Collectivization of Vascular Smooth Muscle Cells via TGF-β–Cadherin-11–Dependent Adhesive Switching
Bibliographic record
Abstract
OBJECTIVE: Smooth muscle cells (SMCs) in healthy arteries are arranged as a collective. However, in diseased arteries, SMCs commonly exist as individual cells, unconnected to each other. The purpose of this study was to elucidate the events that enable individualized SMCs to enter into a stable and interacting cell collective. APPROACH AND RESULTS: Human SMCs stimulated to undergo programmed collectivization were tracked by time-lapse microscopy. We uncovered a switch in the behavior of contacting SMCs from semiautonomous motility to cell-cell adherence. Central to the cell-adherent phenotype was the formation of uniquely elongated adherens junctions, up to 60 μm in length, which appeared to strap adjacent SMCs to each other. Remarkably, these junctions contained both N-cadherin and cadherin-11. Ground-state depletion super-resolution microscopy revealed that these hybrid assemblies were comprised of 2 parallel nanotracks of each cadherin, separated by 50 nm. Blocking either N-cadherin or cadherin-11 inhibited collectivization. Cell-cell adhesion and adherens junction elongation were associated with reduced transforming growth factor-β signaling, and exogenous transforming growth factor-β1 suppressed junction elongation via the noncanonical p38 pathway. Imaging of fura-2-loaded SMCs revealed that SMC assemblies displayed coordinated calcium oscillations and cell-cell transmission of calcium waves which, together with increased connexin 43-containing junctions, depended on cadherin-11 and N-cadherin function. CONCLUSIONS: SMCs can self-organize, structurally and functionally, via transforming growth factor-β-p38-dependent adhesive switching and a novel adherens junction architecture comprised of hybrid nanotracks of cadherin-11 and N-cadherin. The findings define a mechanism for the assembly of SMCs into networks, a process that may be relevant to the stability and function of blood vessels.
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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) | 0.000 | 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 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".