The Immunological Profile of Adipose Mesenchymal Stromal/Stem Cells after Cell Expansion and Inflammatory Priming
Bibliographic record
Abstract
Background: AT-MSCs display great immunoregulatory features, making them potential candidates for cell-based therapy. This study aimed to evaluate the “RBC lysis buffer” isolation protocol and immunological profiling of the so-obtained AT-MSCs. Methods: We established an immune-comparative screening of AT-MSCs throughout in vitro cell expansion (PM, P1, P2, P3, P4) and inflammatory priming regarding the expression of 28 cell-surface markers, 6 cytokines/chemokines, and 10 TLR patterns. Findings: AT-MSCs were highly expandable and sensitive to microenvironment challenges, hereby showing plasticity in distinct expression profiles. Both cell expansion and inflammation differentially modulated the expression profile of CD34, HLA-DR, CD40, CD62L, CD200 and CD155, CD252, CD54, CD58, CD106, CD274 and CD112. Inflammation resulted in a significant increase in the expression of the cytokines IL-6, IL-8, IL-1β, IL-1Ra, CCL5, and TNFα. Depending on the culture conditions, the expression of the TLR pattern was distinctively altered with TLR1–4, TLR7, and TLR10 being increased, whereas TLR6 was downregulated. Protein network and functional enrichment analysis showed that several trophic and immune responses are likely linked to these immunological changes. Conclusions: AT-MSCs may sense and actively respond to tissue challenges by modulating distinct and specific pathways to create an appropriate immuno-reparative environment. These mechanisms need to be further characterized to identify and assess a molecular target that can enhance or impede the therapeutic ability of AT-MSCs, which therefore will help improve the quality, safety, and efficacy of the therapeutic strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
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".