Looking into the Black Box: Insights into the Mechanisms of Somatic Cell Reprogramming
Bibliographic record
Abstract
The dramatic discovery that somatic cells could be reprogrammed to induced pluripotent stem cells (iPSCs), by the expression of just four factors, has opened new opportunities for regenerative medicine and novel ways of modeling human diseases. Extensive research over the short time since the first iPSCs were generated has yielded the ability to reprogram various cell types using a diverse range of methods. However the duration, efficiency, and safety of induced reprogramming have remained a persistent limitation to achieving a robust experimental and therapeutic system. The field has worked to resolve these issues through technological advances using non-integrative approaches, factor replacement or complementation with microRNA, shRNA and drugs. Despite these advances, the molecular mechanisms underlying the reprogramming process remain poorly understood. Recently, through the use of inducible secondary reprogramming systems, researchers have now accessed more rigorous mechanistic experiments to decipher this complex process. In this review we will discuss some of the major recent findings in reprogramming, pertaining to proliferation and cellular senescence, epigenetic and chromatin remodeling, and other complex cellular processes such as morphological changes and mesenchymal-to-epithelial transition. We will focus on the implications of this work in the construction of a mechanistic understanding of reprogramming and discuss unexplored areas in this rapidly expanding field.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".