TINC - a method to dissect transcriptional complexes at single locus resolution - reveals novel <i>Nanog</i> regulators in mouse embryonic stem cells
Bibliographic record
Abstract
Abstract Cellular identity is ultimately controlled by transcription factors (TFs), which bind to specific regulatory elements (REs) within the genome to regulate gene expression and cell fate changes. While recent advances in genome-wide epigenetic profiling techniques have significantly increased our understanding of which REs are utilized in which cell type, it remains largely unknown which TFs and cofactors interact with these REs to modulate gene expression. A major hurdle in dissecting the whole composition of a multi-protein complex formed at a specific RE is the shortage of appropriate techniques. We have developed a novel method termed TALE-mediated Isolation of Nuclear Chromatin (TINC). TINC utilizes epitope-tagged TALEs to isolate a specific genomic region from the mammalian genome and includes a nuclei isolation and chromatin enrichment step for increased specificity. Upon cross-linking of the cells and isolation of the chromatin, the target region is purified based on affinity purification of the TALE and associated nucleic acid and protein molecules can be subjected to further analyses. A key TF in the pluripotency network and therefore in embryonic stem cells (ESCs) is NANOG. It is currently not fully understood how Nanog expression is regulated and consequently it remains unclear how the ESC state is maintained. Using TINC we dissected the protein complex formed at the Nanog promoter in mouse ESCs and identified many known and numerous novel factors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".