Humoral Immunity to Allogeneic Immunoproteasome-Expressing Mesenchymal Stromal Cells Requires Efferocytosis by Endogenous Phagocytes
Bibliographic record
Abstract
The extensive use of mesenchymal stromal cells (MSCs) over the last decade has revolutionized modern medicine. From the delivery of pharmacological proteins to regenerative medicine and immune modulation, these cells have proven to be highly pleiotropic and responsive to their surrounding environment. Nevertheless, their role in promoting inflammation has been fairly limited by the questionable use of interferon-gamma, as this approach has also been proven to enhance the cells' immune-suppressive abilities. Alternatively, we have previously shown that de novo expression of the immunoproteasome (IPr) complex instills potent antigen cross-presentation capabilities in MSCs. Interestingly, these cells were found to express the major histocompatibility class (MHC) II protein, which prompted us to investigate their ability to stimulate humoral immunity. Using a series of in vivo studies, we found that administration of allogeneic ovalbumin (OVA)-pulsed MSC-IPr cells elicits a moderate antibody titer, which was further enhanced by the combined use of pro-inflammatory cytokines. The generated antibodies were functional as they blocked CD4 T-cell activation following their co-culture with OVA-pulsed MSC-IPr and mitigated E.G7 tumor growth in vivo. The therapeutic potency of MSC-IPr was, however, dependent on efferocytosis, as phagocyte depletion prior to vaccination abrogated MSC-IPr-induced humoral responses while promoting their survival in the host. In contrast, antibody-mediated neutralization of CD47, a potent "do not eat me signal", enhanced antibody titer levels. These observations highlight the major role played by myeloid cells in supporting antibody production by MSC-IPr and suggest that the immune outcome is dictated by a net balance between efferocytosis-stimulating and -inhibiting signals.
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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 | 0.001 |
| 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".