A Single TCF Transcription Factor, Regardless of Its Activation Capacity, Is Sufficient for Effective Trilineage Differentiation of ESCs
Bibliographic record
Abstract
Co-expression and cross-regulation of the four TCF/LEFs render their redundant and unique functions ambiguous. Here, we describe quadruple-knockout (QKO) mouse ESCs lacking all full-length TCF/LEFs and cell lines rescued with TCF7 or TCF7L1. QKO cells self-renew, despite gene expression patterns that differ significantly from WT, and display delayed, neurectoderm-biased, embryoid body (EB) differentiation. QKO EBs have no contracting cardiomyocytes and differentiate poorly into mesendoderm but readily generate neuronal cells. QKO cells and TCF7L1-rescued cells cannot efficiently activate TCF reporters, whereas TCF7-rescued cells exhibit significant reporter responsiveness. Surprisingly, despite dramatically different transactivation capacities, re-expression of TCF7L1 or TCF7 in QKO cells restores their tri-lineage differentiation ability, with similar lineage marker expression patterns and beating cardiomyocyte frequencies observed in EBs. Both factors also similarly affect the transcriptome of QKO cells. Our data reveal that a single TCF, regardless of its activation capacity, is sufficient for effective trilineage differentiation of ESCs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".