Identification of a sub-population of synovial mesenchymal stem cells with enhanced treatment efficacy in a rat model of Osteoarthritis
Bibliographic record
Abstract
Abstract Osteoarthritis (OA) is a painful and debilitating disease which has no cure and there are no treatments which can predictably stop/reverse its progression. Treating this disease is particularly difficult since the articular cartilage lacks intrinsic repair capacity even though mesenchymal stem cells (MSCs) are present in the joint environment and have robust chondrogenic potential. We have previously shown that there is heterogeneity of MSC sub-types within the human synovium, yet it remains unclear if any of these MSC types can regenerate cartilage and/or impact OA disease progression. Therefore, we have undertaken this study focusing on clonally derived MSC populations derived from the synovium of normal and OA patients to characterize if any MSC populations can positively impact OA disease trajectory in a rat model of OA. MSCs were clonally isolated by indexed flow cytometry, expanded in culture and then characterized for differentiation capacity and by quantitative proteomics. MSC clones were then transplanted into a xenograft rat OA model and treatment effect was determined by histology and immunofluorescence outcomes. We identified heterogeneity in putative MSCs derived from within and between patient groups (normal vs. OA) and the ability of these cells to effect repair in a rat OA model. However, these different sub-types of MSCs could not be distinguished by traditional cell surface markers showing the need for a better understanding of these populations at the single cell level. Using an unbiased proteomics approach, CD47 was identified a novel marker of human MSCs. Using the same rat model of OA, CD47Hi expressing cells were found to have robust treatment efficacy and directly contributed to the formation of new articular cartilage tissue. Characterizing MSCs is essential to understand which sub-types are appropriate for further clinical investigation. If OA patients still have functional MSCs in their synovium, then it is possible these cells can be exploited for cartilage regeneration / OA treatment strategies.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".