Bovine Fibroblast-Derived Extracellular Matrix Promotes the Growth and Preserves the Stemness of Bovine Stromal Cells during In Vitro Expansion
Bibliographic record
Abstract
Cultivated meat is a fast-growing research field and an industry with great potential to overcome the limitations of traditional meat production. Cultivated meat utilizes cell culture and tissue engineering technologies to culture a vast number of cells in vitro and grow/assemble them into structures mimicking the muscle tissues of livestock animals. Stem cells with self-renewal and lineage-specific differentiation abilities have been considered one of the key cell sources for cultivated meats. However, the extensive in vitro culturing/expansion of stem cells results in a reduction in their abilities to proliferate and differentiate. Extracellular matrix (ECM) has been used as a culturing substrate to support cell expansion for cell-based therapies in regenerative medicine due to its resemblance to the native microenvironment of cells. In this study, the effect of the ECM on the expansion of bovine umbilical cord stromal cells (BUSC) in vitro was evaluated and characterized. BUSCs with multi-lineage differentiation potentials were isolated from bovine placental tissue. Decellularized ECM prepared from a confluent monolayer of bovine fibroblasts (BF) is free of cellular components but contains major ECM proteins such as fibronectin and type I collagen and ECM-associated growth factors. Expansion of BUSC on ECM for three passages (around three weeks) resulted in about 500-fold amplification, while cells were amplified less than 10-fold when cultured on standard tissue culture plates (TCP). Moreover, the presence of ECM reduced the requirement for serum in the culture medium. Importantly, the cells amplified on ECM retained their differentiation abilities better than cells cultured on TCP. The results of our study support the notion that monolayer cell-derived ECM may be a strategy to expand bovine cells in vitro effectively and efficiently.
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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".