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Record W2153171087 · doi:10.1016/j.celrep.2013.01.016

The ROSA26-iPSC Mouse: A Conditional, Inducible, and Exchangeable Resource for Studying Cellular (De)Differentiation

2013· article· en· W2153171087 on OpenAlexaff
Lieven Haenebalcke, Steven Goossens, Pieterjan Dierickx, Sonia Bartunkova, Jinke D’Hont, Katharina Haigh, Tino Hochepied, Dagmar Wirth, András Nagy, Jody J. Haigh

Bibliographic record

VenueCell Reports · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai Hospital
FundersVrije Universiteit BrusselWellcome Trust
KeywordsReprogrammingBiologyCell biologyInduced pluripotent stem cellCellular differentiationCell fate determinationEmbryonic stem cellEpigeneticsGeneGeneticsTranscription factor

Abstract

fetched live from OpenAlex

Control of cellular (de)differentiation in a temporal, cell-specific, and exchangeable manner is of paramount importance in the field of reprogramming. Here, we have generated and characterized a mouse strain that allows iPSC generation through the Cre/loxP conditional and doxycycline/rtTA-controlled inducible expression of the OSKM reprogramming factors entirely from within the ROSA26 locus. After reprogramming, these factors can be replaced by genes of interest-for example, to enhance lineage-directed differentiation-with the use of a trap-coupled RMCE reaction. We show that, similar to ESCs, Dox-controlled expression of the cardiac transcriptional regulator Mesp1 together with Wnt inhibition enhances the generation of functional cardiomyocytes upon in vitro differentiation of such RMCE-retargeted iPSCs. This ROSA26-iPSC mouse model is therefore an excellent tool for studying both cellular reprogramming and lineage-directed differentiation factors from the same locus and will greatly facilitate the identification and ease of functional characterization of the genetic/epigenetic determinants involved in these complex processes.

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 score0.056
Threshold uncertainty score0.383

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 head0.019
GPT teacher head0.249
Teacher spread0.230 · 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

Citations40
Published2013
Admission routes1
Has abstractyes

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