Regulating the transcriptomes that mediate the conversion of fibroblasts to various nervous system neural cell types
Bibliographic record
Abstract
Our understanding of the mechanism of cell fate transition during the direct reprogramming of fibroblasts into various central nervous system (CNS) neural cell types has been limited by the lack of a comprehensive analysis on generated cells, independently and in comparison with other CNS neural cell types. Here, we applied an integrative approach on 18 independent high throughput expression data sets to gain insight into the regulation of the transcriptome during the conversion of fibroblasts into induced neural stem cells, induced neurons (iNs), induced astrocytes, and induced oligodendrocyte progenitor cells (iOPCs). We found common down-regulated genes to be mostly related to fibroblast-specific functions, and suggest their potential as markers for screening of the silencing of the fibroblast-specific program. For example, Tagln was significantly down-regulated across all considered data sets. In addition, we identified specific profiles of up-regulated genes for each CNS neural cell types, which could be potential markers for maturation and efficiency screenings. Furthermore, we identified the main TFs involved in the regulation of the gene expression program during direct reprogramming. For example, in the generation of iNs from fibroblasts, the Rest TF was the main regulator of this reprogramming. In summary, our computational approach for meta-analyzing independent expression data sets provides significant details regarding the molecular mechanisms underlying the regulation of the gene expression program, and also suggests potentially useful candidate genes for screening down-regulation of fibroblast gene expression profile, maturation, and efficiency, as well as candidate TFs for increasing the efficiency of the reprogramming process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".