Large-Scale Human Dendritic Cell Differentiation Revealing Notch-Dependent Lineage Bifurcation and Heterogeneity
Bibliographic record
Abstract
The ability to generate large numbers of distinct types of human dendritic cells (DCs) in vitro is critical for accelerating our understanding of DC biology and harnessing them clinically. We developed a DC differentiation method from human CD34 + precursors leading to high yields of plasmacytoid DCs (pDCs) and both types of conventional DCs (cDC1s and cDC2s). The identity of the cells generated in vitro and their strong homology to their blood counterparts were demonstrated by phenotypic, functional, and single-cell RNA-sequencing analyses. This culture system revealed a critical role of Notch signaling and GM-CSF for promoting cDC1 generation. Moreover, we discovered a pre-terminal differentiation state for each DC type, characterized by high expression of cell-cycle genes and lack of XCR1 in the case of cDC1. Our culture system will greatly facilitate the simultaneous and comprehensive study of primary, otherwise rare human DC types, including their mutual interactions.
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 | 0.000 |
| 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".