CD146 Defines a Mesenchymal Stromal Cell Subpopulation with Enhanced Suppressive Properties
Bibliographic record
Abstract
Mesenchymal stromal cells (MSCs) are largely known for their immune-suppressive capacity, hence, their common use in the control of unwanted inflammation. However, novel concepts related to their biology, combined with the urgent need to identify MSC subpopulations with enhanced suppressive properties, drive the search for isolation protocols optimized for clinical applications. We show, in this study, that MSCs expressing high CD146 levels exhibit altered surface expression profiles of CD44 and secrete elevated levels of interleukin (IL)-6, amongst other factors. In addition, CD146hi MSCs surpass the polyclonal parental populations in inhibiting alloreactive T cells in vitro, in both a soluble- and cell-contact-dependent manner. Despite the lack of CD146hi MSC-mediated activation of peritoneal macrophages to release the suppressive factor IL-10 in vitro, their administration in animals with graft-versus-host disease alleviates inflammation and leads to 40% survival rate up to 7 weeks post-transplantation. This pronounced inhibitory property is driven by CD146-mediated in situ efferocytosis by myeloid cells. Altogether, this study provides the impetus to adopt an isolation protocol for MSCs based on a CD146 expression profile before their therapeutic use and suggests a major role played by CD146 as a novel “eat-me” signal, capable of enhancing MSC uptake by competent phagocytes.
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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 | 0.001 |
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".