID Compound ChEBI Compound ChEMBL Compound SMILES Compound InChI Compound InChIKey Compound Name Compound Synonym Classification of Compound based on targe Compound protein target domains Compound protein target domains synonymes type of control channel of acquired imaging data identifier of object the object identifier, unique for each object in each image Predicted class of object based on manually trained Ilastik classifier Probability of misslabeling of object based on manually trained Ilastik classifier Probability of object to be an organoid based on manually trained Ilastik classifier Probability of object to be a big sphere based on manually trained Ilastik classifier Probability of object to be a small sphere based on manually trained Ilastik classifier Probability of object to be a cluster of organoids on manually trained Ilastik classifier Probability of object to be debris based on manually trained Ilastik classifier Probability of object to be an air bubble based on manually trained Ilastik classifier Probability of object to be a well edge based on manually trained Ilastik classifier area of object coordinate of object center coordinate of object center standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output standard Illastik output length to width ratio of object area of object
# Processed Data Files
Processed Data File Name
Processed Data File Format
Processed Data File Description
Processed Data Column Name
Processed Data Column Type
Processed Data Column Annotation Level
Processed Data Column Description
Processed Data Column Link To Library File