Engineered niches support the development of human dendritic cells in humanized mice
Bibliographic record
Abstract
Abstract Classical dendritic cells (cDCs) are rare sentinel cells specialized in the regulation of adaptive immunity. Modeling cDC development is both crucial to study cDCs and harness their potential in immunotherapy. Here we addressed whether cDCs could differentiate in response to trophic cues delivered by mesenchymal components of the hematopoietic niche where they physiologically develop and maintain. We found that expression of the membrane bound form of human FLT3L and SCF together with CXCL12 in a bone marrow mesenchymal stromal cell line is sufficient to induce the contact-dependent specification of both type 1 and type 2 cDCs from CD34 + hematopoietic stem and progenitor cells (HSPCs). Engraftment of these engineered mesenchymal stromal cells (eMSCs) together with CD34 + HSPCs creates an in vivo synthetic niche in the dermis of immunodeficient mice. Cell-to-cell contact between HSPCs and stromal cells within these organoids drive the local specification of cDCs and CD123 + AXL + CD327 + pre/AS-DCs. cDCs generated in vivo display higher levels of resemblance with human blood cDCs unattained by in vitro generated subsets. Altogether, eMSCs provide a novel and unique platform recapitulating the full spectrum of cDC subsets enabling their functional characterization in vivo .
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 | 0.001 | 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) | 0.002 | 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".