Regulatory B Cells Contribute to the Clinical Response After Bone Marrow-Derived Mesenchymal Stromal Cell Infusion in Patients With Systemic Sclerosis
Bibliographic record
Abstract
Mesenchymal stromal cells (MSCs) have recently emerged as an interesting therapeutic approach for patients with progressive systemic sclerosis (SSc), a rare and life-threatening orphan autoimmune disease. Whereas MSC immunomodulatory potential is considered as a central mechanism for their clinical benefit, very few data are available on the impact of MSCs on immune cell subsets in vivo. In the current extended study of a phase I/II clinical trial exploring the injection of a single dose of allogeneic bone marrow-MSCs (alloBM-MSCs) in patients with severe SSc (NCT02213705), we performed a longitudinal in-depth characterization of circulating immune cells in 19 MSC-treated patients, including 14 responders and 5 non-responders. By a combination of flow cytometry and transcriptomic analyses, we highlighted an increase in circulating CD24hiCD27posCD38lo/neg memory B cells, the main IL-10-producing regulatory B cell (Breg) subset, and an upregulation of IL10 expression in ex-vivo purified B cells, specifically in responder patients, early after the alloBM-MSC infusion. In addition, a deeper alteration of the B-cell compartment before alloBM-MSC treatment, including a higher expression of profibrotic cytokines IL6 and TGFβ by sorted B cells was associated with a non-responder clinical status. Finally, BM-MSCs were able to directly upregulate IL-10 production in activated B cells in vitro. These data suggest that cytokine-producing B cells, in particular Breg, are pivotal effectors of BM-MSC therapeutic activity in SSc. Their quantification as activity biomarkers in MSC potency assays and patient selection criteria may be considered to reach optimal clinical benefit when designing MSC-based clinical trials.
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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 | 0.001 |
| 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".