An in vitro model of tissue boundary formation for dissecting the contribution of different boundary forming mechanisms
Bibliographic record
Abstract
During development and in adult tissues separation of phenotypically distinct cell populations is necessary to ensure proper organization and function of tissues and organs. Various phenomena, such as differential adhesion, differential mechanical tension and cell-cell repulsion, are proposed to cause boundary formation. Moreover, emerging evidence suggests that interplay between multiple such phenomena can underlie boundary formation. Boundary-forming mechanisms are commonly studied in vivo in complex embryo models or in vitro using simple model systems not reflective of in vivo boundary complexity. To better elucidate the interplay between multiple boundary formation mechanism, there is therefore a need for more relevant in vitro model systems that allow quantitative and concomitant studies of the multiple changes in cell/tissue behaviour that lead to boundary establishment. Here, we develop such a model using patterned co-cultures of two cell populations. Using a set of quantitative tools, we demonstrate that our approach allows us to study the mechanisms underlying boundary formation. We demonstrate that in our specific system differential mechanical tension and modulation of migratory behavior of cells accompany boundary formation. The design of our in vitro model system will allow researchers to obtain quantitative, integrative mechanistic data facilitating a faster and more thorough understanding of the fundamental principles underlying boundary formation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".