Inherent genomic properties underlie the epigenomic heterogeneity of human induced pluripotent stem cells
Bibliographic record
Abstract
Human induced pluripotent stem cells (hiPSCs) show variable differentiation potential due to their epigenomic heterogeneity, whose extent/attributes remain unclear, except for well-studied elements/chromosomes such as imprints and the X chromosomes. Here, we show that seven hiPSC lines with variable germline potential exhibit substantial epigenomic heterogeneity, despite their uniform transcriptomes. Nearly a quarter of autosomal regions bear potentially differential chromatin modifications, with promoters/CpG islands for H3K27me3/H2AK119ub1 and evolutionarily young retrotransposons for H3K4me3. We identify 145 large autosomal blocks (≥100 kb) with differential H3K9me3 enrichment, many of which are lamina-associated domains (LADs) in somatic but not in embryonic stem cells. A majority of these epigenomic heterogeneities are independent of genetic variations. We identify an X chromosome state with chromosome-wide H3K9me3 that stably prevents X chromosome erosion. Importantly, the germline potential of female hiPSCs correlates with X chromosome inactivation. We propose that inherent genomic properties, including CpG density, transposons, and LADs, engender epigenomic heterogeneity in hiPSCs.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".