Engineering Adaptive Immunity in 3D: A Patient‐Specific Lymphoid Model Using Stromal Networks and Peripheral Blood Mononuclear Cells
Bibliographic record
Abstract
Abstract Tertiary lymphoid organs (TLOs) are non‐encapsulated immune structures that emerge in response to chronic inflammation, orchestrating local adaptive immune responses. However, recapitulating their complexity in vitro remains challenging due to the difficulty in generating physiologically relevant stromal‐immune interactions. Here, a 3D lymphoid tissue model is presented, engineered using human adipose‐derived stem cells (ADSCs) differentiated into fibroblastic reticular cell (FRC)‐like populations within collagen matrices. Differentiation is induced using TNF‐α and LT‐α, with or without IL‐4, generating two stromal phenotypes: FRC P1 and FRC P2 . These subsets exhibit matrix remodeling, distinct transcriptional signatures, and surface markers consistent with lymph node‐resident T cell reticular and follicular dendritic cell subsets. Upon co‐culture with peripheral blood mononuclear cells (PBMCs) and SARS‐CoV‐2 S1‐primed mature dendritic cells, the model supports antigen‐specific B cell activation and cytokine environments indicative of Th1 or Th2 polarization. FRC P1 favors B cell support and IgM secretion, whereas FRC P2 promotes dendritic cell activation and Th1‐type chemokine expression. This platform demonstrates the functional diversification of stem cell‐derived FRC‐like subsets and their role in orchestrating immune microenvironments. It enables investigation of lymphoid tissue remodeling, stromal‐immune crosstalk, and antibody generation using total PBMCs, providing a scalable, customizable system for personalized vaccine screening, autoimmune modeling, and therapeutic development.
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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".