In vitro biomaterial priming of human mesenchymal stromal/stem cells : implication of the Src/JAK/STAT3 pathway in vasculogenic mimicry
Bibliographic record
Abstract
Mesenchymal stromal/stem cells (MSC) play a crucial role in promoting neovascularization, which is essential for wound healing. They are commonly utilized as an autologous source of progenitor cells in various stem cell-based therapies. However, incomplete MSC differentiation towards a vascular endothelial cell phenotype questions their involvement in an alternative process to angiogenesis, namely vasculogenic mimicry (VM), and the signal transducing events that regulate their in vitro priming into capillary-like structures. Here, human MSC were primed on top of Cultrex matrix to recapitulate an in vitro phenotype of VM. Total RNA was extracted, and differential gene expression assessed through RNA-Seq analysis and RT-qPCR. Transient gene silencing was achieved using specific siRNA. AG490, Tofacitinib, and PP2 pharmacological effects on VM structures were analyzed using the Wimasis software. In vitro VM occurred within 4 h and was prevented by the JAK/STAT3 inhibitors AG490 and Tofacitinib, as well as by the Src inhibitor PP2. RNA-Seq highlighted STAT3 as a signaling hub contributing to VM when transcripts from capillary-like structures were compared to those from cell monolayers. Concomitant increases in IL6, IL1b, CSF1, CSF2, STAT3, FOXC2, RPSA, FN1, and SNAI1 transcript levels suggest the acquisition of a combined angiogenic, inflammatory and epithelial-to-mesenchymal transition phenotype in VM cultures. Increases in STAT3, FOXC2, RPSA, Fibronectin, and Snail protein expression were confirmed during VM. STAT3 and RPSA gene silencing abrogated in vitro VM. In conclusion, in vitro priming of MSC into VM structures requires Src/JAK/STAT3 signaling. This molecular evidence indicates that a clinically viable MSC-mediated pseudo-vasculature process could temporarily support grafts through VM, allowing time for the host vasculature to infiltrate and remodel the injured tissues.
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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) | 0.001 | 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".