A simple, cost-effective method for generating murine colonic 3D enteroids and 2D monolayers for studies of primary epithelial cell function
Bibliographic record
Abstract
Cancer cell lines have been the mainstay of intestinal epithelial experimentation for decades, due primarily to their immortality and ease of culture. However, because of the inherent biological abnormalities of cancer cell lines, many cellular biologists are currently transitioning away from these models and toward more representative primary cells. This has been particularly challenging, but recent advances in the generation of intestinal organoids have brought the routine use of primary cells within reach of most epithelial biologists. Nevertheless, even with the proliferation of publications that use primary intestinal epithelial cells, there is still a considerable amount of trial and error required for laboratories to establish a consistent and reliable method to culture three-dimensional (3D) intestinal organoids and primary epithelial monolayers. We aim to minimize the time other laboratories spend troubleshooting the technique and present a standard method for culturing primary epithelial cells. Therefore, we have described our optimized, high-yield, cost-effective protocol to grow 3D murine colonoids for more than 20 passages and our detailed methods to culture these cells as confluent monolayers for at least 14 days, enabling a wide variety of potential future experiments. By supporting and expanding on the current literature of primary epithelial culture optimization and detailed use in experiments, we hope to help enable the widespread adoption of these innovative methods and allow consistency of results obtained across laboratories and institutions. NEW & NOTEWORTHY Primary intestinal epithelial monolayers are notoriously difficult to maintain culture, even with the recent advances in the field. We describe, in detail, the protocols required to maintain three-dimensional cultures of murine colonoids and passage these primary epithelial cells to confluent monolayers in a standardized, high-yield and cost-effective manner.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".