Direct lineage conversion with pluripotency factors: A risky detour through transient pluripotency?
Bibliographic record
Abstract
The advent of induced pluripotent stem cells (iPSCs) marked a giant step forward towards the reality of converting one type of primary somatic cells into different lineages capable of clinically repairing damaged tissues and organs. However, the major drawbacks of iPSCs hinder their quick translation to the bedside. These drawbacks include the time-, cost-, and labor-intensive process in production of clinical products from iPSCs, and the inherent risk of long-term tumorigenesis due to the forced expression of transcription factors associated with pluripotency, which are often implicated as aberrations within the cancerous gene circuitry. The recent reports of the direct conversion of one somatic lineage into other types following a short-term pulse of pluritotent transcription factors pointed to a more efficient and more lineage–versatile alternative to those using only lineage-restricted transcription factors.1,2 This new approach has also been applauded for its perceived safety merits due to need for fewer perturbations of the genes in the target cells and, perhaps, without generating “true” iPSCs. However, the question remains whether this short-term approach represents a mechanistically different method, which avoids the total erasure of restricted epigenetic imprints as does iPSCs generation, and whether it therefore bypasses the pluripotency stage.3,4 Two recent articles published in the same issue of Nature Biotechnology offered some definitive answers.5,6 Using different, but reliable lineage tracing methods, the two groups of scientists came to the same conclusion that the majority of the converted new lineage offspring cells did indeed come from the intermediate precursors reprogrammed through the short-term pulse of transcription factors. These precursors bear the genomic and proteomic hallmarks, as well as having biological properties, similar to those found in iPSCs. Although the short-term approach still has its merits of efficiency and simplicity when generating desired somatic lineages for research and clinical application, these fresh insights will certainly shape the guidebook for its clinical translation, which requires that the risk of tumorigenesis be examined with the same rigor as in the case of iPSC-derived lineage cells.
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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 | 0.005 | 0.005 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".