Augmented expansion of Treg cells from healthy and autoimmune subjects via adult progenitor cell co-culture
Bibliographic record
Abstract
Abstract Recent clinical experience has demonstrated that adoptive regulatory T cell therapy is a safe and feasible strategy to suppress immunopathology via induction of host tolerance to allo- and autoantigens. However, clinical trials continue to be compromised due to an inability to manufacture a sufficient Treg cell dose. Multipotent adult progenitor cells (MAPC Ⓡ ) promote regulatory T cell differentiation in vitro , suggesting they may be repurposed to enhance ex vivo expansion of Tregs for adoptive cellular therapy. Here, we use a GMP compatible Treg expansion platform to demonstrate that MAPC cell-co-cultured Tregs (MulTreg) exhibit a log-fold increase in yield across two independent cohorts, reducing time to target dose by an average of 30%. Enhanced expansion is linked with a distinct Treg cell-intrinsic transcriptional program, characterized by diminished levels of core exhaustion ( BATF, ID2, PRDM1, LAYN, DUSP1 ), and quiescence ( TOB1, TSC22D3 ) related genes, coupled to elevated expression of cell-cycle and proliferation loci ( MKI67, CDK1, AURKA, AURKB ). In addition, MulTreg display a unique gut homing (CCR7lo β 7 hi) phenotype and importantly, are more readily expanded from patients with autoimmune disease compared to matched Treg lines, suggesting clinical utility in gut and/or Th1-driven pathology associated with autoimmunity or transplantation. Relative to expanded Tregs, MulTreg retain equivalent and robust purity, FoxP3 TSDR demethylation, nominal effector cytokine production and potent suppression of Th1-driven antigen specific and polyclonal responses in vitro and xeno graft vs host disease (xGvHD) in vivo . These data support the use of MAPC cell co-culture in adoptive Treg therapy platforms as a means to rescue expansion failure and reduce the time required to manufacture a stable, potently suppressive product.
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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) | 0.003 | 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".