Developing a model of autoimmune diseases with human tonsil organoids
Bibliographic record
Abstract
Abstract Mouse models have contributed significantly to our knowledge of the pathogenesis of autoimmune diseases. However, differences in mouse and human immune systems limit the translation of the knowledge in mechanism and therapeutic outcomes from mouse to human. Therefore, a human model of autoimmune diseases is urgently needed. Recently, our lab has developed a functional tonsil organotypic system that can recapitulate key T cells responses and germinal center response to influenza vaccine in vitro. We now want to utilize this tonsil system to study autoimmunity. Regulatory T cells (Tregs) have been suggested to play a central role in maintaining self-tolerance. Reduced Treg cell frequencies and impaired suppressive function have been reported in a wide range of autoimmune diseases. In this study, we show that we can efficiently knock out FOXP3 in T cells from human tonsil organoids by using CRISPR technology. We also show that tonsil organoids with FOXP3 knocked-out T cells are able to induce humoral immune responses towards autoantigens stimulation and influenza vaccination. This system could be useful for studying underlying mechanisms in the functions of Tregs in the autoimmune condition in humans and provides insights into the therapeutic strategy for autoimmune diseases.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".