Anchored screening identifies transcription factor blueprints underlying dendritic cell diversity and subset-specific anti-tumor immunity
Bibliographic record
Abstract
Transcription factor cooperation is essential for specifying the heterogeneous dendritic cell (DC) lineages that orchestrate adaptive immunity, yet how it drives subset diversification remains poorly understood. Here, we employed a sequential anchored screen of 70 transcription factors using direct cellular reprogramming to identify regulators that specify type 2 conventional DCs (cDC2s) and plasmacytoid DCs (pDCs). We identified PU.1, IRF4, and PRDM1 as inducers of a pro-inflammatory cDC2B-like fate and SPIB, IRF8, and IKZF2 as mediators of an immature lymphoid DC program. Transcriptomic profiling linked these triads to lineage-specific signatures and demonstrated their requirement for subset identity. Mechanistically, lineage divergence was driven by chromatin co-engagement at subset-specific sites early in reprogramming. Functionally, reprogrammed DCs employed distinct immune mechanisms to elicit orthogonal anti-tumor responses in different tumor models. Collectively, our findings uncover transcriptional circuits that control DC diversification and pave the way to generate patient-tailored DC subsets for cancer immunotherapy.
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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 | 0.000 | 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 | 0.000 |
| 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".