Engineering a Pro-Osteogenic Secretome through the Transient Silencing of the Gene Encoding Secreted Frizzled Related Protein 1
Bibliographic record
Abstract
The evidence sustaining the regenerative properties of mesenchymal stem cells’ (MSCs) secretome has prompted a paradigm change, where MSCs have shifted from being considered direct contributors to tissue regeneration toward being seen as cell factories for producing biotech medicines. We have previously designed a method to prime MSCs towards osteogenic differentiation by silencing the Wnt/β-Catenin inhibitor Sfpr1. This approach produces a significant increase in bone formation in osteoporotic mice. In this current work, we set to investigate the contribution of the secretome from the MSCs where Sfrp1 has been silenced, to the positive effect seen on bone regeneration in vivo. The conditioned media (CM) of the murine MSCs line C3H10T1/2, where Sfrp1 has been transiently silenced (CM-Sfrp1), was found to induce, in vitro, an increase in the osteogenic differentiation of this same cell line, as well as a decrease of the expression of the Wnt inhibitor Dkk1 in murine osteocytes ex vivo. A reduction in the RANKL/OPG ratio was also detected ex vivo, suggesting a negative effect of CM-Sfrp1 on osteoclastogenesis. Moreover, this CM significantly increases the mineralization of human primary MSCs isolated from osteoportotic patients in vitro. Proteomic analysis identified enrichment of proteins involved in osteogenesis within the soluble and vesicular fractions of this secretome. Altogether, we demonstrate the pro-osteogenic potential of the secretome of MSCs primmed in this fashion, suggesting that this is a valid approach to enhance the osteo-regenerative properties of MSCs’ secretome.
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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".