Genome damage in induced pluripotent stem cells: Assessing the mechanisms and their consequences
Bibliographic record
Abstract
In 2006, Shinya Yamanaka and colleagues discovered how to reprogram terminally differentiated somatic cells to a pluripotent stem cell state. The resulting induced pluripotent stem cells (iPSCs) made a paradigm shift in the field, further nailing down the disproval of the long-held dogma that differentiation is unidirectional. The prospect of using iPSCs for patient-specific cell-based therapies has been enticing. This promise, however, has been questioned in the last two years as several studies demonstrated intrinsic epigenetic and genomic anomalies in these cells. Here, we not only review the recent critical studies addressing the genome integrity during the reprogramming process, but speculate about the underlying mechanisms that could create de novo genome damage in iPSCs. Finally, we discuss how much an elevated mutation load really matters considering the safety of future therapies with cells heavily cultured in vitro.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".