Differentiation of Murine Bone Marrow-Derived Smooth Muscle Progenitor Cells Is Regulated by PDGF-BB and Collagen
Bibliographic record
Abstract
Smooth muscle cells (SMCs) are key regulators of vascular disease and circulating smooth muscle progenitor cells may play important roles in vascular repair or remodelling. We developed enhanced protocols to derive smooth muscle progenitors from murine bone marrow and tested whether factors that are increased in atherosclerotic plaques, namely platelet-derived growth factor-BB (PDGF-BB) and monomeric collagen, can influence the smooth muscle specific differentiation, proliferation, and survival of mouse bone marrow-derived progenitor cells. During a 21 day period of culture, bone marrow cells underwent a marked increase in expression of the SMC markers α-SMA (1.93 ± 0.15 vs. 0.0008 ± 0.0003 (ng/ng GAPDH) at 0 d), SM22-α (1.50 ± 0.27 vs. 0.005 ± 0.001 (ng/ng GAPDH) at 0 d) and SM-MHC (0.017 ± 0.004 vs. 0.001 ± 0.001 (ng/ng GAPDH) at 0 d). Bromodeoxyuridine (BrdU) incorporation experiments showed that in early culture, the smooth muscle progenitor subpopulation could be identified by high proliferative rates prior to the expression of smooth muscle specific markers. Culture of fresh bone marrow or smooth muscle progenitor cells with PDGF-BB suppressed the expression of α-SMA and SM22-α, in a rapidly reversible manner requiring PDGF receptor kinase activity. Progenitors cultured on polymerized collagen gels demonstrated expression of SMC markers, rates of proliferation and apoptosis similar to that of cells on tissue culture plastic; in contrast, cells grown on monomeric collagen gels displayed lower SMC marker expression, lower growth rates (319 ± 36 vs. 635 ± 97 cells/mm2), and increased apoptosis (5.3 ± 1.6% vs. 1.0 ± 0.5% (Annexin 5 staining)). Our data shows that the differentiation and survival of smooth muscle progenitors are critically affected by PDGF-BB and as well as the substrate collagen structure.
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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 | 0.001 |
| 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".