Culture Methods to Study Apical-Specific Interactions using Intestinal Organoid Models
Bibliographic record
Abstract
The lining of the gut epithelium is made up of a simple layer of specialized epithelial cells that expose their apical side to the lumen and respond to external cues. Recent optimization of in vitro culture conditions allows for the re-creation of the intestinal stem cell niche and the development of advanced 3-dimensional (3D) culture systems that recapitulate the cell composition and the organization of the epithelium. Intestinal organoids embedded in an extracellular matrix (ECM) can be maintained for long-term and self-organize to generate a well-defined, polarized epithelium that encompasses an internal lumen and an external exposed basal side. This restrictive nature of the intestinal organoids presents challenges in accessing the apical surface of the epithelium in vitro and limits the investigation of biological mechanisms such as nutrient uptake and host-microbiota/host-pathogen interactions. Here, we describe two methods that facilitate access to the apical side of the organoid epithelium and support the differentiation of specific intestinal cell types. First, we show how ECM removal induces an inversion of the epithelial cell polarity and allows for the generation of apical-out 3D organoids. Second, we describe how to generate 2-dimensional (2D) monolayers from single cell suspensions derived from intestinal organoids, comprised of mature and differentiated cell types. These techniques provide novel tools to study apical-specific interactions of the epithelium with external cues in vitro and promote the use of organoids as a platform to facilitate precision medicine.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".