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Record W3125485476 · doi:10.3389/fcell.2020.618552

A Semi-automated Organoid Screening Method Demonstrates Epigenetic Control of Intestinal Epithelial Differentiation

2021· article· en· W3125485476 on OpenAlexaff
Jenny Ostrop, Rosalie T. Zwiggelaar, Marianne Terndrup Pedersen, François Gerbe, Korbinian Bösl, Håvard T. Lindholm, Alberto Díez-Sánchez, N. Parmar, Silke Radetzki, Jens Peter von Kries, Philippe Jay, Kim B. Jensen, C.H. Arrowsmith, Menno J. Oudhoff

Bibliographic record

VenueFrontiers in Cell and Developmental Biology · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsStructural Genomics ConsortiumPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
FundersLeibniz-GemeinschaftHorizon 2020 Framework ProgrammeCentre for Digital Life NorwayNorges ForskningsrådKreftforeningen
KeywordsOrganoidEpigeneticsBiologyCellular differentiationStem cellCell biologyIntestinal epitheliumEpitheliumComputational biologyGeneticsGene

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.239
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations27
Published2021
Admission routes1
Has abstractyes

Explore more

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