The lower in vitro chondrogenic potential of canine adipose tissue-derived mesenchymal stromal cells (MSC) compared to bone marrow-derived MSC is not improved by BMP-2 or BMP-6
Bibliographic record
Abstract
Mesenchymal stromal cells (MSC) are used for cell-based treatment for canine osteoarthritis (OA). Compared with human MSCs, detailed information on the functional characterisation of canine MSCs is limited. In particular, the chondrogenic differentiation of canine adipose tissue-derived MSCs (cAT-MSCs) is challenging. In this study, we aimed to compare cAT-MSCs with bone marrow-derived MSCs (cBM-MSCs), focusing specifically on their in vitro chondrogenic potential, with or without bone morphogenetic proteins (BMP). cBM-MSCs and cAT-MSCs were characterised using flow cytometry and reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The chondrogenic differentiation potential of all cMSC preparations in the presence of TGF-β1 alone or when supplemented with 10, 100, or 250 ng/mL BMP-2 or BMP-6 was investigated using RT-qPCR, and biochemical, histochemical and immunohistological analyses. Both cBM-MSCs and cAT-MSCs expressed the surface markers CD90, CD73, and CD29, and were negative for CD45 and CD34, although the expression of CD73 and CD271 varied with donor and tissue origin. Interestingly, expression of ACAN and SOX9 was higher in cBM-MSCs than cAT-MSCs. In contrast with cBM-MSCs, cAT-MSCs could not differentiate toward the chondrogenic lineage without BMP-2/-6, and their in vitro chondrogenesis was inferior to cBM-MSCs with BMP-2/-6. Thus, cAT-MSCs have lower in vitro chondrogenic capacity than cBM-MSC under the studied culture conditions with 10, 100, or 250 ng/mL BMP-2 or BMP-6. Therefore, further characterisation is necessary to explore the potential of cAT-MSCs for cell-based OA treatments.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".