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Record W3203823955 · doi:10.1016/j.stemcr.2021.09.002

Large-scale analysis of imprinting in naive human pluripotent stem cells reveals recurrent aberrations and a potential link to FGF signaling

2021· article· en· W3203823955 on OpenAlexfundno aff
Gal Keshet, Nissim Benvenisty

Bibliographic record

VenueStem Cell Reports · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Syndromes and Imprinting
Canadian institutionsnot available
FundersAzrieli FoundationIsrael Science FoundationRosetrees Trust
KeywordsBiologyImprinting (psychology)Genomic imprintingInduced pluripotent stem cellDNA methylationReprogrammingGeneticsEmbryonic stem cellEpigeneticsGenomicsComputational biologyGeneGenomeGene expression

Abstract

fetched live from OpenAlex

Genomic imprinting is a parent-of-origin dependent monoallelic expression of genes. Previous studies showed that conversion of primed human pluripotent stem cells (hPSCs) into naive pluripotency is accompanied by genome-wide loss of methylation that includes imprinted loci. However, the extent of aberrant biallelic expression of imprinted genes is still unknown. Here, we analyze loss of imprinting (LOI) in a large cohort of both bulk and single-cell RNA sequencing samples of naive and primed hPSCs. We show that naive hPSCs exhibit high levels of non-random LOI, with bias toward paternally methylated imprinting control regions. Importantly, we show that different protocols used for the primed to naive conversion led to different extents of LOI, tightly correlated to FGF signaling. This analysis sheds light on the process of LOI occurring during the conversion to naive pluripotency and highlights the importance of these events when modeling disease and development or when utilizing the cells for therapy.

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)0.001
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 score0.024
Threshold uncertainty score0.721

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.009
GPT teacher head0.242
Teacher spread0.232 · 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

Citations18
Published2021
Admission routes1
Has abstractyes

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