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Record W2254528831 · doi:10.1242/dev.121020

New markers for tracking endoderm induction and hepatocyte differentiation from human pluripotent stem cells

2015· article· en· W2254528831 on OpenAlexaff
Audrey Holtzinger, Philip R. Streeter, Farida Sarangi, Scott Hillborn, Maryam Niapour, Shinichiro Ogawa, Gordon Keller

Bibliographic record

VenueDevelopment · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institutes of Health
KeywordsEndodermBiologyInduced pluripotent stem cellGerm layerCell biologyPopulationProgenitor cellStem cellCellular differentiationEmbryonic stem cellGeneticsGene

Abstract

fetched live from OpenAlex

The efficient generation of hepatocytes from human pluripotent stem cells (hPSCs) requires the induction of a proper endoderm population, broadly characterized by the expression of the cell surface marker CXCR4. Strategies to identify and isolate endoderm subpopulations predisposed to the liver fate do not exist. In this study, we generated mouse monoclonal antibodies against human embryonic stem cell-derived definitive endoderm with the goal of identifying cell surface markers that can be used to track the development of this germ layer and its specification to a hepatic fate. Through this approach, we identified two endoderm-specific antibodies, HDE1 and HDE2, which stain different stages of endoderm development and distinct derivative cell types. HDE1 marks a definitive endoderm population with high hepatic potential, whereas staining of HDE2 tracks with developing hepatocyte progenitors and hepatocytes. When used in combination, the staining patterns of these antibodies enable one to optimize endoderm induction and hepatic specification from any hPSC line.

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 score0.053
Threshold uncertainty score0.591

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 head0.048
GPT teacher head0.270
Teacher spread0.222 · 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

Citations28
Published2015
Admission routes1
Has abstractyes

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