Three-dimensional tissue stiffness mapping in the mouse embryo supports durotaxis during early limb bud morphogenesis
Bibliographic record
Abstract
ABSTRACT Numerous biophysical hypotheses invoke tissue stiffness as a key parameter for shaping tissue during development and for influencing cell behaviours during disease progression. However, currently available methods are insufficient to test hypotheses that concern the physical properties of bulk tissues. Here we introduce, validate and apply a new 3D magnetic device that generates a uniform magnetic field gradient within a space that is sufficient to accommodate a vertebrate, organ-stage embryo under live conditions. The device allows for rapid, nontoxic measurement of the spatial variation of absolute elastic modulus and viscosity deep within mesenchymal tissues and within epithelia. By applying the device to map the spatiotemporal variation of viscoelastic properties within the early mouse limb bud, we identified an anteriorly biased mesodermal stiffness gradient along which cells move collectively to shape the early bud. Tissue stiffness corresponds to the nascent expression domain of fibronectin that is Wnt5a -dependent. The findings challenge the notion that Wnt5a regulates cell movements by chemotaxis, and raises the possibility that Wnt5a modifies the tissue microenvironment to promote durotaxis in vivo . Importantly, the ability to precisely measure tissue stiffness in 3D has the potential to instigate and refine mechanisms of development and disease progression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".