Gene expression profiles of normal proliferating and differentiating human intestinal epithelial cells: A comparison with the Caco-2 cell model
Bibliographic record
Abstract
cDNA microarray technology enables detailed analysis of gene expression throughout complex processes such as differentiation. The aim of this study was to analyze the gene expression profile of normal human intestinal epithelial cells using cell models that recapitulate the crypt-villus axis of intestinal differentiation in comparison with the widely used Caco-2 cell model. cDNA microarrays (19,200 human genes) and a clustering algorithm were used to identify patterns of gene expression in the crypt-like proliferative HIEC and tsFHI cells, and villus epithelial cells as well as Caco-2/15 cells at two distinct stages of differentiation. Unsupervised hierarchical clustering analysis of global gene expression among the cell lines identified two branches: one for the HIEC cells versus a second comprised of two sub-groups: (a) the proliferative Caco-2 cells and (b) the differentiated Caco-2 cells and closely related villus epithelial cells. At the gene level, supervised hierarchical clustering with 272 differentially expressed genes revealed distinct expression patterns specific to each cell phenotype. We identified several upregulated genes that could lead to the identification of new regulatory pathways involved in cell differentiation and carcinogenesis. The combined use of microarray analysis and human intestinal cell models thus provides a powerful tool for establishing detailed gene expression profiles of proliferative to terminally differentiated intestinal cells. Furthermore, the molecular differences between the normal human intestinal cell models and Caco-2 cells clearly point out the strengths and limitations of this widely used experimental model for studying intestinal cell proliferation and differentiation.
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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 | 0.001 | 0.001 |
| 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".