Modeling Inflammatory Immune Cell Recruitment and Response on Human Colon Intestine-Chip
Bibliographic record
Abstract
Abstract Objective Immune cell recruitment into tissues is an essential step in inflammatory responses. This occurs in a highly tissue- and stimulus-specific manner, which presents a significant challenge to modeling disease and testing therapeutics ex vivo. We previously developed an advanced primary human vascularized Colon Intestine-Chip model and showed that it recapitulates physiologic cell composition, morphology and barrier function. The goal of this study was to test the ability of this system to model inflammatory bowel disease (IBD)-like immune cell reactions ex vivo. Methods We perfused primary human peripheral blood mononuclear cells (PBMC) across the vascular channel in untreated ‘resting’ or TNFα/chemokine-treated ‘inflamed’ Colon Intestine-Chips. We analyzed total cell recruitment, inflammatory cytokine secretion and barrier function in the following 24–72 hours. Results We show that the perfused PBMC efficiently adhered and transmigrated to the epithelial channel in an inflammation-specific manner. This was followed by an accumulation of proinflammatory cytokines characteristic of IBD (e.g., INFγ, IL1β, IL18), as well as loss of barrier function, the hallmark feature of IBD. We further showed that 1) the recruited cells were strongly enriched in the ‘gut trophic’ α4β7+/CCR9+ PBMC subsets and 2) this recruitment could be blocked with the IBD therapeutic Entyvio, which targets the α4β7-MAdCAM-1 interaction. Conclusion Our findings indicate that our Colon Intestine-Chip can model inflammatory immune cell recruitment and in situ immune reactions that reflect key clinical correlates of IBD. This model may prove effective for development of new anti-inflammatory therapeutics for human intestinal diseases. Supported by Emulate Bio
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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) | 0.002 | 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".