Syngeneic adipose-derived stromal cells modulate the immune response but have limited persistence within decellularized adipose tissue implants in C57BL/6 mice
Bibliographic record
Abstract
Abstract The delivery of adipose-derived stromal cells (ASCs) on cell-instructive decellularized adipose tissue (DAT) scaffolds is a promising strategy for stimulating host-derived soft tissue regeneration. However, a better understanding of the mechanisms through which ASCs modulate regeneration in vivo is needed to harness these cells more effectively. In this study, DAT scaffolds, both with and without seeded syngeneic DsRED + mouse ASCs, were implanted into immunocompetent C57BL/6 mice. Downstream analyses focused on assessing donor ASC persistence and phenotype, as well as the effects of ASC seeding on host macrophage polarization and the perfused host vascular network. Notably, most donor ASCs were cleared from the scaffolds by 2 weeks. Mass spectrometry-based proteomics indicated that the transplanted ASCs maintained their pre-implantation phenotype up to 1 week in vivo , suggesting that the cells were not undergoing programmed cell death. A higher fraction of the infiltrating host macrophages expressed CD68 and Arginase-1 in the ASC-seeded implants up to 1-week post-implantation. Interestingly, a small population of phagocytic macrophages, identified by uptake of DsRED protein, was present in the DAT implants in the first 2 weeks and showed enhanced expression of CD68, Arginase-1, and CD163, along with reduced expression of iNOS. MicroCT angiography revealed a similar perfused vessel network in the seeded and unseeded groups at 4- and 8-weeks post-implantation. Overall, seeding with syngeneic ASCs modulated the host macrophage response to the DAT bioscaffolds at early timepoints, but did not impact long-term regenerative outcomes, potentially due to the rapid clearance of the donor cell population in this model.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".