Design principles for generating robust gene expression patterns in dynamic engineered tissues
Bibliographic record
Abstract
Recapitulating native tissue organization is a central challenge in regenerative medicine as it is critical for generating functional tissues. One strategy to generate engineered tissues with predictable and appropriate organization is to mimic the gene expression patterning process that organizes tissues in the developing embryo. In a developing embryo, correct organization is accomplished by tissue patterning via the generation of temporal and spatial patterns of gene expression coupled with, and leading to, extensive cellular re-organization. Methods to pattern gene expression in vitro could therefore provide both better models for understanding the cellular and molecular events taking place during tissue morphogenesis and novel strategies for engineering tissues with more realistic and complex architectures. While a few attempts have been made to genetically pattern tissues in vitro, these do not produce sharp predictable patterning. In both the embryo and an in vitro tissue, patterning often occurs during extensive cell re-organization but how the dynamics of gene induction and cell re-distribution interact to impact the final outcome of patterning and ultimately tissue organization is not known. Understanding this relationship and the system parameters that dictate robust pattern formation is critical for engineering genetic patterning in vitro to organize artificial tissues. We set out to identify key requirements for pattern formation by patterning gene expression in vitro in sheets of re-distributing cells using a drug-inducible gene expression system and patterned drug delivery to mimic morphogen gene induction. Based on our experimental observations, we develop a mathematical model that allows us to identify and experimentally verify the conditions under which generation of sharp gene expression patterns is possible in vitro. Our results highlight the importance of coordinating gene induction dynamics and cellular movement in order to achieve robust pattern 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".