Derivation of naïve human embryonic stem cells
Bibliographic record
Abstract
The naïve pluripotent state has been shown in mice to lead to broad and more robust developmental potential relative to primed mouse epiblast cells. The human naïve ES cell state has eluded derivation without the use of transgenes, and forced expression of OCT4, KLF4, and KLF2 allows maintenance of human cells in a naïve state [Hanna J, et al. (2010) Proc Natl Acad Sci USA 107(20):9222-9227]. We describe two routes to generate nontransgenic naïve human ES cells (hESCs). The first is by reverse toggling of preexisting primed hESC lines by preculture in the histone deacetylase inhibitors butyrate and suberoylanilide hydroxamic acid, followed by culture in MEK/ERK and GSK3 inhibitors (2i) with FGF2. The second route is by direct derivation from a human embryo in 2i with FGF2. We show that human naïve cells meet mouse criteria for the naïve state by growth characteristics, antibody labeling profile, gene expression, X-inactivation profile, mitochondrial morphology, microRNA profile and development in the context of teratomas. hESCs can exist in a naïve state without the need for transgenes. Direct derivation is an elusive, but attainable, process, leading to cells at the earliest stage of in vitro pluripotency described for humans. Reverse toggling of primed cells to naïve is efficient and reproducible.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".