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Record W4367322453 · doi:10.1016/j.stemcr.2023.04.001

Generation, genomic characterization, and differentiation of triploid human embryonic stem cells

2023· article· en· W4367322453 on OpenAlexfundno aff
Guy Haim‐Abadi, Tamar Golan‐Lev, Amnon Koren, Nissim Benvenisty

Bibliographic record

VenueStem Cell Reports · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsnot available
FundersAzrieli FoundationRosetrees Trust
KeywordsBiologyPloidyEmbryonic stem cellTranscriptomeGeneticsStem cellCell biologyEmbryoCellular differentiationGeneGene expression

Abstract

fetched live from OpenAlex

Humans are diploid organisms, and triploidy in human embryos is responsible for ∼10% of spontaneous miscarriages. Surprisingly, some pregnancies proceed to triploid newborns that suffer from many neuro-developmental disorders. To investigate the impact of triploidy on human development, we generate triploid human embryonic stem cells (hESCs) by fusing isogenic haploid and diploid hESCs. Comparison of the transcriptome, methylome, and genome-wide replication timing shows general similarity between diploid and triploid hESCs. However, triploid cells have a larger volume than diploid cells, demonstrating decreased surface-area-to-volume ratio. This leads to a significant downregulation of cell surface ion channel genes, which are more essential in neural progenitors than in undifferentiated cells, leading to inhibition of differentiation, and it affects the neuronal differentiation ability of triploid hESCs, both in vitro and in vivo. Notably, our research establishes a platform to study triploidy in humans and points to their pathology as observed in triploid embryos.

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 score0.026
Threshold uncertainty score0.620

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 head0.021
GPT teacher head0.250
Teacher spread0.229 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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