Comprehensive transcriptome mining of the direct conversion of mesodermal cells
Bibliographic record
Abstract
The direct reprogramming of somatic cells is a promising approach for regenerative medicine, especially in the production of mesoderm layer-derived cells. Meta-analysis studies provide precise insight into the undergoing processes and help increase the efficiency of reprogramming. Here, using 27 high-throughput expression data sets, we analyzed the direct reprogramming of mesodermal cells in humans and mice. Fibroblast-derived cells showed a common expression pattern of up- and down-regulated genes that were mainly involved in the suppression of the fibroblast-specific gene expression program, and may be used as markers of the initiation of reprogramming. Furthermore, we found a specific gene expression profile for each fibroblast-derived cell studied, and each gene set appeared to play specific functional roles in its cell type, suggesting their use as markers for their mature state. Furthermore, using data from protein-DNA interactions, we identified the main transcription factors (TFs) involved in the conversion process and ranked them based on their importance in their gene regulatory networks. In summary, our meta-analysis approach provides new insights on the direct conversion of mesodermal somatic cells, introduces a list of genes as markers for initiation and maturation, and identifies TFs for which manipulating their expression may increase the efficiency of direct conversion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".