ANIMAL COMPONENT-FREE WORKFLOWS TO DERIVE TENOCYTE-LIKE CELLS FROM HUMAN MESENCHYMAL STROMAL CELLS AND PLURIPOTENT STEM CELLS
Bibliographic record
Abstract
Cell-based therapies offer a promising strategy to treat tendon injuries and diseases. Both mesenchymal stromal cells (MSCs) and pluripotent stem cells (PSCs) are good candidates for such applications due to their self-renewing and differentiation capacity. However, the translation of cell-based therapies from bench to bedside can be hindered by the use of animal-derived components in ancillary materials and by the lack of standardised media and protocols for in vitro tenogenic differentiation. To address this, we have optimized animal component-free (ACF) workflows for differentiating human MSCs and PSCs to tenocyte-like cells (TLCs) respectively. MSCs isolated from bone marrow (n = 3) or adipose tissue (n = 3) were expanded using MesenCult™-ACF Plus Culture Kit for at least 2 passages, and differentiated to TLCs in 21 days using a step-wise approach. Briefly, confluent cultures were treated with an ACF tenogenic induction medium for 3 days, followed by treatment with an ACF maturation medium for 18 days. Monolayer cultures were maintained at high density without passaging for the entire duration of the protocol, and the medium was changed every 2 – 3 days. In a similar fashion, embryonic (n = 3) or induced PSCs (n = 3) were first differentiated to acquire a mesenchymal progenitor cell (MPC) phenotype in 21 days using STEMdiff™ Mesenchymal Progenitor Kit, followed by the aforementioned tenogenic protocol for an additional 21 days. In all cases, the optimized workflows using ACF formulations consistently activated a tenogenic transcriptional program, leading to the generation of elongated, spindle-shaped tenomodulin-positive (TNMD+) cells and deposition of an extracellular matrix predominantly composed of collagen type I. In summary, here we describe novel workflows that can robustly generate TLCs from MSCs and hPSC-derived MPCs for potential translational applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".