An in vitro model of tissue boundary formation for dissecting the contribution of different boundary forming mechanisms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it