Human Colon-on-a-Chip Enables Continuous In Vitro Analysis of Colon Mucus Layer Accumulation and Physiology
Bibliographic record
Abstract
BACKGROUND & AIMS: The mucus layer in the human colon protects against commensal bacteria and pathogens, and defects in its unique bilayered structure contribute to intestinal disorders, such as ulcerative colitis. However, our understanding of colon physiology is limited by the lack of in vitro models that replicate human colonic mucus layer structure and function. Here, we investigated if combining organ-on-a-chip and organoid technologies can be leveraged to develop a human-relevant in vitro model of colon mucus physiology. METHODS: A human colon-on-a-chip (Colon Chip) microfluidic device lined by primary patient-derived colonic epithelial cells was used to recapitulate mucus bilayer formation, and to visualize mucus accumulation in living cultures noninvasively. RESULTS: The Colon Chip supports spontaneous goblet cell differentiation and accumulation of a mucus bilayer with impenetrable and penetrable layers, and a thickness similar to that observed in the human colon, while maintaining a subpopulation of proliferative epithelial cells. Live imaging of the mucus layer formation on-chip showed that stimulation of the colonic epithelium with prostaglandin E2, which is increased during inflammation, causes rapid mucus volume expansion via an Na-K-Cl cotransporter 1 ion channel-dependent increase in its hydration state, but no increase in de novo mucus secretion. CONCLUSIONS: This study shows the production of colonic mucus with a physiologically relevant bilayer structure in vitro, which can be analyzed in real time noninvasively. The Colon Chip may offer a new preclinical tool to analyze the role of mucus in human intestinal homeostasis as well as diseases, such as ulcerative colitis and cancer.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| 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".